EOS Data Products

Handbook

Volume 2

ACRIMSAT • Aqua • Jason-1 • Landsat 7

• Meteor 3M/SAGE III •

QuikScat • QuikTOMS • VCL
EOS Data Products

Handbook

Volume 2

ACRIMSAT • ACRIM III

Aqua

• AIRS

• AMSR-E

• AMSU-A

• CERES

• HSB

• MODIS

Jason-1 • DORIS

• JMR

• Poseidon-2

Landsat 7 • ETM+

Meteor 3M • SAGE III

QuikScat

• SeaWinds

QuikTOMS • TOMS

VCL

• MBLA
Editors

Claire L. Parkinson

NASA Goddard Space Flight Center

Reynold Greenstone

Raytheon ITSS

Design and Production

Sterling Spangler

Raytheon ITSS

For Additional Copies:

EOS Project Science Office

Code 900

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

eos.nasa.gov

Phone: (301) 867-2037

Internet: leon_middleton@ssaihq.com

NASA Goddard Space Flight Center

Greenbelt, Maryland

Printed October 2000

EOS Data Products

Handbook

Volume 2

Acknowledgments

Special thanks are extended to Michael King for guidance throughout 

and to the many additional individuals who also provided information 

necessary for the completion of this 

second volume of the EOS Data 

Products Handbook. These include most prominently Stephen 

W. Wharton and Monica Faeth 

Myers, whose tremendous efforts 

brought about Volume 1, and the 

members of the science teams for 

each of the instruments covered in 

this volume.

Support for production of this 

document, provided by William 

Bandeen, Jim Closs, Steve Graham, and Hannelore Parrish, is 

also gratefully acknowledged.
Abstract

The EOS Data Products Handbook provides brief descriptions of the data 

products that will be produced from a range of missions of the Earth Observing 

System (EOS) and associated projects. Volume 1, originally published in 1997, 

covers the Tropical Rainfall Measuring Mission (TRMM), the Terra mission 

(formerly named EOS AM-1), and the Data Assimilation System, while this 

volume, Volume 2, covers the Active Cavity Radiometer Irradiance Monitor 

Satellite (ACRIMSAT), Aqua, Jason-1, Landsat 7, Meteor 3M/Stratospheric 

Aerosol and Gas Experiment III (SAGE III), the Quick Scatterometer (QuikScat), 

the Quick Total Ozone Mapping Spectrometer (QuikTOMS), and the Vegetation 

Canopy Lidar (VCL) missions. Volume 2 follows closely the format of Volume 

1, providing a list of products and an introduction and overview descriptions 

of the instruments and data processing, all introductory to the core of the book, 

which presents the individual data product descriptions, organized into 11 topical chapters. The product descriptions are followed by five appendices, which 

provide contact information for the EOS data centers that will be archiving and 

distributing the data sets, contact information for the science points of contact 

for the data products, references, acronyms and abbreviations, and a data products index.

I
Abstract I

Foreword IV

List of Data Products (Organized by Mission Name) 1

Introduction 11

Instrument Descriptions and Data Processing Overviews 15

Radiance/Reflectance and Irradiance Products 47

Precipitation and Atmospheric Humidity 71

Cloud and Aerosol Properties and Radiative Energy Fluxes 87

Atmospheric Chemistry 123

Atmospheric Temperatures 131

Winds 139

Sea Surface Height and Ocean Wave Dynamics 147

Surface Temperatures of Land and Oceans, 

 Fire Occurrence, and Volcanic Effects 151

Vegetation Dynamics, Land Cover, and Land Cover Change 165

Phytoplankton and Dissolved Organic Matter 187

Snow and Ice Cover 203

Appendix A: EOS Distributed Active Archive Centers 

 (DAACs) Contact Information 217

Appendix B: Points of Contact 218

Appendix C: References 225

Appendix D: Acronyms and Abbreviations 246

Appendix E: Data Products Index (Organized by Instrument Name) 252

Table of Contents

III
Foreword

This is Volume 2 in a planned three-part series known as the Earth Observing System (EOS) Data 

Products Handbook (DPH). Each of the three volumes in the series will contain product descriptions for the science data products that are, or will be, available as standard products from sensors 

on existing and planned EOS satellites. The objective of the information presented in the series 

of Handbooks is to promote a broader understanding of how EOS data products can be used to 

contribute to and facilitate science research that will lead to improved monitoring and analysis 

of Earth phenomena, and ultimately to improved understanding and prediction of global climate 

change. 

DPH Volume 1 is dedicated to data products from instruments of the first era of EOS platforms, the 

Tropical Rainfall Measuring Mission (TRMM) and Terra (formerly known as AM-1). The reader 

is therefore referred to Volume 1 for information on the TRMM precipitation-related instrument 

products derived from the TRMM Microwave Imager (TMI), the Precipitation Radar (PR), the 

Visible Infrared Scanner (VIRS) and the Lightning Imaging Sensor (LIS). TRMM also carried the 

Clouds and the Earthʼs Radiant Energy System (CERES) instrument, and its products will be found 

in Volume 1 as well. There are also products based on the analyses performed by the Goddard Data 

Assimilation Office (DAO). 

The instruments on board Terra are the Advanced Spaceborne Thermal Emission and Reflection 

Radiometer (ASTER), Clouds and the Earthʼs Radiant Energy System (CERES), the Multi-Angle 

Imaging Spectroradiometer (MISR), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Measurements of Pollution in the Troposphere (MOPITT). All standard products for 

these instruments are described in Volume 1.

This Volume 2 of the DPH brings us to the era featuring the second major EOS mission, Aqua 

(formerly PM-1) and includes product descriptions not only for the instruments on board Aqua 

but also for those instruments flying in the same era on missions not included in Volume 1. The 

additional missions and their sensors are ACRIMSAT (Active Cavity Irradiance Monitor, ACRIM 

III), Jason-1 (Doppler Orbitography and Radiopositioning Integrated by Satellite, DORIS; Jason 

Microwave Radiometer, JMR; and a radar altimeter, Poseidon-2), Landsat 7 (Enhanced Thematic 

Mapper +, ETM+), Meteor 3M/SAGE III (Stratospheric Aerosol and Gas Experiment, SAGE III), 

QuikScat (SeaWinds), QuikTOMS (Total Ozone Mapping Spectrometer, TOMS), and Vegetation 

Canopy Lidar (VCL, Multi-Beam Laser Altimeter, MBLA). 

The Aqua instruments whose products are featured in Volume 2 are: Atmospheric Infrared Sounder 

(AIRS), Advanced Microwave Sounding Unit-A (AMSU-A), Humidity Sounder for Brazil (HSB), 

Advanced Microwave Scanning Radiometer-E (EOS version, AMSR-E), Clouds and the Earthʼs 

Radiant Energy System (CERES), and Moderate Resolution Imaging Spectroradiometer (MODIS). 

 

Note that there has been a deliberate plan to have CERES fly on three missions, TRMM, Terra, and 

Aqua, with the intent of achieving maximum near-simultaneous coverage but at different viewing 

angles of Earthʼs radiances and clouds. 

Processing Level – Data set processing level is referred to throughout this document (see definitions on facing page). Level 1 data (radiances, brightness temperatures, etc.) require knowledge 

regarding the instrument calibration and characterization, agreed-upon algorithms, and computer 

processing capability for conversion from Level 0 (unprocessed raw data in counts). Level 2 data 

measure the biogeophysical properties of Planet Earth derived from the calibrated and geolocated 

Level 1 data using scientific remote sensing principles. 

IV Data Products Handbook - Volume 2 Foreword
Level 3 (mapped/gridded) and Level 4 (modeled) products can be used by interdisciplinary 

scientists to combine data from different areas of knowledge without necessarily having to 

know the details or undertake all of the processing that would otherwise be necessary (to 

process Level 1 to Level 3 or 4). The availability of Level 3 and 4 data is a major advantage 

of the EOS Data and Information System (EOSDIS) in promoting data usage from outside 

the science field in which the data were generated. Some data sets are being produced and 

archived in both a Level 2 (unmapped) and Level 3 (mapped and/or temporally sampled) 

form.

Access to the actual data from the EOS instruments can be obtained most readily through 

the EOS Distributed Active Archive Centers (DAACs), and they are listed in Appendix A 

of this volume. In addition, names and addresses of key investigators are to be found in 

Appendix B: Points of Contact. Other information concerning all the EOS instruments can 

be found in the 1999 EOS Reference Handbook, which can be found, along with Volume 1 

of the Data Products Handbook, by accessing the EOS Project Science Office home page at 

http://eos.nasa.gov/.

 

As was typical of the information supplied in Volume 1, the information supplied here in 

Volume 2 has primarily been obtained from the principal investigators of the various instruments or from key staff members of the science teams for the instruments. We are extremely 

grateful to all the many contributors who have made it possible for us to assemble this 

material. We hope the information contained here will be of great use to a wide variety of 

potential users. 

 Michael D. King

 EOS Senior Project Scientist

Foreword

Level 0 - Reconstructed unprocessed instrument/payload data at full resolution; any 

and all communications artifacts (e.g., synchronization frames, communications headers) removed.

Level 1A - Reconstructed unprocessed instrument data at full resolution, time-referenced, and annotated with ancillary information, including radiometric and geometric 

calibration coefficients and georeferencing parameters (i.e., platform ephemeris) computed and appended, but not applied, to the Level 0 data. 

Level 1B - Level 1A data that have been processed to sensor units (not all instruments 

have a Level 1B equivalent).

Level 2 - Derived geophysical variables at the same resolution and location as the 

Level 1 source data.

Level 3 - Variables mapped on uniform space-time grid scales, usually with some 

completeness and consistency.

Level 4 - Model output or results from analyses of lower level data (e.g., variables 

derived from multiple measurements).

Data Products Handbook - Volume 2 V
9

List of Data Products

(Organized by Mission Name)

ACRIMSAT • ACRIM III

Aqua

• AIRS

• AMSR-E

• AMSU-A

• CERES

• HSB

• MODIS

Jason-1 • DORIS

• JMR

• Poseidon-2

Landsat 7 • ETM+

Meteor 3M • SAGE III

QuikScat

• SeaWinds

QuikTOMS • TOMS

VCL

• MBLA
10 Data Products Handbook - Volume 2 List of Data Products (Organized by Mission Name) 

ACRIM III Radiometric Products 1, 2 Radiance/ 51

 Reflectance and 

Irradiance Products

AIRS Level 1A Radiance Counts 1A AIR 01 Radiance/ 52

 Reflectance and 

Irradiance Products

AIRS Level 1B Calibrated, 1B AIR 02 Radiance/ 52

 Geolocated Radiances Reflectance and

 Irradiance Products

AIRS/ Trace Constituent Product 2 AIR 03 Atmospheric 126

AMSU-A/HSB Chemistry

AIRS/ Cloud Product 2 AIR 04 Cloud and Aerosol 91 

AMSU-A/HSB Properties and 

 Radiative

Energy Fluxes

AIRS/ Humidity Product 2 AIR 05 Precipitation and 75

AMSU-A/HSB Atmospheric Humidity

AIRS/ Surface Analysis Product 2 AIR 06 Surface Temperatures 155

AMSU-A/HSB of Land and Oceans, 

 Fire Occurrence, 

and Volcanic Effects

AIRS/ Atmospheric Temperature 2 AIR 07 Atmospheric 134

AMSU-A/HSB Product Temperatures

AIRS/ Ozone Product 2 AIR 08 Atmospheric 126

AMSU-A/HSB Chemistry

AIRS/ Level 2 Cloud-Cleared 2 AIR 09 Radiance/ 53

AMSU-A/HSB Radiances Reflectance and

 Irradiance Products

AIRS/ Flux Product 2 AIR 10 Cloud and Aerosol 92

AMSU-A/HSB Properties and 

 Radiative

Energy Fluxes

AMSR-E Columnar Cloud Water 2,3 AE_Ocean, Cloud and Aerosol 94

 AE_DyOcn, Properties and

 AE_WkOcn, Radiative

 AE_MoOcn Energy Fluxes

AMSR-E Columnar Water Vapor 2,3 AE_Ocean, Precipitation and 79

 AE_DyOcn, Atmospheric 

 AE_WkOcn, Humidity

 AE_MoOcn

AMSR-E Level 2A Brightness 2 AE_L2A Radiance/ 53

 Temperatures Reflectance and

 Irradiance Products

 Mission Instrument Data Set Proc. Prod. ID Chapter Page # Level

ACRIMSAT

Aqua

Aqua

Aqua

Aqua
List of Data Products (Organized by Mission Name) Data Products Handbook - Volume 2 11

 Mission Instrument Data Set Proc. Prod. ID Chapter Page # Level

Aqua

AMSR-E Rainfall - Level 2 2 AE_Rain Precipitation and 76

 Atmospheric 

Humidity

AMSR-E Rainfall - Level 3 3 AE_RnGd Precipitation and 78

 Atmospheric 

Humidity

AMSR-E Sea Ice Concentration 3 AE_SI25 Snow and Ice 207

 Cover

AMSR-E Sea Ice Temperatures 3 AE_SI25 Snow and Ice 209

 Cover

AMSR-E Sea Surface 2,3 AE_Ocean Surface Temperatures 157

 Temperature of Land and Oceans,

 Fire Occurrence, and

 Volcanic Effects

AMSR-E Sea Surface Wind Speed 2,3 AE_Ocean, Winds 142

 AE_DyOcn,

 AE_WkOcn,

 AE_MoOcn

AMSR-E Snow Depth on Sea Ice 3 AE_SI12 Snow and Ice 208

 Cover

AMSR-E Snow-Water Equivalent 3 AE_DySno, Snow and Ice 210

 and Snow Depth AE_5Dsno, Cover

 AE_MoSno

AMSR-E Surface Soil Moisture 2,3 AE_Land, Vegetation Dynamics, 169

 AE_Land3, Land Cover, and

 Land Cover Change

AMSU-A Level 1A Radiance Counts 1A AMS 01 Radiance/ 54

 Reflectance and

Irradiance Products

AMSU-A Level 1B Calibrated, 1B AMS 02 Radiance/ 55

 Geolocated Radiances Reflectance and

 Irradiance Products

 

CERES Bi-Directional Scans Product 0,1 CER/BDS Radiance/ 55

 Reflectance and

Irradiance Products

CERES ERBE-like Instantaneous 2 CER/ES-8 Cloud and Aerosol 96

 TOA Estimates Properties and 

 Radiative

Energy Fluxes

CERES ERBE-like Monthly 3 CER/ES-4, Cloud and Aerosol 97

 Regional Averages (ES-9) and CER/ES-9 Properties and

 ERBE-like Monthly Geographical Radiative

 Averages (ES-4) Energy Fluxes 

 

Aqua
12 Data Products Handbook - Volume 2 List of Data Products (Organized by Mission Name) 

 Mission Instrument Data Set Proc. Prod. ID Chapter Page # Level

Aqua

Aqua

CERES Single Scanner TOA/ 2 CER/SSF Cloud and Aerosol 99

 Surface Fluxes and Clouds Properties and 

 Radiative

Energy Fluxes

CERES Clouds and Radiative 2 CER/CRS Cloud and Aerosol 101

 Swath Properties and 

 Radiative

Energy Fluxes

CERES Monthly Gridded Radiative 3 CER/FSW Cloud and Aerosol 103

 Fluxes and Clouds Properties and 

 Radiative

Energy Fluxes

CERES Synoptic Radiative Fluxes 3 CER/SYN Cloud and Aerosol 104

 and Clouds Properties and 

 Radiative

Energy Fluxes

CERES Monthly Regional Radiative 3 CER/AVG, Cloud and Aerosol 105

 Fluxes and Clouds (AVG) CER/ZAVG Properties and

 and Monthly Zonal and Global Radiative

 Radiative Fluxes and Clouds (ZAVG) Energy Fluxes

 

CERES Monthly Gridded TOA/ 3 CER/SFC Cloud and Aerosol 107

 Surface Fluxes and Clouds Properties and 

 Radiative

Energy Fluxes

CERES Monthly TOA/Surface 3 CER/ Cloud and Aerosol 108

 Averages SRBAVG Properties and

 Radiative

Energy Fluxes

HSB Level 1A Radiance Counts 1A HSB 01 Radiance/ 57

 Reflectance and

Irradiance Products

HSB Level 1B Calibrated, 1B HSB 02 Radiance/ 57

 Geolocated Radiances Reflectance and

 Irradiance Products

MODIS Level 1A Radiance Counts 1A MOD 01 Radiance/ 58

 Reflectance and

Irradiance Products

MODIS Level 1B Calibrated, 1B MOD 02 Radiance/ 58

 Geolocated Radiances Reflectance and

 Irradiance Products

MODIS Geolocation Data Set 1B MOD 03 Radiance/ 60

 Reflectance and

Irradiance Products

Aqua
List of Data Products (Organized by Mission Name) Data Products Handbook - Volume 2 13

 Mission Instrument Data Set Proc. Prod. ID Chapter Page # Level

Aqua

Aqua

MODIS Aerosol Product 2 MOD 04 Cloud and Aerosol 109

 Properties and 

Radiative

Energy Fluxes

MODIS Total Precipitable Water 2 MOD 05 Precipitation and 80

 Atmospheric 

Humidity

MODIS Cloud Product 2 MOD 06 Cloud and Aerosol 112

 Properties and 

Radiative

Energy Fluxes

MODIS Atmospheric Profiles 2 MOD 07 Precipitation and 82

 Atmospheric 

Humidity;

Atmospheric 136

 Temperatures

MODIS Level 3 Atmosphere 3 MOD 08 Cloud and Aerosol 115

 Products Properties and 

 Radiative

Energy Fluxes

MODIS Surface Reflectance; 2 MOD 09 Vegetation Dynamics, 170

 Atmospheric Correction Land Cover, and 

 Algorithm Products Land Cover Change

MODIS Snow Cover 2,3 MOD 10 Snow and Ice 211

 Cover

MODIS Land Surface 2,3 MOD 11, Surface Temperatures 159

 Temperature (LST) and 11comb, of Land and Oceans,

 Emissivity 11adv Fire Occurrence, and

 Volcanic Effects

 

MODIS Land Cover Type 3 MOD 12 Vegetation Dynamics, 171

 Land Cover, and 

Land Cover Change

MODIS Vegetation Indices 3 MOD 13 Vegetation Dynamics, 173

 Land Cover, and 

 Land Cover Change

MODIS Thermal Anomalies – 2,3 MOD 14 Surface Temperatures 161

 Fires of Land and Oceans,

 Fire Occurrence, and

 Volcanic Effects

MODIS Leaf Area Index and Fraction 4 MOD 15 Vegetation Dynamics, 176

 of Photosynthetically Active Land Cover, and

 Radiation – Moderate Resolution Land Cover Change
14 Data Products Handbook - Volume 2 List of Data Products (Organized by Mission Name) 

 Mission Instrument Data Set Proc. Prod. ID Chapter Page # Level

Aqua

Aqua

MODIS Surface Resistance and 4 MOD 16 Vegetation Dynamics, 178

 Evapotranspiration Land Cover, and

 Land Cover Change

MODIS Vegetation Production and 4 MOD 17 Vegetation Dynamics, 179

 Net Primary Production Land Cover, and

 Land Cover Change

MODIS Normalized Water-Leaving 2,3 MOD 18 Radiance/ 60

 Radiance Reflectance and

 Irradiance Products

MODIS Pigment Concentration 2,3 MOD 19 Phytoplankton and 190

 Dissolved Organic 

Matter

MODIS Chlorophyll Fluorescence 2,3 MOD 20 Phytoplankton and 193

 Dissolved Organic 

Matter

MODIS Chlorophyll a Pigment 2,3 MOD 21 Phytoplankton and 195

 Concentration Dissolved Organic 

 Matter

MODIS Photosynthetically 2,3 MOD 22 Radiance/ 62

 Active Radiation Reflectance and

 Irradiance Products

MODIS Suspended Solids 2,3 MOD 23 Phytoplankton and 190

 Concentration Dissolved Organic 

 Matter

MODIS Organic Matter 2,3 MOD 24 Phytoplankton and 190

 Concentration Dissolved Organic 

 Matter

MODIS Coccolith 2,3 MOD 25 Phytoplankton and 197

 Concentration Dissolved Organic 

 Matter

MODIS Ocean Water Attenuation 2,3 MOD 26 Phytoplankton and 190

 Coefficient Dissolved Organic 

 Matter 

MODIS Ocean Primary Productivity 4 MOD 27 Phytoplankton and 198

 Dissolved Organic 

Matter

MODIS Sea Surface Temperature 2,3 MOD 28 Surface Temperatures 162

 of Land and Oceans,

 Fire Occurrence, and

 Volcanic Effects

MODIS Sea Ice Cover 2,3 MOD 29 Snow and Ice 213

 Cover
List of Data Products (Organized by Mission Name) Data Products Handbook - Volume 2 15

 Mission Instrument Data Set Proc. Prod. ID Chapter Page # Level

Aqua

MODIS Phycoerythrin 2,3 MOD 31 Phytoplankton and 200

 Concentration Dissolved Organic 

 Matter

MODIS Processing Framework 2 MOD 32 Radiance/ 63

 and Match-up Database Reflectance and

 Irradiance Products

MODIS Cloud Mask 2 MOD 35 Cloud and Aerosol 118

 Properties and 

Radiative

Energy Fluxes

MODIS Absorption Coefficients 2,3 MOD 36 Phytoplankton and 195

 Dissolved Organic 

Matter

MODIS Aerosol Optical Depth 2,3 MOD 37 Radiance/ 60

 Reflectance and

Irradiance Products

MODIS Clear-Water Epsilon 2,3 MOD 39 Radiance/ 64

 Reflectance and

Irradiance Products

MODIS Burn Scars 4 MOD 40 Surface Temperatures 161

 of Land and Oceans,

Fire Occurrence, and

 Volcanic Effects

MODIS Surface Reflectance BRDF/ 3 MOD 43 Vegetation Dynamics, 180

 Albedo Parameter Land Cover, and

 Land Cover Change

MODIS Vegetation Cover Conversion 4 MOD 44 Vegetation Dynamics, 182

 Land Cover, and

Land Cover Change

MODIS Snow and Sea Ice 3 MODISALB Snow and Ice 214

 Albedo Cover

JMR Columnar Water Vapor 2 Precipitation and 85

 Content Atmospheric Humidity

Poseidon-2 Normalized Radar 2 Winds 143

 Backscatter Coefficient and

 Wind Speed

Poseidon-2 Significant Wave Height 2 Sea Surface Height 150

 and Ocean Wave

Dynamics

Jason-1
16 Data Products Handbook - Volume 2 List of Data Products (Organized by Mission Name) 

 Mission Instrument Data Set Proc. Prod. ID Chapter Page # Level

Poseidon-2 Total Electron Content 2 Atmospheric 127

 Chemistry

Poseidon-2/ Sea Surface Height 2 Sea Surface Height 149

JMR/DORIS and Ocean Wave

 Dynamics

ETM+ Raw Digital Numbers 0R L70R Radiance/ 66

 Reflectance and

Irradiance Products

ETM+ Calibrated Radiances 1R L71R Radiance/ 67

 Reflectance and

Irradiance Products

ETM+ Calibrated Radiances 1G L71G Radiance/ 68

 Reflectance and

Irradiance Products

SAGE III Atmospheric Slant-Path 1B SAGE III 1B Radiance/ 69

 Transmission Product Reflectance and 

 Irradiance Products

SAGE III Aerosol and Cloud 2 SAGE III/ Cloud and Aerosol 120

 Data Products 01, 02 Properties and 

 Radiative 

Energy Fluxes

SAGE III Water Vapor Products 2 SAGE III 03 Precipitation and 86

 Atmospheric 

Humidity

SAGE III NO2, NO3, O3, and 2 SAGE III/ Atmospheric 128

 OClO Data Products 04, 05, 06, Chemistry

 07

SAGE III Temperature and Pressure 2 SAGE III 08 Atmospheric 138

 Data Products Temperatures

SeaWinds Grouped and Surface- 2A Winds 145

 Flagged Backscatter and

 Attenuations in 25 km Swath

 Grid

SeaWinds Normalized Radar 1B Winds 144

 Cross-Section and

 Ancillary Data

SeaWinds Ocean Wind Vectors 3 Winds 145

 in 25 km Grid

SeaWinds Ocean Wind Vectors 2B Winds 145

 in 25 km Swath Grid

Landsat-7

Meteor 

3M/SAGE 

QuikScat

Jason-1
List of Data Products (Organized by Mission Name) Data Products Handbook - Volume 2 17

 Mission Instrument Data Set Proc. Prod. ID Chapter Page # Level

TOMS Aerosol Product 3 Aerosol Cloud and Aerosol 122

 Properties and 

Radiative

Energy Fluxes

TOMS Ozone Product 3 Ozone Atmospheric 129

 Chemistry

MBLA Geolocated Ground Elevations 2 Vegetation Dynamics, 184

 Land Cover, and

Land Cover Change

MBLA Geolocated Canopy- 2 Vegetation Dynamics, 186

 Top Heights Land Cover, and

 Land Cover Change

MBLA Geolocated Vertical 2 Vegetation Dynamics, 185

 Distribution of Intercepted Land Cover, and

 Surfaces Land Cover Change

QuikTOMS

VCL
19

Introduction

ACRIMSAT • ACRIM III

Aqua

• AIRS

• AMSR-E

• AMSU-A

• CERES

• HSB

• MODIS

Jason-1 • DORIS

• JMR

• Poseidon-2

Landsat 7 • ETM+

Meteor 3M • SAGE III

QuikScat

• SeaWinds

QuikTOMS • TOMS

VCL

• MBLA
20 Data Products Handbook - Volume 2 Introduction 

Introduction

Background and Context

The Earth Observing System (EOS) is an international program, centered within NASAʼs Earth 

Science Enterprise, to address fundamental issues 

regarding Earth system science through collection 

and analysis of satellite data. The program involves 

numerous individual satellite missions, teams of scientists to develop and apply techniques to convert 

the satellite data to meaningful geophysical products, 

and an extensive EOS Data and Information System 

(EOSDIS) to process, archive, and distribute the data 

products. To assist potential users of the EOS data, the 

EOS Data Products Handbook is meant as a convenient reference on the data products being produced 

from the EOS and associated missions.

 The current, second volume of the Data Products 

Handbook provides information regarding the data 

products from the Active Cavity Radiometer Irradiance Monitor Satellite (ACRIMSAT), Aqua, Jason-1, Landsat 7, Meteor 3M/SAGE III, the Quick 

Scatterometer (QuikScat), the Quick Total Ozone 

Mapping Spectrometer (QuikTOMS), and the Vegetation Canopy Lidar (VCL) missions. Volume 1 (Wharton and Myers, 1997) covers the data products from 

the Tropical Rainfall Measuring Mission (TRMM), 

Terra (formerly named EOS AM-1), and the Data 

Assimilation System; and Volume 3 is scheduled to 

cover the data products from the Gravity Recovery 

and Climate Experiment (GRACE), Earth Observing 1 (EO-1), the Ice, Clouds, and Land Elevation 

Satellite (ICESat), the Solar Radiation and Climate 

Experiment (SORCE), Pathfinder Instruments for 

Cloud and Aerosol Spaceborne Observations-Climatologie Etendue des Nuages et des Aerosols 

(PICASSO-CENA), CloudSat, the International 

Space Station SAGE III, and Aura. Because two of 

the instruments, the Clouds and the Earthʼs Radiant 

Energy System (CERES) and the Moderate Resolution Imaging Spectroradiometer (MODIS), are on 

both the Terra and Aqua missions, with CERES being 

on TRMM as well, their data products are covered in 

both Volumes 1 and 2. Hence the descriptions in this 

volume of the CERES and MODIS products repeat 

some of the material in Volume 1, as well as providing 

updates where appropriate. Amongst the updates, in 

this volume some of the data product descriptions 

are illustrated by actual CERES and MODIS images 

from the Terra and TRMM missions, something that 

was not possible in the original printing of Volume 1, 

which was published prior to the TRMM and Terra 

satellite launches.

 This volume can be used either as a source for 

determining what data products are being produced 

from the eight missions covered or, for individuals 

seeking information on specific products, as a reference providing specifics and contact information on 

those products. In the latter case, individuals seeking 

information about a particular data product should 

turn to the Data Products Index in Appendix E or to 

the expanded listing of the data products, with product 

IDs, immediately following the Table of Contents and 

Foreword, to find the appropriate page numbers for 

the desired product.

Volume Synopsis

Volume 2 follows closely the format of Volume 1, 

beginning with a list of products and an introduction 

and overview descriptions of the instruments and data 

processing, followed by the core of the book, which 

presents the individual data product descriptions. 

The data products are grouped into the following 11 

topical chapters:

• Radiance/Reflectance and Irradiance Products

• Precipitation and Atmospheric Humidity

• Cloud and Aerosol Properties and Radiative Energy Fluxes

• Atmospheric Chemistry

• Atmospheric Temperatures

• Winds

• Sea Surface Height and Ocean Wave Dynamics

• Surface Temperatures of Land and Oceans, Fire 

Occurrence, and Volcanic Effects

• Vegetation Dynamics, Land Cover, and Land 

Cover Change

• Phytoplankton and Dissolved Organic Matter

• Snow and Ice Cover.

Each topical chapter begins with a short overview 

that discusses the relationship of the chapterʼs subject matter to global change issues, overviews the 

products presented in the chapter, indicates some of 

the product interdependencies and product heritages, 

and lists suggested relevant readings.

 The data product sections are mostly 1- or 2-page 

summaries, divided into short subsections on:

• Product Description

• Research and Applications

• Data Set Evolution (from precursor data sets)

• Suggested Reading

• A boxed product summary highlighting the following specifics about the data product:

- Coverage (the portion of the globe covered)
Introduction Data Products Handbook - Volume 2 21

- Spatial/Temporal Characteristics (space and time 

resolutions)

- Wavelengths or Frequencies (for the products 

in the chapter titled “Radiance/Reflectance and 

Irradiance Products”)

- Key Science Applications

- Key Geophysical Parameters

- Processing Level (indicating processing levels 

as defined in the Foreword)

- Product Type (indicating whether the product 

is a standard product or a research product and 

whether the computer coding for it will be available at-launch or post-launch; standard products 

are routinely processed for each applicable data 

acquisition, whereas research products are not)

- Maximum File Size

- File Frequency

- Primary Data Format

- Browse Available (indicating whether or not a 

browse is available, what type of browse is available, or a location for browse information)

- Additional Product Information (indicating a 

web site for the information)

- DAAC (for the productʼs data archival and distribution)

- Science Team Contact/Contacts (with the full 

contact information provided in Appendix B).

Not all the product descriptions contain each of the 

items in the above list, as in some cases the item is 

not applicable and in others the relevant information was not available at the time of printing. Most 

contain most of the items, however, and some additionally contain illustrative material. More detailed 

information about the data products and the theory 

and background behind their derivation is in the 

respective Algorithm Theoretical Basis Documents 

(ATBDs), available over the internet at the EOS Project Science Office Home Page (http://eos.nasa.gov). 

This site also contains electronic versions of both 

Volumes 1 and 2 of the Data Products Handbook.

 Following the data product descriptions are five appendices, providing contact information for the EOS 

data centers that will be archiving and distributing the 

data sets to the user community (Appendix A), contact 

information for the science points of contact for the 

data products (Appendix B), references (Appendix 

C), acronyms and abbreviations (Appendix D), and 

a data products index (Appendix E).

Data Availability

The data products described in the EOS Data 

Products Handbook will be archived and distributed through the various Distributed Active Archive 

Centers (DAACs) of the EOS Data and Information 

System (EOSDIS). Contact information for these 

DAACs is included in Appendix A. It is hoped that 

these data will be widely used, and although the data 

are not copyrighted, it is requested that researchers 

publishing results using EOS data sets include an 

acknowledgment along the lines of:

 

Data used in this research include data produced 

through the funding of NASAʼs Earth Science Enterprise (ESE) Earth Observing System (EOS) 

program.

 

Furthermore, publications using data provided by 

EOSDIS should include the following:

 

Data used in this research include data provided to 

the authors by the NASA-funded EOS Data and Information System archive at [insert the name of the 

appropriate DAAC].

Further Information Sources

The reader is referred to Volume 1 (Wharton and 

Myers, 1997) for information about the TRMM and 

Terra data products and additional background on the 

objectives of the EOS Data Products Handbook. The 

reader is further referred to the MTPE/EOS Reference 

Handbook (Asrar and Greenstone, 1995) and the 1999 

EOS Reference Handbook (King and Greenstone, 

1999) for background on NASAʼs Earth Science 

Enterprise (formerly called Mission to Planet Earth) 

and for details on the entire EOS program, including 

its scope, purposes, individual mission elements and 

instruments, science teams, interdisciplinary science 

investigations, and educational outreach programs. 

Finally, the reader is referred to the Science Strategy 

for the Earth Observing System (Asrar and Dozier, 

1994) and the EOS Science Plan (King, 1999) and its 

accompanying Executive Summary (Greenstone and 

King, 1999) for details on the science issues being 

addressed by the EOS program and the approaches 

being used.
22 Data Products Handbook - Volume 2 Introduction

Suggested Reading

(full references are listed in Appendix C)

Asrar, G., and J. Dozier, 1994.

Asrar, G., and R. Greenstone, 1995.

Greenstone, R., and M.D. King, 1999.

King, M.D., 1999.

King, M.D., and R. Greenstone, 1999.

Wharton, S.W., and M.F. Myers, 1997.
15

Instrument Descriptions

and

Data Processing Overviews

ACRIMSAT • ACRIM III

Aqua

• AIRS

• AMSR-E

• AMSU-A

• CERES

• HSB

• MODIS

Jason-1 • DORIS

• JMR

• Poseidon-2

Landsat 7 • ETM+

Meteor 3M • SAGE III

QuikScat

• SeaWinds

QuikTOMS • TOMS

VCL

• MBLA
16 Data Products Handbook - Volume 2 Instrument Descriptions and Data Processing Overviews 

Overview

The data products described in this volume are 

derived from the data of eight satellite missions 

and 15 satellite instruments. Of the 15 instruments, 

six are scheduled to fly on board the Aqua satellite, 

three are scheduled to fly on board Jason-1, and one 

each is scheduled to fly on board the Active Cavity 

Radiometer Irradiance Monitor Satellite (ACRIMSAT), Landsat 7, Meteor 3M/SAGE III, Quick Scatterometer (QuikScat), Quick Total Ozone Mapping 

Spectrometer (QuikTOMS), and Vegetation Canopy 

Lidar (VCL) satellites. Of the eight missions, ACRIMSAT, Landsat 7, and QuikScat were launched 

in 1999, while the other five are all scheduled for 

launch in 2001 or 2002. The table entitled “Satellite 

missions covered in this volume” (below) provides 

further information about the eight missions. Briefly, 

in alphabetical order the 15 instruments are:

• The Active Cavity Radiometer Irradiance Monitor 

III (ACRIM III), aimed at monitoring the total solar 

irradiance with a high level of precision. ACRIM 

III is on board ACRIMSAT, launched in December 

1999. It continues the long-term measurements of 

the total amount of solar energy arriving at the 

Earth begun with the precursor ACRIM I and ACRIM II instruments, launched in 1980 and 1991 

on the Solar Maximum Mission (SMM) and the 

Upper Atmosphere Research Satellite (UARS), 

respectively.

• The Advanced Microwave Scanning Radiometer 

for EOS (AMSR-E), a 12-channel microwave 

radiometer provided by Japan and aimed at monitoring a range of hydrologic variables including 

water vapor, cloud liquid water, rainfall, sea surface temperature and sea ice temperature, sea ice 

concentration, snow depth on sea ice, snow water 

equivalent over land, and soil moisture. Surface 

variables will be monitored at a coarse spatial resolution (ranging approximately from 5 to 56 km) but 

will be obtainable day and night and under almost 

all weather conditions. AMSR-E will be on the 

Aqua satellite.

• The Advanced Microwave Sounding Unit (AMSUA), a 15-channel microwave sounder designed priMission Orbit Type Altitude Equatorial Launch date or

 crossing time scheduled month

Landsat 7 Sun-synchronous, 705 km 10:05 a.m., April 15, 1999

 98.2° inclination descending node

QuikScat Sun-synchronous, 803 km 5:55 a.m., June 19, 1999

 98.6° inclination ascending node

ACRIMSAT Sun-synchronous 685 km 10:50 a.m., December 20, 1999

 98.13° inclination descending node

Jason-1 Circular, non-sun- 1336 km ---------- February 2001

 synchronous,

 66° inclination

Meteor 3M/ Sun-synchronous, 1020 km 9:30 a.m., March 2001

SAGE III 99.5° inclination ascending node

QuikTOMS Sun-synchronous, 800 km 10:30 a.m., April 2001

 97.3° inclination descending node

Aqua Sun-synchronous, 705 km 1:30 p.m., July 2001

 98.2° inclination ascending node

VCL Circular, non-sun- 390-410 km ----------- May 2002

 synchronous,

 67° inclination

Satellite missions covered in this volume
Instrument Descriptions and Data Processing Overviews Data Products Handbook - Volume 2 17

marily to obtain temperature profiles in the upper 

atmosphere (especially the stratosphere) and to 

provide a cloud-filtering capability for tropospheric 

temperature observations. The first AMSU was 

launched in May 1998 on board the National Oceanic and Atmospheric Administrationʼs (NOAAʼs) 

NOAA 15 satellite. The EOS AMSU-A will be on 

board the Aqua satellite, as part of a closely coupled 

triplet of instruments including also the AIRS and 

HSB.

• The Atmospheric Infrared Sounder (AIRS), an 

advanced sounder containing 2378 infrared channels and four visible/near-infrared channels, aimed 

at obtaining highly accurate temperature profiles 

within the atmosphere plus a variety of additional 

Earth/atmosphere products. AIRS will be on board 

the Aqua satellite, as the highlighted instrument in 

the AIRS/AMSU-A/HSB triplet centered on measuring accurate temperature and humidity profiles 

throughout the atmosphere.

• The Clouds and the Earthʼs Radiant Energy System 

(CERES), a 3-channel radiometer measuring shortwave radiation in the wavelength band 0.3-5 μm, 

longwave radiation in the band 8-12 μm, and total 

radiation from 0.3 μm to over 100 μm. These data 

will be used to determine radiative fluxes and 

balances, and, in combination with MODIS data, 

detailed information about clouds. The first CERES 

was launched on the Tropical Rainfall Measuring 

Mission (TRMM) in November 1997; the second 

and third CERES were launched on the Terra satellite in December 1999; and the fourth and fifth 

CERES will be on board the Aqua satellite.

• The Doppler Orbitography and Radiopositioning 

Integrated by Satellite (DORIS), a precise orbit 

determination system measuring the doppler shift 

of radiofrequency signals transmitted from groundbased beacons at 2.03625 GHz and 401.25 MHz. 

The 2.03625 GHz measurements are for precise 

doppler determinations, while the 401.25 MHz 

measurements are for ionospheric corrections. A 

DORIS is on board the TOPEX/Poseidon mission, 

and an updated version is scheduled to fly on board 

the Jason-1 satellite.

• The Enhanced Thematic Mapper Plus (ETM+), an 

8-band imaging radiometer aimed at providing high 

spatial resolution, multispectral images of the sunlit 

land surface, using visible, near-infrared, shortwave 

infrared, and thermal infrared wavelength bands, 

along with a panchromatic band. The ETM+ is on 

board the Landsat 7 satellite, launched in April 

1999. It is an enhanced version of the Thematic 

Mapper (TM) on board earlier Landsat satellites.

• The Humidity Sounder for Brazil (HSB), a 4-channel microwave sounder provided by Brazil and 

aimed at obtaining humidity profiles throughout 

the atmosphere. The HSB will be on board the Aqua 

satellite, as the instrument in the AIRS/AMSU-A/

HSB triplet that will allow humidity measurements 

even under conditions of heavy cloudiness and 

haze.

• The Jason-1 Microwave Radiometer (JMR), a 

3-frequency microwave radiometer measuring 

brightness temperatures at 18.7, 23.8, and 34 GHz. 

The primary objective of the JMR is to measure 

total water vapor along the path viewed by the 

Poseidon-2 altimeter, both for range correction 

for the Poseidon-2 topography measurements 

and for determining atmospheric columnar water 

vapor content. JMR will be on board the Jason-1 

satellite.

• The Moderate Resolution Imaging Spectroradiometer (MODIS), a 36-band spectroradiometer measuring visible and infrared radiation and 

obtaining data that will be used to derive products 

ranging from vegetation, land surface cover, and 

ocean chlorophyll fluorescence to cloud and aerosol 

properties, fire occurrence, snow cover on the land, 

and sea ice cover on the oceans. The first MODIS 

instrument was launched on board the Terra satellite in December 1999, and the second MODIS will 

be on board the Aqua satellite.

• The MultiBeam Laser Altimeter (MBLA), a fivebeam laser instrument to measure the heights of 

vegetation canopies and land surface topographies 

and to provide the first global inventory of the 

vertical structure of the Earthʼs forests. The measurements are expected to enable by far the most 

accurate estimates of the global forest biomass. 

MBLA is scheduled to fly on the VCL mission, 

the first mission of NASAʼs Earth System Science 

Pathfinder (ESSP) program.

• Poseidon-2, a dual-frequency radar altimeter aimed 

at mapping the topography of the sea surface and 

measuring ocean wave height and wind speed, 

thereby allowing monitoring of the global ocean 

circulation and more precise determinations of the 

large-scale coupling between the oceans and atmosphere. Poseidon-2 is being provided by France 
18 Data Products Handbook - Volume 2 Instrument Descriptions and Data Processing Overviews 

(Overview, continued)

and is an enhanced version of the Poseidon-1 

instrument flown on TOPEX/Poseidon, launched 

in 1992. Poseidon-2 will be on board the Jason-1 

satellite.

• SeaWinds, a Ku-band scatterometer aimed primarily at measuring near-surface wind velocities 

over the global oceans under almost all weather 

and cloud conditions and secondarily at providing information for vegetation classification and 

ice-type monitoring. SeaWinds is a follow-on to 

the NASA Scatterometer (NSCAT), which flew on 

the Japanese Advanced Earth Observing Satellite 

(ADEOS) in 1996-1997. The first SeaWinds is on 

board QuikScat, launched in June 1999, and the 

second SeaWinds is scheduled for ADEOS II.

• The Stratospheric Aerosol and Gas Experiment 

III (SAGE III), an Earth limb-scanning grating 

spectroradiometer aimed at retrieving global profiles of atmospheric aerosols, ozone, water vapor, 

nitrogen oxides, chlorine dioxide, temperature, 

and pressure. SAGE III is an enhanced version 

of the earlier Stratospheric Aerosol Measurement 

II (SAM II), SAGE I, and SAGE II instruments, 

launched in 1978, 1979, and 1984 on board Nimbus 

7, the Applications Explorer Mission 2 (AEM 2), 

and the Earth Radiation Budget Satellite (ERBS), 

respectively. It will fly on board the Russian Meteor 

3M satellite, with an additional SAGE III instrument scheduled to fly on board the International 

Space Station.

• The Total Ozone Mapping Spectrometer (TOMS), 

a six-band instrument measuring ultraviolet radiation with the primary objective of continuing 

the daily global (sunlit) mapping of total column 

atmospheric ozone, thereby monitoring the degeneration and/or regeneration or stabilization of the 

Earthʼs protective ozone layer. TOMS also measures aerosols and sulfur dioxide, the latter enabling 

monitoring of volcanic eruption plumes. Several 

TOMS instruments have flown, the first launched 

in October 1978 on the Nimbus 7 satellite and two 

others launched in 1996 on ADEOS and in 1996 

on Earth Probe TOMS. An additional TOMS will 

fly on board QuikTOMS.

Greater detail on each of the instruments is provided 

in the remainder of this chapter, arranged alphabetically according to mission name.
Instrument Descriptions and Data Processing Overviews • Data Products Handbook - Volume 2 19

ACRIM III

Active Cavity Radiometer Irradiance Monitor 

Sustained changes in the total solar irradiance (TSI) 

smaller than 0.25% may have been the prime causal 

factor for significant climate change in the past, on 

time scales of centuries to millennia. The long-range 

objective of TSI monitoring is to provide a precision 

database for comparison with the climate record that 

will be capable of resolving systematic variability of 

a tenth of one percent on time scales of a century. The 

continuation of the climate TSI database during solar 

cycle 23, establishment of its precise relationship to 

previous and successive components of the database, 

and analysis of TSI variability on all time scales with 

respect to the climatological and solar physics significance constitute the primary objectives of the 

ACRIM experiment.

 The primary approach of the experiment is observational: to monitor the variability of total solar 

irradiance, extending the NASA high-precision solar 

total irradiance variability database into solar cycles 

23 and 24. This database has been compiled as part 

of NASAʼs Earth radiation budget ʻprincipal thrustʼ 

responsibility in the National Climate Program. ACRIM flight experiments have provided the precision 

database from the peak of solar cycle 21 (1980) to 

the present, except for a two-year gap between the 

end of the SMM/ACRIM I (1989) mission and the 

beginning of the UARS/ACRIM II (1991) mission. 

 The detection of subtle solar luminosity variability 

during solar cycles 21, 22, and 23 underscores the 

need to extend the irradiance database indefinitely 

with maximum precision. There may be other luminosity variabilities with longer periodicities and/or 

proportionately larger amplitudes that could have 

significant climatic implications. Subtle trends in 

the total irradiance of as little as 0.1% per century 

could eventually produce the extreme range of climates known to have existed in the past, from warm 

periods without permanent ice, to the great ice ages. 

Accumulation of a database capable of detecting 

such trends will necessarily require the results of 

many individual solar-monitoring experiments. If 

these experiments last an average of half a decade 

each, about the maximum that can be expected from 

todayʼs technology, they will have to be related with 

a precision smaller than 10 parts per million (ppm). 

 A careful measurement strategy will be required 

to sustain adequate precision for the database since 

the uncertainty of current satellite instrumentation 

on an absolute basis is inadequate for this purpose 

(no better than +/- 0.1%). An approach must be used 

that relates successive solar-monitoring experiments 

at a precision level defined by the operation of the instrumentation. Overlapping flights and comparing the 

observations accomplishes this. A relative precision 

of less than 5 ppm is achievable for the data from 

overlapped solar monitors with the current state-ofthe-art, given sufficient comparison observations. 

 The principal remaining uncertainty for the 

“overlap strategy” is the degradation of solar-monitor sensors during extended missions. Calibration 

of degradation, using redundant sensors in phased 

operation, can sustain the precision of the longterm TSI database at the level of 10 ppm or less. 

Considerable effort will be expended to implement 

the degradation comparisons and to compare with 

preceding (UARS/ACRIM II and SOHO/VIRGO) 

and succeeding (TSIM) TSI experiments.

 Level 0 TSI data are first processed to Level 1 

by conversion to SI units of watts per square meter (W/m2

) at the satellite. The satellite TSI is then 

reconciled to 1 Astronomical Unit using the Solar 

Elevation, Azimuth, and Range computation of the 

Satellite Tool Kit software (Analytical Graphics, 

Inc.), based on ACRIMSATʼs NORAD two-line orbital elements. The primary archived data products 

are Level 2 shutter cycle and daily means. The time 

scale of the shutter cycle means is 2.048 minutes.

 As summarized in the figure on the following 

page, the ACRIM III experiment has been deployed 

on ACRIMSAT, a dedicated small satellite. A ground 

station at the Table Mountain Observatory of the Jet 

Propulsion Laboratory (JPL) controls ACRIMSAT 

and will be the downlink site for Level 0 science data. 

The Level 0 data will be provided via the Internet 

to the Science Teamʼs Science Computing Facility 

(SCF)/Science Investigator-led Processing System 

(SIPS) in Coronado, California. Level 0 data, ancillary data, and Level 2 results will be provided to 

the Langley DAAC by the ACRIM III Science Team 

SIPS.

ACRIMSAT Home Pages

 Science:

 http://acrim.com

 Satellite and Instrument: 

 http://acrim.jpl.nasa.gov/

ACRIMSAT / ACRIM III

ACRIMSAT Mission
20 Data Products Handbook - Volume 2 ACRIMSAT / ACRIM III • Instrument Descriptions and Data Processing Overviews 

ECS-ACRIM III SIPS Functional Interface

ACRIMSAT Mission

Key ACRIM III Facts

Selected for flight on ACRIMSAT, launched 

December 20, 1999

Heritage: ACRIM I, ACRIM II

Contains three active cavity radiometer 

type IV sensors

Measures total solar irradiance

ACRIMSAT

JPL’s

Table Mountain

Product Delivery Records

ACRIM III Products

Br

Metadata

owse

Delivered Algorithm Packages

Product Acceptance Notices

Product Delivery Record Discrepancies

QA Metadata Updates

ACRIM III SIPS

with Product Delivery 

Record Server

EOSDIS Core System (ECS)

 at Langley DAAC
Instrument Descriptions and Data Processing Overviews • Data Products Handbook - Volume 2 21

AIRS/AMSU-A/HSB

• Atmospheric InfraRed Sounder (AIRS)

• Advanced Microwave Sounding Unit (AMSU-A)

• Humidity Sounder for Brazil (HSB)

AIRS is a high-resolution infrared (IR) sounder 

selected to fly on the EOS Aqua platform with two 

operational microwave sounders, AMSU-A and 

HSB. Measurements from the three instruments will 

be analyzed jointly to filter out the effects of clouds 

from the IR data in order to derive clear-column airtemperature profiles and surface temperatures with 

high vertical resolution and accuracy. Together, these 

three instruments constitute an advanced operational 

sounding system whose data will:

a) improve global modeling efforts and numerical 

weather prediction;

b) enhance studies of the global energy and water 

cycles, the effects of greenhouse gases, and atmosphere-surface interactions; and

c) facilitate monitoring of climate variations and 

trends.

AIRS measures upwelling radiances in 2378 spectral 

channels covering the IR spectral band, 3.74 micrometers to 15.4 micrometers. A set of four channels in 

the visible/Near-IR (VIS) observes wavelengths from 

0.4 to 1.0 micrometers to provide cloud cover and 

spatial-variability characterization. The microwave 

sounders provide sea ice concentration, snow cover, 

and additional temperature-profile information as 

well as precipitable water and cloud liquid-water 

content. If cloud cover is too great for IR retrievals, 

the microwave (MW) measurements alone will provide a coarse, low-precision atmospheric-temperature 

profile and surface characterization.

 The figure on page 23 provides an overview of the 

data processing architecture and the products which 

originate at the various stages.

 Three independent Product Generation Executives 

(PGEs), one each for AIRS/VIS, AMSU-A, and HSB, 

execute at the DAAC to ingest Level 0 data to produce Level 1A geolocated science data counts and 

engineering parameters in Hierarchical Data Format 

(HDF) swath format.

 Four independent PGEs, one for each instrument, 

execute at the DAAC to ingest Level 1A data to 

produce a Level 1B calibrated IR radiance product, 

a microwave brightness temperature product, and associated calibration coefficients. The AIRS L1B PGE 

also produces a subset data file of selected cloudy IR 

radiances for ingest by the AIRS L1B daily browse 

PGE. The AIRS and AMSU-A/HSB L1B daily 

browse PGEs subsequently produce browse products from this subset file and the L1B brightness 

temperature product data.

 A combined AIRS/AMSU-A/HSB/VIS Level 

2 PGE executes at the DAAC to ingest the Level 

1B data products and ancillary data and to produce 

Level 2 standard products and support products, all 

in HDF swath format. A Level 2 subset data file is 

also produced for ingest by the Level 2 daily browse 

PGE. This daily browse PGE produces browse products from this subset file as well as summary data to 

be ingested by follow-on Level 2 pentad and Level 2 

monthly browse PGEs. All browse PGEs are executed 

at the DAAC.

 Quality Assurance (QA) Support Products are produced at the Team Leader Science Computing Facility 

(TLSCF) in HDF swath format for a fraction of the 

total data set and are used to monitor the operation 

of the instrument and validate the retrieval algorithm 

and products.

 The microwave Level 1B data sets and Level 2 

standard products are ingested at the TLSCF along 

with the ancillary radiosonde observations (RAOBS) 

to produce a daily statistic by comparison of profiles 

observed by radiosondes with spatially and temporally matched retrieval profiles.

 The combined Level 2 PGE consists of three major 

retrieval stages:

• Microwave-Only Retrieval

• Cloud Clearing

• Combined MW/IR/VIS Retrieval

The Microwave-Only Retrieval employs only the 

AMSU-A and HSB data in combination with an ancillary general circulation model (GCM) (providing 

general climatology and accurate surface pressure) 

and a digital elevation model (DEM) (providing 

topography and land/water fractions) to retrieve atmospheric temperature, water vapor, and liquid-water 

profiles as well as surface skin temperature and microwave spectral surface emissivity. A rain flag is also 

produced. These profiles are passed to the combined 

MW/IR/VIS retrieval as an initial guess. In the event 

of rain or overwhelming cloud cover, the combined 

retrieval is not attempted and the MW-Only Retrieval 

is passed through to the standard product.

 The Cloud Clearing Stage employs MW and 

selected IR channels to estimate the clear-column 

IR radiances for all IR channels. It also employs IR 

window channels to retrieve the surface skin temperature, emissivity, and reflectivity. The temperature 

and water-vapor profiles are corrected for the effects 

of the clouds. This rapid stage produces reasonably 

accurate AIRS/AMSU-A products that are suitable 

Aqua / AIRS/AMSU-A/HSB

Aqua Mission
22 Data Products Handbook - Volume 2 • Instrument Descriptions and Data Processing Overviews 

for assimilation into operational numerical weather 

prediction models.

 The Combined MW/IR/VIS Retrieval Stage recursively refines the temperature and water-vapor profiles and surface parameters, while retrieving ozone 

and multiple cloud-layer parameters and calculating 

uncertainties for all retrieved parameters. If a profile is rejected, a new retrieval is produced, starting 

from the Microwave-Only product and utilizing the 

AMSU-A/HSB channels together with those AIRS 

channels which are insensitive to cloud effects. The 

final profiles and improved clear-column radiances 

for all IR channels are passed to the standard product.

AIRS Home Page

 http://www-airs.jpl.nasa.gov

AIRS

AMSU-A

HSB

Key AIRS/AMSU-A/HSB Facts

Selected for flight on Aqua

Heritage: HIRS/MSU

AIRS is a high-spectral-resolution, grating 

infrared sounder operated in a crosstrack-scanning mode; AMSU-A and 

HSB are both multi-channel microwave 

radiometers

Provides global atmospheric temperature 

and humidity profiles, ozone burden, 

cloud emissivity, cloud-top temperatures 

and pressures, and surface skin temperature and emissivity

(AIRS/AMSU-A/HSB Overview, continued)

Aqua / AIRS/AMSU-A/HSB

Aqua Mission
Instrument Descriptions and Data Processing Overviews • Aqua / AIRS/AMSU-A/HSB Data Products Handbook - Volume 2 23

Aqua Mission

AIRS/AMSU-A/HSB Data Processing Architecture and Products Level 0

Engineering

Archival

Product

Daily Browse Product

(HDF Format) Daily Browse Product

(HDF Format)

Daily Browse 

Product

(HDF Format)

Pentad/Monthly 

Browse Products

(HDF Format)

Support

Products

(HDF Swath)

QA Support

Products

(HDF Swath)

Statistics of

Matched 

Profiles

AIRS

L0 Data

VIS

L0 Data

AIRS/VIS L1A PGE

AMSU-A

L0 Data

HSB

L0 Data

AMSU-A L1A PGE HSB L1A PGE

Geo-Located

Science Data Counts and

Engineering Parameters

(HDF Swath Format)

Geo-Located

Science Data Counts and

Engineering Parameters

(HDF Swath Format)

AMSU-A L1B PGE HSB L1B PGE

Calibrated, Geo-Located 

Brightness Temperatures 

and Calibration 

Coefficients

(HDF Swath Format)

Calibrated, Geo-Located 

Brightness Temperatures 

and Calibration 

Coefficients

(HDF Swath Format)

AIRS L1B Daily

Browse PGE

AMSU-A

and HSB

L1B

Products

and

RAOBS

Daily Stat PGE

Surface Skin Temperature 

Surface Emissivity

Surface Snow/Ice Detection

Rain Flag

Temperature Profile

H2O Vapor and Liquid Profiles

Cloud Cleared IR Radiances

Refined T, H2O Profiles

At TLSCF

Standard

Products

(HDF Swath)

Combined AIRS/AMSU-A/HSB/VIS Level 2 PGE

Combined

MW/IR/VIS

Retrieval Stage

Cloud Cleared IR Radiances

Surface Skin T, Emissivity

T, H2O, O3 Profiles

Cloud Cover, Emissivity, T, p

Microwave-Only Retrieval Stage

Cloud Clearing Stage

Geo-Located

Science Data Counts and

Engineering Parameters

(HDF Swath Format)

AIRS L1B PGE VIS L1B PGE

AIRS L1B 

Subset Data

(HDF Swath)

AIRS L1B Daily

Browse PGE

Calibrated, Geo-Located 

Radiances and Calibration 

Coefficients

(HDF Swath Format)

Calibrated, Geo-Located 

Radiances and Calibration 

Coefficients

(HDF Swath Format)

Ancillary

Data

GCM

DEM

RAOBS

TOMS

etc.

L2 Pentad/Monthly

Browse PGE

L2 Subset Data

(HDF Swath)

L2 Daily

Browse PGE

Summary

Data

(HDF Swath)

Level 1A Level 1B Level 2
24 Data Products Handbook - Volume 2 Aqua / AMSR-E • Instrument Descriptions and Data Processing Overviews 

AMSR-E

 Advanced Microwave Scanning Radiometer for EOS

AMSR-E will be carried aboard the EOS Aqua satellite and will monitor a range of hydrologic processes 

critical to our knowledge of climate variability. A 

unique capability of AMSR-E, versus the other EOS 

instruments, is its ability to make most of its measurements through cloud cover. Over the ocean, the 

instrument will measure rainfall, cloud-water and water-vapor abundance, sea ice parameters, sea surface 

temperature, and near-surface wind speed. Over land, 

vegetation amounts, snow cover, and soil wetness will 

be monitored. AMSR-E will provide unprecedented 

detail and accuracy in the global, all-weather measurement of these variables and thereby will allow a 

more-complete understanding of climate variability, 

ultimately enabling better climate prediction.

 The instrument is a twelve-channel, six-frequency, 

total power passive-microwave radiometer system. 

It measures brightness temperatures at 6.925, 10.65, 

18.7, 23.8, 36.5, and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all 

channels. The Earth-emitted microwave radiation is 

collected by an offset parabolic reflector 1.6 meters 

in diameter that scans across the Earth along an 

imaginary conical surface, maintaining a constant 

Earth incidence angle of 55° and providing a swath 

width of 1445 km. The reflector focuses radiation 

into an array of six feedhorns which then carry the 

radiation to radiometers for measurement. Calibration 

is accomplished with observations of the cosmic 

background radiation and an on-board warm target. 

Spatial resolution of the individual measurements 

varies from 5.4 km at 89 GHz to 56 km at 6.9 GHz.

The AMSR-E will provide instantaneous measurements for the following data products:

• Level 2 rainfall over ocean (land) with an accuracy 

of 1 mm/hr (2 mm/hr) or 20% (40%), whichever is 

greater, at a spatial resolution of 5.4 km.

• Level 3 monthly rainfall averages over ocean (land) 

with an accuracy of 1 mm/day (2 mm/day) or 20% 

(40%), whichever is greater, at a spatial resolution 

of 5° latitude × 5° longitude.

• Sea surface temperature, through most clouds, with 

an accuracy of 0.5° C at two different spatial resolutions: 56 and 38 km.

• Total integrated water vapor over the ocean with 

an accuracy of 1.0 mm at a resolution of 24 km.

• Total integrated cloud water over the ocean with 

an accuracy of 0.02 mm at a resolution of 12 km.

• Sea surface wind speed with an accuracy of 1.0 m/s 

at two resolutions: 38 and 24 km. 

• Surface soil moisture with an accuracy of 0.06 

gm/cm3 in low-vegetation areas (biomass less 

than 1.5 kg/m2), at a grid spacing of 25 km and a 

spatial resolution of 56 km. 

• Sea ice concentration at two resolutions (SSM/I 

grid): 12.5 and 25 km.

• Snow depth over sea ice at a resolution of 

12.5 km.

• Sea ice temperature at a resolution of 25 km.

• Snow-cover water equivalent over land. 

AMSR-E Home Page: 

 http://www.ghcc.msfc.nasa.gov/AMSR

Key AMSR-E Facts

Selected for flight on EOS Aqua spacecraft

Heritage: Nimbus 7 and Seasat SMMR, DMSP 

SSM/I

Multi-frequency, dual polarization conically 

scanning passive-microwave radiometer

Measures hydrologic processes

AMSR-E

Aqua Mission
Instrument Descriptions and Data Processing Overviews • Data Products Handbook - Volume 2 25

AMSR-E Standard Products

Aqua / AMSR-E

Aqua Mission

Level 1* Level 2 Level 3

2A (spatially consistent Tbs)

Ocean Suite:

SST

Water vapor

Cloud water

Wind Speed

Brightness Temperatures (Tbs)

Rainfall

over land and 

ocean, rain type

Azimuthal Equal

area EASE grid

Cylindrical Equal 

area EASE grid

Surface soil

moisture

Gridded brightness

temperature (pol. ste.)

Sea Ice Products:

Concentration

Temperature

Snow depth on ice

Ocean Suite

SST

Water vapor

Cloud water

Wind Speed

Monthly rainfall 

over land and 

ocean

Snow water

equivalent

Surface soil

moisture

gridded brightness 

temperature

* produced by NASDA
26 Data Products Handbook - Volume 2 Aqua / CERES • Instrument Descriptions and Data Processing Overviews 

Aqua Mission

CERES

Clouds and the Earth’s Radiant Energy System

The U.S. Global Change Research Program classifies 

the role of clouds and radiation as its highest scientific 

priority (CEES, 1994). There are many excellent summaries of the scientific issues (IPCC, 1992; Hansen 

et al., 1993; Ramanathan et al., 1989; Randall et al., 

1989, Wielicki et al., 1995) concerning the role of 

clouds and radiation in the climate system. These 

issues naturally lead to a requirement for improved 

global observations of both radiative fluxes and cloud 

physical properties. The CERES Science Team, in 

conjunction with the EOS Investigators Working 

Group representing a wide range of scientific disciplines from oceans, to land processes, to atmosphere, 

has examined these issues and proposed an observational system with the following objectives:

1) For climate-change analysis, provide a continuation of the ERBE record of radiative fluxes at the 

top of the atmosphere (TOA), analyzed using the 

same algorithms that produced the existing ERBE 

data.

2) Double the accuracy of estimates of radiative 

fluxes at TOA and the Earthʼs surface. 

3) Provide the first long-term global estimates of the 

radiative fluxes within the Earthʼs atmosphere.

4) Provide cloud-property estimates which are consistent with the radiative fluxes from the surface 

to the top of the atmosphere.

To accomplish these goals, the CERES data products 

are divided into three major categories, as shown in 

the figure on the following page: ERBE-like Products 

(top row), CERES Surface/TOA Products (middle 

row), and CERES Surface/TOA/Atmosphere (bottom row).

 The ERBE-like products address the first objective 

of long-term continuity of the ERBE TOA fluxes. The 

TOA/Surface Products address the second objective 

and attempt to provide the most direct tie between 

surface radiative-flux estimates and TOA flux measurements. The TOA/Surface/Atmosphere products 

focus on the last two objectives and attempt to derive 

an internally consistent set of atmosphere, cloud, and 

surface-to-TOA radiative fluxes, all within the context of a state-of-the-art radiative-transfer model. In 

this last case, the observed TOA fluxes are used as a 

direct constraint on the model calculations in order 

to implicitly account for non-plane-parallel and other 

poorly modeled atmospheric radiative effects. As in 

ERBE, CERES radiative fluxes will be separately determined for both clear- and cloudy-sky conditions.

 One of the major advances of CERES over ERBE is 

the availability of high spatial and spectral resolution 

cloud imagers for cloud masking, cloud height, and 

cloud-optical-property determination (i.e., VIRS on 

TRMM, MODIS on Terra and Aqua). A second major 

advance is the use of one CERES scanner in a crosstrack mode (global spatial coverage) and a second 

scanner in a rotating-azimuth-plane mode (complete 

angular sampling of viewing zenith and azimuth). 

The rotating-azimuth-plane scanner data will be combined with nearly simultaneous cloud-imager data to 

develop a new set of improved empirical models of 

the SW and LW anisotropy of radiation as a function 

of surface and cloud type. While it is estimated to 

take two years of these data to develop new angular 

models, they are expected to reduce instantaneous 

TOA flux errors by a factor of three from the ERBE 

levels.

 A detailed listing of the CERES data products 

and their individual parameters can be found in 

the CERES Data Products Catalog (http://asdwww.larc.nasa.gov/DPC/DPC.html). Documentation 

of the Release 1 CERES analysis algorithms can be 

found in the CERES Algorithm Theoretical Basis 

Documents (ATBDs) Volumes 0 through 12, where 

each volume covers one of the CERES Data Products and its associated technical algorithms (http:

//eospso.gsfc.nasa.gov/atbd/cerestables.html). A 

summary of the CERES experiment can be found 

in Wielicki et al., Bulletin of the American Meteorological Society, May, 1996.

CERES Home Page

 http://asd-www.larc.nasa.gov/ceres/docs.html
Instrument Descriptions and Data Processing Overviews • Data Products Handbook - Volume 2 27

Key CERES Facts

Selected for flight on TRMM, Terra, and Aqua

Heritage: ERBE

Broadband, scanning radiometer capable of 

operating in cross-track mode, or rotating 

plane mode (bi-axial scanning)

Measures Earth’s radiation budget and 

atmospheric radiation from the top of the 

atmosphere to the surface

First instrument (cross-track scanning) will 

essentially continue the ERBE mission; the 

second (biaxially scanning) will provide 

angular radiance information that will 

improve the accuracy of angular models 

used to derive the Earth’s radiative balance

Dual scanners on Aqua

CERES

CERES Data Processing Architecture and Products

Aqua / CERES

Aqua Mission

BDS

Level 1

Geolocated 

Radiance

ES-8

Level 2

ERBE-like Instantaneous

TOA Estimates

ES-4. ES-9

Level 3

ERBE-like Monthly

Averages

SSF

Level 2

Single Scanner TOA/

Surface Fluxes and 

Clouds

CRS

Level 2

Clouds and 

Radiative Swath

FSW

Level 3

Monthly Gridded

Radiative Fluxes

and Clouds

SYN

Level 3

Synoptic Radiative

Fluxes and Clouds

AVG, ZAVG

Level 3

Monthly Regional, Zonal,

and Global Radiative 

Fluxes and Clouds

SFC

Level 3

Monthly Gridded 

TOA/Surface Fluxes 

and Clouds

SRBAVG

Level 3

Monthly TOA/

Surface Averages

ERBE-Like Surface/TOA Surface/TOA/ Atmospher

e
28 Data Products Handbook - Volume 2 Aqua / MODIS • Instrument Descriptions and Data Processing Overviews 

Aqua Mission

MODIS

 Moderate Resolution Imaging Spectroradiometer

MODIS is an EOS facility instrument designed 

to measure biological and physical processes on a 

global basis every one to two days. Slated for both the 

Terra and Aqua satellites, the instrument will provide 

long-term observations from which to derive an enhanced knowledge of global dynamics and processes 

occurring on the surface of the Earth and in the lower 

atmosphere. This multidisciplinary instrument will 

yield simultaneous, congruent observations of highpriority atmospheric (cloud cover and associated 

properties), oceanic (sea-surface temperature and 

chlorophyll), and land-surface features (land-cover 

changes, land-surface temperature, and vegetation 

properties). The instrument is expected to make major contributions to the understanding of the global 

Earth system, including interactions between land, 

ocean, and atmospheric processes.

 The MODIS instrument employs a conventional 

imaging-radiometer concept, consisting of a crosstrack scan mirror and collecting optics, and a set of 

linear detector arrays with spectral interference filters 

located in four focal planes. The optical arrangement 

will provide imagery in 36 discrete bands from 0.4 

to 14.5 μm, selected for diagnostic significance in 

Earth science. The spectral bands will have spatial 

resolutions of 250 m, 500 m, or 1 km at nadir; signalto-noise ratios of greater than 500 at 1-km resolution 

(at a solar zenith angle of 70°); and absolute irradiance accuracies of ±5 percent from 0.4 to 3 μm (2 

percent relative to the Sun) and 1 percent or better in 

the thermal infrared (3 to 14.5 μm). MODIS instruments will provide daylight reflection and day/night 

emission spectral imaging of any point on the Earth 

at least every two days, operating continuously.

 Many MODIS products are made over time intervals ranging from about one week to a month or a 

season. These products will be better suited for use 

by investigators interested in seasonal phenomena. In 

some cases these products are made to reduce data 

volume; in others they are made to provide cloud-free 

coverage within a defined time period. Because the 

ground track of the EOS spacecraft follows a 16-

day repeat cycle, and because there may be biases 

in the multiday data sets based on viewing geometry 

at specific locations, the MODIS “week” has been 

defined as an 8-day period. The land and ocean data 

products are based upon 8-day weeks beginning on 

January 1 of each year. The atmosphere products are 

also based on 8-day weeks, but these begin with the 

first 8-day period of the Aqua MODIS sensor that is 

concurrent with the Terra MODIS schedule and run 

in an unbroken sequence thereafter. Thus, the land 

and ocean weeks will not, in general, coincide with 

the atmosphere weeks.

 MODIS will provide specific global data products, 

which include the following:

• Surface temperature with 1-km resolution, day and 

night, with absolute accuracy of 0.2 K for oceans 

and 1 K for land;

• Ocean color, defined as ocean-leaving spectral 

radiance within 5 percent from 415 to 653 nm, 

enabled by atmospheric correction from near-infrared sensor channels;

• Chlorophyll fluorescence within 50 percent at 

surface-water concentrations of 0.5 mg m-3;

• Concentrations of chlorophyll a within 35%;

• Vegetation/land-surface cover, conditions, and 

productivity

– Net primary productivity, leaf-area index, and 

intercepted photosynthetically active radiation

– Land cover type, with change detection and 

identification

– Vegetation indices corrected for atmosphere, soil, 

and directional effects;

• Snow cover and sea ice cover;

• Land-surface reflectance (atmospherically corrected) with bidirectional reflectance and albedo;

• Cloud cover with 250-m resolution by day and 

1,000-m resolution at night;

• Cloud properties characterized by cloud-droplet 

phase, optical thickness, droplet size, cloud-top 

pressure, and emissivity;

• Aerosol properties defined as optical thickness, 

particle size, and mass transport;

• Fire occurrence, size, and temperature;

• Global distribution of precipitable water; and

• Cirrus-cloud cover.

 
Instrument Descriptions and Data Processing Overviews • Data Products Handbook - Volume 2 29

The data-product descriptions in the main part of 

this handbook refer to MODIS spectral bands by 

number. These are all defined in the table on the 

following page.

MODIS Home Page

 http://ltpwww.gsfc.nasa.gov/MODIS/ 

 MODIS.html

Key MODIS Facts

Selected for flight on Terra and Aqua 

missions

Heritage: AVHRR, HIRS, Landsat TM, and 

Nimbus 7 CZCS

Medium-resolution, multi-spectral, crosstrack-scanning radiometer

MODIS

Aqua / MODIS

Aqua Mission
30 Data Products Handbook - Volume 2 Aqua / MODIS • Instrument Descriptions and Data Processing Overviews 

Aqua Mission

 Center Pixel

Primary Use Band Wavelength Bandwidth Size Ltyp SNR @ Ltyp

 (nm) (nm) (m) 

Land/Cloud/Aerosol 1 645.0 48.0 250 21.8 129.0

Boundaries 2 856.5 38.4 250 24.7 200.8

Land/Cloud/Aerosol 3 465.6 18.8 500 35.3 243.4

Properties 4 553.6 19.8 500 29.0 228.3

 5 1241.6 24.0 500 5.4 74.0

 6 1629.1 28.6 500 7.3 270.4

 7 2114.1 55.7 500 1.0 111.1

Ocean Color/ 8 411.3 14.8 1000 44.9 880.4

Phytoplankton/ 9 442.0 9.7 1000 41.9 838.0

Biogeochemistry 10 486.9 10.6 1000 32.1 802.5

 11 529.6 12.0 1000 27.9 754.1

 12 546.8 10.3 1000 21.0 750.0

 13 665.5 10.1 1000 9.5 913.5

 14 676.8 11.3 1000 8.7 1087.5

 15 746.4 9.9 1000 10.2 600.0

 16 866.2 15.5 1000 6.2 516.7

Atmospheric 17 904.0 35.0 1000 10.0 166.7

Water Vapor 18 935.5 13.6 1000 3.6 57.1

 19 935.2 46.1 1000 15.0 250.0

Cirrus Clouds 26 1383.0 35.0 1000 6.0 150.0

 

 Center Pixel

Wavelength Bandwidth Size SNR @ Ltyp =

Primary Use Band (μm) (μm) (m) Ltyp(Ttyp) Ltyp/NE∆L

Surface/Cloud 20 3.785 .19 1000 0.45(300K) 900.0

Temperature 21 3.990 .08 1000 2.38(335K) 203.4

 22 3.970 .09 1000 0.67(300K) 837.5

 23 4.056 .09 1000 0.79(300K) 987.5

Atmospheric 24 4.472 .09 1000 0.17(250K) 141.7

Temperature 25 4.545 .09 1000 0.59(275K) 453.8

Water Vapor 27 6.752 .25 1000 1.16(240K) 252.2 28 7.334 .33 1000 2.18(250K) 641.2

Cloud Properties 29 8.518 .35 1000 9.58(300K) 2661.1

Ozone 30 9.737 .30 1000 3.69(250K) 444.6

Surface/Cloud 31 11.017 .54 1000 9.55(300K) 2808.8

Temperature 32 12.032 .52 1000 8.94(300K) 1824.5

Cloud Top 33 13.359 .31 1000 4.52(260K) 452.0

Altitude 34 13.675 .33 1000 3.76(250K) 298.4

 35 13.907 .33 1000 3.11(240K) 220.6 36 14.192 .29 1000 2.08(220K) 106.7

SNR = Signal-to-Noise Ratio

NE = Noise Equivalent 

Ltyp (in W/m2-μm-sr) = Spectral radiance at typical conditions for this product

Ttyp = Temperature at typical conditions for this product

MODIS Spectral Band Properties
Instrument Descriptions and Data Processing Overviews • Data Products Handbook - Volume 2 31

MODIS Data Processing Architecture and Products, general

Aqua / MODIS

Aqua Mission

Level 1

MODIS Processing

 MOD 02

Calibrated L1B Radiances

MOD 03

Geolocation Data Set

Level 0

MOD 01

Level 1A

Level 2/Level 2 Gridded (L2G)

MOD 07

• Total Ozone Burden

• Stability Indices

• Temperature and

 Moisture Profiles 

• Precipitable Water (IR)

Grid Pointer

L2G Land

Pointer Files 

MOD 35

Cloud Mask

 MOD 02 MOD 03

MOD 02, MOD 03, MOD 07, MOD 35, Grid Pointer, Grid Angular

 Level 1, Level 2 Atmosphere, Level 2 Ocean, Grid Angular, Grid Pointer

Ocean Color 

(see Oceans Chart)

Ocean Color

(L3 prior 8-day period)

MOD 28

Sea Surface

Temperature 

(L3 prior 8-day period)

Grid Angular

L2G Land

Angular Data 

L2, L2G, L3, Legend:

MOD 04

Aerosol Product

MOD 06

Cloud Product

Instrument Packet Data

MOD 02, MOD 03, MOD 04, MOD 07, MOD 35, Grid Pointer, Grid Angular

Oceans

Land

Atmos

Snow Cover

(L3 prior 8-day period)

MOD 10 MOD 05

Precipitable Water

MOD 28

MOD 11 Sea Surface Temperature 

L2G Land Temperature

MOD 10

(L3 prior 8-day period)

MOD 43

(16-day)

BRDF/Albedo

Snow Cover
32 Data Products Handbook - Volume 2 Aqua / MODIS • Instrument Descriptions and Data Processing Overviews 

Aqua Mission

MODIS Data Processing Architecture and Products, general (continued)

MOD 28

Sea Surface

Temperature

(1-, 8-, 24-day,

monthly, yearly)

MOD 27

Ocean Productivity

(8-day, yearly) 

Ocean Color

(1-, 8-, 24-day,

monthly, yearly)

MODIS Processing

(Continued)

Level 2 Gridded (L2G)

MOD 08

L3 Atmosphere

Products

(1-, 8-day, monthly) 

Level 3 Level 4

MOD 43

BRDF/Albedo

(16-day) 

MOD 10

Snow Cover

(1-, 8-day)

MOD 13

Vegetation

Indices (8-, 16-day,

monthly)

MOD 12

Land Cover

Type

(96-day) 

MOD 11

Land Surface

Temp. & Emis.

(8-day, monthly) 

MOD 12

Land Cover Type

(L3 prior period or

at-launch)

MOD 11

(L3 prior period)

MOD 10

Snow Cover

MOD 11

Land Surface

Temp. & Emis.

MOD 09

Surface

Reflectance 

MOD 29

Sea Ice Cover

Level 2 Atmosphere, Level 2 Ocean, Level 2G Land

MOD 17

Vegetation Production (8-day, yearly)

MOD 16

Evapotranspiration (8-day, yearly)

MOD 15

LAI and FPAR

(1-, 8-day) 

MOD 29

Sea Ice Cover

(1-, 8-day) 

MOD 14

Thermal Anomalies

(1-, 8-day, monthly) 

MOD 43

(L3 prior period)

 Level 1, Level 2 Atmosphere, Level 2 Ocean , L2G Grid Pointer, L2G Grid Angular

MOD 12

(L3 prior period or

at-launch)

MOD 44

(L4) 

MOD 14

Thermal

Anomalies

MOD 44

Vegetation Cover

Conversion

(3-month. yearly)

MOD 40

Burn Scars

(8-, 16-day,

monthly) 

L2, L2G, L3, Legend:

Oceans

Land

Atmos

BRDF/Albedo

Land Cover Type

Vegetation Cover

Conversion

Surface Resistance and
Instrument Descriptions and Data Processing Overviews • Data Products Handbook - Volume 2 33

MODIS Data Processing Architecture and Products, land

Aqua / MODIS

Aqua Mission

MODIS 

Land Processing

MOD 09

Surface Reflectance 

MOD 14

Thermal Anomalies

MOD 05

Precipitable

Water 

MOD 35

Cloud Mask

MOD 04

Aerosol Product

 MOD 29

Sea Ice Cover

MOD 10

Snow Cover

Level 2 Gridded (L2G) Level 1

 MOD 02

Calibrated L1B Radiances

MOD 03

Geolocation Data Set

Instrument Packet Data

Level 0

MOD 01

Level 1A

MOD 02, Grid Pointer, Grid Angular

MOD 11

Land Surface

Temp. & Emis.

MOD 35

Cloud Mask

Level 2G

MOD 43

BRDF/Albedo

(16-day) 

MOD 10

Snow Cover

(1-, 8-day)

MOD 13

Vegetation Indices

(8-, 16-day, monthly)

MOD 12

Land Cover Type

(96-day) 

MOD 11

Land Surface

Temp. & Emis.

(8-day, monthly) 

MOD 17

Vegetation Production

(8-day, yearly) 

MOD 16

Surface Resistance and 

Evapotranspiration

(8-day, yearly)

MOD 15

LAI and FPAR

(1-, 8-day) 

MOD 29

Sea Ice Cover

(1-, 8-day) 

MOD 14

Thermal Anomalies

(1-, 8-day, monthly)

Level 3 Level 4

Grid Pointer

Product

Grid Angular

Data Product

MOD 12 

Land Cover Type

(L3 prior period or

at-launch)

MOD 11

(L3 prior period)

MOD 12

(L3 prior period or

at-launch)

MOD 43

BRDF/Albedo

(L3 prior period) 

MOD 43

BRDF/Albedo

(Prior period) 

MOD 44

Vegetation Cover Conversion 

(3-month, yearly) 

MOD 40

Burn Scars

(8-, 16-day,

monthly) 

MOD 04

Aerosol

Product

MOD 44

Vegetation Cover

Conversion (L4) 
34 Data Products Handbook - Volume 2 Aqua / MODIS • Instrument Descriptions and Data Processing Overviews 

Aqua Mission

MODIS Data Processing Architecture and Products, atmosphere Level 1

 MOD 02

Calibrated L1B Radiances

MOD 03

Geolocation Data Set

Level 

0

MOD 01

Level 1A

 MOD 02 MOD 03

Level 

2

MOD 07

Atmospheric Profiles

• Total Ozone Burden

• Stability Indices

• Temperature and 

 Moisture Profiles

• Precipitable Water (IR)

MOD 35

Cloud Mask

MODIS 

Atmosphere Processing

MOD 06

Cloud Product

• Cloud Top Properties

 (temperature, height,

 emissivity) 

• Cloud Particle Phase

• Cloud Fraction

• Cloud Optical Properties

 (optical thickness and

 particle size) 

Level 3

MOD 03, Level 2 Atmosphere Products

MOD 02, MOD 03, MOD 07, MOD 35

MOD 08

L3 Atmosphere Products

• Aerosol • Water Vapor

• Clouds • Stability Indices

• Ozone

(1-, 8-day, monthly)

Instrument Packet Data

MOD 05

Precipitable

Water Product

• Solar Algorithm

• IR Algorithm

MOD 04 

Aerosol Product

• Aerosol Optical

 Thickness 

• Aerosol Size

 Distribution 

MOD 11

MOD 10

L2G Land Temperature

Snow Cover

(L3 prior 8-day period)

MOD 43

BRDF/Albedo

(16-day)

MOD 28

Sea Surface Temperature

(L3 prior 8-day period)

MOD 10

Snow Cover

(L3 prior 8-day

period)
Instrument Descriptions and Data Processing Overviews • Data Products Handbook - Volume 2 35

MODIS Data Processing Architecture and Products, oceans. A complete set of these products will be produced for both Terra and 

Aqua MODIS sensor data. In addition, a merged Terra/Aqua MODIS Ocean product set will be produced at Levels 3 and 4 in the 

post-Aqua launch period.

Aqua Mission

Aqua / MODIS

Level 

2 Level 3 Level 

4

MODIS 

Ocean Processing

MOD04

Sea Aerosol 

Product

MOD 28

Sea Surface

Temperature

(1-day) 

MOD 28

Sea Surface

Temperature

(1-, 8-, 24-day,

monthly, yearly) 

MOD 35

Cloud Mask

MOD 04

Aerosol

Product 

MOD 27

Ocean

Productivity

(8-day, yearly) 

Ocean Color:

MOD 18 Water-Leaving Rad.

MOD 19 Pigment Conc.

MOD 20 Chlorophyll Fluor.

MOD 21 Chlorophyll Conc.

MOD 22 PAR/Interim PAR

MOD 23 Suspended Solids Conc.

MOD 24 Organic Matter Conc. 

MOD 25 Coccolith Conc.

MOD 26 Water Attenuation Coef.

MOD 31 Phycoerythrin Conc.

MOD 36 Absorption Coefficients

MOD 37 Aerosol Optical Depth

MOD 39 Clear-Water Epsilon 

MOD 05

Precip. Water

MOD 28

Sea Surface

Temperature

(L3 prior 

8-day period)

Level 

0 Level 1

 MOD 02

Calibrated L1B Radiances

MOD 03

Geolocation Data Set

MOD 01

Level 1A

 MOD 02 MOD 03

Ocean Color

(1-, 8-, 24-day,

monthly, yearly) 

Ocean Color

(L3 prior 

8-day period)

Instrument Packet Data
36 Data Products Handbook - Volume 2 Jason-1 / Poseidon-2/JMR/DORIS • Instrument Descriptions and Data Processing Overviews 

Jason-1 Mission

Poseidon-2/JMR/DORIS

Jason-1 is a follow-on mission to TOPEX/Poseidon 

(T/P) to continue high-accuracy measurement of the 

sea surface topography of the worldʼs oceans. The 

missionʼs measurements will be used to study: 1) the 

global ocean circulation and its variability in relation 

to climate variability, 2) the global ocean wave field, 

and 3) the global radar backscatter in relation to sea 

state and gas exchange with the atmosphere. The 

data are organized as sequential records along the 

ground track of the satellite. Each record contains all 

the information regarding the instrumentʼs measurements averaged over a period close to one second of 

flight time, including the measurements of sea surface 

height, wave height, radar backscatter cross-section, 

and wind speed, along with a host of auxiliary data, 

including atmospheric, geophysical, and instrumental 

corrections as well as other relevant information such 

as ocean-tide model products and atmospheric-pressure products.

 The Jason-1 mission was defined in a way that allows an optimum continuation of the T/P scientific 

mission and fulfillment of the long-term observation 

requirements. The satellite is designed for a minimum 

duration of three years, with a potential for an additional two years. Every 10 days, it will overfly within 

1 km the reference T/P ground-tracks (66° inclination, 

1336 km orbit), measuring sea level with the same 

or better accuracy than T/P. The Jason-1 payload 

includes a similar package as for T/P. CNES (Centre 

National dʼEtudes Spatiales) provides the Poseidon2 altimeter, which is a solid-state, low-power-consumption, low-mass instrument. It is derived from 

the Poseidon-1 altimeter of T/P but works at two 

frequencies (13.6 GHz and 5.3 GHz) to enable an 

ionospheric correction accuracy in accordance with 

the required error budget. The DORIS Precise Orbit 

Determination system, whose ultimate performance 

has been demonstrated on T/P, is also provided by 

CNES. JPL/NASA is responsible for developing the 

3-frequency Jason-1 Microwave Radiometer (JMR) 

which will be used to compute the troposphere 

path-delay correction with the required accuracy. A 

TurboRogue Space GPS Receiver and a Laser Retroreflector Array, also provided by JPL/NASA, will 

contribute to the Precise Orbit Determination effort. 

The US Delta II vehicle will be used to launch the 

Jason-1 satellite, jointly with the TIMED satellite, 

from the Vandenberg Air Force Base.

 The raw engineering Jason-1 data will be recorded 

on board the satellite, then transmitted to the ground 

when over-flying the two main telemetry stations 

located in Aussagel (near Toulouse, France) and in 

Poker-Flat (Alaska). One backup station is located at 

the Wallops Flight Facility in Virginia. In addition to 

these Earth terminals, the control ground system will 

include a Satellite Control Center located in CNES 

and a Project Operation Control Center (POCC) at 

JPL. The mission ground system will be responsible 

for instrument programming/monitoring as well as 

scientific product generation and distribution. It will 

comprise: 1) a CNES center called SSALTO (Segment Sol Multimission Altimetry and Orbitography), 

which will work not only for Jason-1, but also for 

other DORIS missions and the Envisat altimeter mission, and 2) a NASA center, which is part of the JPL 

POCC and derived from the T/P ground system.

 The Geophysical Data Records (GDRs) will constitute the final and fully validated products. They will 

be systematically delivered to data users within 30 

days and eventually archived as permanent records. 

They will contain, at a rate of one record per second, 

the best estimates of range measurement, the time tag 

and Earth location, plus the best associated instrumental and environmental corrections and the most 

accurate orbit altitude. All these parameters will be 

validated during the verification phase of the mission, 

covering the first 6-8 months after launch. The GDR, 

structured in a way similar to what has been used 

with T/P, will contain the following additional geophysical parameters: wave-height, sigma-naught and 

derived wind speed, atmospheric surface pressure, 

tides, mean sea surface, and the geoid. The altimeter 

radar instrument bias and drift and the relative bias 

between T/P and Jason-1 time series will be provided. 

For specific applications (over ocean, coastal areas, 

lake, land, or ice), the Sensor Geophysical Data Records (SGDR), containing waveforms, will be made 

available to users. 

 The Interim GDRs (IGDRs) and Operational Sensor Data Records (OSDR) will support near-real-time 

applications, including mesoscale and coastal applications, climate forecasting, and marine meteorology. 

Wind speed, significant wave-height (SWH), and their 

forecasts are of interest to many activities related to 

maritime industries. The key requirement for operational use of altimetric data in models is to provide 

the data within a very short time period. IGDRs will 

be delivered within three days, mainly for mesoscale 

and climate applications. They will contain the same 

parameters as GDRs, but not fully validated and not 

necessarily in their ultimate precision. OSDRs will 

be provided within three hours, mainly for supporting 

marine meteorology applications. They will contain, 

at a rate of one record per second, all parameters 

needed for such applications. OSDR products will 
Instrument Descriptions and Data Processing Overviews • Data Products Handbook - Volume 2 37

have a lower accuracy than IGDR products, as they 

are based on on-board processing for radar altimetry 

and orbit determination (DORIS navigator). SWH 

and wind speed will have respective accuracies of 

10% or 0.5 m (whichever is greater) and 2 m/s. 

Jason-1 Home Page

 http://topex-www.jpl.nasa.gov/jason1/

Jason-1

Key Poseidon-2/JMR/DORIS Facts

Selected for flight on Jason-1

Heritage: Poseidon-1, TMR, and DORIS on 

TOPEX/Poseidon

Poseidon-2 is a dual-frequency solid-state 

radar altimeter (13.6 GHz and 5.3 GHz); 

JMR is a 3-frequency microwave radiometer; DORIS is a 2-frequency receiver 

measuring signals received from groundbased beacons

Provides high-accuracy global observations of ocean topography, wave height, 

and wind speed, plus, from the JMR, 

columnar water vapor content and, from 

Poseidon-2, total electron content

Jason-1 / Poseidon-2/JMR/DORIS

Jason-1 Mission
38 Data Products Handbook - Volume 2 • Instrument Descriptions and Data Processing Overviews 

ETM+

 Enhanced Thematic Mapper Plus

The Enhanced Thematic Mapper Plus (ETM+) has 

flown on the Landsat 7 satellite as of 15 April 1999, 

and provides synoptic, repetitive, multispectral, highresolution, digital imagery of the Earthʼs land surfaces. Since 1972, the Landsat Program has provided 

calibrated land-surface digital imagery to a broad user 

group including the agricultural community, global 

change researchers, state and local governments, 

commercial users, international users, and the military. The Landsat 7 mission will extend the database 

of the Earthʼs surface into the next century.

 The ETM+ instrument is an improved version of 

the Thematic Mapper instruments that flew on Landsat 4 and 5. Like the earlier instruments, the ETM+ 

will acquire data for six visible, near-infrared, and 

shortwave infrared spectral bands at a spatial resolution of 30 meters. 

 The ETM+ instrument also incorporates a 15-meter 

resolution panchromatic band as well as improved 

ground resolution for the thermal infrared band (60 

m vs. 120 m). Incorporation of in-flight full- and partial-aperture solar calibration will improve the overall 

radiometric accuracy to 5 percent. The ETM+ will 

provide data that are sufficiently consistent in terms of 

acquisition geometry, spatial resolution, spectral characteristics, and calibration with previous Landsat data 

to meet requirements for global change research.

 The Landsat 7 satellite operates in a circular, sunsynchronous orbit with an inclination of 98.2°, an 

altitude of 705 km, and a descending node equatorial 

crossing time of 10:00 AM plus or minus 15 minutes. 

This orbit allows Landsat 7 to precede the Terra satellite by 30 minutes along a common ground track. The 

185 km swath of coverage provided by the ETM+ 

field-of-view affords a global view every 16 days.

 ETM+ data will be used primarily to characterize 

and monitor change in land cover and land-surface 

processes. The high spatial resolution and seasonal 

global coverage of ETM+ will allow improved assessment of both the rates of land cover change, and 

the local processes responsible for those changes. 

Deforestation, ecosystem fragmentation, agricultural 

productivity, glacier dynamics, coastal hazards, and 

volcano monitoring are all science targets for ETM+. 

The common orbit with Terra offers additional opportunities for data fusion with the data from the 

ASTER, MISR, and MODIS sensors. 

 The mission will generate and periodically refresh 

a global archive with substantially cloud-free, sunlit 

data. To facilitate this objective, a long-term acquisition plan has been devised to ensure maximal global 

data acquisition, taking into account vegetation seasonality, cloud cover, and instrument gain.

 Data stored on board, as well as real-time 

continental U.S. data, will be downlinked to the 

Landsat 7 ground station located at the EROS Data 

Center (EDC) in South Dakota. Data received at the 

EDC will be processed for archiving and distribution 

by the Land Processes DAAC. All data received at 

EDC will be available at L0R (no radiometric calibration; limited geometric correction). In addition, 

a subset of these scenes can be processed to Level 1 

(radiometric and geometric corrections applied). 

ETM+/Landsat Home Page

 http://landsat.gsfc.nasa.gov/

Key ETM+ Facts

Selected for flight on Landsat 7, launched 

April 15, 1999

Heritage: Landsat 4, 5 and 6

Imaging radiometer with 7 spectral bands from 

visible to thermal infrared plus a panchromatic band; 16-day repeat cycle

Provides high spatial resolution, multispectral 

images of the sunlit land surface

Landsat 7

Landsat 7 / ETM+

Landsat 7 Mission
Instrument Descriptions and Data Processing Overviews • Meteor 3M / SAGE III Data Products Handbook - Volume 2 39

SAGE III

 Stratospheric Aerosol and Gas Experiment III 

 

The SAGE III instrument is designed to enhance our 

understanding of natural and human-derived atmospheric processes by providing accurate long-term 

measurements of the vertical structure of aerosols, 

clouds, temperature, ozone, water vapor, and other 

important trace gases (NO2, NO3, and OClO) in the 

upper troposphere and stratosphere. The instrument 

is mounted on board the Russian Meteor 3M spacecraft and is scheduled for launch in March 2001. 

The SAGE III instrument has a design that has been 

advanced over that of its predecessors, permitting 

measurements during both solar and lunar occultation 

events. For the SAGE III instrument on Meteor 3M, 

solar events will occur between 50°N and 80°N and 

between 30°S and 60°S. Lunar occultation events will 

be limited to periods when the lunar disk is more than 

50% illuminated. These measurement opportunities, 

however, will extend over all latitudes.

 The SAGE III data processing function converts the 

instrumentʼs Level 0 data into Level 1B and Level 2 

data products. The procedure involves the conversion 

of instrument responses to solar or lunar flux, at 70- 

to-80 wavelengths between 290 and 1550 nm, into 

vertical profiles of the molecular density of gaseous 

species, aerosol extinction at eight wavelengths, 

temperature, and pressure. This procedure consists 

of two main parts: the transmission algorithm and 

the inversion algorithm, as illustrated in the figure 

on the following page.

 The function of the transmission algorithm is to 

calibrate the radiance data and to produce multiwavelength slant-path transmission profiles from time 

sequences of radiometric and engineering measurements by the SAGE III instrument. This process can 

be separated into five components: data screening, 

position registration, wavelength registration, radiance calibration, and data grouping and statistics. 

 The inversion algorithm converts the multi-wavelength slant-path transmission data from the transmission algorithm into vertical profiles of molecular 

density for SAGE III-measured gas species, aerosol 

extinction at eight wavelengths, and atmospheric 

temperature and pressure. Procedures such as the 

multiple linear regression (MLR) technique and the 

Marquardt nonlinear least-squares algorithm are used 

in the retrieval algorithm to produce the Level 2 data 

products. The detailed algorithm is discussed in the 

SAGE III ATBDs. 

 The SAGE III data processing algorithm has been 

developed at the NASA Langley Research Center 

(LaRC). Routine processing of the SAGE III data 

will be performed at the SAGE III Scientific Computing Facility (SCF) at LaRC. All products will be 

delivered to the Langley Distributed Active Archive 

Center (DAAC) for archive and public distribution. 

SAGE III Home Page

http://www-sage3.larc.nasa.gov/

Key SAGE III Facts

Selected for flights on the Russian Meteor 

3M satellite and on the International Space 

Station

Heritage: SAM II, SAGE I, and SAGE II

Multi-spectral sun/moon-looking photometer

Provides long-term global ozone and aerosol 

data

SAGE III

Meteor 3M/SAGE III Mission
40 Data Products Handbook - Volume 2 • Instrument Descriptions and Data Processing Overviews 

SAGE III Data Processing Architecture and Products

Meteor 3M/SAGE III Mission

Meteor 3M / SAGE III

Output Data Products

Profile Inversion

Convergence Test

Temperature and

Pressure Retrieval

H2O Retrieval

O3, Aerosol Slant-Path

Calculations using Least Squares

NO2, O3, Slant-Path

Calculations using MLR

NCEP

Meteorological Data

Level 0 (Telemetry) Data

Processing

Data Screening

Position Registration

(Refraction Correction)

Wavelength Registration

Radiance Calibration

(Transmission Calculations)

Data Grouping and Statistics

Transmission

Rayleigh Scattering

Calculations

INVERSION TRANSMISSION
Instrument Descriptions and Data Processing Overviews • QuikScat / SeaWinds Data Products Handbook - Volume 2 41

SeaWinds

The SeaWinds instruments are specialized microwave 

radars designed to make moderate-resolution, broadswath measurements of global radar backscatter and 

ocean-surface wind speed and direction under nearly 

all-weather conditions. SeaWinds vector-wind measurements are critical for investigating and modeling 

upper-ocean circulation, marine meteorological processes, and large-scale air-sea interaction and climate. 

The data are provided to the U.S. National Oceanic 

and Atmospheric Administration (NOAA) and the 

EOS Data Assimilation Office (DAO) in near-realtime for use in regional and global weather prediction 

and marine forecasting. NOAA distributes near-realtime SeaWinds wind-velocity data to international 

operational weather prediction centers.

 The SeaWinds radar backscatter cross-section 

measurements over land provide information on 

vegetation type and both seasonal and interannual 

change. Measurements over continental and seaice-covered areas are used to monitor ice extent and 

freeze/melt processes on diurnal through interannual 

time scales.

 SeaWinds is a dual, conically scanning pencil-beam 

radar operating at 13.4 GHz. Vertically polarized 

backscatter cross-section measurements at ~54° 

incidence angle are acquired over an 1800-km-wide 

swath centered on the satellite subtrack. Horizontally 

polarized data are acquired over a concentric 1400-

km-wide swath at ~46° incidence angle. The antenna spins at 18 rpm, yielding measurements with 

25-×-35-km IFOV. Modulation of the transmitted 

pulses allows extraction of cross-section data with 

~6-×-25-km spatial resolution. SeaWinds vector-wind 

measurements are obtained with 25-km resolution 

across nearly the entire swath, and cover more than 

90% of the global, ice-free oceans daily.

 Significant ground processing is required to 

extract accurate backscatter cross-section and vector-wind measurements from raw SeaWinds radar 

measurements. The surface normalized radar crosssection is calculated from the received power and 

knowledge of the viewing geometry and instrument 

characteristics. Over the ocean, backscattering results primarily from centimetric ocean roughness 

elements whose amplitudes and directional distributions are in local equilibrium with the local wind. 

The ocean backscatter cross-section thus varies with 

wind speed and direction relative to the radar beam. 

Vector-wind measurements are thus obtained by 

combining multiple, near-simultaneous, collocated 

backscatter measurements acquired from different 

viewing geometries as the satellite passes overhead. 

SeaWinds ocean vector-wind products include flags 

to indicate the presence of contamination resulting 

from heavy rain conditions.

 Two SeaWinds instruments will be flown in the first 

set of EOS missions. The QuikScat mission, with SeaWinds as the sole science instrument, was launched 

on 19 June 1999 and has been returning high-quality 

data since 19 July 1999. A second SeaWinds will be 

launched in late 2001 as part of the National Space 

Development Agency (NASDA) of Japan's ADEOS-II 

mission; this mission will also include a broad-swath, 

multi-channel microwave radiometer (an AMSR). A 

subset of the ADEOS AMSR measurements will be 

used in the ADEOS SeaWinds processing to provide 

atmospheric corrections and advanced rain flagging 

to supplement the autonomous SeaWinds flags and 

to enhance the accuracy of the vector-wind measurements. The figure on the following page outlines the 

complete SeaWinds processing chain and standard 

products.

SeaWinds Homepage

 http://winds.jpl.nasa.gov/

Key SeaWinds Facts

Selected for flights on QuikScat, launched 

June 19, 1999, and the Japanese ADEOS II 

satellite

Heritage: SEASAT, NSCAT

Ku-band scatterometer

Provides near-surface wind velocities over 

the oceans, plus information for vegetation 

classification and ice-type investigations

QuikScat Mission 

SeaWinds on

QuikScat
42 Data Products Handbook - Volume 2 • Instrument Descriptions and Data Processing Overviews 

Pre-Processors (PP) and Level Processors (LP)

SeaWinds Data Flow

SeaWinds L0

SWS L0

Raw Telemetry

SWS L1A

Unpacked

Telemetry

SWS L1B

Backscatter

Power

SWS L2A

Sigma0 w/AMSR

SWS L2B

Ocean Winds

PP L0:

Ingest Telemetry

LP L1A:

Convert Telemetry

LP L1B:

Calculate Sigma0

LP L2A:

Group Sigma0 &

AMSR L1B

LP L2B:

Determine Winds &

Flag Rain

ADEOS II AMSR L1A

Convert AMSR 1A to

Brightness Temperatures

SeaWinds Data Processing Architecture and Products

QuikScat Mission

QuikScat / SeaWinds
Instrument Descriptions and Data Processing Overviews • QuikTOMS / TOMS Data Products Handbook - Volume 2 43

TOMS

 Total Ozone Mapping Spectrometer 

The QuikTOMS mission is to continue daily mapping of the global distribution of the Earth's total 

column of atmospheric ozone with the Total Ozone 

Mapping Spectrometer Flight Model 5 (TOMS-FM5). 

TOMS-FM5 was scheduled to be launched in the year 

2000 aboard a Russian satellite, but that mission was 

terminated in April 1999. Because of the requirement 

for monitoring changes in the ozone layer, NASA 

had to formulate a new mission to fly TOMS-5 in a 

very short time. The continuous observation of the 

global ozone past the year 2000 is critical in order 

to monitor the expected recovery of ozone as levels 

of chlorofluorocarbons (CFCs) decrease from their 

current maximum as a result of the Montreal Protocol 

limits. The planned three-year QuikTOMS mission 

is intended to provide overlap with the current Earth 

Probe TOMS, launched in July 1996, and with the 

Ozone Monitoring Instrument (OMI), scheduled for 

launch on Aura in 2003.

 The main objectives of QuikTOMS are: 1) the determination of long term change in global total ozone 

level, 2) the understanding of the processes related to 

the “ozone hole” formation and local anomalies in the 

equatorial region, and 3) improved understanding of 

processes that govern the generation, depletion, and 

distribution of global total ozone. Additional objectives are: the detection and measurement of volcanic 

ash clouds and sulfur dioxide gas, the detection and 

measurement of aerosol and pollution plumes, and 

the estimation of surface ultraviolet irradiance

 TOMS-5 is a f/5 single grating Ebert-Fastie type 

monochromator with multiple exit slits for six different wavelengths. The nominal center wavelength 

values of the six bands are 308.6, 312.5, 317.5, 322.3, 

331.2, 360 nm, and the nominal bandwidth for each 

band is 1.2 nm. The wavelength is selected with a 

chopper wheel near the slit plate that gates the pair of 

the entrance and exit slits for each wavelength band. 

The instantaneous field of view (IFOV) of TOMS-5 

is 3° × 3°, which projects to approximately 42 km ×

42 km at the subsatellite point on the Earth for the 

800 km satellite altitude. TOMS-5 scans ±18 scenes 

from the nadir in a 3° interval to cover approximately 

2300 km cross track.

 The TOMS instrument records daily global measurements of total column ozone, aerosols, Erythemal UV exposure, and reflectivity. In the event of a 

volcanic eruption it can also measure sulfur dioxide 

in the eruption plume.

 Data are provided in a gridded 1° latitude by 1.25° 

longitude format. All data processing will be done at 

the Science Operations Center at NASA Goddard 

Space Flight Center, Greenbelt, Maryland.

QuikTOMS Home Page

 http://toms.gsfc.nasa.gov/.

Key TOMS Facts

Selected for flights on:

Nimbus 7, launched October 24, 1978; 

Meteor 3, launched August 15, 1991; 

ADEOS I, launched August 17, 1996; 

Earth Probe, launched July 2, 1996; and 

QuikTOMS

Heritage: the earlier TOMS instruments

Six-band monochromator

Provides global daytime measurements of 

total column ozone, aerosols, Erythemal 

ultraviolet exposure, and reflectivity

QuikTOMS Mission
44 Data Products Handbook - Volume 2 • Instrument Descriptions and Data Processing Overviews 

QuikTOMS Data Processing Architecture

2 Kbps Uplink

400 Kbps Downlink

Satellite Radio Frequency Up/Down

Front End Processing

Temporary Data Storage

EPGN Tracking Stations

Alaska, McMurdo, Norway,

Wallops

Tracking Files/Data

G/S Commands

S/C Commands

Instrument Commands

S/C Telemetry

Instrument Data

G/S Status Telemetry

128 Kbps or T1

NASA Integrated

Services Network

NASA Central Standard 

Autonomous File Server

NASA Communications

GSFC, MD

256 Kbps

Point-to-Point Circuit

Pass Schedules

S/C Telemetry

Instrument Data

G/S Status Telemetry

Position Vector

Schedule Requests

S/C Ephemeris

S/C Commands

Instrument Commands

TOMS Mission Control 

Center

Dulles, VA

FTP

(Internet)

Instrument

Command

Requests

Instrument Data

Ancillary Data

S/C Ephemeris

Command

 Request

 Verification

Pass Schedule

TOMS Science Operations Center

GSFC, MD

QuikTOMS Mission

QuikTOMS / TOMS
Instrument Descriptions and Data Processing Overviews • VCL / MBLA Data Products Handbook - Volume 2 45

MBLA

 MultiBeam Laser Altimeter

The Vegetation Canopy Lidar (VCL) is the first selected mission of NASAʼs new Earth System Science 

Pathfinder (ESSP) program. The related data products 

listed in this handbook are derived from the MultiBeam Laser Altimeter (MBLA), the instrument that 

will fly on the VCL platform. The principal goal of 

VCL is the characterization of the three-dimensional 

structure of the Earth: in particular, canopy vertical 

and horizontal structure and land-surface topography. 

Its primary science objectives are: land cover characterization for terrestrial ecosystem modeling, monitoring and prediction; land cover characterization for 

climate modeling and prediction; and production of a 

global reference data set of topographic spot heights 

and transects. VCL will provide unique data sets for 

understanding important environmental issues including climatic change and variability, biotic erosion, and sustainable land use, and will dramatically 

improve our estimation of global biomass and carbon 

stocks, fractional forest cover, forest extent and condition. Furthermore, it will provide canopy data critical 

for biodiversity studies as well as for natural hazard 

and climate studies. Scheduled for launch in late 

2000, VCL is an active lidar remote sensing system 

consisting of a five-beam instrument with 25-m contiguous along-track resolution. The five beams are in 

a circular configuration 8 km across, and each beam 

traces a separate ground track spaced 2 km apart, 

eventually producing 2-km coverage between 67° N 

and S latitude. VCLʼs core measurement objectives 

are: 1) canopy top heights; 2) vertical distribution of 

intercepted surfaces, e.g., leaves and branches; and 3) 

ground-surface topographic elevations. These measurements are used to derive a variety of science data 

products, including canopy heights, canopy vertical 

distribution, and ground elevations gridded at various 

temporal and spatial resolutions.

 The VCL standard data products will be produced 

by the VCL Data Center (VDC). The VDC consists 

of three major science-processing subsystems. These 

science subsystems are:

• Altimeter Return Echo Analysis System (AREAS) 

will process the altimeter waveform acquired with 

each laser shot. These waveforms will be used to 

determine the range to the canopy top and ground, 

provide information on the Vertical Distribution of 

Intercepted Surfaces (VDIS), and perhaps deliver 

other information, e.g., reflectance, useful in the 

characterization of the surface spot being ranged to. 

For example, the laser data can be used to identify 

water, unvegetated land surfaces, surface slopes, 

terrain roughness, and related information.

• The VCL Precision Geolocation System (VPGS) 

will be responsible for providing precise orbital 

ephemeris information for VCL through the proper 

modeling of the forces acting on the satellite, reduction and analysis of the GPS, Satellite Laser 

Ranging (SLR), and laser-altimeter data, and 

incorporation of specialized orbit-determination 

methodologies. The system will also utilize the 

altimeter ranges acquired over the ocean during 

times of special-pointing, off-nadir spacecraft 

maneuvers to derive pointing corrections for the 

spacecraftʼs attitude and laser instrument offsets 

due to mounting and thermal effects, and to geolocate the spot on the surface being ranged to. This 

orbit-position and spacecraft-attitude information 

will be interpolated to the time of each VCL altimeter range to locate the surface footprint while 

retaining the level of accuracy in the geolocation 

and pointing solutions. The VPGS will also process 

the raw star tracker, gyro, and magnetometer data 

to provide improved attitude information in support 

of the pointing solution.

• The Level 3 Products Generation Subsystem. This 

science processing system, of less complexity, is a 

gridding package which will generate the missionʼs 

Level 3 data products, using a variety of windowing, averaging, and parsing strategies.

The VDC also supports other important functions, 

which include:

• Processing data received from telemetry packets 

into various science and intermediate products. 

This includes unpacking and uncompressing telemetry to provide gyro/attitude, star tracker, GPS 

receiver, GPS NAV orbit, and MBLA waveform 

data at Level 0. 

• Support expedient data delivery (through both 

a subscription and random ordering capability) 

and specialized analysis functions and flexibility 

needed by the Science Team to verify system performance, improve processing algorithms, calibrate 

instruments, and add value to the processing approaches as the mission evolves.

• Provide all required data and data product distribution to the EROS Data Center in Sioux Falls, 

South Dakota, which is designated as the DAAC 

interface with the outside science community.

VCL Mission
46 Data Products Handbook - Volume 2 • Instrument Descriptions and Data Processing Overviews

• Provide on-site data archiving, information management, and data-management functions.

• Perform gridding, and visualization functions to 

assist the Science Team in their evaluation of these 

data and provide interim and final higher level data 

products for testing and accuracy assessment and 

distribution to the EROS Data Center.

• Provide data back-up and security management to 

protect the VDC and its data holdings from unauthorized use and catastrophic failure/events.

• Acquire all data sets in a timely and regular fashion 

externally located from the VDC which are needed 

to support AREAS, pointing, and orbit processing. 

This includes Earth-orientation information, solar 

and magnetic flux, SLR data, snow cover grids, sea 

state, and like information.

• Provide on-line/near-line access to all subsidiary 

data sets which are needed to process, characterize, 

and evaluate data products. This includes DEM, 

urban masks, land/ocean masks, surface-type/

characterization files, surface-slope masks, etc.

VCL Home Page

 http://www.inform.umd.edu/geog/vcl

Key MBLA Facts

Selected for flight on VCL, the first mission of 

NASA’s ESSP program

Heritage: Shuttle Laser Altimeter (SLA),

 Mars Orbiter Laser Altimeter (MOLA), 

 Laser Vegetation Imaging Sensor (LVIS)

Laser altimeter system using five, 1064 nmwavelength lasers in a circular configuration

Measures the three-dimensional structure 

of the Earth, in particular canopy vertical 

and horizontal structure and land surface 

topography

VDC System Context within the VCL Mission

VCL Mission

 VCL / MBLA

(MBLA Overview, continued)

CDDIS/GSFC

• GPS Ground Data

• GPS Constellation Info.

• GPS Precise Orbits

• SLR Tracking

Commercial Ground Stations

VCL

Control

Center

File Transfer

FTP/Tape

Flight Engineering

Data

Ancillary Data

FTP

VDC at UMD

Data Processing

Limited Archival

and Distribution

Instrument Data

USNO

Polar Motion

A1-UT1

NOAA

Flux +

Solar Mag

GSFC/NOAA

Ocean

Surface Data

NOAA

Surface

Met. Data

Data Products

FTP

GPS Sat. +SLR

FTP/Tape

EROS Data Center

Full Archival and

Distribution

GPS IGS Receiver Sites

Navigation Signals

(Differenced GPS)

X-Band Navigation Signals Science Data

Laser

Tracking

VCL
47

Radiance/Reflectance 

and

Irradiance Products

ACRIMSAT • ACRIM III

Aqua

• AIRS

• AMSR-E

• AMSU-A

• CERES

• HSB

• MODIS

Landsat 7 • ETM+

Meteor 3M • SAGE III
48 Data Products Handbook - Volume 2 Overview • Radiance/Reflectance and Irradiance Products 

Radiance/Reflectance and 

Irradiance Products – An 

Overview

Relationship to Global Change Issues

Remote sensing of the Earth and its atmosphere 

requires the detection of electromagnetic radiation, 

something that has been done effectively from satellites for a wide range of wavelengths and wavelength 

bands from the ultraviolet to the microwave. Instruments used to detect this radiation can be broadly 

classified as either passive or active, the distinction 

being based on whether the instruments carry only 

detectors or detectors plus electromagnetic radiation 

sources. The fundamental products of passive and 

active remote sensing instruments can be classified 

as radiance/reflectance products, irradiance products, or radar/lidar products. Radiance products are 

a measure of the radiant flux per unit area and solid 

angle originating from an Earth scene. Reflectance 

products are a measure of the incoming radiant flux 

reflected from an Earth scene, usually measured using 

a reference target of known reflectance. Reflectance 

products can be converted to radiance products provided that an accurate solar-irradiance spectrum is 

available or is measured. Irradiance products are a 

measure of radiant flux incident on a surface per unit 

area of that surface. Products that can be classified 

in the radar category are produced by active remote 

sensing instruments. Examples of these instruments in 

this volume are microwave radar, such as SeaWinds 

on the QuikScat satellite, and lidar instruments, such 

as MBLA on the VCL satellite. For all products, the 

geophysical parameters derived from the fundamental 

instrument measurements drive the selection of instrument operating wavelengths and bandpasses.

 The derivation of accurate higher order, geophysical data products used in global change studies depends on the accuracy and precision of the 

fundamental products. Accuracy and precision in 

the fundamental instrument products are sought 

through careful prelaunch and on-orbit instrument 

calibration and characterization and through precise 

geolocation of those products. Moreover, the need 

for accurate geophysical data over the time frame 

of decades requires that fundamental instrument 

products be calibrated between successive versions 

of the same instrument on different platforms and 

between synergistic instruments operating in similar 

or different wavelength regions.

Product Overview

Radiance/Reflectance: Calibrated and geolocated 

radiance/reflectance products are generated by passive remote sensing instruments designed to measure 

1) emitted thermal and microwave radiation from the 

Earth and its atmosphere in the 2.5-to-100- micrometer wavelength and the 6-to-89-GHz frequency 

regions, and/or 2) solar radiation reflected from the 

Earthʼs land, ocean, cryosphere, and atmosphere in 

the 0.3-to-2.5-micrometer wavelength region.

 On the EOS Aqua platform, radiances will be measured from the visible through microwave wavelength 

regions by the MODIS, CERES, AIRS, AMSU-A, 

HSB, and AMSR-E instrument suite. Radiance 

products will be generated by the first five of these 

instruments. The AMSR-E instrument will make calibrated, geolocated radiance measurements, but will 

produce a brightness temperature as its fundamental 

product made available to the remote sensing community. The MODIS instrument team plans to generate a reflectance product in addition to a radiance 

product. These radiance/reflectance and brightness 

temperature products will be used to generate higher 

order geophysical products used in the study of atmospheric radiation, cloud formation, atmospheric 

temperatures and humidities, precipitation, radiative 

balance, snow, sea ice, sea surface temperature, land 

vegetation, and ocean productivity.

 The Landsat 7 ETM+ instrument produces radiances from the visible through infrared in seven 

spectral bands. The ETM+ spectral coverage, coupled 

with its small footprint on the Earth, facilitates highresolution remote sensing studies of the Earthʼs surface.

 The QuikTOMS instrument will make high-resolution measurements of upwelling ultraviolet radiance 

in six spectral bands between 0.309 and 0.360 micrometers. These radiance measurements will be used 

in studies of the atmospheric ozone layer.

 The JMR instrument on Jason-1 will make microwave radiance measurements in three channels 

between 18 and 34 GHz. These measurements will be 

used to determine atmospheric water concentrations 

and to perform path-delay corrections for the Poseidon-2 altimeter on board the Jason-1 platform.

Irradiance: Remote sensing instruments that produce 

irradiance products measure radiant flux incident 

per unit instrument-aperture area. Irradiance measurements include solar irradiance, in which solar 

flux is measured directly, and irradiance reflected or 

scattered through interaction with the Earth and its 

atmosphere. Irradiance-measuring instruments are 

typically passive remote sensing instruments.
Radiance/Reflectance and Irradiance Products • Overview Data Products Handbook - Volume 2 49

 ACRIM III is a solar-irradiance-measuring instrument which, with its predecessor instruments, has 

contributed greatly to the long-term solar-irradiance 

database. ACRIM III is an active cavity radiometer 

that measures total solar irradiance in the 0.001-

to-1000- micrometer wavelength range. ACRIM 

measurements have been, and will continue to be, of 

paramount importance in monitoring the variability of 

total solar irradiance in climatic interactions between 

the Sun and the Earth.

 The SAGE III instrument measures the ratio of 

Earth-occulted solar and lunar irradiance transmitted 

through the Earthʼs atmosphere to the un-occulted 

solar and lunar irradiance. SAGE III will not produce 

an irradiance product but will produce a transmissionbased product, the slant-path optical depth, at a number of wavelengths from 0.29 to 1.55 micrometers. 

These optical depths will be used to retrieve a number 

of atmospheric constituent concentrations and parameters as a function of altitude. These measurements 

include O3, H2O, NO2, NO3, OClO, air density, and 

atmospheric aerosols.

Product Interdependency and Continuity 

with Heritage Data

The accuracy and precision of higher order geophysical data products is directly dependent on the 

accuracy and precision of the fundamental radiance/

reflectance, irradiance, and radar products. Fundamental products of high accuracy and precision are 

produced by mechanically, optically, and electronically well-designed instruments that have been completely calibrated and characterized prelaunch and 

whose calibrations are carefully monitored while in 

orbit. The importance of cross-calibration of remote 

sensing instruments is amplified for those instances in 

which a product from one instrument is needed for a 

product derived from the data of another instrument, 

either to generate products or to improve the accuracy and precision of products. A good example of 

the use of data from multiple instruments to produce 

data products of high quality is the AIRS, AMSUA, HSB instrument suite. Collocated microwave 

measurements from AMSU-A in the 27-to-89 GHz 

channels and from HSB in the 89-to-183 GHz channels will be directly used to increase the accuracy of 

AIRS atmospheric temperature retrievals under partly 

cloudy conditions. Additional examples of product 

interdependency are the use of MODIS scene classification in processing CERES radiance data and the use 

of collocated AMSR-E precipitation and atmospheric 

water-vapor measurements in improving the accuracy 

of QuikScat retrievals of wind velocities. 

 Long-term monitoring of instrument on-orbit calibration and performance is necessary to produce the 

long time series remote sensing data products needed 

in global climate-change studies. Ideally, data products measured by identical or different versions of 

the same instrument on successive platforms should 

produce continuous, seamless data products. Careful calibration of generations of instruments is of 

fundamental importance in the generation of useful 

long-term remote sensing data products. In addition, 

the production of high-quality, long-term data products is facilitated when future generations of remote 

sensing instruments share a strong heritage with predecessor instruments. The instruments addressed in 

this second volume of the Data Products Handbook 

take advantage of invaluable lessons learned from 

heritage instruments. The instruments highlighted in 

this volume and their heritage instruments are listed 

on the next page.

Suggested Reading

Brest, C.L., and S.N. Goward, 1987.

Chomko, R., and H.R. Gordon, 1998.

Evans, R.H., and H.R. Gordon, 1994.

Foukal, P.A., and J.L. Lean, 1990.

Gillespie, A.R. et al., 1986.

Lee III, R.B. et al., 1996.

Robinson, W.D. et al., 1992.

Slater, P.N. et al., 1987.

Smith, G.L. et al., 1986.

Wielicki, B.A. et al., 1998.

Willson, R.C., and A.V. Mordvinov, 1999.
50 Data Products Handbook - Volume 2 Overview • Radiance/Reflectance and Irradiance Products 

DPH2 Instrument

ACRIM III

AIRS

AMSR-E

AMSU-A

CERES

DORIS

ETM+

HSB

JMR

MBLA

MODIS

Poseidon-2 

TOMS

SeaWinds

SAGE III

Heritage Instruments

ACRIM I/SMM, ACRIM II/UARS

HIRS

SSM/I, TMI/TRMM

MSU, AMSU-A/NOAA K

ERB, ERBE, CERES/TRMM,

CERES/Terra

DORIS/SPOT-2, DORIS/ 

TOPEX/Poseidon

TM/Landsat 4, TM/Landsat 5

MSU

TMR/TOPEX/Poseidon

Various airborne lidars

AVHRR, CZCS, MODIS/Terra

GEOSAT, ERS-1, TOPEX/Poseidon

TOMS/Nimbus 7, TOMS/EP, 

TOMS/ADEOS

S-193/Skylab, SASS/SEASAT,

AMI/ERS-1, NSCAT/ADEOS

SAM/Apollo-Soyuz, SAM II/

Nimbus 7, SAGE I/AEM II,

SAGE II/ERBS

Instruments highlighted in this volume (DPH2, for Data Products Handbook, Volume 2) and their 

heritage instruments
Radiance/Reflectance and Irradiance Products • ACRIMSAT / ACRIM III Data Products Handbook - Volume 2 51

ACRIM III Radiometric 

Products

Product Description

ACRIM III uses three redundant cavity pyrheliometers to monitor the total solar irradiance (TSI) 

with state-of-the-art precision. One sensor monitors 

full time; a second is compared to the first, monthly; 

and a third is compared with the other two every third 

month, as a calibration for solar-exposure-induced 

degradation. TSI precision will be better than 20 ppm 

over ACRIM IIIʼs 5-year mission lifetime. Overlapping comparisons with preceding (UARS/ACRIM II 

and SOHO/VIRGO) and succeeding (TIM/SIM) TSI 

monitors will be made that are essential for database 

continuity.

 The Level 1A data product (not archived) will 

consist of the experiment data converted to watts 

per square meter in the International System of units 

(SI) at the ACRIMSAT satellite.

 Level 1B data will be Level 1A corrected for shutter 

offsets, pointing errors, temperature dependencies, 

and other minor instrumental effects. 

 Level 1C data will be the calculated TSI time-ordered series in units of watts per square meter for each 

ACRIM shutter-open cycle as observed in situ by the 

ACRIM instrument on ACRIMSAT. The basic time 

resolution will therefore be an average TSI centered 

on 131.076-second intervals.

 Level 2 data will be Level 1C corrected to 1 

Astronomical Unit. The satellite TSI is reconciled 

to 1 Astronomical Unit using the Solar Elevation, 

Azimuth and Range computation of the Satellite 

Tool Kit software (Analytical Graphics, Inc.) based 

on ACRIMSATʼs NORAD two-line orbital elements. 

The primary archived data products are Level 2 shutter cycle and daily means. The time scale of the shutter cycle means is 2.048 minutes. 

Suggested Reading

Beer, J. et al., 1991.

Eddy, J. A., 1976.

Foukal, P. A., and J. L. Lean, 1988.

Foukal, P. A., and J. L. Lean, 1990.

Hays, J.D. et al., 1976.

Hickey, J.R. et al., 1980.

Lee III, R.B. et al., 1995.

Willson, R.C., 1984.

Willson, R.C., 1997.

Willson, R.C., and A.V. Mordvinov, 1999.

Willson, R.C. et al., 1981.

Woodard, M., and R. Noyes, 1985.

ACRIM III Radiometric Products Summary

Coverage: N/A

Processing Level: 1, 2

Product Type: Standard, at-launch

Maximum File Size: 1.2 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Browse Available: Daily Solar Irradiance 

Graphs (10 MB)

Additional Product Information:

http://eosweb.larc.nasa.gov/PRODOCS/

acrimIII/table_acrimIII.html

DAAC: NASA Langley Research Center 

Science Team Contact:

R. C. Willson
52 Data Products Handbook - Volume 2 • Radiance/Reflectance and Irradiance Products 

AIRS Level 1A Radiance 

Counts (AIR 01)

Product Description

This Level 1A data set contains geolocated counts 

for 2378 infrared channels and 4 visible/near-infrared 

channels, along with engineering and spacecraft ancillary data.

 The Level 1A data are used as input for calibration 

and processing. Quality indicators are added to the 

data to indicate instrument mode and missing or 

otherwise bad data.

 Infrared and visible observations are made during 

both the day and the night portions of the orbits.

 This product includes all AIRS infrared and visible 

data in counts from all channels and all detector views 

(Earth, space, and calibrated-source views). Included 

also are engineering data in engineering units, geolocation data, and spacecraft ancillary data.

AIRS Level 1A Radiance Counts Summary

Coverage: Global

Spatial/Temporal Characteristics:

 Infrared: 13.5 km FOV at nadir/twice daily 

(daytime and nighttime)

Visible: 2.3 km FOV at nadir/twice daily 

(daytime and nighttime)

Wavelengths:

 Infrared: 2378 channels

 (3.74-4.61 μm, 6.20-8.22 μm, 8.8-15.4 μm)

Visible/Near-Infrared: 4 channels 

 (0.40-0.44 μm, 0.58-0.68 μm, 0.71-0.93 μm, 

0.48-0.95 μm)

Product Type: Standard, at-launch

Maximum File Size: 75 MB (total size of 4 

files)

File Frequency: 240/day for each of 4 files

Primary Data Format: HDF-EOS

Browse Available: No

Additional Product Information: 

 http://www-airs.jpl.nasa.gov/

DAAC: NASA Goddard Space Flight Center

Science Team Contact:

 H. H. Aumann

AIRS Level 1B Calibrated, 

Geolocated Radiances (AIR 02)

Product Description

This Level 1B data set contains calibrated and 

geolocated radiances for 2378 infrared channels 

and 4 visible/near-infrared channels, along with 

engineering, calibration, and spacecraft ancillary 

data. The units of radiance for infrared channels are 

mW/(m2 cm sr). The units of radiance for visible/

near-infrared channels are W/(m2 μm sr).

 Infrared and visible/near-infrared observations are 

made during both the day and the night portions of 

the orbits.

 This product includes all AIRS IR and visible/nearinfrared data in radiance units from all channels and 

all detector views (Earth, space, and calibrated-source 

views). Included also are engineering and spacecraft 

ancillary data, quality flags, error estimates, and calibration coefficients.

AIRS Level 1B Calibrated, Geolocated 

Radiances Summary

Coverage: Global

Spatial/Temporal Characteristics:

 Infrared: 13.5 km FOV at nadir/twice 

daily (daytime and nighttime)

Visible: 2.3 km FOV at nadir/twice daily 

(daytime and nighttime)

Wavelengths: 

 Infrared: 2378 channels

 (3.74-4.61 μm, 6.20-8.22 μm,

8.8-15.4 μm) 

 Visible/Near-Infrared: 4 channels

 (0.40-0.44 μm, 0.58-0.68 μm, 

 0.71-0.93 μm, 0.48-0.95 μm)

Product Type: Standard, at-launch

Maximum File Size: 147 MB (total size of 2 

files)

File Frequency: 240/day for each of 2 files

Primary Data Format: HDF-EOS

Browse Available: Yes

Additional Product Information:

 http://www-airs.jpl.nasa.gov/

DAAC: NASA Goddard Space Flight Center

Science Team Contact:

 H. H. Aumann

Aqua / AIRS
Radiance/Reflectance and Irradiance Products • Aqua / AIRS/AMSU-A/HSB and AMSR-E Data Products Handbook - Volume 2 53

AIRS/AMSU-A/HSB Level 2 

Cloud-Cleared Radiances 

(AIR 09)

Product Description

The Cloud-Cleared Radiance product is included in 

the Level 2 retrieval data granules and is geolocated 

to an AMSU-A footprint.

 This Level 2 data set contains calibrated and geolocated radiances for 2378 infrared channels. The 

units of radiance are mW/(m2 cm sr).

 This product includes all AIRS infrared data in 

radiance units from all channels. The cloud-cleared 

radiances from Earth views are the radiances that 

would have been observed by the IR detectors if 

there were no clouds in the field of view.

AIRS/AMSU-A/HSB Level 2 Cloud-Cleared 

Radiances Summary

Coverage: Global

Spatial/Temporal Characteristics:

40.6-km FOV at nadir/

twice daily (daytime and nighttime)

Wavelengths: 

Infrared: 2378 channels within

3.74-4.61 μm, 6.20-8.22 μm, 

and 8.8-15.4 μm

Product Type: Standard, at-launch

Maximum File Size: 14 MB

File Frequency: 240/day

Primary Data Format: HDF-EOS

Browse Available: Yes

Additional Product Information:

http://www-airs.jpl.nasa.gov/

DAAC: NASA Goddard Space Flight Center

Science Team Contacts:

H. H. Aumann

J. Susskind

AMSR-E Level 2A Brightness 

Temperatures

Product Description

Due to diffraction, radiometers of differing frequencies using a common antenna do not generally produce equivalent gain patterns on the Earthʼs 

surface. Consequently, direct comparison of such 

observations is complicated by the fact that the 

measurements do not describe identical locations. 

The Level 2A algorithm will alleviate this problem 

by producing several spatially consistent data sets 

of brightness temperatures, corresponding to the 

footprint sizes of the 6.9, 10.7, 18.7, 23.8, 36.5, 

and 89-GHz observations. These six sets of antenna 

patterns are referred to as resolutions 1 through 6 

of the Level 2A data set, corresponding to footprint 

sizes of approximately 56, 38, 21, 24, 12, and 5 km, 

respectively. Observations will be produced at spatial 

intervals of approximately 10 km, except at 89 GHz, 

for which the spatial interval is 5 km. The Level 2A 

product will serve as input for all AMSR-E standard 

Level 2 algorithms.

Research and Applications

The resampling that is done during Level 2A 

processing is a logical first step for most types of 

geophysical retrievals. Bringing the AMSR-E observations to a common spatial resolution will improve 

the retrieval accuracy for those algorithms requiring 

multi-spectral observations.

Data Set Evolution

For previous microwave radiometers such as SSM/I, 

individual investigators employed various resampling 

schemes in order to do geophysical retrievals (see 

Suggested Reading). For AMSR-E, we will be providing the resampled brightness temperatures as a 

standard data product. 

Suggested Reading

Farrar, M. R., and E. A. Smith, 1992.

Farrar, M. R. et al., 1994.

Poe, G. A., 1990.

Robinson, W. D. et al., 1992.

Stogryn, A., 1978.
54 Data Products Handbook - Volume 2 Aqua / AMSR-E and AMSU-A • Radiance/Reflectance and Irradiance Products 

AMSR-E Level 2A Brightness Temperatures 

Summary

Coverage: Global

Spatial/Temporal Characteristics: Swath pixels 

at resolutions from 5 to 56 km

Key Science Applications: Input to all AMSR-E 

standard Level 2 algorithms

Key Geophysical Parameters: Brightness temperatures at common resolutions

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 86 MB

File Frequency: 29/day

Primary Data Format: HDF-EOS

Browse Available: Accompanies data product

Additional Product Information: 

 http://wwwghcc.msfc.nasa.gov/AMSR/

html/amsr_products.html

DAAC: National Snow and Ice Data Center

Science Team Contacts:

 F. J. Wentz

 P. Ashcroft

AMSU-A Level 1A Radiance 

Counts (AMS 01)

Product Description

This Level 1A data set contains geolocated counts 

for 15 microwave channels, along with engineering 

and spacecraft ancillary data.

 The Level 1A data are used as input for calibration and processing. Quality indicators are added to 

the data to indicate instrument mode and missing or 

otherwise bad data.

 Microwave observations are made during both the 

day and the night portions of the orbits.

 This product includes all AMSU-A microwave 

data in counts from all channels and all detector 

views (Earth and space views). Included also are 

engineering data in engineering units, spacecraft 

ancillary data, and geolocation data.

AMSU-A Level 1A Radiance Counts Summary

Coverage: Global

Spatial/Temporal Characteristics:

 40.6-km FOV at nadir/twice daily (daytime 

and nighttime)

Center Frequencies (MHz):

 23800, 31400, 50300, 52800, 53596±115,

 54400, 54940, 55500, 57290.344,

 57290.344±217, 57290.344±322.4±48,

 57290.344±322.4±22, 

57290.344±322.4±10, 

57290.344±322.4±4.5, 89000

Processing Level: 1A

Product Type: Standard, at-launch

Maximum File Size: 1 MB

File Frequency: 240/day

Primary Data Format: HDF-EOS

Additional Product Information: 

 http://orbit-net.nesdis.noaa.gov/crad/st/

amsuclimate/index.html

DAAC: NASA Goddard Space Flight Center

Science Team Contact:

 H. H. Aumann

(AMSR-E Level 2A Brightness Temperatures, continued)
Radiance/Reflectance and Irradiance Products • Data Products Handbook - Volume 2 55

AMSU-A Level 1B Calibrated, 

Geolocated Radiances (AMS 02)

Product Description

This Level 1B data set contains calibrated and geolocated radiances for 15 microwave channels, along 

with engineering, calibration, and spacecraft ancillary 

data. The radiances are expressed as brightness temperatures in K. Accuracy of radiances varies from 

channel to channel and is expected to be approximately ±1.5 K absolute and ±0.5 K relative.

 Microwave observations are made during both the 

day and the night portions of the orbits.

 This product includes all AMSU-A microwave 

data in counts from all channels and all detector 

views (Earth, space, and calibrated-source views). 

Included also are engineering and spacecraft ancillary 

data, quality flags, error estimates, and calibration 

coefficients.

AMSU-A Level 1B Calibrated, Geolocated 

Radiances Summary

Coverage: Global

Spatial/Temporal Characteristics:

 40.6-km FOV at nadir/twice daily (daytime 

and nighttime)

Center Frequencies (MHz):

 23800, 31400, 50300, 52800, 53596±115, 

54400, 54940, 55500, 57290.344, 

57290.344±217, 57290.344±322.4±48,

 57290.344±322.4±22, 

57290.344±322.4±10, 

57290.344±322.4±4.5, 89000

Processing Level: 1B

Product Type: Standard, at-launch

Maximum File Size: 1 MB

File Frequency: 240/day

Primary Data Format: HDF-EOS

Browse Available: Daily global browse images

Additional Product Information: 

 http://orbit-net.nesdis.noaa.gov/crad/st/

amsuclimate/index.html

DAAC: NASA Goddard Space Flight Center

Science Team Contact:

 H. H. Aumann

Aqua / AMSU-A and CERES 

CERES Bi-Directional Scans 

Product (BDS)

Product Description

The Bi-Directional Scans (BDS) Product contains 

24 hours of instantaneous Level 1B Clouds and the 

Earthʼs Radiant Energy System (CERES) data for 

a single scanner instrument. The BDS contains instantaneous radiance measurements recorded every 

0.01 second for views of space, internal calibration, 

solar calibration, and Earth. It contains all elevation 

scan modes, which include the normal Earth scan 

and the short Earth scan modes and both the fixed 

and rotating azimuth-plane scan modes. The BDS 

also contains Level 0 raw (unconverted) science 

and instrument data as well as the geolocation of the 

radiance measurements. The BDS product contains 

additional data not found in the Level 0 input file, 

including converted satellite position and velocity 

data, celestial data, converted digital status data, and 

parameters used in the equations for radiance count 

conversions.

 A complete listing of parameters for this data product can be found in the CERES Data Products Catalog 

at http://asd-www.larc.nasa.gov/DPC/DPC.html, and 

detailed definitions of each parameter can be found 

in the CERES Collection Guide at http://asdwww.larc.nasa.gov/ceres/collect_guide/list.html.

Research and Applications

These radiance data products are the primary inputs 

to all the CERES product algorithms. The spacecraft 

ephemeris and sensor telemetry are inputs to this subsystem, which uses instrument calibration coefficients 

to convert the spacecraft telemetry inputs into geolocated filtered radiances and housekeeping data into 

engineering units. 

Data Set Evolution

The BDS product algorithm operates on the Level 

0 reformatted raw sensor counts and produces calibrated radiances in the three channels. The steps in 

the processing are: 1) Convert the raw housekeeping 

telemetry into engineering units; 2) calculate the geographical location of the CERES footprints; 3) revise 

the radiometric detector count conversion coefficients 

when required; 4) convert the radiometric detector 

signals into filtered radiances; and 5) archive the BDS 

standard products.
56 Data Products Handbook - Volume 2 Aqua / CERES • Radiance/Reflectance and Irradiance Products 

 Documentation on the BDS algorithms can be 

found in the CERES ATBD for Subsystem 1.0, in 

PDF format, at http://eospso.gsfc.nasa.gov/atbd/

cerestables.html.

Suggested Reading

Hoffman, L. H. et al., 1987.

Jarecke, P. J. et al., 1991.

Lee III, R. B. et al., 1996.

Priestley, K. J. et al., 2000.

Smith, G. L. et al., 1986.

Wielicki, B. A. et al., 1998.

(CERES Bi-Directional Scans Product, continued) CERES Bi-Directional Scans Product 

Summary

Coverage: Global

Spatial/Temporal Characteristics:

 20 km at nadir/0.01 second

Key Geophysical Parameters: Filtered total, 

shortwave, and window radiances, geolocation data

Processing Level: 0, 1

Product Type: Standard, at-launch

Maximum File Size: 900 MB

File Frequency: 1/day

Primary Data Format: HDF

Browse Available: No

Additional Product Information: 

 http://eosweb.larc.nasa.gov/PRODOCS/

ceres/table_ceres.html

DAAC: NASA Langley Research Center

Science Team Contacts:

 R. B. Lee

 K. J. Priestley

Bi-Directional Scans Product: Nighttime total channel radiance measurements taken from the CERES 

instrument on Terra on February 28, 2000.

No 35 40 45 50 55 60 65 70 75 80 85

Data Watts per square meter per steradian
Radiance/Reflectance and Irradiance Products • Data Products Handbook - Volume 2 57

HSB Level 1B Calibrated, 

Geolocated Radiances (HSB 02)

Product Description

This Level 1B data set contains calibrated and geolocated radiances for 4 microwave channels, along 

with engineering, calibration, and spacecraft ancillary data. The radiances are expressed as brightness 

temperatures in K. Accuracy of radiances varies from 

channel to channel and is expected to be approximately ±1.0 K absolute and ±0.6 K relative.

 Microwave observations are made during both the 

day and the night portions of the orbits.

 This product includes all HSB microwave data in 

counts from all channels and all detector views (Earth, 

space, and calibrated-source views). Included also 

are engineering and spacecraft ancillary data, quality 

flags, error estimates, and calibration coefficients.

HSB Level 1B Calibrated, Geolocated 

Radiances Summary

Coverage: Global

Spatial/Temporal Characteristics:

 13.5-km FOV at nadir/

 twice daily (daytime and nighttime)

Center Frequencies (MHz):

 150000, 183310±1000, 183310±3000,

 183310±7000

Processing Level: 1B

Product Type: Standard, at-launch

Maximum File Size: 2 MB

File Frequency: 240/day

Primary Data Format: HDF-EOS

Browse Available: Daily global browse images

Additional Product Information:

 http://www.dss.inpe.br/programas/hsb/ingl/

Produto/Default.htm

Science Team Contact:

 H. H. Aumann

Aqua / HSB

HSB Level 1A Radiance Counts 

(HSB 01)

Product Description

This Level 1A data set contains geolocated counts for 

4 microwave channels, along with engineering and 

spacecraft ancillary data.

 The Level 1A data are used as input for calibration and processing. Quality indicators are added to 

the data to indicate instrument mode and missing or 

otherwise bad data.

 Microwave observations are made during both the 

day and the night portions of the orbits.

 This product includes all HSB microwave data in 

counts from all channels and all detector views (Earth 

and space views). Included also are engineering data, 

in engineering units, spacecraft ancillary data, and 

geolocation data.

HSB Level 1A Radiance Counts Summary

Coverage: Global

Spatial/Temporal Characteristics:

 13.5-km FOV at nadir/

 twice daily (daytime and nighttime)

Center Frequencies (MHz):

 150000, 183310±1000, 183310±3000,

 183310±7000

Processing Level: 1A

Product Type: Standard, at-launch

Maximum File Size: 1 MB

File Frequency: 240/day

Primary Data Format: HDF-EOS

Additional Product Information: 

 http://www.dss.inpe.br/programas/hsb/

ingl/Produto/Default.htm

Science Team Contact:

 H. H. Aumann
58 Data Products Handbook - Volume 2 Aqua / MODIS • Radiance/Reflectance and Irradiance Products 

MODIS Level 1B Calibrated, 

Geolocated Radiances (MOD 02)

Product Description

The Level 1B data set contains calibrated and geolocated at-aperture radiances for 36 bands generated 

from MODIS Level 1A sensor counts (MOD 01). 

The radiances are in W/(m2 μmsr). In addition, Earth 

BRDF may be determined for the solar reflective 

bands (1-19, 26) through knowledge of the solar irradiance (e.g., determined from MODIS solar-diffuser 

data, and from the target-illumination geometry). Additional data are provided, including quality flags, 

error estimates, and calibration data.

 Visible, SWIR, and NIR measurements are made 

during daytime only, while radiances for TIR are 

measured continuously. Only Channels 1 and 2 

have 250-m resolution, Channels 3-7 have 500-m 

resolution, and the rest have 1-km resolution.

MODIS Level 1B Calibrated, Geolocated 

Radiances Summary

Coverage: Global

Spatial/Temporal Characteristics:

 0.25, 0.5, and 1 km resolutions/daily (daytime and nighttime)

Wavelengths: 20 channels 0.4-3.0 μm, 

16 channels 3-15 μm

Processing Level: 1B

Product Type: Standard, at-launch

Maximum File Size: 345 MB (1 km), 276 MB 

(500 m), 287 MB (250 m)

File Frequency: 288/day (1 km), 144/day 

(500 m), 144/day (250 m)

Primary Data Format: HDF-EOS

Additional Product Information: 

http://daac.gsfc.nasa.gov/CAMPAIGN_

DOCS/MODIS/rad_geo_products.html

DAAC: NASA Goddard Space Flight Center

Science Team Contact:

 B. W. Guenther

MODIS Level 1A Radiance 

Counts (MOD 01)

Product Description

This Level 1A data set contains counts for 36 MODIS 

channels, along with raw instrument engineering and 

spacecraft ancillary data. The Level-1A data are used 

as input for geolocation, calibration, and processing. 

Quality indicators are added to the data to indicate 

missing or bad pixels and instrument modes. Visible, SWIR, and NIR measurements are made during 

daytime only, while radiances for TIR are measured 

during both the day and the night portions of the orbit. 

This product includes all MODIS data in digitized 

(counts) form for all bands, all spatial resolutions, 

all time tags (converted), all detector views (Earth, 

solar diffuser, spectroradiometric calibration assembly, black body, and space view), and all engineering 

and ancillary data.

MODIS Level 1A Radiance Counts Summary

Coverage: Global

Spatial/Temporal Characteristics:

 0.25, 0.5, and 1 km resolutions/

 daily (daytime and nighttime)

Wavelengths: 20 channels 0.4-3.0 μm, 

16 channels 3-15 μm

Processing Level: 1A

Product Type: Standard, at-launch

Maximum File Size: 534 MB

File Frequency: 288/day

Primary Data Format: HDF-EOS

Additional Product Information: 

 http://daac.gsfc.nasa.gov/CAMPAIGN_

DOCS/MODIS/rad_geo_products.html

DAAC: NASA Goddard Space Flight Center

Science Team Contact: 

V. V. Salomonson
Radiance/Reflectance and Irradiance Products • Data Products Handbook - Volume 2 59

View of eastern North America on March 6, 2000, obtained from data of the MODIS 

instrument on Terra.

Aqua / MODIS
60 Data Products Handbook - Volume 2 • Radiance/Reflectance and Irradiance Products 

MODIS Geolocation Data Set 

(MOD 03)

Product Description

The MODIS Geolocation product contains geodetic 

coordinates, ground elevation, and solar and satellite zenith, and azimuth angle for each MODIS 1-km 

sample. These data are provided as a ʻcompanionʼ 

data set to the Level 1B calibrated radiances and the 

Level 2 data sets to enable further processing. These 

geolocation fields are determined using the spacecraft 

attitude and orbit, instrument telemetry, and a digital 

elevation model.

MODIS Geolocation Data Set Summary

Coverage: Global

Spatial/Temporal Characteristics:

 1 km resolution/daily (daytime and nighttime)

Wavelengths: n/a

Processing Level: 1B

Product Type: Standard, at-launch

Maximum File Size: 60 MB

File Frequency: 288/day

Primary Data Format: HDF-EOS

Additional Product Information: 

http://daac.gsfc.nasa.gov/CAMPAIGN_

DOCS/MODIS/rad_geo_products.html

DAAC: NASA Goddard Space Flight Center

Science Team Contacts: 

V. V. Salomonson

 E. J. Masuoka

Aqua / MODIS

MODIS Normalized WaterLeaving Radiance (MOD 18) 

and Aerosol Optical Depth 

(MOD 37)

Product Description

This Level 2 and Level 3 product contains ocean 

water-leaving radiances for 7 of the 36 wavelengths/

spectral bands (Bands 8 through 14, 412 through 

681 nm) of MODIS. These are the “ocean” bands; 

the water-leaving radiances in these bands are used 

to derive nearly all of the MODIS ocean products. 

In addition, another key parameter generated by the 

algorithm is provided as product MOD 37: Aerosol 

Optical Depth. The Level 2 product is provided 

daily at 1-km resolution for cloud-free pixels, while 

the Level 3 product is provided daily, 8-day weekly, 

monthly, and yearly. The product constraints are 

that only cloud-free pixels are used (with Sun glitter 

below a threshold), and all valid pixels are outside 

a distance threshold from land. The product availability is at-launch.

Research and Applications

Normalized Water-Leaving Radiance is the radiance that would exit the ocean in the absence of the 

atmosphere, if the Sun were at the zenith. It is used 

in the bio-optical algorithms to estimate chlorophyll 

a concentration and ocean primary productivity on 

a global scale. The algorithm evolved from experience from the CZCS experiment, which proved the 

feasibility of measuring ocean color from space. Extensive testing of the algorithm is being conducted 

using SeaWiFS imagery and will be continued using 

the Terra and Aqua MODIS instruments. This is the 

fundamental product for recovering most of the 

MODIS ocean products.

Data Set Evolution

Inputs to the algorithm are the Level 1 radiances 

in Bands 8-14, screened for clouds and land, and 

estimates of the surface wind speed, atmospheric 

pressure, and ozone concentration derived from National Centers for Environmental Prediction (NCEP, 

formerly the NMC) products. The success of the 

algorithm depends on the accurate characterization 

of the atmospheric effect, which for water typically 

constitutes 90 percent of the at-satellite radiance. 

The algorithm includes single scattering, multiple 
Radiance/Reflectance and Irradiance Products • Aqua / MODIS Data Products Handbook - Volume 2 61

scattering effects, and whitecap removal, and uses 

six types of ancillary data. There are three major 

sources of uncertainty in the product: 1) The candidate aerosol models chosen to describe the aerosol 

may be unrepresentative of the natural aerosol, 2) 

there is uncertainty in the estimate of the whitecap 

reflectance/radiance, and 3) there is uncertainty 

in the sensorʼs radiometric calibration. Efforts to 

reduce these uncertainties include development of 

improved aerosol models, direct measurements of 

the properties of oceanic whitecaps, and utilization 

of the Marine Optical Buoy (MOBY) calibration 

site to improve radiometric calibration. The product 

will be validated by comparing simultaneous surface-based measurements (including drifting-buoy 

radiometers) and MODIS-derived values at a set of 

locations.

Suggested Reading

Chomko, R., and H. R. Gordon, 1998.

Deschamps, P. Y. et al., 1983.

Evans, R. H., and H. R. Gordon, 1994.

Gordon, H. R., 1997.

Gordon, H. R., and D. K. Clark, 1981.

Gordon, H. R., and A. Y. Morel, 1983.

Gordon, H. R., and M. Wang, 1994.

Gordon, H. R. et al., 1997.

MODIS Normalized Water-Leaving Radiance 

and Aerosol Optical Depth Product 

Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 1 km/daily 

(Level 2), 4.6 km, 36 km, 1°/daily, 8-day, 

monthly, yearly (Level 3)

Key Science Applications: Ocean chlorophyll, 

ocean productivity

Key Geophysical Parameters: Water-leaving 

radiances in the ocean bands, aerosol optical depth

Processing Level: 2, 3

Product Type: Standard, at-launch

Maximum File Size: 88 MB (Level 2); 640 MB 

binned, 134 MB mapped (Level 3)

File Frequency: 144/day (Daily Level 2); 7/day 

(Daily Level 3), 7/8-day (8-day Level 3),7/

month (Monthly Level 3), 7/year (Yearly 

Level 3)

Primary Data Format: HDF-EOS

Additional Product Information: http://modisocean.gsfc.nasa.gov/dataprod.html

DAAC: NASA Goddard Space Flight Center

Science Team Contact: 

H. R. Gordon
62 Data Products Handbook - Volume 2 • Radiance/Reflectance and Irradiance Products 

effects of scattering, absorption by ozone, absorption 

by gas, and water vapor. This spectrum is then binned 

and weighted appropriately to give the irradiance in 

each of the MODIS ocean bands. Next, the below-surface values are computed and summed to give IPAR. 

Inputs to the algorithm are Water-Leaving Radiance 

(MOD 18), PAR (MOD 22) and Absorption Coefficients (MOD 36).

Suggested Reading

Gordon, H. R., and M. Wang, 1994.

Gregg, W. W., and K. L. Carder, 1990.

Iqbal, M., 1983.

Paltridge, G. W., and C. M. R. Platt, 1976.

MODIS Photosynthetically 

Active Radiation (PAR-MOD 22)

Product Description

This Level 2 and 3 product consists of four related 

parameters which describe the irradiance at the ocean 

surface. The first is Downwelling Solar Irradiance, 

which is the incident irradiance just above the sea 

surface in each of the visible MODIS ocean bands 

(8, 9, 10, 11, 12, 13, 14). The second is Instantaneous 

Photosynthetically Active Radiation (IPAR), which 

is the total downwelling flux of photons just below 

the sea surface at the instant MODIS views the pixel, 

integrated over the wavelength range of 400-to-700 

nm. The third is PAR, which is the irradiance averaged over an entire day. Since measurements are 

only available at the time MODIS views the pixel, this 

parameter is estimated from IPAR by the algorithm. 

The product is produced at 1 km daily for Level 2 

and gridded at 4.6 km, 36 km, and 1° daily, 8-day 

weekly, monthly, and yearly for Level 3. The fourth 

is ARP (Absorbed Radiation by Phytoplankton) averaged over the first optical depth. This parameter 

is critical for calculation of fluorescence efficiency, 

a product useful for conditioning phytosynthesis 

models (MOD 23).

Research and Applications

This product is critical for determining photosynthetic 

rate of growth of phytoplankton and primary ocean 

production. Downwelling irradiance is needed as 

an input to the chlorophyll a algorithm (MOD 21), 

and it is used to convert the normalized water-leaving radiance values (MOD 18) into remote sensing 

reflectance values. A series of irradiance models 

specific for terrigenous aerosols have been available; however, the aerosol characteristics of these 

models differ greatly in size and optical characteristics from marine aerosols. The irradiance model of 

Gregg and Carder (1990) uses a mixture of marine 

and terrigenous aerosols and forms the basis for the 

algorithm for the MODIS PAR product.

Data Set Evolution

Product generation begins with solar-irradiance data 

at 1-nm resolution taken from the revised Neckel and 

Labs data and corrected for Earth-Sun distance for the 

current day. Atmospheric correction is made for the 

Aqua / MODIS
Radiance/Reflectance and Irradiance Products • Aqua / MODIS Data Products Handbook - Volume 2 63

MODIS Processing Framework 

and Match-up Database 

(MOD 32)

Product Description

This Level 2 product consists of the calibration data 

set to be used in the generation of all the MODIS 

Oceans products. It consists of a database which 

contains in situ observations of ocean parameters 

matched with satellite measurement data. This 

match-up database will initially be populated with 

existing ocean-surface data matched temporally 

and spatially with CZCS and AVHRR data and with 

SeaWiFS data as they are obtained and then MODIS 

data post-launch. The product includes the definition 

of the processing framework in which all the MODIS 

ocean-product algorithms will operate. It is produced 

daily and supports ocean products at 1 km, but the 

product itself does not have a spatial resolution.

Research and Applications

This is a calibration product for MODIS ocean processing. It is used through a vicarious calibration 

scheme to update MODIS radiometric calibration 

coefficients, which directly relate MODIS Level 

1A raw counts to Level 2 calibrated water-leaving 

radiances, and to monitor long-term performance 

of the MODIS instrument. MOD 32, as a matchup database, is also used to validate derived ocean 

geophysical parameters such as SST and for quality 

control of the ocean-team retrievals.

Data Set Evolution

Heritage programs provide the basis for MODIS 

algorithms. A program developed for the Pathfinder 

ocean SST product forms the framework for analyzing AVHRR-derived SST, algorithm development 

and validation, and application of the match-up database. Development for ocean-color algorithms is 

based on experience gained in the transition from 

CZCS to SeaWiFS algorithms. Development of the 

SeaWiFS program involves integration of algorithms 

generated by H. Gordon (atmospheric correction) and 

K. Carder (chlorophyll). In addition, daily validity 

tests are being developed through collaboration of 

the SeaWiFS cal/val team and our group. The in 

situ records are first temporally matched against the 

MODIS Photosynthetically Active Radiation 

Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 1 km/daily 

(Level 2); 4.6 km, 36 km, 1°/daily, 8-day, 

monthly, yearly (Level 3)

Key Science Applications: Ocean chlorophyll, 

productivity

Key Geophysical Parameters: Photosynthetically available irradiance

Processing Level: 2, 3

Product Type: Validation, at-launch

Maximum File Size: 83 MB (Level 2);

 640 MB binned, 134 MB mapped

(Level 3)

File Frequency: 144/day (Daily Level 2); 2/day 

(Daily Level 3), 2/8-day (8-day Level 3), 

2/month (Monthly Level 3), 2/year (Yearly 

Level 3)

Primary Data Format: HDF-EOS

Browse Available: 36 km sample imagery available at the Goddard DAAC (Level 3 only)

Additional Product Information:

 http://modis-ocean.gsfc.nasa.gov/

dataprod.html

DAAC: NASA Goddard Space Flight Center

Science Team Contacts:

 K. Carder

 W. Esaias

 M. Abbott
64 Data Products Handbook - Volume 2 • Radiance/Reflectance and Irradiance Products 

(MODIS Processing Framework and Match-up Database, 

continued) MODIS Clear-Water Epsilon 

(MOD 39)

Product Description

This product provides a single parameter, the ratio 

of clear-water-leaving radiance at 531 nm to that at 

667 nm and is called the clear-water epsilon. This 

quantity relates directly to the iron content of aerosols 

over clear waters mostly in the ±35° latitude range. 

The Level 2 product is produced daily, at 1-km spatial 

resolution, whereas the Level 3 product is produced 

daily, 8-day weekly, monthly, and yearly, at 4.6 km, 

36 km, and 1° resolution.

Research and Applications

The primary purpose of this algorithm is to estimate 

aerosol iron content over ocean waters. The aerosol 

iron influences the validity of other MODIS products. 

The secondary objective is to flag instances when 

normalized water-leaving-radiance retrievals may 

need adjustment due to aerosol absorption at blue 

and green wavelengths. Such errors will affect chlorophyll a calculations. The third objective is to provide 

a check on the Angstrom exponent derived at red and 

infrared wavelengths. The algorithm is valid for pigment concentrations up to 2 mg/m3

. When pigment 

concentrations are larger than this, the algorithm can 

no longer be applied. A research algorithm is planned 

post-launch that will use a coupled ocean-atmosphere 

radiance model to address, interactively, aerosols in 

high-pigment water.

Data Set Evolution

The algorithm is based on methods developed for 

obtaining clear-water epsilon values from CZCS 

data. Modifications for the MODIS algorithm 

include extension of the clear-water concept to include waters with higher pigment concentrations and 

modification of the values of the normalized waterleaving radiance at 520, 550, and 670 nm for CZCS to 

the slightly different MODIS bands by means of the 

water-absorption curve. Product validation will use 

SeaWiFS data in the pre-launch period and MODIS 

data post-launch. Scattering and optical-thickness 

data plus ship data of water-leaving radiances will 

be acquired to test the clear-water radiance assumptions. This is an interim validation product which 

may not be archived.

Aqua / MODIS

AVHRR extractions. AVHRR data for the match-up 

database were extracted for 3 × 3 pixel boxes centered 

at each in situ sea-surface-temperature-measurement 

location. Sea-surface observations are from two main 

types of platforms: moored buoys and drifting buoys. 

After the SeaWiFS and Pathfinder programs are validated using actual sensor data, they will be converted 

from a scalar to a parallel implementation and coded 

using the FORTRAN 90 language and EOS coding 

standards. Code for the MODIS algorithms initially 

will be based on SeaWiFS and Pathfinder programs. 

These implementations will evolve to support product production for the MODIS ocean investigators 

and will be coded using a combination of the C and 

FORTRAN 90 programming languages and EOS 

coding standards. 

Suggested Reading

Evans, R. H., and H. R. Gordon, 1994.

Gordon, H. R., 1987.

Gordon, H. R., and A. Y. Morel, 1983.

Slater, P. N. et al., 1987.

Smith, R. C., and W. H. Wilson, 1981.

MODIS Processing Framework and Match-up 

Database Summary

Coverage: Global ocean surface

Spatial/Temporal Characteristics: 1 km/daily

Key Science Applications: Ocean chlorophyll, 

ocean productivity, all ocean products

Key Geophysical Parameters: Match-up databases, water-leaving radiances, in situ

measurements

Processing Level: 2

Product Type: Standard, at-launch

Science Team Contact:

 R. H. Evans

 H. R. Gordon
Radiance/Reflectance and Irradiance Products • Data Products Handbook - Volume 2 65

Suggested Reading

Carder, K. L. et al., 1991.

Gordon, H. R., 1978.

Gordon, H. R., and D. K. Clark, 1981.

Gordon, H. R., and A. Y. Morel, 1983.

Gordon, H. R., and M. Wang, 1994.

MODIS Clear-Water Epsilon Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 1 km/daily 

(Level 2); 4.6 km, 36 km, 1°/daily, 8-day, 

monthly, yearly (Level 3)

Key Science Applications: Aerosol iron estimation, water-leaving radiance correction

Key Geophysical Parameters: Clear-water 

epsilon, aerosol iron content

Processing Level: 2, 3

Product Type: Validation, at-launch

Maximum File Size: 88 MB (Level 2);

 865 MB binned, 134 MB mapped (Level 3)

File Frequency: 144/day (Daily Level 2); 36/day 

(Daily Level 3), 36/8-day (8-day Level 

3), 36/month (Monthly Level 3), 36/year 

(Yearly Level 3)

Primary Data Format: HDF-EOS

Browse Available: 36 km sample imagery available at the Goddard DAAC (Level 3 only)

Additional Product Information:

http://modis-ocean.gsfc.nasa.gov/

dataprod.html

DAAC: NASA Goddard Space Flight Center

Science Team Contact:

 K. Carder

Aqua / MODIS
66 Data Products Handbook - Volume 2 • Radiance/Reflectance and Irradiance Products 

ETM+ L70R Raw Digital 

Numbers

Product Description

This Level 0R data set contains raw digital numbers 

from the eight channels of the ETM+ instrument. The 

data are in 8-bit form. Ephemeris profiles and other 

information required for radiometric correction and 

geometric rectification accompany the image bands. 

Descriptive information and data quality metrics are 

also included in the product metadata.

 Data are collected during the day over land. Some 

nighttime acquisitions are made. The ETM+ is commanded to collect all channels simultaneously. Radiometric calibration is performed using ground testing 

and in-flight data. The instrument has three onboard 

calibrators: a partial-aperture solar calibrator, a fullaperture solar calibrator, and an internal- lamp calibrator. Data from the calibrators are ground processed 

and used to update pre-launch calibration coefficients 

for bands 1 through 5, 7, and 8. An onboard blackbody 

source is used to update the calibration of band 6. All 

parameters required for radiometric and geometric 

processing are included in the Calibration Parameter 

File (CPF). The most current CPF accompanies the 

product.

 The ETM+ is similar to other EOS radiance imagers, and data from this instrument may be used 

for a variety of terrestrial and atmospheric research 

studies. First, however, the data must be radiometrically corrected. Band 6 radiances can be converted 

to brightness temperatures for thermal studies.

Landsat 7 / ETM+

ETM+ L70R Raw Digital Numbers Summary

Coverage: 233 orbits, each having a 185-km 

swath width

Spatial/Temporal Characteristics: 30 meter 

(bands 1-5, 7), 60 meter (band 6), 15 

meter (band 8)

Wavelengths: .45-.52, .53-.61, .63-.69, .78-.90, 

1.55-1.75, 10.4-12.5, 2.09-2.35, .52-.90 μm

Product Type: Standard, at-launch

Maximum File Size: 450 MB

File Frequency: 250/day

Primary Data Format: HDF-EOS

Secondary Data Format: Fast, GeoTIFF

Browse Available: Yes

Additional Product Information: 

http://eosdatainfo.gsfc.nasa.gov/eosdata/

landsat7/dataproducts.html

DAAC: EROS Data Center

Science Team Contact:

D. L. Williams
Radiance/Reflectance and Irradiance Products • Landsat 7 / ETM+ Data Products Handbook - Volume 2 67

ETM+ L71R Calibrated 

Radiances

Product Description

This Level 1R data set contains calibrated radiances 

in W/(m2 μm sr) from the eight channels of the ETM+ 

instrument. The data are scaled to 16-bit integers. 

The data can be converted back to radiance units by 

dividing pixel values by 100. Ephemeris profiles and 

other information required for geometric rectification 

accompany the image bands. Descriptive information 

and data quality metrics are also included in the product metadata.

 Data are collected during the day over land. Some 

nighttime acquisitions are made. The ETM+ is 

commanded to collect all channels simultaneously. 

Radiometric calibration is performed using ground 

testing and in-flight data. The instrument has three 

onboard calibrators: a partial-aperture solar calibrator, 

a full-aperture solar calibrator, and an internal lamp 

calibrator. Data from the calibrators are ground processed and used to update pre-launch calibration coefficients for bands 1 through 5, 7, and 8. An onboard 

blackbody source is used to update the calibration 

of band 6. Processing to compensate for undesirable 

image artifacts is performed, but the data are not geometrically corrected or resampled.

 The ETM+ is similar to other EOS radiance imagers, and data from this instrument may be used for 

a variety of terrestrial and atmospheric research studies. Band 6 radiances can be converted to brightness 

temperatures for thermal studies. 

ETM+ L71R Calibrated Radiances Summary

Coverage: 233 orbits, each having a 185-km 

swath width

Spatial/Temporal Characteristics: 30 meter 

(bands 1-5, 7), 60 meter (band 6), 15 

meter (band 8)

Wavelengths: .45-.52, .53-.61, .63-.69, .78-.90, 

1.55-1.75, 10.4-12.5, 2.09-2.35, .52-.90 μm

Product Type: Standard, at-launch

Maximum File Size: 900 MB

File Frequency: 250/day

Primary Data Format: HDF-EOS

Secondary Data Format: Fast, GeoTIFF

Browse Available: Yes

Additional Product Information: 

http://eosdatainfo.gsfc.nasa.gov/eosdata/

landsat7/dataproducts.html

DAAC: EROS Data Center

Science Team Contact:

D. L. Williams
68 Data Products Handbook - Volume 2 • Radiance/Reflectance and Irradiance Products 

ETM+ L71G Calibrated 

Radiances

Product Description

This Level 1G data set contains calibrated radiances 

in W/(m2 μm sr) from the eight channels of the ETM+ 

instrument. The data are scaled to 8-bit integers. The 

data can be converted back to radiance units by applying radiometric correction coefficients. These are 

included in the Fast-formatted product. Descriptive 

information and data-quality metrics are also included 

in the product metadata.

 Data are collected during the day over land. Some 

nighttime acquisitions are made. The ETM+ is commanded to collect all channels simultaneously. Radiometric calibration is performed using ground testing 

and in-flight data. The instrument has three onboard 

calibrators: a partial-aperture solar calibrator, a fullaperture solar calibrator, and an internal- lamp calibrator. Data from the calibrators are ground processed 

and used to update pre-launch calibration coefficients 

for bands 1 through 5, 7, and 8. An onboard blackbody source is used to update the calibration of band 

6. Processing to compensate for undesirable image 

artifacts is performed, and the data are geometrically 

corrected and resampled according to user-specified 

parameters.

 The ETM+ is similar to other EOS radiance imagers, and data from this instrument may be used for 

a variety of terrestrial and atmospheric research studies. Band 6 radiances can be converted to brightness 

temperatures for thermal studies.

ETM+ L71G Calibrated Radiances Summary

Coverage: 233 orbits, each having a 

185-km swath width

Spatial/Temporal Characteristics: 30 meter 

(bands 1-5, 7), 60 meter (band 6), 15 

meter (band 8)

Wavelengths: .45-.52, .53-.61, .63-.69, .78-.90, 

1.55-1.75, 10.4-12.5, 2.09-2.35, .52-.90 μm

Product Type: Standard, at-launch

Maximum File Size: 600 MB

File Frequency: 250/day

Primary Data Format: HDF-EOS

Secondary Data Format: Fast, GeoTIFF

Browse Available: Yes

Additional Product Information: 

http://eosdatainfo.gsfc.nasa.gov/eosdata/

landsat7/dataproducts.html

DAAC: EROS Data Center

Science Team Contact:

D. L. Williams

Landsat 7 / ETM+
Radiance/Reflectance and Irradiance Products • Data Products Handbook - Volume 2 69

SAGE III Atmospheric SlantPath Transmission Product 

(SAGE III 1B)

Product Description

This Level 1B product (SAGE III 1B) consists of 

atmospheric slant-path transmission profiles at 85 

spectral channels in 0.5-km intervals from sea level 

to 100-km height. The estimated uncertainty for 

each transmission value is also included. These data 

have been geolocated and normalized to the exo-atmospheric solar measurements so that independent 

retrieval can be performed on these data sets to produce Level 2 atmospheric species profiles.

SAGE III Atmospheric Slant-Path 

Transmission Product Summary

Coverage: 30 solar events per day, 15 

northern high latitude, 15 southern high 

latitude.

Spatial/Temporal Characteristics: 

Altitude resolution: 0.5 km vertical

 Wavelengths: 290 nm to 1550 nm, 

85 wavelengths/30 events per day

Processing Level: 1B

Product Type: Standard, at-launch

Maximum File Size: 0.21 MB

File Frequency: 196/week

Primary Data Format: HDF-EOS, Big Endian 

IEEE Binary

Browse Available: Yes

Additional Product Information: 

http://eosweb.larc.nasa.gov/PRODOCS/

sage3/table_sage3.html

DAAC: NASA Langley Research Center

Science Team Contact:

W. P. Chu

Meteor 3M / SAGE III
71

Precipitation

and 

Atmospheric Humidity

Aqua

• AIRS

• AMSR-E

• AMSU-A

• HSB

• MODIS

Jason-1 • JMR

Meteor 3M • SAGE III
72 Data Products Handbook - Volume 2 Overview • Precipitation and Atmospheric Humidity 

Precipitation and Atmospheric 

Humidity – An Overview

Relationship to Global Change Issues

NASA now recognizes the combined water and energy cycle as one of five key Earth-science disciplines. 

The latent heat associated with the phase transitions 

of water is a significant, in some cases the dominant, 

portion of the energy flux in the atmosphere. Monitoring precipitation and other forms of water is critical 

to obtaining hydrological-cycle parameterizations 

needed for developing and improving global climate 

models and detecting climate change. The processes 

that generate rainfall are central to the dynamical, 

biological, and chemical processes in the atmosphere, 

in the oceans, and on the land surfaces; and spacebased measurement is the only practical means to an 

accurate documentation of global rainfall.

 The AMSR-E instrument on Aqua is designed 

to provide greatly improved measurements of rain 

rates over both land and ocean compared to current 

methods at high latitudes and to extend the temporal 

coverage of the Tropical Rainfall Measuring Mission 

(TRMM) in the tropics. TRMM has been referred to 

as a ʻflying rain gauge.ʼ Due to the lack of a precipitation radar on the Aqua platform, the rain measurements from Aqua will not provide the same insight 

as provided by TRMM. The use of physically based 

algorithms will nonetheless offer the opportunity to 

take understandings acquired through the combined 

radar/radiometer system on TRMM and apply them to 

AMSR-E. The final product thus offers a large contribution to the World Climate Research Programmeʼs 

(WCRPʼs) rain climatology initiative.

 Microwave measurements are particularly well 

suited to the measurement of water in all its phases. 

AMSR-E, in addition to rain rates, will also be used 

to derive total integrated water vapor, total integrated 

cloud water, soil moisture, snow cover, and sea ice 

coverage. The AIRS/AMSU-A/HSB instruments, 

also on Aqua, will be used to determine the vertical 

profiles of water vapor and will allow estimation of 

precipitation rates over land and sea.

 From Jason-1, JMR data will be used to obtain 

columnar water vapor content; and from Meteor 3M, 

SAGE III data will be used to obtain water-vapor 

concentration, water-vapor mixing ratios, and relative 

humidities, as well as total-column water vapor. 

 This chapter describes the EOS products that support the monitoring of precipitation and atmospheric 

humidity. Although the majority of global precipitation occurs in the tropics, between latitudes 30°N 

and 30°S, extratropical precipitation also affects 

global climate change, the global heat balance, and 

the global water cycle. To understand and predict 

large variations in weather and climate, it is critical 

to understand the coupling between the atmosphere 

and the underlying oceans, which cover 70% of the 

Earthʼs surface. 

Precipitation: Atmospheric circulation transports 

both energy and water, moving heat from the tropics 

to polar regions. Water, evaporated from the oceans 

and the land surface, falls as rain or snow, often in 

places far removed from its point of origin. Rainfall 

variability is influenced by wind and pressure patterns 

and storm tracks and in turn influences aspects of the 

underlying surface, such as sea surface temperature. 

Fluctuations in rainfall amounts impact climate on 

both short-term and long-term time scales. In fact, 

variation in rainfall is considered by some to be the 

primary means by which climate changes will impact 

human welfare.

 The long-term, time-averaged circulation and precipitation fields show large-scale features associated 

with the annual evolution of the monsoons, trade-wind 

systems, and oceanic convergence zones. Large-scale 

features are also evident on a monthly and seasonal 

scale. Interannual variability is dominated by tropical Pacific ocean-atmosphere interactions associated 

with the ENSO cycle, whose major swings occur at 

irregular intervals of 2-7 years and are associated with 

pronounced year-to-year precipitation variations over 

large areas of the Earth. The large-scale spatial coherence and systematic evolution of both interseasonal 

and interannual tropical precipitation anomalies is 

of great significance to seasonal climate prediction. 

They are also important to the development of optimum sampling strategies for estimating accumulated 

precipitation over periods of a few weeks to a few 

months.

Humidity: The atmosphere gains water through evaporation from ocean and land areas, transpiration from 

vegetation, and sublimation from ice and snow. Once 

in the atmosphere, the water vapor is transported both 

vertically and horizontally by local and larger-scale 

atmospheric circulations, thereby being redistributed. 

In general, there is a net outflow of atmospheric moisture from the tropics, where sea surface temperatures 

are high and the warm atmosphere can support large 

amounts of water vapor, to higher latitudes. At all 

latitudes, condensation occurs when the air becomes 

saturated and clouds form, after which precipitation 

returns the water, in the form of rain or snow, to the 

surface. Current numerical weather prediction models give interesting but inconsistent results about the 

future distribution of precipitation.
Precipitation and Atmospheric Humidity • Overview Data Products Handbook - Volume 2 73

 Humidity is a local measure of the amount of water 

vapor in the atmosphere. Water-vapor concentration 

depends on temperature, which determines the total 

amount of water per unit volume that the atmosphere 

can support without saturation, as well as evaporation. 

The greater the temperature, the greater is the amount 

of water vapor that the atmosphere can support, and 

consequently, in the large-scale, water-vapor amounts 

decrease from equator to pole and with increasing altitude. Furthermore, water amounts are generally less 

over the continents than over the oceans, the oceans 

providing the overwhelmingly larger water source for 

evaporation. Superimposed on these large-scale patterns are smaller variations of water-vapor amounts 

that determine locally the formation and properties 

of clouds and rainfall.

Product Overview

The products included in this chapter are produced 

by the AMSR-E, AIRS/AMSU-A/HSB, and MODIS 

instruments on Aqua, the JMR instrument on Jason1, and the SAGE III instrument on Meteor 3M. The 

product list includes both instantaneous and timeaveraged entries. Among the instantaneous products, 

land and ocean rainfall are generated separately in 

the AMSR-E data processing because of the different 

physics accessible to the retrieval process. The products are generated through a single algorithm that 

determines the nature of the background and adjusts 

accordingly in a manner transparent to the data user. 

The user need only be aware that the background 

significantly affects the accuracy of the results from 

AMSR-E. Both the land and ocean components of 

the algorithm have a long heritage and history of 

numerous improvements.

 The Level 3 AMSR-E product aggregates the instantaneous rainfall retrieval and also generates an 

independent estimate of the monthly rainfall over 

the oceans based on a simpler, and presumably more 

robust, algorithm that makes use of the statistical 

properties of rainfall. The independent method only 

works over the ocean; there is no parallel algorithm 

for the land areas.

 The Level 2 AIRS/AMSU-A/HSB precipitation-rate retrievals have 50-km spatial resolution 

and differ from AMSR-E products in their heavy 

reliance on opaque atmospheric bands near 54 and 

183 GHz, which reduces surface effects and tends 

to yield comparable accuracies over both land and 

sea. Humidity profiles will be generated from the 

AIRS/AMSU-A/HSB data with a global root-meansquare (rms) accuracy of ±10% in 2-km layers from 

the surface to the 200 mb pressure level.

 MODIS products include total precipitable water 

(MOD 05) and atmospheric water vapor profiles 

(MOD 07), both processed at Level 2. SAGE III 

products include water vapor concentration and volume mixing ratios, both with a vertical resolution of 

0.5 km and both processed at Level 2.

Product Interdependency and Continuity 

with Heritage Data

The heritage of the measurement of rainfall with passive-microwave measurements began with the 1972 

launch of Nimbus 5 carrying the 19.35-GHz Electrically Scanning Microwave Radiometer (ESMR), 

which was used for the first retrievals of oceanic 

rainfall. The launch of the dual-polarized 37-GHz 

version of ESMR on Nimbus 6 in 1975 enabled early 

exploration of the mapping, if not measurement, of 

rain over land. The discovery of the importance of 

scattering by ice particles from the NASA Convair 

990 aircraft in the mid-1970s paved the way for the 

Special Sensor Microwave/Imager (SSM/I) on the 

Defense Meteorological Satellite Program (DMSP) 

satellite, which was first launched in 1987. The SSM/I 

has been used for the routine production of oceanic 

rainfall data sets for more than a decade and for extensive research in algorithm development.

 The TRMM satellite, which was launched in 

1997, carries a microwave radiometer, the TRMM 

Microwave Imager, or TMI, of very similar capability 

to AMSR-E as far as rain is concerned. It also carries 

the first precipitation radar to be flown in space. This 

combination has enabled detailed examination of the 

assumptions in the microwave retrieval of rainfall. 

The non-sun-synchronous orbit of TRMM has additionally enabled the exploration of the impact of 

the diurnal cycle of rainfall on totals derived from 

sensors on sun-synchronous spacecraft such as DMSP 

and Aqua. Level 2 and Level 3 rainfall products have 

been routinely produced from TRMM. Thus, between 

DMSP and TRMM we have global coverage of rainfall extending back to 1987.

 Heritage data for the MODIS water vapor profiles 

include the HIRS and TOVS data, while heritage 

data for the MODIS precipitable water calculations 

come from the AVIRIS instrument on an ER-2 aircraft, MODIS being the first satellite instrument to 

use near-IR bands along with IR bands to retrieve total 

precipitable water. Heritage data for the JMR water 

vapor products come from the TOPEX/Poseidon Microwave Radiometer; and heritage data for the SAGE 

III water vapor products come from the earlier SAGE 

instruments, SAGE I and SAGE II.
74 Data Products Handbook - Volume 2 Overview • Precipitation and Atmospheric Humidity 

Suggested Reading

Chang, A. T. C. et al., 1993.

Ferraro, R. R, 1997. 

Kummerow, C. et al., 1998.

Olson, W. et al., 1999.

Rosenkranz, P. W., 1998.

Smith, E. A. et al., 1998.

Staelin, D. H., and F. W. Chen, 2000.

(Precipitation and Atmospheric Humidity – An Overview, 

continued)
Precipitation and Atmospheric Humidity • Overview Data Products Handbook - Volume 2 75

AIRS/AMSU-A/HSB Humidity 

Product (AIR 05)

Product Description

The AIRS Humidity Product (AIR 05) is Level 2 data 

providing tropospheric column water-vapor profiles, 

total precipitable water, cloud liquid-water content, 

precipitation flags, and cloud-ice flags.

 A microwave-only retrieval algorithm is first applied to estimate atmospheric temperature and surface temperature and to derive the total precipitable 

water, cloud liquid-water content, precipitation, 

and cloud-ice states. A combined microwave and 

infrared retrieval algorithm is then applied to refine 

these quantities and to derive the atmospheric column 

water-vapor profile and burden.

 The product is generated daily (day and night) at 

AMSU-A FOV resolution, with some information at 

AIRS FOV resolution.

 

Research and Applications

Water vapor is the most variable, radiatively active constituent of the atmosphere and the main 

contributor to the greenhouse effect. In addition to 

being the primary gaseous absorber of both longwave 

and shortwave radiation, water vapor is also connected to the prediction of cloudiness that controls 

the Earthʼs radiation budget. Water vapor in the lower 

troposphere is the primary determinant of moist convective activities and of convective precipitation under unstable atmospheric conditions. Boundary-layer 

water vapor is also important for calculation of latent 

heat flux, which plays a role in the exchange of energy 

between the Earthʼs surface and the atmosphere. Existing observational knowledge is insufficient to meet 

even the simplest requirements of general circulation 

models (GCMs). High-spectral-resolution observations by AIRS will provide improved accuracy over 

the entire globe. The AIRS humidity profile will be a 

significant quantity for data assimilation to improve 

numerical weather prediction.

Data Set Evolution

The column water-vapor profiles are derived from 

selected AIRS radiances in the 6.3-μm water-vapor 

band (in the regions 1234 cm-1 to 1612 cm-1 and 

2606 cm-1 to 2657 cm-1) and the 11-μm windows, 

which are sensitive to the water-vapor continuum. 

The radiance measured in these channels depends on 

atmospheric and surface temperature and the distribution of humidity in the atmosphere.

 The 6.3-μm channels are more sensitive to humidity in the middle and upper troposphere, while 

the narrow channels in the 11-μm continuum are 

more sensitive to humidity in the lower troposphere. 

Simultaneous determination of surface temperature 

and spectral emissivity is essential for obtaining an 

accurate low-level water-vapor distribution.

 The total precipitable water, cloud liquid-water 

content, and precipitation flags are derived from 

AMSU-A and HSB radiances. The AMSU-A window 

channels will also be used to provide information on 

sea ice concentration and snow cover.

 The humidity-profile rms error is expected to be 

±10% for precipitable water in 2-km layers from the 

surface to 200 mb. The rms error of total precipitable 

water is expected to be ±20%.

 HSB channels in the 183-GHz water-vapor line 

are used to improve the accuracy of atmospheric humidity profiles and total precipitable water vapor.

 Data validation will be accomplished through 

comparison of the retrieved water-vapor profiles 

with simultaneous water-vapor measurements by: 

1) ground-based Raman lidar, 2) in situ researchquality radiosondes, 3) Lidar Atmospheric Sensing 

Experiment (LASE) on the NASA DC-8 aircraft, and 

4) NPOES Airborne Sounder Testbed-Interferometer 

(NASTI) on the NASA ER-2 aircraft. Locations will 

be DoE ARM/CART and other EOS instrument-validation sites.

Suggested Reading

Aumann, H. H., and C. Miller, 1995.

Aumann, H. H. et al., 1999.

Chahine, M. T. et al.,1997c.

Fleming, H. F. et al., 1986.

Grody, N. C. et al., 1980.

Haskins, R. D. et al., 1997.

Hofstadter, M. et al., 1999.

Janssen, M. A. 1993. 

Kuo, C. C. et al., 1994. 

Lambrigtsen, B., 1996.

Rosenkranz, P. W., 1995.

Rosenkranz, P. W., 1998. 

Smith, W. L., and H. M. Woolf, 1976.

Susskind, J. et al., 1993.

Aqua / AIRS/AMSU-A/HSB
76 Data Products Handbook - Volume 2 • Precipitation and Atmospheric Humidity 

(AIRS/AMSU-A/HSB Humidity Product, continued)

Susskind, J. et al., 1998.

Wilheit, T. T., 1990.

AIRS/AMSU-A/HSB Humidity Product 

Summary

Coverage: Global

Spatial/Temporal Characteristics: 40.6 km 

FOV at nadir; 2-km vertical layers, surface to tropopause/twice daily (daytime 

and nighttime)

Key Science Applications: Global moisture 

transport; hydrological cycle; climatology

Key Geophysical Parameters: Atmospheric 

column water-vapor profile and burden; 

total precipitable water; cloud liquid-water content, precipitation flag; cloud-ice 

flag

Processing Level: 2

Product Type: Standard, at-launch; precipitation flag is a research product, postlaunch

Maximum File Size: 5 MB

File Frequency: 240/day

Primary Data Format: HDF-EOS

Browse Available: Yes

Additional Product Information: 

http://www-airs.jpl.nasa.gov/

DAAC: NASA Goddard Space Flight Center

Science Team Contacts:

 J. Susskind

 P. W. Rosenkranz

 D. H. Staelin

Aqua / AIRS/AMSU-A/HSB and AMSR-E

AMSR-E Rainfall – Level 2

Product Description

This product contains surface rainfall and its classification into convective and stratiform components. This information is obtained by matching 

the observed brightness temperatures to a database 

of a priori cloud profiles derived from a variety of 

sources including cloud dynamical models as well 

as ground-based radars. This a priori information is 

then used in a Bayesian inversion scheme to derive 

a linear combination of profiles that most resembles 

the observed brightness temperatures. Despite differences, the Land and Ocean algorithms have been 

combined into a single framework to insure communication between the algorithm components. The 

Level 2 product has not been resampled; scan time, 

latitude, longitude, and scan line/pixel number are 

included for each pixel in this product.

 This product uses the Level 2A AMSR-E brightness temperatures as input. There is also a Level 3 

AMSR-E rainfall product, which uses the Level 2 

product over land directly for accumulations but uses 

the Level 2 product over ocean only as a means of 

independent validation.

Research and Applications

The research objectives of this product include

• Improve the availability of high-quality rainfall 

products and latent-heat-release estimates over 

the globe.

• Validate and improve models that promote the 

understanding of the formation and organization 

of convection and their interactions with the ocean 

and ambient atmosphere and the climate system; 

and

• Advance understanding of the Earthʼs global energy 

and water cycles by determining how rainfall and 

its variability influence global circulation.

Data Set Evolution and Applications

The AMSR-E instantaneous rainfall product is very 

similar to the TRMM TMI rainfall algorithm. There 

is also a parallel version of this algorithm running for 

the DMSP SSM/I sensor. There is a long heritage for 
Precipitation and Atmospheric Humidity • Data Products Handbook - Volume 2 77

this rainfall algorithm, the physical nature of which 

makes the transition from these previous sensors to 

AMSR-E straightforward.

Suggested Reading

Hong, Y. et al., 1999.

Kummerow, C. et al., 1996.

Aqua / AMSR-E

60N

50N

40N

30N

20N

10N

EQ

10S

20S

30S

40S

0 90E 180 90W 0

50 100 200 300 400 500

Passive-Microwave Satellite Global Monthly Rainfall Estimates for April 2000, derived from data from the DMSP SSM/I 

based on a similar algorithm to the algorithm that will be used for Level 2 rainfall estimates from AMSR-E data. This image 

illustrates the global rainfall patterns that will be produced from AMSR-E. Units are mm/month.

AMSR-E Rainfall – Level 2 Summary

Coverage: 70°N-70°S ice-free and snow-free 

land and ocean

Spatial/Temporal Characteristics: Satellite 

orbit track, 5-km resolution

Key Geophysical Parameters: Rainfall, rainfall 

type (Convective/Stratiform)

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 18 MB

File Frequency: 29/day

Primary Data Format: HDF-EOS

Browse Available: Accompanies data product

Additional Product Information: 

http://wwwghcc.msfc.nasa.gov/AMSR/

html/amsr_products.html

DAAC: National Snow and Ice Data Center

Science Team Contacts:

 R. Ferraro (Level 2 Land)

 C. Kummerow (Level 2 Ocean)
78 Data Products Handbook - Volume 2 Aqua / AMSR-E • Precipitation and Atmospheric Humidity 

AMSR-E Rainfall – Level 3

Product Description

This product consists of mean monthly rainfall 

amounts over 5° × 5° cells. Retrievals over ocean 

are independent of the Level 2 algorithm. Monthly 

accumulations over land use the Level 2 land product 

as input. Over oceans, this approach sacrifices some 

of the accuracy of the instantaneous retrieval in order 

to minimize assumptions that might contaminate a climate record of rainfall. The frequency of occurrence 

of rain intensities in different rate categories over a 

specified area can be plotted as a histogram or as a 

smoothed curve that fits the histogram. This product 

is obtained by considering histograms of 19-GHz, 21-

GHz, and 19-21 GHz combinations. Monthly rainfall 

indices are computed by statistically matching the histograms with the model-calculated probability density 

functions (pdf). The main purpose of this product is 

to provide a robust baseline of surface-rainfall rates. 

There is no equivalent independent method for estimating rainfall over land, and the Level 2 product is 

therefore used as input to the pdf scheme. 

 This Level 3 rainfall product uses the Level 2A 

AMSR-E brightness temperatures as well as the 

AMSR-E Level 2 rainfall product as input. The Level 

3 product uses the Level 2A brightness temperatures 

to estimate rainfall independently over oceans and the 

Level 2 rainfall product over land to compute monthly 

accumulations.

Research and Applications

This product is designed to advance understanding 

of the Earthʼs global energy and water cycles by determining how rainfall and its variability influence 

global circulation. A reliable measurement of rainfall, 

particularly over the oceans, will provide the longterm record needed to distinguish natural variability 

from any anthropogenic changes.

Data Set Evolution

The AMSR-E instrument is very similar to the TRMM 

TMI and the DMSP SSM/I. The algorithm over 

oceans will be a slight modification of the algorithm 

for the TMI standard product and the WMO Global 

Precipitation Climatology Project (GPCP) algorithm 

applied to SSM/I. Data are already available from 

these algorithms for the period since 1987.

Suggested Reading 

Chang, A. T. C. et al., 1999.

Wilheit, T. T. et al., 1991.

AMSR-E Rainfall – Level 3 Summary

Coverage: 70oN-70oS

Spatial/Temporal Characteristics: 5o × 5o/

monthly

Key Geophysical Parameters: Rainfall amount 

and statistics

Processing Level: 3

Product Type: Standard, at-launch

Maximum File Size: 0.016 MB

File Frequency: 1/month

Primary Data Format: HDF-EOS

Browse Available: Accompanies data product

Additional Product Information:

http://wwwghcc.msfc.nasa.gov/AMSR/

html/amsr_products.html

DAAC: National Snow and Ice Data Center

Science Team Contact:

T. T. Wilheit
Precipitation and Atmospheric Humidity • Data Products Handbook - Volume 2 79

temporal mismatch between the point radiosonde 

observation and the 25-km SSM/I footprint. Computer simulations show that the true rms accuracy of 

the microwave water-vapor retrievals is about 1 mm. 

The AMSR-E retrievals should be more accurate than 

those from SSM/I because AMSR-E has a dual-polarization water-vapor channel whereas SSM/I only 

had vertical polarization. Also, the AMSR-E watervapor signal will not saturate in the tropics because 

its water-vapor channel is at 23.8 GHz rather than 

at the center of the water-vapor absorption line 

(22.235 GHz). (SSM/I operated at 22.235 GHz and 

experienced partial saturation in the tropics.)

Suggested Reading

Alishouse, J. C. et al., 1990.

Chahine, M. T., 1992.

Chang A. T. C., and T. T. Wilheit, 1979.

Chang, H. D. et al., 1984.

Grody, N. C., 1976.

Liu, W. T. et al., 1992.

Ramanathan, V., 1988.

Staelin, D. H. et al., 1976.

Wentz, F. J., 1997.

Aqua / AMSR-E

AMSR-E Columnar Water 

Vapor

Product Description

The columnar water vapor derived from the AMSR-E 

microwave radiances is a measure of the total vertically integrated water vapor in the marine atmosphere. It is expressed in units of millimeters, which 

represent the depth of the column of liquid water that 

would result if the vapor were to precipitate completely. The spatial resolution of this product is 24 

km, and its expected rms accuracy at this resolution 

is 1.0 mm. This product is available for clear and 

cloudy conditions and up-to-moderate rain rates. The 

product will be produced for individual pixels across 

the AMSR-E swath (Level 2) as well as an Earth-gridded product (0.25° in latitude and longitude) averaged 

daily, weekly, and monthly (Level 3).

Research and Applications

Column water vapor is a critical component coupling 

the atmosphere and oceans, transporting energy in 

the form of latent heat from the ocean surface to 

the atmosphere, where it is released through condensation. Furthermore, water vapor is the Earthʼs 

principal greenhouse gas, trapping sufficient heat to 

maintain the average surface temperature above freezing, and therefore is responsible for the habitability 

of the planet. Within the context of global-change 

studies, the possibility of positive feedback between 

anthropogenic carbon dioxide and water vapor has 

become a particularly active research area, largely 

due to model predictions of significant amplification 

of greenhouse heat trapping by concomitant rises in 

atmospheric water vapor. Sensitive and accurate longterm measurements of spatial and temporal variability 

in this quantity also constitute, along with measurements of air and sea-surface temperature, a valuable 

geophysical data set for monitoring global change.

Data Set Evolution

The water-vapor retrieval algorithm is based on experience gained from previous microwave radiometers, 

including SMMR, SSM/I, and TMI. In particular, the 

SSM/I has demonstrated the ability of microwave 

radiometers to measure water vapor to an accuracy 

of about 1 mm. For 35,000 SSM/I overflights of radiosondes (1987-1991), the rms difference between 

the SSM/I and radiosonde water vapor is 3.9 mm, 

with most of this difference being due to the spatial-
80 Data Products Handbook - Volume 2 • Precipitation and Atmospheric Humidity 

MODIS Total Precipitable 

Water (MOD 05)

Product Description

The MODIS Precipitable Water product (MOD 05) 

consists of column water-vapor amounts. During the 

daytime, a near-infrared algorithm is applied over 

clear land areas of the globe and above clouds over 

both land and ocean. Over clear ocean areas, watervapor estimates are provided over the extended glint 

area. An infrared algorithm for deriving atmospheric 

profiles is also applied both day and night for Level 

2.

 The Level 2 data are generated at the 1-km spatial 

resolution of the MODIS instrument using the nearinfrared algorithm during the day, and at 5 × 5 1-km 

pixel resolution both day and night using the infrared 

algorithm when at least nine FOVs are cloud free. The 

infrared-derived precipitable water vapor is generated 

as one component of product MOD 07, and simply 

added to product MOD 05 for convenience.

 The solar retrieval algorithm relies on observations 

of water-vapor attenuation of reflected solar radiation 

in the near-infrared MODIS channels so that the 

product is produced only over areas where there is a 

reflective surface in the near IR.

Research and Applications

The near-infrared total-column precipitable water is 

very sensitive to boundary-layer water vapor since 

it is derived from attenuation of reflected solar light 

from the surface. This data product is essential to 

understanding the hydrological cycle, aerosol properties, aerosol-cloud interactions, energy budget, 

and climate. Of particular interest is the collection 

of water-vapor data above cirrus cloudiness, which 

has important applications to climate studies. MODIS 

will also provide finer horizontal-scale atmospheric 

water-vapor gradient estimates than are currently 

available from the Polar-orbiting Operational Environmental Satellites (POES).

Data Set Evolution

The solar-column water-vapor parameter is derived 

from the attenuation by water vapor of near-IR solar 

radiation. Techniques employing ratios of watervapor-absorbing channels 17, 18, and 19 with the 

atmospheric window channels 2 and 5 are used. 

The ratios remove partially the effects of variation 

of surface reflectance with wavelength and result 

in the atmospheric water-vapor transmittances. The 

column-water-vapor amounts are derived from the 

AMSR-E Columnar Water Vapor Summary

Coverage: Global ocean surface, clear and 

cloudy skies and up-to-moderate rainfall 

rates

Spatial/Temporal Characteristics:

Level 2: swath pixels at 20-km resolution

Level 3: 0.25° latitude-longitude grid/daily, 

weekly, and monthly

Key Science Applications: Climate variability, air-sea interaction, radiation balance, 

hydrological cycle

Key Geophysical Parameters: Columnar water 

vapor

Processing Level: 2, 3

Product Type: Standard, post-launch

Maximum File Size: 10 MB

File Frequency: 29/day

Primary Data Format: HDF-EOS

Browse Available: Accompanies data product

Additional Product Information: 

http://wwwghcc.msfc.nasa.gov/AMSR/html/

amsr_products.html

DAAC: National Snow and Ice Data Center

Science Team Contact:

F. J. Wentz

(AMSR-E Columnar Water Vapor, continued)

Aqua / AMSR-E and MODIS
Precipitation and Atmospheric Humidity • Aqua / MODIS Data Products Handbook - Volume 2 81

transmittances based on theoretical radiative-transfer calculations and using look-up-table procedures. 

MODIS is the first space instrument to use near-IR 

bands together with the traditional IR bands to retrieve total precipitable water. Experience in this 

retrieval is based on an AVIRIS instrument aboard 

an ER-2 aircraft. Atmospheric water vapor should be 

determined with an accuracy of 5-10%.

 The thermal column water-vapor parameter is derived by integrating the moisture profile through the 

atmospheric column. Other, split-window, methods 

also exist. This class of techniques uses the difference 

in water-vapor absorption that exists between channel 

31 (11 μm) and channel 32 (12 μm).

 Data validation will be conducted by comparing 

these data with water-vapor measurements from the 

National Weather Service (NWS) radiosonde network, from ground-based upward-looking microwave 

radiometers, from a ground-based GPS network, and 

from a ground-based sunphotometer network. Quality 

control will be performed in two dimensions. The first 

will be comparisons of specific validation sites across 

as many different climatic and geographic regions as 

possible. The second will be a statistical analysis of 

the entire data set.

 The related MODIS Cloud product ATBDs can 

be found in PDF format at http://eos.nasa.gov/atbd/

modistables.html.

Suggested Reading

Gao, B. C., and A. F. H. Goetz, 1990.

Gao, B. C. et al., 1993a,b.

Green, R. O., and J. E. Conel, 1995.

Jedlovec, G. J., 1987.

Kaufman, Y. J., and B. C. Gao, 1992.

King, M. D. et al., 1992.

Kleepsies, T. J., and L. M. McMillan, 1984.

MODIS Total Precipitable Water Summary

 Coverage: Global

Spatial/Temporal Characteristics: Varies with 

retrieval technique; 1 km near-infrared daylight only, and 5 km infrared day and night

Key Science Applications: Hydrological cycle 

climatology, effect on aerosol and clouds, 

atmospheric correction, characterization of 

the atmosphere

Key Geophysical Parameters: Atmospheric total 

column water vapor

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 15 MB 

File Frequency: 288/day

Primary Data Format: HDF-EOS

Browse Available: 

 http://modis-atmos.gsfc.nasa.gov/MOD05_

L2/sample.html

Additional Product Information:

 http://modis-atmos.gsfc.nasa.gov/MOD05_

L2/index.html

Science Team Contacts:

 B. C. Gao 

Y. J. Kaufman

 D. Tanré

 W. P. Menzel

Sample Columnar Atmospheric Water Vapor from Terra MODIS data from the vicinity of Tibet and India on March 2, 2000, at UTC 

0525. These results are derived from data from channels located within and around the 0.94-μm water vapor band. The left panel 

shows an image processed from MODIS channels centered at 0.645 μm. The lower part of the scene covers portions of India and the 

nearby Indian Ocean. The upper part of the scene covers Nepal and part of the Tibetan Plateau. The middle panel shows the MODIS 

column water vapor. The right panel shows the corresponding image of surface elevations. It is seen from these images that column 

water vapor values vary significantly with surface elevation.

5.0

4.0

3.0

2.0

1.0

0.0

Meters

6000

5000

4000

3000

2000

1000

0

Reflectance (0.645 μm) Water Vapor (cm) Elevation
82 Data Products Handbook - Volume 2 • Precipitation and Atmospheric Humidity 

MODIS Atmospheric Profiles 

(MOD 07)

Product Description

The MODIS Atmospheric Profiles product (MOD 07) 

consists of several parameters: they are total-ozone 

burden, atmospheric stability, temperature and 

moisture profiles, and atmospheric water vapor. All 

of these parameters are produced day and night for 

Level 2 at 5 × 5 1-km pixel resolution when at least 

9 FOVs are cloud free.

 The MODIS total-ozone burden is an estimate of 

the total-column tropospheric and stratospheric ozone 

content. The MODIS atmospheric stability consists 

of three daily Level 2 atmospheric stability indices. 

The Total Totals (TT), the Lifted Index (LI), and the 

K index (K) are each computed using the infrared 

temperature- and moisture-profile data, also derived 

as part of MOD 07. The MODIS temperature and 

moisture profiles are produced at 20 vertical levels 

for temperature and 15 levels for moisture. A simultaneous direct physical solution to the infrared 

radiative-transfer equation in a cloudless sky is used. 

The MODIS atmospheric water-vapor product is an 

estimate of the total tropospheric column water vapor 

made from integrated MODIS infrared retrievals of 

atmospheric moisture profiles in clear scenes.

Research and Applications

Total-column ozone estimates at MODIS resolution 

are required by MODIS investigators developing atmospheric correction algorithms. This information is 

crucial for accurate land and ocean-surface-parameter 

retrievals. Furthermore, strong correlations have been 

found to exist between the meridional gradient of total 

ozone and the wind velocity at tropopause levels, 

providing the potential to predict the position and 

intensity of jet streams. Total-column ozone monitoring is also important due to the potential harm to 

the environment caused by anthropogenic depletion 

of ozone.

 Atmospheric instability measurements are predictors of convective-cloud formation and precipitation. The MODIS instrument offers an opportunity 

to characterize gradients of atmospheric stability at 

high resolution and greater coverage. Radiosondederived stability indices are limited by the coarse 

spacing of the point-source data, too large to pinpoint 

local regions of probable convection.

 Atmospheric temperature and moisture sounding 

data at high spatial resolution from MODIS and 

high- spectral-resolution sounding data from AIRS 

will provide a wealth of new information on atmospheric structure in clear skies. The profiles will be 

used to correct for atmospheric effects for some of the 

MODIS products (e.g., sea-surface and land-surface 

temperatures, ocean aerosol properties, water-leaving 

radiances, and PAR) as well as to characterize the 

atmosphere for global greenhouse studies.

 Total-column precipitable-water estimates at MODIS resolution are required by MODIS investigators 

developing atmospheric-correction algorithms. This 

information is crucial for accurate land and ocean 

surface-parameter retrievals. MODIS will also provide finer horizontal-scale atmospheric water-vapor 

gradient estimates than are currently available from 

the POES satellites.

Data Set Evolution

One of two ozone-retrieval methods developed using 

the HIRS will be chosen as best suited for application 

with MODIS data. Both use a first-guess perturbation method and radiances from MODIS channel 

30 (9.6 μm) to solve the radiative-transfer equation. 

The perturbations are with respect to some a priori

conditions that may be estimated from climatology, 

regression, or, more commonly, from an analysis or 

forecast provided by a numerical model. The MODIS 

cloud-mask product (MOD 35) will also be used to 

screen for clouds.

 Atmospheric-stability estimates will be derived 

from the MODIS temperature and moisture retrievals 

contained in this product. Layer temperature and 

moisture values may be used to estimate the temperature lapse rate of the lower troposphere and the 

low-level moisture concentration.

 Temperature and moisture profile retrieval algorithms are adapted from the International TOVS 

Processing Package (ITPP), taking into account 

MODISʼ lack of stratospheric channels and far higher 

horizontal resolution. The profile retrieval algorithm 

requires calibrated, navigated, and coregistered 1-km 

FOV radiances from MODIS channels 20, 22-25, 27-

29, and 30-36. The MODIS cloud mask (MOD 35) is 

used for cloud screening. The algorithm also requires 

NCEP model analyses of temperature and moisture 

profiles as a first guess and an NCEP analysis of 

surface temperature and pressure.

 Several algorithms for determining atmospheric 

water vapor, or precipitable water, exist. It is most 

directly achieved by integrating the moisture profile 

through the atmospheric column. Other, split-window, methods also exist. This class of techniques 

Aqua / MODIS
Precipitation and Atmospheric Humidity • Aqua / MODIS Data Products Handbook - Volume 2 83

uses the difference in water-vapor absorption that 

exists between channel 31 (11 μm) and channel 32 

(12 μm).

 Data validation will be conducted by comparing 

results to in situ radiosonde measurements, NOAA 

HIRS operational retrievals, GOES sounder operational retrievals, NCEP analyses, and retrievals from 

the AIRS/AMSU-A/HSB instrument package on the 

Aqua platform. A field campaign using a profiler 

network in the central U.S. and an aircraft equipped 

with the MODIS Airborne Simulator (MAS) will be 

initiated in the first year after launch. Quality control 

will consist of manual and automatic inspections, with 

regional and global mean temperatures at 300, 500, 

and 700 hPa monitored weekly, along with 700 hPa 

dew-point temperatures. For total ozone, data validation will consist of comparing the TOMS retrievals, 

as well as operational NOAA ozone estimates from 

HIRS, to the MODIS retrievals.

Suggested Reading

Hayden, C. M., 1988.

Houghton, J. T. et al., 1984.

Jedlovec, G. J., 1987.

Kleepsies, T. J., and L. M. McMillan, 1984.

Ma, X. L. et al., 1984.

Prabhakara, C. et al., 1970.

Shapiro, M. A. et al., 1982.

Smith, W. L., and F. X. Zhou, 1982.

Smith, W. L. et al., 1985.

Sullivan, J. et al., 1993.

MODIS Atmospheric Profiles Summary

 

Coverage: Global, clear-sky only

Spatial/Temporal Characteristics: 5 km

Key Science Applications:

•Ozone: atmospheric correction, prediction of cyclogenesis, anthropogenic 

ozone depletion

•Atmospheric stability: atmospheric correction, prediction of convective cloudiness and precipitation, characterization 

of the atmosphere

•Soundings: atmospheric correction algorithm development and use, characterization of the atmosphere

•Total-column water vapor: atmosphericcorrection algorithm development and 

use, characterization of the atmosphere

Key Geophysical Parameters: Total-column 

ozone, atmospheric stability (Total Totals, 

Lifted Index, and K index), atmospheric 

profiles of temperature and moisture, 

atmospheric total-column water vapor

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 28 MB 

File Frequency: 288/day

Primary Data Format: HDF-EOS

Browse Available: 

 http://modis-atmos.gsfc.nasa.gov/

MOD07_L2/sample.html

Additional Product Information:

 http://modis-atmos.gsfc.nasa.gov/

MOD07_L2/index.html

Science Team Contact:

 W. P. Menzel
84 Data Products Handbook - Volume 2 • Precipitation and Atmospheric Humidity 

Level 2 MODIS Atmospheric Temperature (K) at 500 hPa (upper panel) and Level 2 MODIS Total Column Precipitable 

Water Vapor (cm) (lower panel), both derived from data taken over the Himalayas and Northern India at 5:25 UTC 

on April 19, 2000, from the MODIS instrument on Terra.

Atmospheric Temperature at 500 hPa

Total Column Precipitable Water Vapor

Aqua / MODIS

(MODIS Atmospheric Profiles, continued)
Precipitation and Atmospheric Humidity • Jason-1 / JMR Data Products Handbook - Volume 2 85

JMR Columnar Water Vapor 

Content

Product Description

This Level 2 product provides the measurement of the 

total atmospheric columnar water vapor content (in 

g/cm2) made by the Jason-1 Microwave Radiometer 

(JMR). The measurement is repeated every 9.9 days 

along a set of fixed ground tracks. The satellite 

makes 127 revolutions of the Earth every 9.9 days. 

The coverage of the measurement is limited from 66° 

S to 66° N due to the 66° inclination of the satelliteʼs 

orbit plane. The longitudinal distance between the 

ground tracks is [315.cos(latitude)] km, i.e., 315 km 

at the equator, 223 km at 45°, and 128 km at 66°. This 

product is used to generate gridded Level 3 products 

by various Jason-1 Science Working Team members. 

Two kinds of product are available: The Interim Geophysical Data Record (IGDR) is available 3 days after 

the data collection, while the GDR, based on a more 

precisely determined orbit, is available 30 days after 

the data collection.

Research and Applications

Water vapor is the most important greenhouse gas in 

the atmosphere. It plays a large role in keeping the 

Earth at a temperature suitable for intelligent life. 

Furthermore, information on the content of water 

vapor and its variability is important for the study of 

climate in other ways as well. For example, the water 

vapor measurement made by the TOPEX Microwave 

Radiometer was used to study the relationship between the 1997-98 El Niño and the variability of total 

water vapor content of the Earth.

Data Set Evolution

The JMR measures the brightness temperature of the 

sea surface at three frequencies: 18.7, 23.8, and 34 

GHz. These measurements are used to compute the 

range delay of radar altimeter signals due to the water 

vapor in the atmosphere as well as the total columnar 

water vapor content. The antenna temperatures measured by JMR are first converted into brightness temperatures of the sea surface at the three frequencies. 

The brightness temperatures are then used to compute 

the total water vapor content along the path of the 

JMR line of sight. The primary heritage for the JMR 

water vapor product is the corresponding product 

derived from the data of the TOPEX/Poseidon Microwave Radiometer, launched in 1992.

Suggested Reading

Haines, B. J., and Y. E. Bar-Sever, 1998.

Nerem, R. S. et al., 1999.

Ruf, C. S. et al., 1994.

JMR Columnar Water Vapor Content 

Summary

Coverage: Ocean areas from 66° S to 66° N.

Spatial/Temporal Characteristics: 6.2-km 

resolution along-track every 9.9 days; 

[315·cos(latitude)] km longitudinal track 

spacing

Key Science Applications: Greenhouse gas 

monitoring, climate change

Key Geophysical Parameters: Atmospheric 

columnar water vapor content

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 1.5 MB

File Frequency: 25/day

Primary Data Format: Binary

Browse Available: Yes

Additional Product Information:

http://www-aviso.cnes.fr

DAAC: Jet Propulsion Laboratory

Science Team Contacts:

 L.-L. Fu

Y. Ménard
86 Data Products Handbook - Volume 2 • Precipitation and Atmospheric Humidity 

SAGE III Water Vapor Products 

(SAGE III 03)

Product Description

The SAGE III Water Vapor Products (SAGE III 03 

at Level 2) consist of H2O concentration (number 

density in units of molecules/cm3) and volume mixing ratio (ppmv) at each measurement location with 

a vertical resolution of 0.5 km. The water-vapor 

products are retrieved from the solar-occultation 

measurements of slant-path absorption using 29 

contiguous subchannels between 933.19 nm and 

960.12 nm, spaced at approximately 1 nm with a 

resolution of about 1.4 nm.

 Since temperature profiles will also be measured 

by SAGE III, utilizing the oxygen A-band absorption 

spectra in the wavelength region 759-771 nm, relative humidities can be derived accordingly, using 

the SAGE III-retrieved water-vapor profiles and 

temperature profiles.

Research and Applications

SAGE III water-vapor profiles will provide complementary measurements to those of other EOS instruments. With its vertical resolution of 0.5 km, SAGE 

III water-vapor products are necessary for unraveling 

the issues associated with troposphere-stratosphere 

water-vapor exchange. Since the occultation measurements are relatively immune to drift in instrument performance, SAGE III-retrieved water-vapor profiles 

are invaluable for helping to validate observations 

from the other EOS-system instruments. Furthermore, 

given the long-term reliability and self-calibration 

capabilities of the SAGE instruments, SAGE III 

may be in a position to evaluate water-vapor trends 

in the upper troposphere and lower stratosphere during a time period when considerable warming due to 

increased emissions of human-derived greenhouse 

gases may occur.

SAGE III Water Vapor Products Summary

Coverage: 30 solar events per day, 15 

northern high latitude, 15 southern high 

latitude

Spatial/Temporal Characteristics: 0.5 km vertical resolution

Key Science Applications: Greenhouse effect, 

global warming, validation of climate 

models, convective cloud studies, weatherforecast initialization, tropospheric chemistry, stratospheric chemistry

Key Geophysical Parameters: Water-vapor 

concentration, water-vapor mixing ratios, 

relative humidities, total-column water 

vapor, moisture content

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 0.05 MB

File Frequency: 196/week

Primary Data Format: HDF-EOS, Big Endian 

IEEE Binary

Browse Available: Yes

Additional Product Information:

http://eosweb.larc.nasa.gov/PRODOCS/

sage3/table_sage3.html

DAAC: NASA Langley Research Center

Science Team Contact:

 E.-W. Chiou

Meteor 3M / SAGE III
87

Cloud and Aerosol 

Properties

and 

Radiative Energy Fluxes

Aqua

• AIRS

• AMSR-E

• AMSU-A

• CERES

• HSB

• MODIS

Meteor 3M • SAGE III

QuikTOMS • TOMS
88 Data Products Handbook - Volume 2 Overview • Cloud and Aerosol Properties and Radiative Energy Fluxes 

Cloud and Aerosol Properties 

and Radiative Energy Fluxes 

– An Overview

Relationship to Global Change Issues

Clouds and aerosols play a significant role in modifying the flow of energy to and from the Earthʼs surface. Hence, measuring and understanding clouds and 

aerosols are among the keys to predicting climate 

change and to testing global climate models. The 

technology for measuring radiative fluxes, cloud 

properties, and aerosol properties from space is now 

sufficiently advanced that these measurements can be 

used to improve significantly our ability to address 

such critical global change issues as whether clouds 

act as a positive or negative feedback mechanism 

as climate changes and whether aerosols will offset some of the expected warming from increasing 

greenhouse gases.

 In many cases, cloud and/or aerosol studies using 

the data from the missions highlighted in this volume 

will benefit from instruments on other satellite missions as well. In particular, versions of the CERES 

instrument will be operating on the TRMM, Terra, 

and Aqua platforms and will function synergistically 

in conjunction with VIRS and TMI (on TRMM), 

MODIS and MISR (on Terra), and MODIS and 

AMSR-E (on Aqua). Similarly, the MODIS instruments on Terra and Aqua will complement each other 

by providing morning and afternoon measurements, 

respectively.

Clouds: A first step in examining clouds from space 

is to determine their distribution through the atmosphere, i.e, where they are and what their dimensions 

are. Once clouds are identified and mapped, cloud 

properties such as effective particle size, thermodynamic phase (water, ice), cloud-top properties, 

and optical thickness (all of which help determine 

how much radiation passes through them) may be 

measured. The MODIS, AIRS/AMSU-A/HSB, and 

AMSR-E instruments on the EOS Aqua platform, 

the SAGE III instrument on the Meteor 3M platform, the MODIS, MISR, and ASTER instruments 

on the EOS Terra platform (see Volume 1), and the 

VIRS and TMI instruments on TRMM (see Volume 

1) will provide these key cloud measurements. The 

daily observations from these instruments also provide the sampling needed to drive and verify climate 

models. In the longer term, advances are expected 

in the vertical distribution of clouds using the active 

lidar and cloud radar data from the PICASSO-CENA 

and CloudSat missions flying in formation with Aqua 

starting in 2003. 

Aerosols: Aerosols are suspended particles in the 

atmosphere such as dust, sulfate, smoke, and many 

others. Their measurement is an important element 

in describing energy transmission through the atmosphere. Uncertainties regarding aerosols result in their 

being a significant source of uncertainty in climate 

modeling. They affect cloud microphysics by acting 

as condensation nuclei, thereby affecting cloud radiative properties. They also interact directly with solar 

radiation, thus affecting the radiative balance; and 

the geographic locations of anthropogenic aerosols 

are also important considerations in their impact on 

climate. In addition, stratospheric aerosols play a role 

in the ozone depletion issue through heterogeneous 

chemistry. Aerosol products described here, derived 

from MODIS on Aqua, and those from MODIS and 

MISR on Terra, will provide new information on 

aerosol formation, distribution, and sinks over both 

the land and ocean. Aerosol data from SAGE III on 

Meteor 3M will provide altitude profiles of aerosols 

and polar stratospheric cloud information from the 

upper troposphere through the stratosphere in the 

high-latitude regions. In the longer term, advances 

are expected in determining the vertical distribution 

of aerosols in the troposphere using the active lidar 

on the PICASSO-CENA mission.

Radiation: The flow of radiation from the Sun to the 

Earth and its absorption, reflection, and emission 

within the Earth system determine the energy balance 

of the Earth and hence have a significant effect on the 

nature of life on Earth as well. The information from 

products describing optical and physical properties 

of clouds and aerosols is combined with broadband 

radiation measurements from CERES and AIRS to 

provide estimates of solar and infrared radiative 

fluxes, which in turn determine the heating and cooling of the Earth and the atmosphere. The radiation 

estimates needed to do this are both shortwave and 

longwave fluxes at the surface and at the top of the 

atmosphere, both downwelling and upwelling, so that 

the net flow can be determined. Also, fluxes must be 

distinguished between cloudy- and clear-sky regions, 

and the fluxes over a series of elevation intervals are 

needed to test atmospheric models. Improved knowledge of the vertical distribution of radiative fluxes 

will be obtained by the addition of PICASSO-CENA 

and CloudSat data, with these platforms flying in formation with Aqua.

Product Overview

CERES will continue the fifteen-year record of radiation-budget information produced by the ERBE 
Cloud and Aerosol Properties and Radiative Energy Fluxes • Overview Data Products Handbook - Volume 2 89

program. As with Terra, starting 6 months after the 

launch of Aqua (following instrument and analysis 

validation), the CERES science team will begin 

production of ERBE-like data products. These topof-the-atmosphere (TOA) fluxes will use the ERBE 

analysis algorithms to allow consistent comparisons 

to historical ERBE data. Furthermore, the data from 

the first 24 months after the Terra and Aqua launches 

will be used to develop a new set of CERES angular 

distribution models (ADMs). These ADMs are used 

to convert broadband CERES radiances, i.e., radiative 

energy at a single viewing direction, into estimates 

of broadband hemispheric fluxes. The CERES rotating-azimuth-plane scan mode will be used along 

with the cloud properties derived using VIRS and 

MODIS data to derive new and much more accurate 

ADMs. Beginning 30 months after launch, these 

more accurate CERES estimated TOA fluxes will 

be produced in addition to the ERBE-like products. 

Finally, starting 36 months after launch, CERES will 

routinely provide estimates of radiative fluxes at the 

surface and at a selected number of levels within the 

atmosphere. The number of atmospheric levels will 

depend on the accuracies determined during postlaunch validation. 

 All the CERES products except the ERBE-like 

products are classified as “post-launch” for the reasons discussed above. The AMSR-E product in this 

chapter is also post-launch, while the AIRS/AMSUA/HSB, MODIS, SAGE III, and TOMS products are 

“at-launch.”

 The cloud products fall into two classes: 1) cloud 

detection and delineation, and 2) cloud properties. 

Cloud detection and delineation produces masks 

indicating where clouds exist and information on 

layering and overlap to give a geometric picture 

of global cloud coverage. Cloud properties include 

optical thickness, temperature, liquid-water content, 

ice-water content, particle radius, cloud-top altitude, 

and phase, all of which relate to the radiative transmission, reflection, and emission of the clouds. 

 The CERES cloud-detection process uses observations from VIRS on TRMM and MODIS on Terra 

and Aqua to produce a global binary mask indicating 

the presence or absence of a cloud in each imager 

pixel mapped to the CERES fields of view. The 

MODIS cloud mask (MOD 35) is a global product 

that provides a probability that a view of the Earth 

is obstructed by clouds and an indication of whether 

cloud shadow is detected. It also provides information 

on land/sea presence and day/night conditions.

 MODIS cloud-property parameters include particle 

size and phase, optical thickness, cloud-top height, 

emissivity and temperature, and cloud fraction in a 

region, all of which support specific MODIS science 

tasks that complement the radiation studies and products from other instruments. CERES cloud properties 

include cloud-layer mapping, cloud-top and cloudbase pressure, infrared emissivity, liquid-water path, 

particle radius, and cloud overlap, all derived from 

MODIS or VIRS data and remapped onto the broadband CERES fields of view for studies of the Earthʼs 

radiative-energy distribution. AIRS/AMSU-A/HSB 

cloud properties include cloud-top pressure, temperature, and cloud spectral properties, while AMSR-E 

data will be used to derive columnar cloud water.

 Aerosol properties are provided by SAGE III on 

Meteor 3M, TOMS on QuikTOMS, and MODIS 

(MOD 04) on Aqua. Aerosols must be accounted for 

during the retrieval of surface and atmospheric parameters for maximum accuracy. The MODIS aerosol 

products include aerosol optical thickness, aerosol 

size distribution, and aerosol sources and sinks; 

SAGE III products include multi-spectral extinction 

profiles of aerosols with high vertical resolution from 

the upper troposphere through the stratosphere; and 

TOMS products include an aerosol index derived 

from backscattered ultraviolet radiances. These various aerosol products will aid both in understanding 

the role of aerosols in the climate system and in improving corrections for aerosol effects in the retrieval 

of surface parameters.

 Radiant energy flux products from CERES include the radiative fluxes downward and upward at 

the top of the atmosphere, at the Earthʼs surface, and 

at selected intermediate altitudes, as well as the net 

flux, which is used to determine the radiation budget. 

This budget in turn determines the overall heating 

and cooling of the atmosphere and the Earthʼs surface and hence has a central role in climate changes. 

The CERES radiation algorithms use the cloud and 

aerosol products in the calculations for determining 

how much solar radiation is transmitted, reflected, 

and absorbed in the cloud/atmosphere/Earth-surface 

system. They are thus critical in the monitoring of 

global climate.

Product Interdependency and Continuity 

with Heritage Data

There is significant interdependency both amongst the 

products described in this chapter and between these 

products and data from other sources. In many cases, 

output data from one instrument become input ancillary data for another, as in the case of MODIS and/or 

TRMM data being used in the calculation of CERES 

cloud products. Further, the cloud distributions will 

be used in cloud masks for surface products.
90 Data Products Handbook - Volume 2 • Cloud and Aerosol Properties and Radiative Energy Fluxes 

 The AIRS/AMSU-A/HSB cloud products build on 

the heritage of HIRS/MSU, the AMSR-E products 

on the heritage of SMMR, SSM/I, and TMI, the CERES products on the heritage of ERBE, the MODIS 

products on the heritage of AVHRR, the SAGE III 

products on the heritage of SAM II, SAGE I, and 

SAGE II, and the QuikTOMS products on the heritage 

of earlier TOMS instruments. In the case of CERES, 

several data products use ERBE algorithms to ensure 

continuity with the extensively analyzed ERBE data 

set. Similarly, the SAGE III data will be used to 

derive aerosol products with an algorithm similar to 

the SAGE II algorithm, to ensure continuity of the 

SAGE aerosol products. The continuation of existent 

data records will assist the determination of long-term 

trends and the analysis of the role of clouds, aerosols, 

and radiative energy fluxes in global climate and climate change.

Suggested Reading

Barkstrom, B. R., 1984.

Coakley, J. et al., 1983.

Di Girolamo, L., and R. Davis, 1994.

Diner, D. J. et al., 1991.

Dubovik, O. et al., 2000.

Goodman, A. H., and A. Henderson-Sellers, 1988.

Hansen, J. et al., 1993.

Holben, B. N. et al., 1992.

Kaufman, Y. J., and C. Sendra, 1988.

Kaufman, Y. J. et al., 1997a,b.

King, M. D., 1987.

King, M. D. et al., 1992.

King, M. D. et al., 1999.

Ramanathan, V., 1986.

Ramanathan, V., 1987.

Ramanathan, V. et al., 1989.

Randall, D. A. et al., 1989.

Remer, L. A., and Y. J. Kaufman, 1998.

Remer, L. A. et al., 1997.

Remer, L. A. et al., 1998.

Sohn, B.-J., and F. R. Robertson, 1993.

Solomon, S. et al., 1997.

Tanré, D. et al., 1996.

Wang, M., and H. R. Gordon, 1994.

Wielicki, B. A. et al., 1995.

Wielicki, B. A. et al., 1996.

Wielicki, B. A. et al., 1998.

Overview • Cloud and Aerosol Properties and Radiative Energy Fluxes

(Cloud and Aerosol Properties and Radiative Energy Fluxes 

– An Overview, continued)
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 2 91

AIRS/AMSU-A/HSB Cloud 

Product (AIR 04)

Product Description

The AIRS Cloud Product (AIR 04) employs microwave, infrared, and visible observations to determine 

the physical and radiative cloud properties within an 

AMSU-A field of view (FOV).

 The product consists of cloud-top pressure for 

up- to-two cloud layers within an AMSU-A FOV, 

effective fractional cloud-cover values for each AIRS 

footprint and cloud type, and the ratio of the spectral 

emissivity at a number of frequencies to that at 11 μm 

for the top cloud layer. The effective fractional cloud 

cover is the product of the geometrical fractional 

cloud cover and the 11-μm cloud emissivity.

 Cloud parameters are retrieved for each AIRS 

footprint, regardless of whether a microwave/infrared 

(MW/IR) temperature profile can be successfully retrieved.

 The product is generated daily (day and night) at 

AMSU-A FOV resolution, with some information at 

AIRS FOV resolution.

 A visible cloud product is available at AIRS footprint resolution during daylight. The product consists 

of cloud amount (distinguishing between high and 

low clouds) and variability index within the AMSU-A 

footprint.

Research and Applications

Cloud parameters are important for studying interannual variability, and cloudiness is the principal 

modulating factor on shortwave- and longwaveradiation fluxes. Because of their high spatial and 

temporal variability, the effect of clouds is important 

over a wide range of spatial and temporal scales. Most 

current estimates of cloud amount (fractional cloud 

cover) are based on observations made at the ground. 

The accuracy of these values is doubtful except for 

zero or 100% cloudiness. Several definitions of cloudiness from space have been proposed. The definition 

considered appropriate for sounders such as AIRS 

emphasizes that clouds are the forcing parameters 

that make the observed infrared radiance differ from 

clear-column radiance.

 The AIRS effective fractional cloud cover derived from the MW/IR retrieval is effective for 

longwave- flux calculations only and involves the 

cloud emissivity. Shortwave fluxes require effective 

cloud fractions utilizing the cloud albedo as well as 

the geometric cloud fraction.

Data Set Evolution

Radiances are cloud cleared to remove the effects 

of clouds on the radiance, enabling geophysical 

parameters to be determined for the clear portion of 

the scene. Assuming these parameters are valid over 

the entire scene, a set of cloud parameters is found 

which, when substituted in the radiative-transfer 

equations, best matches the observations for a number of channels used to determine cloud properties. 

The geophysical parameters used to determine the 

cloud parameters are from the AIRS solution for 

the AMSU- A footprint, whether it is a combined 

MW/IR solution in the case of accepted retrievals, 

or a primarily MW solution in the case of rejected 

MW/IR profiles.

 Visible observations are made at 2-km resolution 

during day portions of the orbits to determine a visible cloud amount (distinguishing between high and 

low clouds) and variability index within the AMSU-A 

footprint. Visible observations made during the night 

portions of the orbits are expected to consist only of 

noise, except where bright moonlight or man-made 

sources are present. Visible information is combined 

to achieve AIRS IR spatial resolution.

 Data validation will be accomplished through 

comparison of the retrieved cloud parameters with 

correlative data sets from satellite imagers (AVHRR 

and its derivatives).

Suggested Reading

Aumann, H. H., and C. Miller, 1995.

Aumann, H. H. et al., 1999.

Chahine, M. T., 1977.

Chahine, M. T., 1982.

Chahine, M. T. et al., 1977.

Chahine, M. T. et al., 1997a,b,c.

Hofstader, M. et al., 1999.

Lambrigsten, B., 1996.

Susskind, J. et al., 1997.

Susskind, J. et al., 1998.

Aqua / AIRS/AMSU-A/HSB
92 Data Products Handbook - Volume 2 Aqua / AIRS/AMSU-A/HSB • Cloud and Aerosol Properties and Radiative Energy Fluxes 

AIRS/AMSU-A/HSB Cloud Product Summary

Coverage: Global

Spatial/Temporal Characteristics: Resolution of AMSU-A FOV (40.6 km at nadir); 

some parameters at AIRS FOV (13.5 km 

at nadir)/once or twice daily (varies with 

parameter)

Key Science Applications: Cloud parameterization, climate modeling, and monitoring; 

AIRS cloud-cleared radiances

Key Geophysical Parameters:

• Fraction of FOV obscured by clouds and 

number of resolved cloud formations

• Cloud-top pressure, temperature, and 

cloud spectral properties

Processing Level: 2

Product Type: Standard, at-launch (spectral 

emissivity is a research product)

Maximum File Size: 5 MB

File Frequency: 240/day

Primary Data Format: HDF-EOS

Browse Available: Yes

Additional Product Information: 

http://www-airs.jpl.nasa.gov/

DAAC: NASA Goddard Space Flight Center

Science Team Contacts:

 M. T. Chahine

 J. Susskind

AIRS/AMSU-A/HSB Flux 

Product (AIR 10)

Product Description

The AIRS Flux Product is included in the Level 2 

retrieval data granules and is geolocated to an AMSUA footprint.

 AIRS radiative fluxes consist of clear-column radiances, total Outgoing Longwave Radiation (OLR), 

net surface longwave flux, and net surface shortwave 

flux.

 Clear-column radiances represent the radiances 

which would have gone to space at the angle of 

observation if no clouds were present in the field 

of view, and they are the radiances used to compute 

the final AIRS products. Clear-column radiances are 

produced for each AMSU-A footprint in which a successful combined MW/IR retrieval can be produced. 

This occurs in most cases of up to 80% cloudiness in 

the AMSU-A footprint and is expected roughly two 

thirds of the time.

 Clear-column radiances at different zenith angles 

should not be averaged together to produce timemean values because of their substantial zenith-angle 

dependence.

 OLR is a function of surface and atmospheric 

conditions, particularly cloud-top pressure and 

cloud amount. OLR will be produced for every AIRS 

footprint. OLR is important for studying interannual 

variability and has been used as a proxy for precipitation.

 When compared to other measures of OLR, such 

as those from CERES, OLR from AIRS also serves 

as a validation for the detailed AIRS surface and atmospheric products. Net surface longwave flux is the 

difference between the longwave flux leaving the surface, which depends only on surface parameters, and 

longwave flux striking the surface, which depends on 

atmospheric parameters, including cloud-base pressure. This is not a retrieved parameter. Consequently, 

net surface longwave flux is more difficult to compute 

than OLR and is a research product. One value will 

be produced for each AMSU-A footprint.

 Net shortwave flux is the difference between the 

shortwave flux reaching the surface and that reflected 

by the surface. The downwelling shortwave flux is 

a function of the sun irradiance and the atmospheric 

properties, mostly those of clouds (cloud cover and 

cloud optical depth). The upwelling shortwave flux 

depends on the downwelling flux and the surface 

albedo.

(AIRS/AMSU-A/HSB Cloud Product, continued)
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 2 93

 Radiances in selected AIRS cloud-clearing channels, together with all the microwave radiances, are 

used to determine the coefficients for each AMSU-A 

footprint. Error estimates are computed theoretically 

for each channel for each profile. Surface temperature 

and emissivity variations within the AMSU-A footprint will contribute to the error in the clear-column 

radiances.

 The procedure to compute OLR and downward 

flux is analogous to the procedure used with HIRS2/

MSU sounding data. Radiative-transfer calculations 

are done separately for a number of spectral bands as 

a function of the retrieved surface-skin temperature, 

band surface emissivity, temperature-water-vaporozone profile, and cloud parameters including band 

cloud spectral emissivity. The calculation in each band 

is done at a band effective zenith angle, different for 

upwelling and downwelling flux, and multiplied by 

π to give total band flux. These are added together to 

give the total OLR or downwelling longwave flux.

 The procedure to compute the downwelling and 

upwelling shortwave flux employs a radiative-transfer 

model that uses the broad AIRS VIS/IR channel 4, 

surface albedo, aerosol optical depth, and integrated 

water vapor as input. The surface albedo is computed 

from AIRS Channel 4 clear-sky radiances using the 

same radiative-transfer model. Clear-sky detection 

is achieved using a combination of AIRS Channel 1 

and, for regions covered by land surfaces, normalized 

difference vegetation index (NDVI) computed by differencing AIRS Channels 2 and 3. 

 Data validation will be accomplished through 

checking the internal consistency of the Level 2 retrieval and vicarious validation of AIRS and near-IR 

radiances against in situ measurements at DoE ARM/

CART sites, Baseline Upper Air Network (BUAN) 

sites, and dedicated MODIS and ASTER validation 

sites.

Suggested Reading

Aumann, H. H. et al., 1999.

Chahine, M. T. et al., 1977.

Gautier, C., and M. Landsfeld, 1997.

Hofstadter, M. et al., 1999.

Lambrigsten, B., 1996.

Mehta, A., and J. Susskind, 1999.

Research and Applications

The study of the global hydrologic cycle and its coupling to the energy cycle is key to understanding the 

major driving forces of the Earthʼs climate system. 

AIRS will measure the major components of these 

driving forces, including the thermal structure of 

the surface and atmosphere, amount and height of 

clouds, outgoing longwave infrared radiation, and 

distribution of atmospheric water vapor.

 If the net radiative energy flux at the top of the 

atmosphere (TOA) is combined with the net radiative 

energy flux at the Earthʼs surface, the net atmospheric 

radiative cooling is obtained. The atmospheric radiative cooling is the net effect of the infrared emission 

by the atmosphere, the absorption by the atmosphere 

of infrared radiation emitted by the Earthʼs surface, 

and the absorption by the atmosphere of solar radiation.

 AIRS/AMSU-A/HSB will measure the spectral 

changes in the longwave radiation going to space. 

Knowledge of the changes is needed to monitor the 

effects of trace gases on global warming.

 AIRS super-window channels will be able to 

observe the surface with minimum spectral contamination by the atmosphere. In addition, AIRS 

narrow spectral channels in the short-wavelength 

infrared region will observe the atmospheric layers 

above the Earthʼs surface with the highest vertical 

resolution possible with passive remote sensing. The 

observations will enable investigations of the fluxes 

of energy and water vapor between the atmosphere 

and the surface, as well as studies of their effect on 

climate.

Data Set Evolution

The AIRS Flux product is derived by integrating 

the observed radiances in the infrared and near 

infrared.

 Determination of the AIRS clear-column radiances 

is the most important step in the AIRS retrieval process. The methodology implicitly assumes that geophysical parameters in all nine AIRS footprints within 

an AMSU-A footprint are otherwise homogeneous 

except for varying amounts of up to eight different 

types of cloud formations. With this assumption, the 

clear-column radiances for all channels are given as 

linear combinations of the observed radiances in those 

channels in the nine fields of view, with an unknown 

set of channel-independent coefficients, one for each 

cloud formation.

Aqua / AIRS/AMSU-A/HSB
94 Data Products Handbook - Volume 2 • Cloud and Aerosol Properties and Radiative Energy Fluxes 

AIRS/AMSU-A/HSB Flux Product Summary

Coverage: Global

Spatial/Temporal Characteristics: AMSU-A 

FOV resolution (40.6 km at nadir)/twice 

daily

Key Science Applications: Radiation budget, 

climate modeling

Key Geophysical Parameters:

• Clear-column radiance

• TOA outgoing longwave spectral radiative flux

• Spectral features in longwave fluxes, both 

land and ocean

• Net surface longwave fluxes, both land 

and ocean

• Net surface and TOA outgoing shortwave fluxes, both land and ocean

• Surface albedo

Processing Level: 2

Product Type: Research, at-launch

Maximum File Size: 5 MB

File Frequency: 240/day

Primary Data Format: HDF-EOS

Browse Available: Yes

Additional Product Information: 

http://www-airs.jpl.nasa.gov/

DAAC: NASA Goddard Space Flight Center

Science Team Contacts:

 J. Susskind

 C. Gautier

 Aqua / AIRS/AMSU-A/HSB and AMSR-E

(AIRS/AMSU-A/HSB Flux Product, continued) AMSR-E Columnar Cloud 

Water

Product Description

The columnar cloud water (L) derived from the 

AMSR-E microwave radiances is a measure of the 

total vertically integrated liquid cloud water in the 

marine atmosphere. It is expressed in units of millimeters, which represent the depth of the column of 

liquid water that would result if the cloud were to 

precipitate completely. The spatial resolution of this 

product is 12 km, and its expected rms accuracy at 

this resolution is 0.02 mm. The product only includes 

the liquid-water contribution due to cloud droplets, 

and does not include the larger raindrops. Cloud 

droplets are typically smaller than 100 μm, whereas 

raindrops can reach sizes up to several millimeters. At 

microwave frequencies, the radiative characteristics 

of cloud droplets and raindrops are quite different. 

Hence when rain is present, the accuracy of the cloud 

product degrades due to the difficulty in separating 

the cloud water from the rain water. The product will 

be produced for individual pixels across the AMSR-E 

swath (Level 2) as well as an Earth-gridded product 

(0.25° in latitude and longitude) averaged daily, 

weekly, and monthly (Level 3).

Research and Applications

Clouds remain one of the most poorly understood 

constituents of the climate system, primarily because 

of their complex morphology and highly variable radiative effects. They have significant effects on the 

planetary radiation balance through both their high 

albedo in the visible and the direct absorption and 

re-emission of outgoing longwave radiation. Clouds 

contribute to both positive greenhouse feedback 

from increased solar absorption and heat trapping 

and negative feedback from increases in albedo and 

radiative cooling. Formation of clouds is directly 

related to the conversion of latent heat to sensible 

heat, and is strongly correlated with the distribution 

of atmospheric heating. Additionally, advection of 

cloud water is critical in the redistribution of water 

through precipitation, forming a major link in the 

global hydrological cycle, which is a major driving 

force in the Earthʼs climate. Due to the difficulties 

involved in treating their manifold effects accurately, 

clouds are generally regarded as the weak link in 

most current state-of-the-art models of climate, making improvements in the observational cloud data sets 

of particular importance.
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 2 95

Data Set Evolution

The cloud-water retrieval algorithm is based on experience gained from previous microwave radiometers, 

including SMMR, SSM/I, and TMI. In particular, the 

SSM/I has demonstrated the ability of microwave 

radiometers to measure the cloud content over the 

worldʼs oceans. Unfortunately, there is no reliable 

“ground truth” with which to validate the cloud-water 

algorithm. Hence, we rely on computer simulations 

and statistical analyses to assess the performance of 

the algorithm. The AMSR-E L retrievals should be 

more accurate than SSM/I retrievals because AMSRE has two additional sets of dual-polarization channels at 6.9 and 10.7 GHz, which help separate the 

cloud signal from the wind signal and remove crosstalk due to varying sea-surface temperature.

Suggested Reading

Grody, N. C., 1976.

Liu, G., and J. Curry, 1993.

Ramanathan, V. et al., 1989.

Staelin, D. H. et al., 1976.

Trenberth, K. E., 1998.

Webster, P. J., 1994.

Weng, F., and N.C. Grody, 1994.

Wentz, F. J., 1997. 

Wentz, F. J., and R. W. Spencer, 1998.

AMSR-E Columnar Cloud Water Summary

Coverage: Global ocean surface, clear and 

cloudy skies and light rainfall

Spatial/Temporal Characteristics:

 Level 2: swath pixels at 12-km resolution

 Level 3: 0.25° latitude-longitude grid/daily, 

weekly, and monthly

Key Science Applications: Climate variability, 

air-sea interaction, radiation balance, 

hydrological cycle

Key Geophysical Parameters: Columnar cloud 

water

Processing Level: 2, 3

Product Type: Standard, post-launch

Maximum File Size: 10 MB

File Frequency: 29/day

Primary Data Format: HDF-EOS

Browse Available: Accompanies data product

Additional Product Information: 

http://wwwghcc.msfc.nasa.gov/AMSR/

html/amsr_products.html

DAAC: National Snow and Ice Data Center

Science Team Contact:

 F. J. Wentz

Aqua / AMSR-E
96 Data Products Handbook - Volume 2 • Cloud and Aerosol Properties and Radiative Energy Fluxes 

scanners on ERBS, NOAA-9, and NOAA-10. ES-8 

flux data are directly comparable to ERBE fluxes and 

thus effectively extend the time series of ERBE flux 

measurements, allowing global-change experiments 

to continue using ERBE algorithms without the errors 

associated with comparing fluxes estimated by two 

different algorithms. Improved TOA flux-estimation 

algorithms for CERES (using improved ADMs and 

VIRS/MODIS cloud identification) are available in 

the Single-Scanner Footprint (SSF) product. Producing the two versions of TOA fluxes will allow 

continuity with the previous ERBE data along with 

improved accuracy of CERES TOA flux data.

Data Set Evolution

The ERBE-like Inversion Subsystem consists of algorithms that convert filtered radiometric measurements 

to instantaneous flux estimates at the TOA. The basis 

for this procedure is the ERBE Data Management 

System, which produced TOA fluxes from the 

ERBE scanning radiometers. The system consists of 

algorithms for Spectral Correction, Observed Scene 

Type, Radiance-to-Flux Conversion using Angular 

Distribution Models, and finally the Regional Averaging algorithm, which produces regional fluxes. A 

linear-estimation scheme is used to relate the filtered 

and unfiltered radiances. Ancillary parameter files are 

required for processing, including ADMs and spectral 

correction coefficients for converting filtered radiances to unfiltered radiances.

Aqua / CERES

CERES ERBE-like 

Instantaneous TOA Estimates 

(ES-8)

Product Description

The ERBE-like Instantaneous TOA Estimates (ES-8) 

product contains 24 hours of instantaneous CERES 

data for a single scanner instrument. The ES-8 product contains filtered radiances recorded every 0.01 

seconds for the total, shortwave (SW), and window 

(WN) channels and the unfiltered SW, longwave 

(LW), and WN radiances. The SW and LW radiances 

at spacecraft altitude are converted to top-of-the-atmosphere (TOA) fluxes with a scene-identification 

algorithm and Angular Distribution Models (ADMs), 

which are “like” those used for the Earth Radiation 

Budget Experiment (ERBE). The TOA fluxes, scene 

identification, and angular geometry are included in 

the ES-8 product.

 A complete listing of parameters for this data product can be found in the CERES Data Products Catalog at http://asd-www.larc.nasa.gov/DPC/DPC.html 

and detailed definitions of each parameter can be 

found in the CERES Collection Guide at http://asdwww.larc.nasa.gov/ceres/collect_guide/list.html.

Research and Applications

This product is a CERES-produced equivalent to 

the instantaneous fluxes derived from the ERBE 

CERES ERBE-like Instantaneous TOA Estimates: TOA longwave flux measurements taken from the CERES 

instrument on Terra on February 28, 2000.

No 140 154 168 182 196 210 224 238 252 266 280

Data Watts per square meter
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 2 97

CERES ERBE-like Monthly 

Regional Averages (ES-9) 

and ERBE-like Monthly 

Geographical Averages (ES-4)

Product Description

The ERBE-like Monthly Regional Averages (ES-9) 

product contains a month of space-and-time-averaged CERES data for a single scanner instrument. 

The ES-9 product is also produced for combinations 

of scanner instruments. All instantaneous shortwave 

and longwave fluxes at the top of the atmosphere 

(TOA) from the CERES ES-8 product for a month 

are sorted by 2.5° spatial regions, by day number, 

and by the local hour of observation. The mean of 

the instantaneous fluxes for a given region-day-hour 

bin is determined and recorded in the ES-9 product 

along with other flux statistics and scene information. 

For each region, the daily average flux is estimated 

from an algorithm that uses the available hourly 

data, scene identification data, and diurnal models. 

This algorithm is “like” the algorithm used for the 

Earth Radiation Budget Experiment (ERBE). The 

ES-9 product also contains hourly average fluxes 

for the month and an overall monthly average for 

each region. These average fluxes are given for both 

clear-sky and total-sky scenes.

 The ERBE-like Monthly Geographical Averages 

(ES-4) product contains a month of space-and-timeaveraged CERES data for a single scanner instrument. 

The ES-4 product is also produced for combinations 

of scanner instruments. For each observed 2.5-degree 

spatial region, the daily average, the hourly average 

over the month, and the overall monthly average of 

shortwave and longwave fluxes at the TOA from the 

CERES ES-9 product are spatially nested up from 

2.5° regions to 5° and 10° regions, to 2.5°, 5°, and 10° 

zonal averages, and to global monthly averages. For 

each nested area, the albedo and net flux are given. 

For each region, the daily average flux is estimated 

from an algorithm that uses the available hourly data, 

scene-identification data, and diurnal models. This algorithm is “like” the algorithm used for the ERBE.

 A complete listing of parameters for these data 

products can be found in the CERES Data Products 

Catalog at http://asd-www.larc.nasa.gov/DPC/

DPC.html, and detailed definitions of each parameter 

can be found in the CERES Collection Guide at 

http://asd-www.larc.nasa.gov/ceres/collect_guide/

list.html.

 Documentation on the ES-8 algorithms can be found 

in CERES ATBD Subsystem 2.0, in PDF format, at 

http://eospso.gsfc.nasa.gov/atbd/cerestables.html.

Suggested Reading

Avis, L. M. et al., 1984.

Barkstrom, B. R., and G. L. Smith, 1986.

Smith, G. L. et al., 1986.

Wielicki, B. A. et al., 1998.

CERES ERBE-like Instantaneous TOA 

Estimates Summary

Coverage: Global

Spatial/Temporal Characteristics: 20 km at 

nadir/0.01 second

Key Geophysical Parameters: TOA SW, LW 

fluxes and radiances, scene type

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 490 MB

File Frequency: 1/day

Primary Data Format: HDF

Browse Available: No

Additional Product Information: 

http://eosweb.larc.nasa.gov/PRODOCS/

ceres/table_ceres.html

DAAC: NASA Langley Research Center

Science Team Contacts:

 R. N. Green

 N. G. Loeb

Aqua / CERES
98 Data Products Handbook - Volume 2 Aqua / CERES • Cloud and Aerosol Properties and Radiative Energy Fluxes 

Research and Applications

These products provide an equivalent of the ERBEbased radiation-balance component of inputs to 

global climate models, which were developed as part 

of, or over the same period, as the ERBE program 

and will allow continuation of this research while new 

algorithms are developed for CERES data.

 The ES-9 and ES-4 products are equivalent to their 

ERBE analogs (S-9, and S-4) and effectively extend 

the time series of ERBE flux measurements through 

the EOS era. Various spatial scales are provided to 

simplify intercomparisons with earlier Nimbus and 

ERBE non-scanner time series.

Data Set Evolution

These data sets are generated using heritage ERBE algorithms operating on inputs from CERES calibrated 

Level 1 data and ancillary data. Data processing uses 

CERES Subsystem 3.0, which temporally interpolates 

CERES measurements using linear interpolation over 

oceans and half-sine-curve fit over land and desert 

regions. Monthly and monthly-hourly means are 

then computed using the combination of observed 

and interpolated values.

 Documentation on the algorithms used to create 

the data products can be found in CERES ATBD 

Subsystem 3.0, in PDF format, at http://eos.nasa.gov/

atbd/cerestables.html.

Suggested Reading

Brooks, D. R. et al., 1986.

Harrison, E. F. et al., 1990a,b.

Harrison, E. F. et al., 1995.

Ramanathan V. et al., 1989.

Wielicki, B. A. et al., 1998.

CERES ES-9 and ES-4 Products Summary

Coverage: Global

Spatial/Temporal Characteristics: 2.5°, 5.0°,

10.0°, region and zone, global/monthly 

(by day and hour)

Key Geophysical Parameters: TOA shortwave 

and longwave radiant flux, scene type, 

albedo, solar incidence, cloud condition

Processing Level: 3

Product Type: Standard, at-launch

Maximum File Size: 27 MB (ES-4), 1100 MB 

(ES-9)

File Frequency: 1/month

Primary Data Format: HDF

Browse Available: No

Additional Product Information:

http://eosweb.larc.nasa.gov/PRODOCS/

ceres/table_ceres.html

DAAC: NASA Langley Research Center

Science Team Contact:

 D. F. Young

(CERES ERBE-like Monthly Regional Averages and ERBE-like 

Monthly Geographical Averages, continued)

(Left ) Sample CERES ERBE-like Monthly Geographical Averages, presenting the TOA longwave flux for March 2000. (Right) Sample 

CERES ERBE-like Monthly Regional Average, presenting the TOA clear-sky longwave flux for March 2000. Both images are from data 

from the Terra CERES.

No 220 231 242 253 264 275 286 297 308 319 330

Data Watts per square meter No 150 168 186 204 222 240 258 276 294 312 330

Data Watts per square meter
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 2 99

CERES Single Scanner TOA/

Surface Fluxes and Clouds 

(SSF)

Product Description

The Single Scanner Footprint TOA/Surface Fluxes 

and Clouds (SSF) product contains one hour of instantaneous CERES data for a single scanner instrument. 

The SSF combines instantaneous CERES data with 

scene information from a higher-resolution imager 

such as the Visible/Infrared Scanner (VIRS) on 

TRMM or MODIS on Terra and Aqua. Scene identification and cloud properties are defined at the higher 

imager resolution and these data are averaged over the 

larger CERES footprint. For each CERES footprint, 

the SSF contains the number of cloud layers, and for 

each layer, the cloud amount, height, temperature, 

pressure, optical depth, emissivity, ice and liquidwater path, and water-particle size. The SSF also 

contains the CERES filtered radiances for the total, 

shortwave (SW), and window (WN) channels and the 

unfiltered SW, longwave (LW), and WN radiances. 

The SW, LW, and WN radiances at spacecraft altitude 

are converted to top-of-the-atmosphere (TOA) fluxes 

based on the imager-defined scene. These TOA fluxes 

are used to estimate surface fluxes. Only footprints 

with adequate imager coverage are included on the 

SSF, which is much less than the full set of footprints 

on the CERES ES-8 product.

 A complete listing of parameters for this data product can be found in the CERES Data Products Catalog 

at http://asd-www.larc.nasa.gov/DPC/DPC.html, and 

detailed definitions of each parameter can be found 

in the CERES Collection Guide at http://asdwww.larc.nasa.gov/ceres/collect_guide/list.html. The 

algorithms are described in CERES ATBDs 4.0-4.6, 

located, in PDF format, at 

http://eospso.gsfc.nasa.gov/atbd/cerestables.html.

Research and Applications

This product generates the basic data on radiation and 

cloud characteristics needed by the hourly, monthly, 

gridding, averaging, and radiation-budget algorithms 

producing many of the CERES output products.

Data Set Evolution

This product is the first step in the sequence of processes from the geolocated radiances to estimates of 

the global radiation budget.

 The SSF analysis uses coarse-spatial-resolution 

broadband radiance data from CERES, matched 

with simultaneous high-spatial-resolution narrowband radiance data from MODIS on Aqua.

Analysis proceeds in six basic steps:

1) Cloud masking and clear-sky map update;

2) Cloud height and layering determination;

3) Cloud-optical-property determination;

4) Convolution of imager pixel-level cloud/surface 

properties with the CERES point-spread function 

for each CERES field of view;

5) Use of convolved cloud/surface properties to select a broadband anisotropic model for conversion 

of CERES radiance to TOA flux; and

CERES Single Scanner Footprint TOA Longwave Flux from the TRMM Satellite on March 1, 1998, hour 23. This 

hourly product contains data within the area viewed by the VIRS imager on TRMM. For the corresponding Terra 

and Aqua CERES images, the area coverage is defined by the respective MODIS instruments.

Aqua / CERES
100 Data Products Handbook - Volume 2 Aqua / CERES • Cloud and Aerosol Properties and Radiative Energy Fluxes 

6) Estimation of radiative fluxes at the surface, using theoretical/statistical relationships to TOA 

fluxes.

The SSF data product contains the matched cloud 

and radiative-flux data, which will also be used for 

later determination of radiative fluxes within the 

atmosphere.

Suggested Reading

Baum, B. A. et al., 1995a,b.

Dong, X. et al., 1999.

Green, R. N., and B. A. Wielicki, 1995.

Green, R. N. et al., 1995.

Minnis, P. et al., 1995.

Minnis, P. et al., 1998.

Minnis, P. et al., 1999.

Wielicki, B. A. et al., 1995.

Wielicki, B. A. et al., 1998.

Young, D. F. et al., 1998. 

(CERES Single Scanner TOA/Surface Fluxes and Clouds, 

continued) CERES Single Scanner TOA/Surface Fluxes 

and Clouds Summary

Coverage: Global

Spatial/Temporal Characteristics: 20 km at 

nadir/0.01 second

Key Geophysical Parameters: Radiance and 

flux (TOA and surface); cloud and clearsky statistics, optical depth, infrared 

emissivity, liquid-water path, ice-water 

path, cloud-top pressure, cloud effective 

pressure, cloud temperature and cloud 

height, cloud-bottom pressure, water 

and ice-particle radius

Processing Level: 2

Product Type: Standard, post-launch

Maximum File Size: 260 MB

File Frequency: 1/hour

Primary Data Format: HDF

Browse Available: No

Additional Product Information: 

http://eosweb.larc.nasa.gov/PRODOCS/

ceres/table_ceres.html

DAAC: NASA Langley Research Center

Science Team Contact:

 P. Minnis
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 2 101

CERES Clouds and Radiative 

Swath (CRS)

Product Description

The Clouds and Radiative Swath (CRS) product 

contains one hour of instantaneous CERES data for 

a single scanner instrument. The CRS contains all of 

the CERES SSF product data. For each CERES footprint on the SSF the CRS also contains vertical flux 

profiles evaluated at four levels in the atmosphere: 

the surface, 500-, 70-, and 1-hPa. The CRS fluxes 

and cloud parameters are adjusted for consistency 

with a radiative-transfer model, and adjusted fluxes 

are evaluated at the four atmospheric levels for both 

clear-sky and total-sky.

 A complete listing of parameters for this data product can be found in the CERES Data Products Catalog 

at http://asd-www.larc.nasa.gov/DPC/DPC.html.

Research and Applications

The surface and atmospheric radiation budget 

(SARB) is the primary driver of the hydrological 

cycle and the general circulation of the atmosphere. 

Anthropogenically induced changes in the radiatively 

active trace gases and aerosols will affect the SARB 

and will therefore force a climate response. The 

68 radiance and radiant flux and cloud parameters 

provided by this product are inputs to higher level 

algorithms which produce gridded, averaged flux 

and cloud-property products. This product contains 

a further enhancement of the basic data on radiation and cloud characteristics from SSF needed for 

determination of the amount of energy passing from 

space through the atmosphere to the Earth and flowing 

from the Earth back to space, which is provided by 

products SRBAVG, AVG, and ZAVG. It is a global 

product and is used as the input to the hourly gridded 

computation of surface, multiple atmosphere levels, 

and TOA radiative fluxes.

 The analysis algorithms are described in CERES ATBD Subsystem 5.0, which can be found 

in PDF format at http://eospso.gsfc.nasa.gov/atbd/

cerestables.html. 

 An example of the radiative-flux profile, expressed 

in terms of the radiative diabatic heat rate, is given 

in the figure on the following page. The local-scale 

CERES/ARM/GEWEX Experiment (CAGEX) 

provides a prelaunch window to this component 

of CERES with on-line access to input data, calculated fluxes, and validating measurements (see http:

//snowdog.larc.nasa.gov:8081/cagex.html).

Aqua / CERES

Longwave and Shortwave Heating Rates over one ERBE footprint containing low clouds. The figure shows for both LW and 

SW the fluxes at the TOA (as untuned Fu-Liou values, ERBE observations, and tuned Fu-Liou values), fluxes at the surface (as 

untuned Fu-Liou values and tuned Fu-Liou values), and heating rates (untuned are solid and tuned are dashed).
102 Data Products Handbook - Volume 2 Aqua / CERES • Cloud and Aerosol Properties and Radiative Energy Fluxes 

Data Set Evolution

The inputs to this product come from the SSF product, 

and the cloud parameters are further corrected and 

adjusted. The TOA and surface values are corrected 

for satellite and cloud effects to produce observed 

TOA and tuned surface estimates. This data set is 

tailored to provide the needed inputs to the hourly 

gridded flux and surface radiation budget Level 3 

product, FSW.

Suggested Reading

Charlock, T. P., and T. L. Alberta, 1996.

Charlock, T. P. et al., 1993.

Charlock, T.P . et al., 1995.

Fu, Q., and K. Liou, 1993.

Liou, K.-N., 1992.

Whitlock, C. H. et al., 1995.

Wielicki, B. A. et al., 1998.

Wu, M., and L.-P. Chang, 1992.

CERES Clouds and Radiative Swath 

Summary

Coverage: Global

Spatial/Temporal Characteristics: 20 km at 

nadir/0.01 second

Key Geophysical Parameters: Shortwave and 

longwave radiant flux at the surface, multiple atmosphere levels, and TOA, scene 

identification, cloud parameters

Processing Level: 2

Product Type: Standard, post-launch

Maximum File Size: 354 MB

File Frequency: 1/hour

Primary Data Format: HDF

Browse Available: No

Additional Product Information: 

http://eosweb.larc.nasa.gov/PRODOCS/

ceres/table_ceres.html

DAAC: NASA Langley Research Center

Science Team Contact:

 T. P. Charlock

(CERES Clouds and Radiative Swath, continued)
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 2 103

CERES Monthly Gridded 

Radiative Fluxes and Clouds 

(FSW)

Product Description

The Monthly Gridded Radiative Fluxes and Clouds 

(FSW) product contains a month of space-and-timeaveraged CERES data for a single scanner instrument. 

The FSW is also produced for combinations of scanner instruments. All instantaneous fluxes from the 

CERES CRS product for a month are sorted by 1° 

spatial regions, by day number, and by the Universal Time (UT) hour of observation. The mean of 

the instantaneous fluxes for a given region-hour 

bin is determined and recorded on the FSW along 

with other flux statistics and scene information. The 

mean adjusted fluxes at the four atmospheric levels 

defined by CRS are also included for both clear-sky 

and total-sky scenes. In addition, four cloud-height 

categories are defined by dividing the atmosphere 

into four intervals with boundaries at the surface, 

700-, 500-, 300-hPa, and the top of the atmosphere 

(TOA). The cloud layers from CRS are put into one 

of the cloud-height categories and averaged over the 

region. The cloud properties are also column averaged 

and included on the FSW.

 A complete listing of parameters for this data product can be found in the CERES Data Products Catalog 

at http://asd-www.larc.nasa.gov/DPC/DPC.html.

Research and Applications

This product is an interim step in the generation 

of regional and global radiation products from 

CERES footprint data. It contains the gridded data 

on radiation and cloud characteristics needed by the 

synoptic flux and clouds algorithm SYN. The contribution of this product is the averaging and gridding 

operations applied to both the radiative flux and cloud 

property data.

Data Set Evolution

The algorithm is based on ERBE algorithms and 

carries out aggregation of the high-resolution 20-

km (nadir) data products to 1° (110 km) data cells 

using weighted averaging and linear interpolation to 

produce the flux and cloud data in this grid-cell format chosen for final products. This is an equal-angle 

grid consisting of regions that are 1° in latitude by 

1° in longitude. The parameter list is essentially the 

same as for CRS.

 The analysis algorithms are described in CERES 

ATBD Subsystem 6.0, which can be found at http:

//eospso.gsfc.nasa.gov/atbd/cerestables.html.

Suggested Reading

Stowe, L. et al., 1993.

Wielicki, B. A. et al., 1998.

CERES Monthly Gridded Radiative Fluxes 

and Clouds Product Summary

Coverage: Global

Spatial/Temporal Characteristics: 1° region/

hour

Key Geophysical Parameters: Gridded surface, 

atmosphere layer, and TOA fluxes, clearsky and total-sky cloud parameters in 

CRS, (e.g., cloud overlap, cloud pressure, 

cloud altitude, cloud temperature, icewater properties, ice-particle properties, 

liquid-water properties)

Processing Level: 3

Product Type: Standard, post-launch

Maximum File Size: 1130 MB

File Frequency: 11/month

Primary Data Format: HDF

Browse Available: No

Additional Product Information: 

http://eosweb.larc.nasa.gov/PRODOCS/

ceres/table_ceres.html

DAAC: NASA Langley Research Center

Science Team Contact:

D. F. Young

Aqua / CERES
104 Data Products Handbook - Volume 2 • Cloud and Aerosol Properties and Radiative Energy Fluxes 

CERES Synoptic Radiative 

Fluxes and Clouds (SYN)

Product Description

The Synoptic Radiative Fluxes and Clouds (SYN) 

product contains a day of space-and-time-averaged 

CERES data for a single scanner instrument. The 

SYN is also produced for combinations of scanner 

instruments. The 1° regional flux at the hour of observation from the CERES FSW product and concurrent 

diurnal data from geostationary satellites are used to 

estimate the regional flux at 3-hour intervals. Also at 

3-hour intervals are estimates of the adjusted fluxes 

at the four atmospheric levels as defined by the CERES CRS product for both clear-sky and total-sky 

scenes, estimates of the average cloud parameters in 

four cloud-height categories, and column-averaged 

cloud parameters.

 A complete listing of parameters for this data product can be found in the CERES Data Products Catalog 

at http://asd-www.larc.nasa.gov/DPC/DPC.html.

Research and Applications

The synoptic format of this product provides data 

which are global and simultaneous in time. This is 

preferred by climate modelers since the synoptic times 

ensure consistency with ground-truth meteorological 

observations from weather stations and radiosondes 

as well as with geostationary satellites, which provide 

images at synoptic hours. The production of a CERES 

synoptic data product is important because synoptic 

views provide a basis for studying the life cycle of 

cloud systems and for validating the CERES data 

processing, and it provides a regular data structure 

that simplifies the design of algorithms and operation of the data processing system. Synoptic fields 

of radiation and clouds are potentially valuable in 

developing and understanding the role of clouds in 

the generation and dissipation of available potential 

energy, since the calculation of this quantity requires 

integration over approximately horizontal layers 

within the atmosphere.

Data Set Evolution

The CERES time-space averaging and time-interpolation algorithm is based on techniques used in 

previous systems for deriving the Earth radiation 

budget, such as ERBE. The chief input to the time 

interpolation process is the gridded SW and LW 

TOA clear-sky and total-sky fluxes and cloud information provided by the FSW product. Data from the 

three possible systems which could carry CERES 

simultaneously in orbit (TRMM, Terra, and Aqua) 

provide up to six samples per day. This significantly 

reduces temporal sampling errors. Estimates of the 

cloud properties at synoptic times are also provided. 

The cloud-mask output defines clear-sky conditions 

for the 1° cells for the radiative fluxes, which are 

averaged over 3-hour periods. Interpolation to the 

synoptic times uses geostationary data to assist in 

modeling meteorological variations between times 

of observations.

 The analysis algorithms are described in CERES ATBD Subsystem 7.0, which can be found at 

http://eospso.gsfc.nasa.gov/atbd/cerestables.html.

Suggested Reading

Breigleb, B. P., and V. Ramanathan, 1982.

Brooks, D.R. et al., 1986.

Harrison, E. F. et al., 1988.

Minnis, P., and E. F. Harrison, 1984.

Smith, G. L. et al., 1995.

Wielicki, B. A. et al., 1998.

Young, D. F. et al., 1995a.

Aqua / CERES
Cloud and Aerosol Properties and Radiative Energy Fluxes • Aqua / CERES Data Products Handbook - Volume 2 105

CERES Synoptic Radiative Fluxes and Clouds 

Summary

Coverage: Global

Spatial/Temporal Characteristics: 1° region/

3-hour, monthly

Key Geophysical Parameters: Synoptic timeinterval clear-sky, total-sky fluxes at 

surface, atmosphere levels, and TOA, 

cloud parameters including cloud overlap, cloud pressure, cloud altitude, cloud 

temperature, optical depth, ice-water 

properties, ice-particle properties, liquidwater properties, photosynthetically 

active radiation

Processing Level: 3

Product Type: Standard, post-launch

Maximum File Size: 240 MB

File Frequency: 1/3-hour (3-hour Level 3), 

1/month (Monthly Level 3)

Primary Data Format: HDF

Browse Available: No

Additional Product Information:

http://eosweb.larc.nasa.gov/PRODOCS/

ceres/table_ceres.html

DAAC: NASA Langley Research Center

Science Team Contact:

 T. Wong

CERES Monthly Regional 

Radiative Fluxes and Clouds 

(AVG) and Monthly Zonal and 

Global Radiative Fluxes and 

Clouds (ZAVG)

Product Description

The Monthly Regional Radiative Fluxes and Clouds 

(AVG) product contains a month of space-and-timeaveraged CERES data for a single scanner instrument. 

The AVG is also produced for combinations of scanner instruments. The 1° regional flux at the hour of 

observation from the CERES SYN product and 

concurrent diurnal data from geostationary satellites 

are used to estimate the daily regional flux, which 

is averaged to yield the monthly average flux. Adjusted fluxes at the four atmospheric levels defined 

by the CERES CRS product are also estimated for 

both clear-sky and total-sky scenes. In addition, 

four cloud-height categories are defined by dividing 

the atmosphere into four intervals with boundaries 

at the surface, 700-, 500-, 300-hPa, and the top of 

the atmosphere. The cloud layers from SYN are put 

into one of the cloud-height categories and diurnally 

averaged. The cloud properties are also column averaged. The AVG also contains, for each region, the 

hourly average fluxes for the month and an overall 

monthly average.

 The Monthly Zonal and Global Radiative Fluxes 

and Clouds (ZAVG) product contains a month of 

space-and-time-averaged CERES data for a single 

scanner instrument. The ZAVG is also produced for 

combinations of scanner instruments. The space-andtime-averaged fluxes and cloud parameters on the 

CERES AVG product are spatially averaged from 1° 

regions to 1° zonal averages and a global monthly 

average. 

 A complete listing of parameters for these data 

products can be found in the CERES Data Products 

Catalog at http://asd-www.larc.nasa.gov/DPC/

DPC.html.

Research and Applications

These products will be used by meteorological researchers to study climate and improve global climate 

models. Zonal quantities are needed for studying energy transport since averaging on large spatial scales 

minimizes the effects of regional-scale anomalies in 

studying climate change and global dynamics. Global 

averages can be compared with other historical data 

sets derived from different regional scales to detect 

climate temperature trends and evaluate large-scale 
106 Data Products Handbook - Volume 2 • Cloud and Aerosol Properties and Radiative Energy Fluxes 

climate anomalies such as the effects of major volcanic eruptions.

Data Set Evolution

The product algorithm first calculates the means on a 

regional (1° equal-angle grid) basis from one month 

of synoptic maps from the SYN 3-hourly product. 

Regional means are then combined to obtain zonal 

and global averages. The main steps of the monthly 

averaging process are: 1) regionally sort the synoptically ordered data, 2) linearly average all flux data 

to produce monthly and monthly-hourly means, 

3) average the cloud properties using the proper 

weighting schemes, and 4) combine and average the 

regional means into zonal and global means. Once 

regional means are computed for all parameters and 

all regions, these means are combined into zonal and 

global means. Area-weighting factors are used to correct for the variation of grid-box size with latitude. 

 The analysis algorithms are described in CERES ATBD Subsystem 8.0, which can be found at 

http://eospso.gsfc.nasa.gov/atbd/cerestables.html.

Suggested Reading

Harrison, E.F. et al., 1990b.

Li, Z., and H. Leighton, 1993.

Wielicki, B. A. et al., 1998.

Young, D. F. et al., 1995b.

Aqua / CERES

CERES AVG and ZAVG Products Summary

Coverage: Global

Spatial/Temporal Characteristics: 1° region, 

1° zone, global/month

Key Geophysical Parameters: Averaged clearsky, total-sky surface, TOA, and standard 

CERES pressure-level fluxes; cloud parameters include: cloud overlap, pressure, 

temperature, optical depth, ice-particle 

properties, liquid-water properties, PAR

Processing Level: 3

Product Type: Standard, post-launch

Maximum File Size: 1188 MB

File Frequency: 1/month

Primary Data Format: HDF

Browse Available: No

Additional Product Information:

http://eosweb.larc.nasa.gov/PRODOCS/

ceres/table_ceres.html

DAAC: NASA Langley Research Center

Science Team Contact:

 T. Wong

(CERES Monthly Regional Radiative Fluxes and Clouds 

and Monthly Zonal and Global Radiative Fluxes and 

Clouds, continued)
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 2 107

CERES Monthly Gridded TOA/

Surface Fluxes and Clouds 

(SFC)

Product Description

The Monthly Gridded TOA/Surface Fluxes and 

Clouds (SFC) product contains a month of spaceand-time averaged CERES data for a single scanner 

instrument. The SFC is also produced for combinations of scanner instruments. All instantaneous 

shortwave, longwave, and window fluxes at the 

top of the atmosphere (TOA) and surface from the 

CERES SSF product for a month are sorted by 1° 

spatial regions, by day number, and by the local hour 

of observation. The mean of the instantaneous fluxes 

for a given region-day-hour bin is determined and 

recorded in the SFC product along with other flux 

statistics and scene information. These average fluxes 

are given for both clear-sky and total-sky scenes. The 

regional cloud properties are column averaged and 

are included in the SFC product.

 A complete listing of parameters for this data product can be found in the CERES Data Products Catalog 

at http://asd-www.larc.nasa.gov/DPC/DPC.html.

Research and Applications

This product is a necessary step in the transformation 

of raw CERES data to the radiation-budget outputs 

from product SRBAVG. The averaging and gridding 

operations performed on the footprint data serve to 

reduce variability by averaging to a 1-hour period 

and present the data in a uniform grid, which is 

required for temporal comparison of flux and cloud 

parameters.

Data Set Evolution

The parameters generated for this product are derived 

from the SSF product, which contains all the flux 

and cloud parameters. Each of the parameters from 

SSF is averaged to the 1° grid using equal weighting, 

which results in approximately 50 footprints being 

averaged for each geographic cell. These parameters 

are all inputs to the SRBAVG final radiation-budget 

product.

 The analysis algorithms are described in CERES ATBD Subsystem 4.6, which can be found at 

http://eospso.gsfc.nasa.gov/atbd/cerestables.html.

Suggested Reading

Barkstrom, B. R., and B. A. Wielicki, 1995.

Cess, R. et al., 1991.

Gupta, S. K. et al., 1992.

Gupta, S. K. et al., 1995.

Inamdar, A. K., and V. Ramanathan, 1994.

Inamdar, A. K., and V. Ramanathan, 1995.

Kratz, D. P., and Z. Li, 1995.

Li, Z. et al., 1993.

Wielicki, B. A. et al., 1998.

CERES Monthly Gridded TOA/Surface Fluxes 

and Clouds Summary

Coverage: Global

Spatial/Temporal Characteristics: 1° region/

hour

Key Geophysical Parameters: Total-sky, clearsky, and angular-model scene radiative fluxes (TOA and surface), cloud 

parameters, including cloud pressure, 

cloud altitude, cloud temperature, optical depth

Processing Level: 3

Product Type: Standard, post-launch

Maximum File Size: 615 MB

File Frequency: 18/month

Primary Data Format: HDF

Browse Available: No

Additional Product Information: 

http://eosweb.larc.nasa.gov/PRODOCS/

ceres/table_ceres.html

DAAC: NASA Langley Research Center

Science Team Contacts:

 D. F. Young

 D. P. Kratz

Aqua / CERES
108 Data Products Handbook - Volume 2 • Cloud and Aerosol Properties and Radiative Energy Fluxes 

CERES Monthly TOA/Surface 

Averages (SRBAVG)

Product Description

The Monthly TOA/Surface Averages (SRBAVG) 

product contains a month of space-and-time-averaged CERES data for a single scanner instrument. 

The SRBAVG is also produced for combinations of 

scanner instruments. The monthly average regional 

flux is estimated using diurnal models and the 1° 

regional fluxes at the hour of observation from the 

CERES SFC product. A second set of monthly average fluxes is estimated using concurrent diurnal information from geostationary satellites. These fluxes 

are given for both clear-sky and total-sky scenes and 

are spatially averaged from 1° regions to 1° zonal 

averages and a global average. For each region, the 

SRBAVG also contains hourly average fluxes for the 

month and an overall monthly average. The cloud 

properties from SFC are column averaged and are 

included on the SRBAVG.

 A complete listing of parameters for this data product can be found in the CERES Data Products Catalog 

at http://asd-www.larc.nasa.gov/DPC/DPC.html.

Research and Applications

The cloud properties, as a function of LW and SW 

radiative fluxes at TOA and the surface, are critical to 

cloud-forcing issues, and cloud properties as a function of liquid-water and ice-water volume are critical 

to cloud dynamics modeling.

Data Set Evolution

This product is obtained from the chain of processes which start with the 1B radiances and flow 

through products BDS, SSF, SFC, and the CERES 

Instrument Earth Scans (IES), ultimately producing 

the final averaged radiative flux and surface-radiation-budget (SRB) product. The regional fluxes are 

calculated using two different methods, which will 

help produce very stable, accurate long-term data 

sets and averages for use in detailed regional studies 

of radiation and clouds and interdisciplinary studies. 

This is a post-launch product. Currently, there is no 

adopted method available for producing total-sky 

surface LW flux from TOA flux. Data required for 

parameterization must be obtained from either Meteorological Ozone and Aerosol (MOA) atmospheric 

data sets or from CERES and ISCCP cloud properties. 

In addition, data used in this product are limited to 

days in which there is at least one observation. The 

cloud parameters are also averaged monthly and 

monthly-daily. The surface radiation budget is the 

net radiative flux at the surface and is computed 

using the hourly gridded TOA and surface fluxes 

from SFC, parameterized models, atmospheric profiles, cloud parameters, and surface temperatures 

and emissivities to produce the downward and net 

shortwave and longwave fluxes at the surface. The 

LW algorithm is based on parameterized equations 

developed expressly for computing surface LW fluxes 

in terms of meteorological parameters obtained from 

satellite and/or other operational sources.

 The analysis algorithms are described in CERES 

ATBD Subsystem 10.0, which can be found at 

http://eospso.gsfc.nasa.gov/atbd/cerestables.html.

Suggested Reading

Cess, R. et al., 1991.

Gupta, S. K. et al., 1992.

Harrison, E. F. et al., 1995.

Li, Z. et al., 1993.

Suttles, J. T., and G. Ohring, 1986.

Wielicki, B. A. et al., 1998.

Aqua / CERES
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 2 109

MODIS Aerosol Product 

(MOD 04)

Product Description

The MODIS Aerosol Product (MOD 04) monitors the ambient aerosol optical thickness over the 

oceans globally and over a portion of the continents. 

Further, the aerosol size distribution is derived over 

the oceans, and the aerosol type is derived over the 

continents. Daily Level 2 (MOD 04) data are produced at the spatial resolution of a 10 × 10 1-km (at 

nadir)-pixel array.

Research and Applications

Aerosols are one of the greatest sources of uncertainty in climate modeling. Aerosols modify cloud 

microphysics by acting as cloud condensation nuclei 

(CCN), and, as a result, impact cloud radiative properties and climate. Aerosols scatter back to space and 

absorb solar radiation. The MODIS aerosol product 

will be used to study aerosol climatology, sources 

and sinks of specific aerosol types (e.g., sulfates and 

biomass-burning aerosol), interaction of aerosols 

with clouds, and atmospheric corrections of remotely 

sensed surface reflectance over the land.

Data Set Evolution

Prior to MODIS, satellite measurements were limited to reflectance measurements in one (GOES, 

METEOSAT) or two (AVHRR) channels. There was 

no real attempt to retrieve aerosol content over land 

on a global scale. Algorithms had been developed 

for use only over dark vegetation. The blue channel 

on MODIS, not present on AVHRR, offers the possibility to extend the derivation of optical thickness 

over land to additional surfaces. The algorithms will 

use MODIS bands 1 through 7 and 20 and require 

prior cloud screening using MODIS data. Over the 

land, the dynamic aerosol models will be derived 

from ground-based sky measurements and used in 

the net retrieval process.

 Over the ocean, three parameters that describe the 

aerosol loading and size distribution will be retrieved. 

Pre-assumptions on the general structure of the size 

distribution are required in the inversion of MODIS 

data, and the volume-size distribution will be described with two log-normal modes: a single mode 

Aqua / CERES and MODIS

CERES Monthly TOA/Surface Averages 

Summary

Coverage: Global

Spatial/Temporal Characteristics: 1° region/

month

Key Geophysical Parameters: Averaged totalsky, radiative fluxes (TOA and surface), 

net surface radiative fluxes (surface radiation budget), averaged cloud parameters

Processing Level: 3

Product Type: Standard, post-launch

Maximum File Size: 615 MB

File Frequency: 18/month

Primary Data Format: HDF

Browse Available: No

Additional Product Information:

http://eosweb.larc.nasa.gov/PRODOCS/

ceres/table_ceres.html

DAAC: NASA Langley Research Center

Science Team Contact:

 D. F. Young
110 Data Products Handbook - Volume 2 • Cloud and Aerosol Properties and Radiative Energy Fluxes 

(MODIS Aerosol Product, continued)

Terra MODIS Image of a Massive Sandstorm blowing off the northwest African 

desert. This sandstorm blanketed hundreds of thousands of square miles of 

the eastern Atlantic Ocean on February 

29, 2000.

to describe the accumulation mode particles (radius 

<0.5 μm) and a single coarse mode to describe dust 

and/or salt particles (radius >1.0 μm). The aerosol 

parameters we therefore expect to retrieve are: the 

ratio between the two modes, the spectral optical 

thickness, and the mean particle size.

 The quality control of these products will be 

based on comparison with ground stations and climatology.

 The related MODIS Cloud Product ATBD, Algorithm for Remote Sensing of Tropospheric Aerosol 

from MODIS: Optical thickness over land and ocean 

and aerosol size distribution over the ocean, can be 

found in PDF format at http://eospso.gsfc.nasa.gov/

atbd/modistables.html.

Suggested Reading

Chu, D. A. et al., 1998.

Dubovik, O. et al., 2000.

Holben, B. N. et al., 1992.

Holben, B. N. et al., 1998.

Kaufman, Y. J., and C. Sendra, 1988.

Kaufman, Y. J., and L. A. Remer, 1994.

Kaufman, Y. J. et al., 1997a,b.

King, M. D. et al., 1992.

King, M. D. et al., 1999.

Rao, C. R. N. et al., 1989.

Remer, L. A. et al., 1996.

Tanré, D. et al., 1997.

MODIS Aerosol Product Summary

 

Coverage: Global over oceans, nearly global 

over land

Spatial/Temporal Characteristics: 10 km for

Level 2

Key Science Applications: Aerosol climatology, 

biomass-burning aerosols, atmospheric corrections, cloud radiative properties, climate 

modeling

Key Geophysical Parameters: Atmospheric aerosol optical depth (global) and aerosol size 

distribution (oceans)

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 11 MB 

File Frequency: 144/day

Primary Data Format: HDF-EOS

Browse Available:

http://modis-atmos.gsfc.nasa.gov/MOD04_

L2/sample.html

Additional Product Information:

http://modis-atmos.gsfc.nasa.gov/MOD04_

L2/index.html

Science Team Contacts:

Y. J. Kaufman

 D. Tanré

Aqua / MODIS
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 2 111

Aerosol Optical 

Thickness

0.0

0.8

0.4

0.2

0.6

0.0

5.0

3.0

2.0

4.0

1.0

0.0 0.5 1.0 1.5 2.0 2.5

0.0

0.5

1.0

1.5

2.0

2.5

Forest

Sunphotometer optical thickness

Retrieved optical thickness

Cerrado

λ = 0.66 m 

SCAR-B

RGB Composite

(2.19, 0.87, 0.65)

Aerosol Optical 

Thickness

Distance (km)

18.5 10 0 10 18.5

Distance (km)

18.5 10 0 10 18.5

0.6

Retrieved optical thickness

Sunphotometer optical thickness

0.0

0.1

0.2

0.3

0.4

0.5

0.0 0.1 0.2 0.3 0.4 0.5 0.6

λ = 0.55 m

TARFOX

Aerosol Optical 

Thickness

RGB Composite

(0.66, 0.55, 0.46)

Distance (km)

18.5 10 0 10 18.5

Distance (km)

0

100

150

125

25

50

75

Distance (km)

18.5 10 0 10 18.5

MODIS Airborne Simulator (MAS) Images and Corresponding Aerosol Thickness Retrievals for a 150 × 37-km section of flight 

during TARFOX (ocean scene) and SCAR-B (land scene). The bottom two panels are the validation of the retrieved aerosol optical 

thickness in comparison to sunphotometer observations at 0.55 μm (TARFOX; University of Washington C-131A measurements) 

and 0.66 μm (SCAR-B; ground-based AERONET measurements). (From King, M.D. et al., 1999.)

Aqua / MODIS
112 Data Products Handbook - Volume 2 • Cloud and Aerosol Properties and Radiative Energy Fluxes 

Sample Cloud Images from MODIS data on Terra, collected over South America on February 28, 2000. The left panel shows a 0.645 

μm image revealing both the upper level cirrus clouds and the lower level cumulus clouds. The middle panel shows the 1.38 μm channel image. The upper level cirrus clouds are seen clearly in this image. The right panel is an image similar to the one in the left panel, 

except that the cirrus clouds shown in the middle panel have been removed.

Uncorrected Image Cirrus Image (1.38 μm) Cirrus-Corrected Image

Aqua / MODIS

MODIS Cloud Product (MOD 06)

Product Description

The MODIS Cloud Product (MOD 06) combines 

infrared and visible techniques to determine both 

physical and radiative cloud properties. Daily global 

Level 2 (MOD 06) data are provided. Cloud-particle 

phase (ice vs. water, clouds vs. snow), effective cloudparticle radius, and cloud optical thickness are derived 

using the MODIS visible and near-infrared channel 

radiances. An indication of cloud shadows affecting 

the scene is also provided. Cloud-top temperature, 

height, effective emissivity, phase (ice vs. water, 

opaque vs. non-opaque), and cloud fraction are 

produced by the infrared retrieval methods both day 

and night at 5 × 5 1-km-pixel resolution. Finally, the 

MODIS Cloud Product includes the cirrus reflectance 

in the visible at the 1-km-pixel resolution, which is 

useful for removing cirrus scattering effects from the 

land-surface reflectance product.

Research and Applications

A thorough description of global cloudiness and 

its associated properties is essential to the MODIS 

mission for two reasons. First, clouds play a critical 

role in the radiative balance of the Earth and must be 

accurately described in order to assess climate and 

potential climate change accurately. In addition, the 

presence or absence of cloudiness must be accurately 

determined in order to retrieve properly many atmospheric and surface parameters. For many of these 

retrievals, cloud cover, even thin cirrus, represents 

contamination. Key radiative properties of clouds 

such as phase, optical thickness, and temperature 

may be retrieved using MODIS instruments with 

unprecedented resolution.

Data Set Evolution

The determination of cloud-top properties will require the use of MODIS bands 29 and 31-36, along 

with the cloud-mask product (MOD 35), to screen 

for clouds. In addition, NCEP or DAO global model 

analyses of surface temperature and pressure, profiles of temperature and moisture, and blended SST 

analyses will be required in the calculation of cloud 

forcing as a function of atmospheric pressure and 

emissivity. The Menzel cloud-phase algorithm will 

require MODIS bands 29, 31, and 32 and analyses 

of surface emissivity.

 The validation of cloud-top heights will be 

conducted through comparisons with stereo determinations of cloud heights from GOES and 

lidar estimates and aircraft observations of cirrus 

heights. Cloud emissivity will be compared to lidardetermined values. These interim products will be 

used in concert with field campaigns with the MAS 

instrument. The Menzel cloud-phase parameter will 

be validated using HIRS/AVHRR data and by comparison to the King cloud-phase parameter.

 The King cloud-phase algorithm requires product 

MOD 02, calibrated multispectral radiances. Cloudparticle size and optical thickness require these radiances plus the cloud-top parameters within MOD 06 
Cloud and Aerosol Properties and Radiative Energy Fluxes • Aqua / MODIS Data Products Handbook - Volume 2 113

and the Menzel cloud-phase parameter. In addition, 

these parameters require MODIS product MOD 43 

(surface reflectance) and the NCEP or DAO analyses and profiles described above. The validation and 

quality control of these products will be performed 

primarily through the use of in situ measurements 

obtained during field campaigns and with the use of 

the MAS instrument.

 The related MODIS Cloud Product ATBDs, MODIS: Cloud Top Properties and Cloud Phase and 

Cloud Retrieval Algorithms for MODIS: Optical 

Thickness, Effective Particle Radius, and Thermodynamic Phase, can be found in PDF format at http:

//eospso.gsfc.nasa.gov/atbd/modistables.html.

Suggested Reading

Gao, B. C. et al., 1998.

King, M. D. et al., 1992.

King, M.D. et al., 1996.

Nakajima, T. Y., and T. Nakajima, 1995.

Platnick, S. et al., 2000.

Strabala, K. I. et al., 1994.

Wylie, D. P., and W. P. Menzel, 1998.

MODIS Cloud Product Summary

Coverage: Global

Spatial/Temporal Characteristics: Resolutions of 

1 km or 5 km/once or twice per day (varies 

with parameter)

Key Science Applications: Cloud parameterization, climate modeling, climate monitoring, increasing accuracy of other MODIS 

retrievals

Key Geophysical Parameters: Cloud-particle 

phase (two algorithms), cloud-particle size 

and optical thickness, cirrus reflectance 

at 1.375 μm, and cloud-top temperature, 

emissivity, and height

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 65 MB 

File Frequency: 288/day

Primary Data Format: HDF-EOS

Browse Available: 

http://modis-atmos.gsfc.nasa.gov/MOD06_

L2/sample.html

Additional Product Information:

http://modis-atmos.gsfc.nasa.gov/MOD06_

L2/index.html

Science Team Contacts:

 M. D. King

 W. P. Menzel

 B. C. Gao
114 Data Products Handbook - Volume 2 Aqua / MODIS • Cloud and Aerosol Properties and Radiative Energy Fluxes 

(MODIS Cloud Product, continued)

Cloud Optical Thickness

Effective Cloud-Particle Radius

MODIS Retrieved Cloud Optical Thickness (unitless) and Effective Cloud-Particle Radius (in microns) for Tropical 

Cyclone Rosita shortly before it crossed the western Australian coastline at 0220 UTC on April 19, 2000. The images 

are derived from data from the visible and 2.1-μm channels on the Terra MODIS instrument.
Cloud and Aerosol Properties and Radiative Energy Fluxes • Aqua / MODIS Data Products Handbook - Volume 2 115

MODIS Level 3 Atmosphere 

Products (MOD 08)

Product Description

There are three MODIS Level 3 Atmosphere Products, each covering a different temporal scale. The 

Earth Science Data Type names (and time spans) 

for each of the products are: MOD08_D3 (Daily), 

MOD08_E3 (8-Day), MOD08_M3 (Monthly).

 Each of these Level 3 products contains statistics 

derived from over 50 science parameters from the 

Level 2 Atmosphere products: MOD04_L2, MOD05_

L2, MOD06_L2, and MOD07_L2.

 A range of statistical summaries is computed, 

depending on the Level 2 science parameter being 

considered. Statistics for a given Level 2 measurement might include:

• Simple (mean, minimum, maximum, standard 

deviation) statistics 

• Parameters of normal and log-normal distributions 

• Fraction of pixels that satisfy some condition (e.g., 

cloudy, clear)

• Histograms of the quantity within each grid box 

• Histograms of the confidence placed in each measurement

• Histograms and/or regressions derived from comparing one science parameter to another; statistics 

may be computed for a subset that satisfies some 

condition 

• Pixel counts

Statistics are sorted into 1° × 1° cells on an equalangle grid and then summarized over the globe. The 

equal-angle grid has a fixed dimension of 360×180 

pixels. It should be noted that three additional 

MOD 08 products, using the same temporal scales 

but summarized over an equal-area grid, will be 

available after launch.

 The daily product contains nearly 400 statistical 

summary parameters. The 8-day and monthly products, which are identical in format, include over 600 

statistical summary parameters.

 Additional information on the MODIS Level 

3 Atmosphere products can be obtained from the 

MODIS-Atmosphere web site at: http://modisatmos.gsfc.nasa.gov.

Research and Applications

Users should refer to the research applications of Level 2 Atmosphere products from MOD 04, MOD 05, 

MOD 06, and MOD 07 listed in this handbook. Level 

3 statistical products derived from Level 2 measurements lend themselves to longer-term time-series 

studies that help monitor variations in environmental 

conditions and aid research for assessing both natural 

and human-induced global change. 

Data Set Evolution

Definition of Time Spans

For the daily product (MOD08_D3), Level 2 granules 

that overlap any part of the data day (0000 to 2400 

UTC) are included in the computation of statistics. 

Therefore a particular Level 2 granule may be included in two consecutive MOD08_D3 products.

 The 8-day product (MOD08_E3) is computed by 

manipulating and summarizing the daily product 

over eight consecutive days. The running 8-day 

interval begins with the first day of MODIS data on 

Terra (February 25, 2000). The 8-day intervals for 

MODIS on Aqua will be the same 8-day intervals as 

for MODIS on Terra for the period of data overlap. 

It should be noted that the starting day of the 8-day 

interval is not reset to the first, during the rollover of 

a calendar month or calendar year.

 The monthly product (MOD08_M3) is computed 

by manipulating and summarizing the daily product 

over a calendar month.

Definition of Daily Statistics

For the daily product, assume that x1, x2, ..., xn represent the retrieved pixel values of a Level 2 parameter 

over a 1° × 1° grid box, Qi is the quality flag for each 

retrieved pixel value, and wi is the weighting factor 

(1 for the daily case), then the simple statistics are 

defined as:

In these equations, (1) will be referred to as the “regular” mean, (2) as the QA-weighted mean, (3) as the 

regular standard deviation, (4) as the QA-weighted 

Xd = Σi wi • xi

 Σi wi

n

n

XdQ = Σi Qi • xi

 Σi Qi

n

n

Xstd = Σ1 (wi • xi

 – wi • Xd)

2

 Σi wi

2

n

n

XstdQ = Σ1 (Qi • xi

 – Qi • XdQ)

2

 Σi Qi

2

n

n

Xmin = min (x1, x2,..., xn)

Xmax = max (x1, x2,..., xn)

(1)

(2)

(3)

(4)

(5)

(6)
116 Data Products Handbook - Volume 2 Aqua / MODIS • Cloud and Aerosol Properties and Radiative Energy Fluxes 

standard deviation, (5) as the minimum, and (6) as 

the maximum.

 The simple statistics also include daily log regular 

mean, log standard deviation, log QA mean, and log 

QA standard deviation. These log quantities are calculated as shown in equations (1-4), except that x1

, 

x2, ..., xn are replaced by their logarithms.

 Regression statistics, based on the pixels within 

each 1° × 1° grid cell, include the slope, intercept, 

mean squared error (MSE), and the coefficient of 

determination (R2).

 The histograms and joint histograms report the 

counts of the pixels falling into predetermined numerical intervals.

 The pixel counts are used to represent the number 

of pixels for the parameters that do not have QA flags, 

while the confidence-histograms-counts is used to 

represent the number of counts for each parameter 

that fall within each QA bin (e.g., good, very good, 

marginal, and total).

Definition of 8-day and Monthly Statistics

The 8-day and monthly statistics are based on the 

daily statistics with the assumption that the daily statistics from retrieved pixels can represent the statistics 

of the “populations” of each 1° × 1° grid cell. In other 

words, it is assumed that the samples composed of 

retrieved pixels can represent the “populations” composed of all the pixels within each grid cell.

 The simple statistics for 8-day and monthly quantities include mean, standard deviation, and minimum 

and maximum values of the corresponding daily 

means (i.e., daily mean, daily QA mean, daily log 

mean, and daily log QA mean).

 For example, if Xd1, Xd2, ...,Xdn are the daily means 

for a Level 3 parameter, then their monthly simple 

statistics (i.e., mean, standard deviation, minimum, 

and maximum) are represented by equations (1, 3, 5, 

and 6) with Xi replaced by Xdi. It should be noted that 

in the monthly case, the weights in equations (1) and 

(2) are taken either as unweighted (i.e., wi = 1), pixel 

weighted (e.g., weighted by the number of pixels over 

the total pixels), or fraction weighted (e.g., weighted 

by the cloud fraction).

 The fraction statistics represent the mean and 

standard deviation of the daily fraction values.

 The regression statistics, which include four components (slope, intercept, mean-squared-error, and 

the coefficient of determination), are calculated by 

using the daily mean values of the first parameter 

versus the daily mean values of the second parameter. 

Therefore, two daily mean time series are used to 

calculate monthly regression attributes.

 Histogram and joint-histogram statistics represent 

the number of times a parameter falls into each predefined interval.

 The total counts are reported. This is simply the 

summation of all the counts from each interval for 

the parameter.

 The 8-day and monthly product has an additional 

statistical parameter called “mean daily standard deviation.” This parameter is used to represent the mean 

values of the daily standard deviations, such as the 

mean of daily standard deviation, mean of daily QA 

standard deviation, mean of log standard deviation, 

and mean of log QA standard deviation. The standard 

deviation of the mean is used to describe the variation 

of a parameter around its monthly mean, while the 

mean of the standard deviation is used to describe the 

average daily variation of a parameter. For example, 

a parameter may have a large daily standard devia-

(MODIS Level 3 Atmosphere Products, continued)

Level 3 MODIS Global Atmospheric Mean Water Vapor below 850 mb (in cm) for June 6, 2000, derived from data 

from the MODIS instrument on Terra.
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 2 117

tion on each day, but those variations may be similar 

to each other. In this case, the mean daily standard 

deviation will be large, but its standard deviation will 

be small. 

 The related MODIS data product ATBDs can be 

found in PDF format at http://eospso.gsfc.nasa.gov/

atbd/modistables.html.

Suggested Reading

Ackerman, S. A. et al., 1998.

Gao, B. C., and A.F. H. Goetz, 1990.

Gao, B. C. et al., 1993a.

Gao, B. C. et al., 1998.

Green, R. O., and J. E. Conel, 1995.

Gustafson, G. B. et al., 1994.

Hayden, C. M., 1988.

Holben, B. N. et al., 1992.

Houghton, J. T. et al., 1984.

Jedlovec, G. J., 1987.

Kaufman, Y. J., and C. Sendra, 1988.

Kaufman, Y. J., and B. C. Gao, 1992.

Kaufman, Y.J., and L. A. Remer, 1994.

Kaufman, Y. J. et al., 1997a,b.

King, M.D. et al., 1992.

King, M. D. et al., 1996.

King, M. D. et al., 1998.

King, M. D. et al., 1999.

Kleepsies, T. J., and L. M. McMillan, 1984.

Ma, X.L. et al., 1984.

Nakajima, T. Y., and T. Nakajima, 1995.

Platnick, S. et al., 2000.

Prabhakara, C. et al., 1970.

Rao, C. R. N. et al., 1989.

Remer, L.A. et al., 1996.

Rossow, W. B., and L. C. Garder, 1993.

Saunders, R. W., and K. T. Kriebel, 1988.

Shapiro, M. A. et al., 1982

Smith, W. L., and F. X. Zhou, 1982.

Smith, W. L. et al., 1985.

Stowe, L. L. et al., 1991.

Strabala, K. I. et al., 1994.

Sullivan, J. et al., 1993.

Tanré, D. et al., 1997.

MODIS Level 3 Atmosphere Products 

Summary

 

Coverage: Global

Spatial/Temporal Characteristics: 1.0° latitudelongitude equal-angle grid/daily, 8-day, and 

monthly

Key Science Applications: Climate and ecosystem monitoring and modeling, cloud 

radiative properties, atmospheric properties, and atmospheric corrections

Key Geophysical Parameters: Aerosol properties, 

cloud radiative properties, atmospheric water vapor and temperature

Processing Level: 3

Product Type: Standard, at-launch

Maximum File Size: 411 MB (Daily 

Level 3), 713 MB (8-day and Monthly 

Level 3) 

File Frequency: 1/day (Daily Level 3), 1/8-day 

(8-Day Level 3), 1/month (Monthly

Level 3)

Primary Data Format: HDF-EOS

Browse Available: 

http://modis-atmos.gsfc.nasa.gov/

MOD08_E3/browse_main.html

Additional Product Information:

http://modis-atmos.gsfc.nasa.gov/

MOD08_E3/index.html

Science Team Contact:

 M. D. King

Aqua / MODIS
118 Data Products Handbook - Volume 2 • Cloud and Aerosol Properties and Radiative Energy Fluxes 

MODIS Cloud Mask for a difficult winter scene. The top two panels are MODIS Airborne Simulator (MAS) visible (0.66 μm) and 

infrared (11 μm) observations collected over Hudson Bay during the WINter Cloud Experiment (WINCE) on 6 February 1997. The 

cloud is very thin, nearly invisible in the infrared. Ice leads can be seen on the surface. The bottom left panel represents the results 

of a simple snow-mask algorithm that is applied as part of the cloud mask and aids in the final cloud-mask determination. The final 

product for this scene is shown in the bottom right panel, in which the lighter gray depicts clear conditions, white depicts clouds, and 

dark gray represents undecided conditions.

Cloud Mask Example - Thin Cloud Over Ice

MAS WINCE data over Hudson Bay 6 February 1997 - 19:49 UTC 

MAS .66 μm Image MAS 11μm Image

MAS Snow Mask Cloud Mask Result

Aqua / MODIS

MODIS Cloud Mask (MOD 35)

Product Description

The MODIS Cloud Mask product (MOD 35) is a 

daily, global Level 2 product generated at 1-km and 

250-m (at nadir) spatial resolutions. The algorithm 

employs a series of visible and infrared threshold 

and consistency tests to specify confidence that an 

unobstructed view of the Earthʼs surface is observed. 

An indication of shadows affecting the scene is also 

provided. The 250-m cloud-mask flags are based on 

the visible channel data only. Radiometrically accurate radiances are required, so holes in the Cloud 

Mask will appear wherever the input radiances are 

incomplete or of poor quality.

Research and Applications

A determination of the presence of global cloudiness 

is essential to the MODIS mission for two reasons. 

First, clouds play a critical role in the radiative balance of the Earth and must be accurately described to 

assess climate and potential climate change. Second, 

the presence of cloudiness must be accurately determined to retrieve properly many atmospheric and 

surface parameters. For many of these retrieval algorithms even thin cirrus represents contamination.

Data Set Evolution

The MODIS cloud-mask algorithm employs a battery 

of spectral tests, which use methodology applied for 

the AVHRR Processing scheme Over cLoud Land 
Cloud and Aerosol Properties and Radiative Energy Fluxes • Aqua / MODIS Data Products Handbook - Volume 2 119 

and Ocean (APOLLO), International Satellite Cloud 

Climatology Project (ISCCP), CLoud Advanced Very 

high resolution Radiometer (CLAVR), and Support of 

Environmental Requirements for Cloud Analysis and 

Archive (SERCAA) to identify cloudy FOVs. From 

these a clear-sky confidence level (high confident 

clear, probably clear, undecided, cloudy) is assigned 

to each FOV. For inconclusive results, spatial- and 

temporal-variability tests are applied. The spectral 

tests rely on radiance (temperature) thresholds in 

the infrared and reflectance thresholds in the visible 

and near-infrared. Thresholds vary with surface type, 

atmospheric conditions (moisture, aerosol, etc.), and 

viewing geometry. Along with MOD 02 calibrated 

radiances, a 1-km land/water mask, DEM, ecosystem 

analysis, snow/ice cover map, NCEP analysis of surface temperature and wind speed, and an estimate of 

precipitable water will be required as inputs.

 Cloud-mask validation will be conducted using 

MAS data from several field campaigns, all-sky 

cameras, and comparison with NOAA operational 

instruments and, possibly, Terra instruments such as 

ASTER (see Volume 1).

 The related MODIS Cloud Mask ATBD, Discriminating Clear Sky from Cloud with MODIS, can be 

found in PDF format at 

http://eospso.gsfc.nasa.gov/atbd/modistables.html.

Suggested Reading

Ackerman, S. A. et al., 1998.

Gao, B. C. et al., 1993a.

Gustafson, G. B. et al., 1994.

King, M. D. et al., 1992.

King, M. D. et al., 1998.

Rossow, W. B., and L. C. Garder, 1993.

Saunders, R. W., and K. T. Kriebel, 1988.

Stowe, L. L. et al., 1991.

MODIS Cloud Mask Summary

 

Coverage: Global

Spatial/Temporal Characteristics: 250 m and 

1 km/daily

Key Science Applications: Cloud determination 

and screening, climate modeling, climate 

monitoring, increasing accuracy of other 

MODIS retrievals

Key Geophysical Parameters: Presence of 

cloud or shadow

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 48 MB 

File Frequency: 288/day

Primary Data Format: HDF-EOS

Additional Product Information:

http://modis-atmos.gsfc.nasa.gov/

MOD35_L2/index.html

DAAC: NASA Goddard Space Flight Center

Science Team Contacts:

 W. P. Menzel

 S. A. Ackerman
120 Data Products Handbook - Volume 2 • Cloud and Aerosol Properties and Radiative Energy Fluxes 

affected by the presence of aerosols. Other EOS 

sensors like MODIS and MISR (on Terra) require 

SAGE III measurements of stratospheric aerosols 

to achieve optimal performance. 

Clouds play a major role in determining the planetʼs 

solar and longwave energy balance and, thus, are important in governing the Earthʼs climate. SAGE III 

will provide measurements of mid- and high-level 

clouds including thin or “sub-visual” clouds that are 

not detectable by nadir-viewing passive remote sensors. These observations are important because:

• While low clouds primarily reflect incoming solar radiation back into space (acting to cool the 

planet), mid- and high-level clouds enhance the 

“greenhouse” effect by trapping infrared radiation 

(acting to warm the planet).

• Polar Stratospheric Clouds play a significant role 

in heterogeneous chemical processes that lead to 

stratospheric ozone destruction. The presence of 

thin clouds near the tropopause may also contribute 

to ozone loss in high and mid-latitudes.

Clouds play an important role in feedback processes 

(such as changes in altitude or amount of cloud) 

associated with climate change. Currently, neither 

the sign nor magnitude is known for the climatic 

impact of cloud response to changes in radiative 

processes brought about by human-caused increasing levels of greenhouse gases (like carbon dioxide) 

and aerosols.

Suggested Reading

IPCC, 1996.

Solomon, S. et al., 1997.

Stolarski, R. S., and H. L. Wesoky, 1993.

SAGE III Aerosol and Cloud Data 

Products (SAGE III 01, 02) 

Product Description

SAGE III Aerosol and Cloud Data Products 

(SAGE III 01 and 02 at Level 2) include aerosol 

extinction at eight wavelengths (385, 448, 521, 676, 

754, 868, 1019, and 1537 nm) from the ground or 

opaque cloud tops to an altitude of approximately 40 

km. Stratospheric optical depth is reported for each 

channel for which the profile base is at or below the 

tropopause. In addition, the presence of optically 

thin clouds is inferred based upon the wavelength 

dependence of aerosol extinction. These data are produced for each sunrise or sunset encountered by the 

spacecraft. Aerosol extinction profiles are determined 

as a residual following the clearing of previously determined contributions by molecular scattering and 

absorption by ozone and nitrogen dioxide.

Research and Applications

Aerosols play an essential role in the radiative and 

chemical processes that govern the Earthʼs climate. 

Since stratospheric aerosol loading has varied by a 

factor of 30 since 1979, long-term monitoring of 

tropospheric and stratospheric aerosols is crucial. 

SAGE III aerosol measurements will provide important contributions in the following areas: 

• Research demonstrates that the long-term variability of aerosol abundance strongly modulates the 

rate of ozone destruction in the lower stratosphere 

(Solomon et al., 1997). 

• The impact of aircraft exhaust on ozone is strongly 

dependent on the abundance and properties of the 

ambient aerosol. The future of high-speed, stratospheric air travel is, in part, dependent on improved 

understanding of aerosol properties (Stolarski and 

Wesoky, 1993).

• Aerosol radiative forcing is the largest unknown 

in current climate models and, as a result, in predicting future climate. Accurate vertically resolved 

measurements of aerosol optical properties are an 

important element of improved climate prediction 

(IPCC, 1996).

• Observations from space of many geophysical 

properties like sea-surface temperature, vegetation, 

and atmospheric trace-gas species can be adversely 

Meteor 3M / SAGE III
Cloud and Aerosol Properties and Radiative Energy Fluxes • Data Products Handbook - Volume 2 121 

SAGE III Aerosol and Cloud Data Products 

Summary

Coverage: 30 solar events per day, 15 northern 

high latitude, 15 southern high latitude

Spatial/Temporal Characteristics: 0.5 km vertical 

resolution

Key Science Applications: Aerosol trends, global 

change

Key Geophysical Parameters: Extinction (km-1)

Processing Level: 2

Product Type: Research, at-launch

Maximum File Size: 0.78 MB

File Frequency: 1/month

Primary Data Format: HDF-EOS, Big Endian 

IEEE Binary

Browse Available: Yes

Additional Product Information: 

http://eosweb.larc.nasa.gov/PRODOCS/

sage3/table_sage3.html

DAAC: NASA Langley Research Center

Science Contact:

 L. W. Thomason

Meteor 3M / SAGE III
122 Data Products Handbook - Volume 2 • Cloud and Aerosol Properties and Radiative Energy Fluxes

TOMS Aerosol Product

Product Description

The QuikTOMS aerosol index is formed directly 

from the measured radiances in two TOMS channels. 

Generally, positive values represent UV-absorbing 

aerosols while negative values represent non-absorbing aerosols. The two most important UV-absorbing aerosols are the silicate dust that originates 

in the Sahara each year and smoke from extensive 

agricultural burning in Africa and South America. 

Non-absorbing aerosols like sulfate particles from 

industrial pollution sources or from volcanoes are 

also seen. The identification is not perfect because 

of geophysical reasons (e.g., aerosol too low to the 

ground, or a mixture of absorbing and non-absorbing 

aerosols). Aerosol optical depths and single-scattering 

albedos can be calculated from the aerosol index, but 

the accuracy of the result is sensitive to the assumed 

parameters, particularly the height of the aerosol 

layer. Data coverage is the same as for the TOMS 

ozone product.

TOMS Aerosol Product Summary

Coverage: Global maps (90°S to 90°N)

Spatial/Temporal Characteristics: Gridded at 

 1° latitude × 1.25° longitude/daily

Wavelengths: Aerosol index derived from 

331-nm and 360-nm backscattered radiances

Processing Level: 3

Product Type: Standard, at-launch

Maximum File Size: 0.16 MB

File Frequency: 1/day

Primary Data Format: ASCII

Browse Available: Yes

Additional Product Information:

http://quiktoms.gsfc.nasa.gov

DAAC: NASA Goddard Space Flight Center

Science Team Contacts:

 J. R. Herman

 R. D. McPeters

Smoke and Dust over North America on June 20, 1998, from Earth Probe TOMS data. Earth Probe TOMS 

detected smoke from the fires in Florida and desert dust from northern Africa advecting westward towards 

North America using the TOMS aerosol index. This image was created by compositing 2 days of data, with 

the majority of the data coming from June 20, 1998. The Aerosol data product containing measurements 

of UV-absorbing tropospheric aerosols from four NASA TOMS instruments (the TOMS on Nimbus-7 and 

Meteor 3M, using measured 340-nm and 380-nm radiances, and the TOMS on ADEOS and Earth Probe 

TOMS, using the 331-nm and 360-nm wavelength channels) can be used to generate daily global maps of 

these particles. See http://toms.gsfc.nasa.gov/ for more information. 

Earth Probe TOMS

Smoke/Dust over North America for June 20, 1998.

Aerosol Index

0.7 1.2 1.7 2.2 2.7 3.2 3.7 4.2

QukTOMS / TOMS
123

Atmospheric

Chemistry

Aqua

• AIRS

• AMSU-A

• HSB

Jason-1

• Poseidon-2

Meteor 3M • SAGE III

QuikTOMS • TOMS
124 Data Products Handbook - Volume 2 • Atmospheric Chemistry 

Atmospheric Chemistry – An 

Overview

Relationship to Global Change Issues

The distribution of chemical constituents in the 

atmosphere plays an important role in the radiative 

balance of the Earth-atmosphere system and in the 

long-term stability of our environment. Human activities involving combustion of fossil fuels and the 

use of chlorofluoromethane compounds (chlorofluorocarbons-CFCs) during the last century have led to 

drastic changes in the chemical composition of the 

Earthʼs atmosphere. Current estimates assuming continuous levels of human activities well into the 21st 

century indicate the potential for significant future 

global changes. It is well understood that the increases 

of greenhouse gases such as CO2, N2O, CH4, H2O, 

and others could lead to significant global warming. 

Similarly, the increased accumulation of CFCs has 

led to the formation of the northern and southern 

polar ozone “holes” with subsequent global ozone 

depletion.

 Ozone is an important greenhouse gas and is at 

the same time critical to our environment. Stratospheric ozone, which accounts for 90% of the total 

atmospheric ozone, shields us from harmful solar UV 

radiation. Tropospheric ozone in the mid- and uppertroposphere is an important greenhouse gas. In addition, tropospheric ozone in high concentration near 

the Earthʼs surface is a health hazard. Studies of the 

Antarctic ozone hole have determined that ice clouds 

in the stratosphere (Polar Stratospheric Clouds, PSCs) 

are responsible for the rapid destruction of stratospheric ozone during Antarctic springtime. Similar 

types of ozone-destroying chemical reactions have 

also been found to occur with volcanic aerosols after 

major eruptions such as that of the Mount Pinatubo 

volcano in 1991.

 The distribution of ozone in the atmosphere is 

determined by the concentration of the major reacting chemical families such as Ox

, NOx, CLOx, and 

HOx. Aerosols in the stratosphere, either in the form 

of PSCs or volcanic aerosols, also provide reacting 

surface areas for heterogeneous reactions to occur 

that affect the local ozone concentration. Finally, the 

ozone concentration also depends on the sources and 

sinks for ozone, the different reacting chemicals, and 

the dynamical circulation of the atmosphere, which 

continuously controls the redistribution of all the 

chemical species in the atmosphere. To understand 

fully the behavior of ozone in the atmosphere, longterm space-based observation of the different species involved in ozone chemistry, and the associated 

dynamical circulation of the atmosphere, will be 

required.

 In the Aqua time frame, atmospheric chemistry 

measurements made by the different instruments 

will be focused on long-term global ozone behavior, 

with particular emphasis on the Arctic ozone. Latest 

data from the UARS instruments have indicated that 

atmospheric chlorine concentrations have leveled off 

from the continuous increases during the last few 

decades because of the Montreal Protocolʼs success 

in restricting the use of CFCs. Simple chemistry 

would indicate that the recovery of the ozone should 

be occurring. However, current model simulation 

studies have suggested that with the increase in the 

concentration of greenhouse gases, the recovery of 

the global ozone potentially could be delayed, with 

ozone depletion developing in the Arctic regions. 

 Global ozone data will be provided by the SAGE 

III, TOMS, and AIRS/AMSU-A/HSB instruments. 

SAGE III will provide additional measurements 

of stratospheric and upper tropospheric aerosols, 

PSCs, H2O, NO2, NO3, and OClO. TOMS on QuikTOMS will also provide tropospheric aerosol data. 

AIRS/AMSU-A/HSB will additionally provide CO 

and CH4 data. The above data sets will be used to 

monitor global ozone distribution, study Arctic ozone 

behavior, and provide global mapping of tropospheric 

ozone and aerosol distributions. 

 A more complete study of detailed ozone chemistry 

and the associated atmospheric dynamics will be possible using data sets to be acquired during the Aura 

(the former EOS Chemistry mission) time frame. 

Instruments such as HIRDLS, MLS, and OMI will 

provide detailed measurements of the various chemical species in the atmosphere and a detailed mapping 

of the dynamical circulation of the atmosphere. The 

TES instrument will provide measurements on most 

of the species involved in tropospheric ozone chemistry.

Product Overview

The data products described in this chapter are 

produced by the Meteor 3M/SAGE III mission, the 

QuikTOMS mission, the Poseidon-2 instrument on 

the Jason-1 spacecraft, and the AIRS/AMSU-A/HSB 

instrument triplet on the Aqua spacecraft. Ozone profile measurements will be provided by the SAGE III 

instrument on the Meteor 3M spacecraft, extending 

in altitude from the upper troposphere to the lower 

mesosphere. The SAGE III solar occultation measurements will be restricted to measurement sites 

near high latitudes in both northern and southern 

Overview
Atmospheric Chemistry • Data Products Handbook - Volume 2 125

hemispheres. SAGE III lunar measurements of ozone 

will have measurement sites at all latitudes.

 QuikTOMS and AIRS/AMSU-A/HSB will provide 

total-ozone mapped products. The SAGE III ozoneprofile data and the QuikTOMS and AIRS/AMSUA/HSB total-ozone data will be used to continue the 

long-term global data base of ozone profiles and total 

burden. 

 For the Meteor 3M/SAGE III mission, half of the 

solar occultation measurements will be located in 

the Arctic region. The SAGE III OClO, NO2, NO3, 

and PSC measurements together with the total ozone 

measurements from QuikTOMS and AIRS/AMSUA/HSB can be used to monitor the buildup of ozone 

destruction and recovery over the Arctic spring. The 

data can also be used to study the long-term changes 

of the Arctic ozone as the reversal of the buildup of 

atmospheric chlorine is in progress. 

 Higher in the atmosphere, the total electron content 

of the ionosphere will be derived from data of the 

Poseidon-2 radar altimeter. These data will provide a 

synoptic description of the ionosphere every 9.9 days 

and will be used to study the spatial and temporal 

variability of the ionosphere and its geomagnetic 

characteristics.

Product Interdependency and Continuity 

with Heritage Data

Both Meteor 3M/SAGE III and QuikTOMS are 

continuations of previous missions extending a heritage of ozone data products dating back more than 

two decades. SAGE III ozone-profile data products 

will be generated using algorithms similar to those 

used in the previous two SAGE missions, SAGE I 

and SAGE II. QuikTOMS ozone products will be 

produced using an algorithm similar to the one used 

for the long line of TOMS missions, beginning with 

the Nimbus 7 TOMS and extending to the Meteor 3 

TOMS, ADEOS TOMS, and the current Earth Probe 

TOMS. The new ozone data sets produced by continuing past missions will be invaluable for long-term 

trend studies, and analysis of the long-term ozone 

trends will provide better understanding of the natural 

and human-induced changes occurring in atmospheric 

ozone levels.

Overview

Suggested Reading

Anderson, J. et al., 2000.

Shinell, D. et al., 1998.

Solomon, S. et al., 1992.

Solomon, S. et al., 1996.

World Meteorological Organization (WMO), 1992.

World Meteorological Organization (WMO), 1995.
126 Data Products Handbook - Volume 2 • Atmospheric Chemistry 

AIRS/AMSU-A/HSB Trace 

Constituent Product (AIR 03)

Product Description

The Trace Constituent Product is included in the 

Level 2 retrieval data granules and is geolocated to 

an AMSU-A footprint.

 A number of spectral features resolved by AIRS are 

due to strong absorption lines of trace constituents. 

The strength of these features depends on the vertical distribution of these gases and other factors. 

Examples of trace constituents that will be retrieved 

as research products are:

• CH4 total burden (spatial resolution at the Earthʼs 

surface: 50-250 km)

• CO total burden (spatial resolution at the Earthʼs 

surface: approximately 50 km)

The accuracy of measuring these atmospheric constituents in the course of the retrieval of standard 

products will be evaluated during the first year of 

flight. The expected accuracy of the CO total burden, 

approximately 10%, is quite good compared to CO 

variability, whereas the expected accuracy of the CH4

burden is not as good compared to its variability.

AIRS/AMSU-A/HSB Trace Constituent Product 

Summary

Coverage: Global

Spatial/Temporal Characteristics: 40.6 km FOV 

at nadir combined as required to coarser 

resolution to achieve greater signal to noise/

twice daily (daytime and nighttime)

Wavelengths: Infrared

Processing Level: 2

Product Type: Research, post-launch

Maximum File Size: 5 MB

File Frequency: 240/day

Primary Data Format: HDF-EOS

Browse Available: Yes

Additional Product Information:

http://www-airs.jpl.nasa.gov/

DAAC: NASA Goddard Space Flight Center

Science Team Contact:

 L. L. Strow

AIRS/AMSU-A/HSB Ozone 

Product (AIR 08)

Product Description

The Ozone Product is included in the Level 2 retrieval data granules and is geolocated to an AMSU-A 

footprint.

 Ozone profiles and burden are derived from AIRS 

infrared radiances within an AMSU-A footprint. The 

rms error of total ozone burden is expected to be 

±10%.

Derived ozone product is:

• O3 concentration profile (in Dobson Units for 3-5 

layers)

• Total O3 burden (Dobson Units)

AIRS/AMSU-A/HSB Ozone Product Summary

Coverage: Global

Spatial/Temporal Characteristics: 40.6 km FOV 

at nadir/twice daily (daytime and nighttime)

Wavelengths: Infrared

Processing Level: 2

Product Type: Research, at-launch

Maximum File Size: 5 MB

File Frequency: 240/day

Primary Data Format: HDF-EOS

Browse Available: Yes

Additional Product Information:

http://www-airs.jpl.nasa.gov/

DAAC: NASA Goddard Space Flight Center

Science Team Contacts:

L. L. Strow

J. Susskind

Aqua / AIRS/AMSU-A/HSB
Atmospheric Chemistry • Jason-1 / Poseidon-2 Data Products Handbook - Volume 2 127

Suggested Readings

Codrescu, M. V. et al., 1999.

Forbes, J. M. et al., 1997.

Imel, D., 1994.

Vladimer, J. A. et al., 1999.

Poseidon-2 Total Electron Content Summary

Coverage: Ocean areas from 66°S to 66°N 

Spatial/Temporal Characteristics: 6.2-km resolution along-track every 9.9 days [315.

cos 

(latitude) km longitudinal track spacing]

Key Science Applications: Variability and climatology of the ionosphere

Key Geophysical Parameters: Total electron 

content of the ionosphere

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 1.5 MB

File Frequency: 25/day

Primary Data Format: Binary

Browse Available: No

Additional Product Information: 

http://www-aviso.cnes.fr

DAAC: Jet Propulsion Laboratory

Science Team Contacts:

 L.-L. Fu

Y. Menard

Poseidon-2 Total Electron 

Content 

Product Description

This Level 2 product provides the measurement of 

the total electron content (TEC) of the ionosphere (in 

electrons/m2) made by the Poseidon-2 radar altimeter 

on board Jason-1. The measurement is repeated every 9.9 days along a set of fixed ground tracks. The 

satellite makes 127 revolutions of the Earth every 

9.9 days. The coverage of the measurement is limited from 66°S to 66°N due to the 66° inclination of 

the satelliteʼs orbit plane. The longitudinal distance 

between the ground tracks is [315. cos(latitude)]km, 

i.e., 315 km at the equator, 223 km at 45°, and 128 

km at 66°. Two kinds of product are available: The 

Interim Geophysical Data Record (IGDR) is available 

3 days after the data collection, while the GDR, based 

on a more precisely determined orbit, is available 30 

days after the data collection.

Research and Applications

The TEC measurements provide a synoptic description of the ionosphere on global scales (although 

restricted to 66°S - 66°N) every 9.9 days. This information is useful for the study of the spatial and 

temporal variability of the ionosphere and its geomagnetic characteristics. For example, studies based 

on the TOPEX/Poseidon data have been performed 

on the longitude structure of the ionosphere F region, 

including the quasi 2-day oscillation. Climatologies 

of the TEC have also been produced. TEC is also a 

useful indicator for the geomagnetic activities that 

influence telecommunication.

Data Set Evolution

The radar range between the spacecraft and sea 

surface is measured by the radar altimeter at two 

frequencies: 13.575 and 5.3 GHz. The differences in 

the measurements between the two channels are used 

to compute the range delay of the radar signals caused 

by the ionospheric free electrons as well as the TEC. 

The effects of other factors causing range errors, such 

as the sea-state biases, are corrected for before the 

computation of the TEC. The primary heritage for the 

Poseidon-2 TEC product is the corresponding product 

derived from the TOPEX/Poseidon dual-frequency 

altimeter, launched in 1992.
128 Data Products Handbook - Volume 2 • Atmospheric Chemistry 

SAGE III NO2, NO3, O3, and OClO Data 

Products Summary

Coverage: 30 solar events per day, 15 

northern high latitude, 15 southern high 

latitude; up to 30 lunar events per day 

ranging over all latitudes

Spatial/Temporal Characteristics: 0.5 km vertical resolution for species derived from 

solar occultation events and 1 km vertical 

resolution for species derived from lunar 

occultation events

Key Science Applications: Long-term trends 

and interannual variability in ozone, nitrogen dioxide; ozone-loss studies incorporating observations of reactive chlorine, 

reactive nitrogen, aerosols, and PSCs; 

chemical studies of reactive nitrogen in 

the middle stratosphere

Key Geophysical Parameters: Vertically resolved density profiles of ozone, nitrogen 

dioxide, nitrogen trioxide, and chlorine 

dioxide

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 0.05 MB

File Frequency: 196/week

Primary Data Format: HDF-EOS, Big Endian 

IEEE Binary Binary

Browse Available: Yes

Additional Product Information: 

http://eosweb.larc.nasa.gov/PRODOCS/

sage3/table_sage3.html

DAAC: NASA Langley Research Center

Science Team Contact:

 J. M. Zawodny

Meteor 3M / SAGE III

SAGE III NO2, NO3, O3 , and 

OClO Data Products (SAGE III 

04, 05, 06, 07) 

Product Description

The SAGE III reactive species profiles consist of the 

vertical density profiles of nitrogen dioxide (SAGE III 

04), nitrogen trioxide (SAGE III 05), ozone (SAGE 

III 06), and the chlorine dioxide dimer (SAGE III 07). 

The ozone and nitrogen dioxide products are produced 

during both solar- and lunar- occultation events, while 

the nitrogen trioxide and chlorine dioxide species, 

which exist in measurable quantities only at night, 

are produced for lunar-occultation events only. Solar 

Level 2 products are produced with a 0.5-km vertical 

resolution, while lunar Level 2 products are produced 

with a 1-km vertical resolution. Ozone is retrieved 

in the altitude range from 6 km to 85 km, NO2 from 

the tropopause to 50 km, NO3 from 20 km to 55 km, 

and OClO in the lower stratosphere wherever it is 

sufficiently enhanced (winter polar vortex).

 These products are measured using the Occultation 

Technique where the spectral extinction of the atmosphere is observed in the 290-1500 nm region. The 

unique spectral signature of each of these species is 

used to determine the slant-path-column abundance 

along the line of sight. This slant-column abundance 

profile is then inverted to obtain a vertical-density 

profile.

Research and Applications

The calibration insensitivity of the occultation technique allows SAGE III species measurements to be 

ideally suited for monitoring the atmosphere for longterm changes or trends. Like the predecessor SAGE I 

and II measurements, SAGE III results will be useful 

in measuring the recovery of stratospheric ozone levels as anthropogenic perturbations to the chemistry of 

the atmosphere recover to normal levels in response 

to action by the international community.

 The high vertical resolution of the measurements 

will also be beneficial to the modeling community. 

In addition to ozone, SAGE III also measures water 

vapor, aerosol extinction, pressure, and temperature. 

Over suitable time scales these can be used as dynamical tracers in modern analyses using transformation to potential vorticity coordinates. This allows 

the relatively sparse direct coverage to be greatly 

expanded with the addition of dynamical observations.
Atmospheric Chemistry • Data Products Handbook - Volume 2 129

TOMS Ozone Product

Product Description

QuikTOMS measurements of backscattered ultraviolet sunlight are used to derive total-column ozone. 

QuikTOMS is in a polar sun-synchronous orbit with 

approximately 10:30 AM local equator crossing time. 

TOMS scans to the left and right of the orbital track to 

give complete coverage of the sunlit Earth on a daily 

basis. The instrument FOV at nadir is approximately 

40 km square. The data are averaged into a 1°-latitude 

by 1.25°-longitude uniform grid.

 Total column ozone is derived using an algorithm 

identical to the one used for the Nimbus 7 and Earth 

Probe TOMS instruments. A radiative-transfer model 

is used to calculate backscattered radiances as a function of total ozone, latitude, viewing geometry, and 

reflecting surface conditions. Total column ozone 

can then be derived by comparing measured radiances with theoretical radiances calculated for the 

conditions of the measurement and finding the value 

of ozone that gives a computed radiance equal to the 

measured radiance.

TOMS Ozone Product Summary

Coverage: Global maps (90°S-90°N)

Spatial/Temporal Characteristics: Gridded at 

1° latitude × 1.25° longitude/daily

Wavelengths: Ozone is derived by differential absorption from wavelength 

pairs: A 312 /331 nm, B 317/331 nm, 

C 323/331 nm

Processing Level: 3

Product Type: Standard, at-launch

Maximum File Size: 0.16 MB

File Frequency: 1/day

Primary Data Format: ASCII

Browse Available: Yes

Additional Product Information: 

http://quiktoms.gsfc.nasa.gov

DAAC: NASA Goddard Space Flight Center

Science Team Contact:

 P. K. Bhartia

QuikTOMS / TOMS
131

Atmospheric

Temperatures

Aqua

• AIRS

• AMSU-A

• HSB

• MODIS

Meteor 3M • SAGE III
132 Data Products Handbook - Volume 2 • Atmospheric Temperatures 

Atmospheric Temperatures 

– An Overview

Relationship to Global Change Issues

The atmosphere exhibits significant variations in 

temperature and pressure with altitude. In fact, the 

standard layers of the atmosphere are defined based 

on their vertical temperature profiles. The lowest 

10-15 km of the atmosphere, where temperatures 

predominantly decrease with height, is called the 

troposphere, and the region from altitudes of about 

15-50 km, where temperatures increase or stay steady 

with height, is called the stratosphere. The boundary 

between the troposphere and the stratosphere is the 

tropopause.

 Upper-air temperature is an essential atmospheric 

parameter that plays a major role in determining the 

global circulation of the atmosphere and directly 

influences the hydrological cycle. Thus, upper-air 

temperature is a key input to global climate models. 

Tropospheric and stratospheric temperatures are 

central to the problem of climate warming both because tropospheric temperatures are the atmospheric 

temperatures of most immediate importance to life 

at the surface and because general circulation models (GCMs) predict that temperature changes with 

enhanced concentrations of greenhouse gases will 

have a characteristic profile, with warming in the 

troposphere and cooling in much of the stratosphere. 

Cooler stratospheric conditions are an expected consequence of the increased trapping of terrestrial radiation in the troposphere. Accurate knowledge of the 

fluctuations in stratospheric temperature on a global 

scale is also necessary to understand the recovery of 

the ozone layer, as most ozone depletion occurs in 

the lower portion of the stratosphere (15-25 km). 

 Three-dimensional wind and temperature fields 

are the highest priority data for understanding atmospheric dynamics and initializing forecast models. 

Prior to satellite observations, characterizing the 

global temperature distribution was greatly hindered 

by notable gaps over the oceans in the surface-based 

observation network. With satellites, those gaps are 

now filled, although a problem that remains even 

with satellites is the difficulty in inferring sharp 

temperature inversions.

 Even in the absence of complete wind observations, 

model-based data-assimilation systems can provide 

dynamically consistent and statistically optimal 

global data sets at frequent time intervals. The dataassimilation system organizes the observations from 

many diverse instrument sources at different locations and times into a single consistent data product, 

complements the observations by propagating information from observed into unobserved regions and 

times, and supplements the observations by providing 

estimates of quantities that are difficult or impossible 

to measure directly.

 Significant improvement in short-term weather 

forecasting skill is expected as the temperature profile data used as inputs to the weather forecast models 

improve well beyond the accuracy of present infrared 

sounders, which is about 2.5 K at a vertical resolution 

on the order of 5 km.

Product Overview

The AIRS/AMSU-A/HSB complement on the Aqua 

spacecraft will provide atmospheric temperature 

profiles, available globally, twice per day, under all 

cloud conditions in 50-km ground footprints. The 

anticipated accuracy of the temperature profiles is 

given as a global root-mean-square (rms) of 1 K per 

1-km thick layers, matching that of radiosondes. This 

accuracy should lead to improved climate modeling 

and a substantial increase in the mid- and long-range 

weather forecast skill. AIRS/AMSU-A/HSB data will 

be assimilated operationally within 3 hours into the 

forecast GCM by the weather prediction centers in the 

U.S. (National Centers for Environmental Prediction, 

NCEP) and Europe (European Center for MediumRange Weather Forecasts, ECMWF). 

 MODIS Atmospheric Profiles (MOD 07, 08) will 

provide atmospheric temperature information, as well 

as atmospheric stability, water vapor, and total ozone 

burden. Four assimilated products will be available 

in the Terra/Aqua timeframe. The SAGE III Temperature Products (SAGE III 08) will provide profiles 

of temperature and pressure derived from measurements of the oxygen A-band absorption feature near 

762 nm.

Product Interdependency and Continuity 

with Heritage Data

Atmospheric temperature profiles have been obtained 

from several satellite instruments, most relevantly the 

Tiros Operational Vertical Sounder (TOVS) system 

on NOAA operational satellites since 1979 and the 

Geostationary Operational Environmental Satellite 

(GOES) VISSR Atmospheric Sounder since 1978. 

The TOVS system contains a High Resolution Infrared Radiation Sounder (HIRS and the follow-on 

HIRS2) and a microwave sounder, the latter originally 

as a 4-channel Microwave Sounding Unit (MSU) and 

subsequently as a 20-channel Advanced Microwave 

Sounding Unit (AMSU) on NOAA-K. The HIRS2/

Overview
Atmospheric Temperatures • Data Products Handbook - Volume 2 133

MSU temperature data have been assimilated into 

numerical weather forecast models with significant 

improvements in forecast skill. Similar assimilation 

of the AIRS/AMSU-A/HSB temperature and humidity data into forecast models, along with surface 

winds from QuikScat, is critical to the full utilization 

of these data.

Suggested Reading

Daley, R., 1991.

Goldberg, M. D., and L. M. McMillin, 1999.

McMillin, L. M. et al., 1995.

Pfaendtner, J. et al., 1995.

Schubert, S. D. et al., 1993.

Strow, L. L. et al., 1998.

Suarez, M. et al., 1994.

Susskind, J. et al., 1998.

Overview
134 Data Products Handbook - Volume 2 • Atmospheric Temperatures 

AIRS/AMSU-A/HSB 

Atmospheric Temperature 

Product (AIR 07)

Product Description

The AIRS Atmospheric Temperature Product is 

a vertical profile of temperature geolocated to an 

AMSU-A footprint and is included in the Level 2 

retrieval data granules.

 The product consists of temperatures at discrete 

pressures between the surface and 1 mb. Combined 

IR/MW temperature retrievals are expected to have 

an accuracy of better than l K in the rms sense for 

layer mean temperature in each 1-km layer between 

the surface and 200 mb, each 2-km layer between 200 

mb and 100 mb, and each 4-km layer from 100 mb to 

1 mb. The troposphere height is also to be reported.

Derived temperature products are:

• Atmospheric temperature profile for 30 levels

• Tropopause height

• Stratopause height

Research and Applications

The temperature profile is an important parameter 

for data assimilation to improve numerical weather 

prediction. Surface air temperatures are important 

for energy exchange between the Earthʼs surface 

and the atmosphere. All temperatures are important 

to monitor interannual variability and whether any 

significant global or regional temperature trends 

exist.

 The AIRS Temperature Product is a significant 

improvement over what is achievable using current 

HIRS3/AMSU data or what can be expected from 

MODIS data.

Data Set Evolution

The AIRS Team algorithm comprises three sequential 

series of steps. The first series of steps creates the 

microwave product based exclusively on AMSUA/HSB observations. This provides a first guess for 

surface skin temperature, microwave spectral surface 

emissivity, atmospheric temperature and moisture 

profiles, and atmospheric liquid-water profile to be 

used in subsequent steps. The liquid-water profile 

is not subsequently modified and is an AIRS team 

product on an AMSU-A footprint basis.

 The next series of steps begins with a physically based cloud-clearing algorithm to give cloudcleared AIRS radiances for all channels, that is, an 

estimate of what the radiances would have been if 

no clouds were present within the AMSU-A footprint. The cloud-cleared radiances are used to produce the first AIRS/AMSU-A product, which uses 

a statistical regression step followed by a linearly 

constrained physical retrieval step. These steps are 

rapid and can be used to produce reasonably accurate AIRS/AMSU-A products that are practical for 

operational numerical weather prediction use in a 

near-real-time mode.

 The last sequence of steps produces the AIRS 

final product, based on a number of fully non-linear 

physical retrieval steps for surface skin temperature, 

spectral surface emissivity, temperature profile, 

water-vapor profile, and cloud parameters, and utilizing improved clear-column radiances computed 

iteratively.

 Selected IR channels are used to produce the temperature profiles, primarily in the spectral regions 665 

-750 cm-1 and 2378-2400 cm-1.

 The final product steps produce more-refined 

products, including product error estimates, and 

have improved rejection capability, compared to the 

first product. In the event a profile is rejected, a new 

retrieval is produced, starting from the microwave 

product initial guess and utilizing the AMSU-A/HSB 

channels together with those AIRS channels that are 

insensitive to cloud effects.

 Data validation will be accomplished through 

comparison of the retrieved temperature profiles with 

simultaneous in situ measurements via radiosondes, 

dropsondes, lidars, and radio-acoustic sounding systems at selected DoE ARM/CART sites and other 

EOS-dedicated validation sites. Vicarious validation 

will be accomplished through comparison with MODIS, TOVS, and GPS retrievals.

Suggested Reading

Anyamba, E., and J. Susskind, 1998.

Aumann, H. H., and C. Miller, 1995.

Aumann, H. H. et al., 1999.

Chahine, M. T. et al., 1997a.

Fleming, H. F. et al., 1986.

Haskins, R. D. et al., 1997.

Hofstadter, M. et al., 1999.

Janssen, M. A., 1993. 

Lakshmi, V. et al., 1998.

Lambrigtsen, B., 1996.

Aqua / AIRS/AMSU-A/HSB
Atmospheric Temperatures • Data Products Handbook - Volume 2 135

Rosenkranz, P. W., 1995. 

Smith, W. L., and H. M. Woolf, 1976.

Susskind, J. et al., 1993.

Susskind, J. et al., 1998.

AIRS/AMSU-A/HSB Atmospheric Temperature 

Product Summary

Coverage: Global

Spatial/Temporal Characteristics: Resolution 

of AMSU-A FOV (40.6 km at nadir); 

1-km vertical levels, surface-to-200- mb 

pressure level, coarser level separation 

above 200 mb/twice daily (daytime and 

nighttime)

Key Science Applications: Characterization of 

the atmosphere; atmospheric dynamics; 

climatology

Key Geophysical Parameters: Atmospheric 

temperature profile; tropopause height

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 5 MB

File Frequency: 240/day

Primary Data Format: HDF-EOS

Browse Available: Yes

Additional Product Information:

http://www-airs.jpl.nasa.gov/

DAAC: NASA Goddard Space Flight Center

Science Team Contact:

 J. Susskind

Aqua / AIRS/AMSU-A/HSB
136 Data Products Handbook - Volume 2 Aqua / MODIS • Atmospheric Temperatures 

MODIS Atmospheric Profiles 

(MOD 07)

Product Description

The MODIS Atmospheric Profiles product (MOD 07) 

consists of several parameters: total-ozone burden, 

atmospheric stability, temperature and moisture 

profiles, and atmospheric water vapor. All of these 

parameters are produced day and night for Level 2 

at 5 × 5 1-km pixel resolution when at least 9 FOVs 

are cloud-free.

 The MODIS total-ozone burden is an estimate of 

the total column tropospheric and stratospheric ozone 

content. The MODIS atmospheric stability consists 

of three daily Level 2 atmospheric stability indices. 

The Total Totals (TT), the Lifted Index (LI), and the 

K index (K) are each computed using the infrared 

temperature- and moisture-profile data, also derived 

as part of MOD 07. The MODIS temperature and 

moisture profiles are produced at 20 vertical levels 

for temperature and 15 levels for moisture. A simultaneous direct physical solution to the infrared 

radiative-transfer equation in a cloudless sky is used. 

The MODIS atmospheric water-vapor product is an 

estimate of the total tropospheric column water vapor 

made from integrated MODIS infrared retrievals of 

atmospheric moisture profiles in clear scenes.

Research and Applications

Total-column ozone estimates at MODIS resolution 

are required by MODIS investigators developing atmospheric correction algorithms. This information 

is crucial for accurate land- and ocean-surface-parameter retrievals. Furthermore, strong correlations 

have been found to exist between the meridional 

gradient of total ozone and the wind velocity at 

tropopause levels, providing the potential to predict 

the position and intensity of jet streams. Total-column ozone monitoring is also important due to the 

potential harm to the environment caused by anthropogenic ozone depletion.

 Atmospheric instability measurements are predictors of convective-cloud formation and precipitation. The MODIS instrument offers an opportunity 

to characterize gradients of atmospheric stability at 

high resolution and greater coverage. Radiosonde-derived stability indices are limited by the coarse spacing of the point-source data, too coarse to pinpoint 

local regions of probable convection.

 Atmospheric temperature and moisture sounding 

data at high spatial resolution from MODIS and 

high- spectral-resolution sounding data from AIRS 

will provide a wealth of new information on atmospheric structure in clear skies. The profiles will be 

used to correct for atmospheric effects for some of the 

MODIS products (e.g., sea-surface and land-surface 

temperatures, ocean aerosol properties, water-leaving 

radiances, and PAR) as well as to characterize the 

atmosphere for global greenhouse studies.

 Total-column precipitable-water estimates at MODIS resolution are required by MODIS investigators 

developing atmospheric-correction algorithms. This 

information is crucial for accurate land and ocean 

surface-parameter retrievals. MODIS will also provide finer horizontal-scale atmospheric water-vapor 

gradient estimates than are currently available from 

the POES satellites.

Data Set Evolution

One of two ozone-retrieval methods developed using 

the HIRS will be chosen as best suited for application 

with MODIS data. Both use a first-guess perturbation method and radiances from MODIS channel 

30 (9.6 μm) to solve the radiative-transfer equation. 

The perturbations are with respect to some a priori

conditions that may be estimated from climatology, 

regression, or more commonly, from an analysis or 

forecast provided by a numerical model. The MODIS 

cloud-mask product (MOD 35) will also be used to 

screen for clouds.

 Atmospheric-stability estimates will be derived 

from the MODIS temperature and moisture retrievals 

contained in this product. Layer temperature and 

moisture values may be used to estimate the temperature lapse rate of the lower troposphere and the 

low-level moisture concentration.

 Temperature and moisture profile retrieval algorithms are adapted from the International TOVS 

Processing Package (ITPP), taking into account 

MODISʼ lack of stratospheric channels and far higher 

horizontal resolution. The profile retrieval algorithm 

requires calibrated, navigated, and coregistered 1-km 

FOV radiances from MODIS channels 20, 22-25, 27-

29, and 30-36. The MODIS cloud mask (MOD 35) is 

used for cloud screening. The algorithm also requires 

NCEP model analyses of temperature and moisture 

profiles as a first guess and an NCEP analysis of 

surface temperature and pressure.

 Several algorithms for determining atmospheric 

water vapor, or precipitable water, exist. The most 

direct calculations are done by integrating the moisture profile through the atmospheric column. Other, 

split-window methods also exist. This class of tech-
Atmospheric Temperatures • Aqua / MODIS Data Products Handbook - Volume 2 137

niques uses the difference in water-vapor absorption 

between channel 31 (11 μm) and channel 32 (12 

μm).

 Data validation will be conducted by comparing 

results to in situ radiosonde measurements, NOAA 

HIRS operational retrievals, GOES sounder operational retrievals, NCEP analyses, and retrievals from 

the AIRS/AMSU-A/HSB instrument package on the 

Aqua platform. A field campaign using a profiler 

network in the central U.S. and the MAS-equipped 

aircraft will be initiated in the first year after launch. 

Quality control will consist of manual and automatic 

inspections, with regional and global mean temperatures at 300, 500, and 700 hPa monitored weekly, 

along with 700 hPa dew-point temperatures. For 

total ozone, data validation will consist of comparing the TOMS, as well as operational NOAA ozone, 

estimates from HIRS to the MODIS retrievals.

Suggested Reading

Hayden, C. M., 1988.

Houghton, J. T. et al., 1984.

Jedlovec, G. J. 1987.

Kleepsies, T. J., and L. M. McMillan, 1984.

Ma, X. L. et al., 1984.

Prabhakara, C. et al., 1970.

Shapiro, M. A. et al., 1982.

Smith, W. L., and F. X. Zhou, 1982.

Smith, W. L. et al., 1985.

Sullivan, J. et al., 1993.

(See figure on pg. 84.)

MODIS Atmospheric Profiles Summary

 

Coverage: Global, clear-sky only

Spatial/Temporal Characteristics: 5 km

Key Science Applications:

• Ozone: atmospheric correction, prediction of cyclogenesis, anthropogenic ozone 

depletion

• Atmospheric stability: atmospheric correction, prediction of convective cloudiness and precipitation, characterization of 

the atmosphere

• Soundings: atmospheric-correction- algorithm development and use, characterization of the atmosphere

• Total-column water vapor: atmosphericcorrection algorithm development and 

use, characterization of the atmosphere

Key Geophysical Parameters: Total-column 

ozone, atmospheric stability (Total Totals, 

Lifted Index, and K index), atmospheric 

profiles of temperature and moisture, 

atmospheric total-column water vapor

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 28 MB

File Frequency: 288/day

Primary Data Format: HDF-EOS

Browse Available:

http://modis-atmos.gsfc.nasa.gov/

MOD08_E3/browse_main.html

Additional Product Information:

http://modis-atmos.gsfc.nasa.gov/

MOD07_L2/index.html

DAAC: NASA Goddard Space Flight Center

Science Team Contact:

 W. P. Menzel
138 Data Products Handbook - Volume 2 • Atmospheric Temperatures

SAGE III Temperature and 

Pressure Data Products 

(SAGE III 08) 

Product Description

The SAGE III Temperature and Pressure Data Products (SAGE III 08 at Level 2) consist of profiles of 

temperature and pressure derived from measurements 

of the oxygen A-band absorption feature near 762 nm. 

The profiles extend from an altitude of 0.5 km (or 

cloud top) up to an altitude of 85 km in 0.5-km increments. In addition, the temperature profiles are 

also reported on pressure surfaces from 1000 hPa 

to 0.004 hPa. Associated temperature and pressure 

uncertainty profiles are also produced.

Research and Applications

SAGE III temperature measurements will provide 

a unique data set for monitoring and understanding 

atmospheric temperature changes. In particular, the 

long-term stability and self-calibration capabilities 

of SAGE III may permit the detection of trends in 

stratospheric and mesospheric temperature that would 

be important diagnostics of climate change. SAGE III 

temperature and pressure measurements will provide 

important contributions in the following areas:

• SAGE III temperature measurements in the upper stratosphere and mesosphere will be the only 

source of long-term temperature measurements in 

this region of the atmosphere.

• Short-term temperature variations are important 

in understanding the photochemical interactions 

and feedback in the formation and destruction of 

stratospheric ozone.

• SAGE III temperature measurements will allow 

the monitoring of periodic temperature changes, 

such as those associated with the solar cycle and 

quasi-biennial oscillation, and the effects of radiative forcing by aerosols.

• An improved temperature climatology will lead to 

better radiative-dynamical-chemical general circulation models and enhance the understanding of the 

interactions between temperature and pressure and 

the state of the climate system.

• Temperature and pressure measurements are 

essential to SAGE III for computing molecular 

scattering and the retrieval of mass mixing ratios 

of gaseous species on pressure surfaces.

SAGE III Temperature and Pressure Data 

Products Summary

Coverage: 30 solar events per day, 15 

northern high latitude, 15 southern high 

latitude

Spatial/Temporal Characteristics: 0.5 km 

vertical resolution/weekly

Key Science Applications: Monitoring and 

understanding atmospheric temperature 

changes

Key Geophysical Parameters: Profiles of 

temperature and pressure

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 0.05 MB

File Frequency: 196/week

Primary Data Format: Binary

Browse Available: Yes

Additional Product Information: 

http://eosweb.larc.nasa.gov/PRODOCS/

sage3/table_sage3.html

DAAC: NASA Langley Research Center

Science Team Contact:

 M. C. Pitts

Meteor 3M / SAGE III
139

Winds

Aqua

• AMSR-E

Jason-1

• Poseidon-2

QuikScat

• SeaWinds
140 Data Products Handbook - Volume 2 Overview • Winds 

Winds – An Overview

Relationship to Global Change Issues

Winds are critical to transports of heat and chemical 

constituents within the atmosphere and thus are 

critical to understanding atmospheric dynamics and 

climate change. For example, stratospheric wind 

estimates provide information about the sources, 

sinks, and transport mechanisms of stratospheric 

trace gases important to resolving global warming 

issues. Winds are also a vehicle for addressing various 

multidisciplinary questions involving the global biogeochemical and hydrologic cycles. The 1991-1992 

El Niño was the first El Niño to be predicted in advance, based on winds as the driving force of a global 

circulation model. Because wind field profiles have 

been primarily measured by land-based rawinsondes, 

measurements have been too irregularly distributed to 

provide adequate data for global climate studies. 

 Areas largely devoid of data, especially prior to 

satellites, include, importantly, oceanic areas (covering 70% of the Earthʼs surface), and some lessdeveloped southern hemisphere land areas. Wind 

measurements historically have been inferred from 

satellite cloud-tracking imagery or computed from 

pressure gradients. Tracking of clouds by satellites 

provides estimates of low- and high-level winds over 

portions of the Earth, but uncertainties in heights introduce errors, and there are many gaps in the data. 

The desired accuracy is ±10%, which corresponds to 

about 1 m/s in the lower troposphere and 5 m/s in the 

upper troposphere. Even with these data, however, 

there are significant gaps in global coverage.

 Knowledge of wind velocity over the ocean is of 

critical importance for understanding and predicting 

many oceanographic, meteorological, and climate 

phenomena. Wind stress is the single largest source 

of momentum to the upper ocean, and winds drive 

oceanic motions on scales ranging from surface 

waves to basin-wide current systems. Winds over 

the oceans regulate the crucial coupling between the 

air and the sea that maintains global and regional climate, by modulating air-sea fluxes of heat, moisture, 

and chemical substances. Measurements of surfacewind velocity can be assimilated into regional and 

global numerical weather prediction systems, thereby 

extending and improving our ability to predict future 

weather patterns on many scales.

 Until the advent of satellite-borne measurements, 

available ocean-wind-velocity data sets have been 

severely deficient in coverage and accuracy. Ship reports are geographically and phenomenologically biased and are inaccurate owing to untrained observers, 

poor instrumentation, badly placed anemometers, 

inadequate correction for shipʼs velocity, and data 

transcription and transmission errors (Pierson, 1990). 

Measurements from moored meteorological buoys 

are highly accurate, but they are few in number and 

tend to be concentrated in near-coastal regions in the 

northern hemisphere. 

 Only satellite-borne instruments can acquire wind 

data with global coverage, high spatial resolution, 

and frequent sampling. Wind speeds over ocean 

areas can be derived from both passive-microwave 

and active-microwave instruments. However, satellite-borne radar scatterometers are the only remote 

sensing systems presently capable of providing accurate, frequent, high-resolution measurements of 

ocean near-surface wind speed and direction in both 

clear-sky and cloudy conditions. The scatterometer 

measurements, however, are highly indirect, and 

significant processing is required to estimate nearsurface wind velocity from the backscattered power 

measured directly by the scatterometer. 

 A Ku-band scatterometer, “SeaWinds,” is now flying on the QuikScat platform, and another version of 

SeaWinds will be flying on the ADEOS II Japanese 

spacecraft during the EOS era. These two instruments 

will provide accurate multiple measurements of the 

radar cross-section of the ocean surface, leading to 

estimates of near-surface oceanic wind velocity and 

related quantities such as the horizontal component 

of wind stress.

Product Overview

The AMSR-E surface wind product provides wind 

speeds over the oceans in terms of a neutral stability 

wind at 10 m above the surface, doing so with a spatial 

resolution of 20 km. The Poseidon-2 product provides 

wind speeds between 66°N and 66°S, at a resolution 

of 6.2 km. SeaWinds provides vector winds, not 

simply wind speeds, over the ice-free portions of 

the global oceans, at a 25-km resolution.

Product Interdependency and Continuity 

with Heritage Data

The AMSR-E wind-speed retrieval algorithm builds 

on earlier research using SMMR, SSM/I, and TMI 

data. The AMSR-E retrievals should be more accurate 

than those from SSM/I because of the AMSR-E having two additional sets of dual-polarization channels 

at 6.9 and 10.7 GHz.

 The primary heritage sequence for SeaWinds dates 

back to the SEASAT-A Satellite Scatterometer (SASS) 

instrument, which was the first dedicated spaceborne 
Winds • Overview Data Products Handbook - Volume 2 141

microwave scatterometer. It flew on the SEASAT mission from June to October, 1978, and operated at a 

frequency of 14.6 GHz. The SASS instrument used 

four fan-beam antennas to illuminate measurement 

swaths on each side of the spacecraft ground track, 

with ground footprints oriented at angles of ±45° and 

±135° with respect to the subsatellite track. As only 

two azimuthal measurements were obtained at each 

location in the swaths, it was not possible to determine 

a unique wind direction based on scatterometer data 

alone.

 Although there were many difficulties associated with the processing and interpretation of SASS 

data, the SASS instrument represented a milestone in 

scatterometer development and a positive scientific 

contribution to oceanographic and meteorological 

research. The SASS measurements proved to be 

an invaluable set of realistic data for the development and testing of wind-retrieval and ambiguityremoval algorithms. Although the SEASAT data set 

was temporally short by oceanographic standards, a 

wide range of scientific studies has been conducted 

using vector wind information calculated from SASS 

measurements.

 The European Space Agency (ESA) ERS-1 mission 

was launched in July 1991, carrying a C-band (5.3 

GHz) Active Microwave Instrument (AMI), which 

operates both as a synthetic aperture radar (SAR) 

and as a scatterometer. In scatterometer mode, the 

ERS-1 AMI illuminates a 500-km-wide swath on the 

starboard side of the spacecraft. Although fan-beam 

antennas are used to achieve the broad swath illumination for the AMI as with SASS, the AMI utilizes 

range gating, rather than Doppler filtering, to achieve 

along-beam spatial resolution. Within the measurement swath, the ERS-1 AMI illuminates footprints 

oriented at 45°, 90°, and 135° with respect to the 

satellite ground track; each location in the swath is 

therefore imaged from three separate viewing geometries. This “3-look” imaging strategy successfully 

reduces the 4-fold directional ambiguity to a 2-fold 

ambiguity for most measurements, with directions approximately 180° apart. This reduction in directional 

ambiguity greatly simplifies the processing required 

to select a single, unique direction.

 The ERS-1 AMI has operated nearly continuously 

since early 1992 and has provided the longest record 

of global scatterometer data yet obtained. Although 

its coverage is limited by its single-swath design and 

narrow swath width, and there appears to be a lack of 

wind-velocity sensitivity at C-band (especially at low 

wind speeds and small incidence angles), the ERS-1 

AMI vector wind data are being actively studied by 

the oceanographic, meteorological, and air-sea-interaction research communities. 

 Both C- and Ku-band scatterometers flew in the 

mid-1990s before the launch of the EOS SeaWinds 

instrument on QuikScat. The ERS-2 mission began in 

early 1995 and carries a 3-look, single-swath, C-band 

AMI identical to that on ERS-1. The planned overlap 

between the ERS-1 and ERS-2 missions should allow on-orbit cross-calibration of the two instruments, 

resulting in a 5-7-year data set of consistent winds 

from C-band scatterometers.

 The NASA Scatterometer (NSCAT) instrument was 

implemented as a follow-on to SASS. While based 

heavily on SASS heritage, NSCAT had significant 

design enhancements that allowed it to obtain moreaccurate and extensive vector wind measurements. 

Like SASS, NSCAT operated at Ku-band and used 

fan-beam antennas to allow measurements to be obtained in two, ~600-km-wide swaths. NSCAT flew in 

a polar orbit as a NASA contribution to the Japanese 

ADEOS mission, which launched in early 1996. 

Suggested Reading

Naderi, F. M. et al., 1991.

Pierson, W. J., 1990.
142 Data Products Handbook - Volume 2 Aqua / AMSR-E • Winds 

channels at 6.9 and 10.7 GHz, which help separate 

the cloud signal from the wind signal and remove 

crosstalk due to varying sea surface temperature.

Suggested Reading

Goodberlet, M. A. et al., 1989.

Halpern, D. et al., 1994.

Hollinger, J. P., 1971.

Nordberg,W. et al., 1971.

Schluessel, P., and H. Luthardt, 1991.

Wentz, F. J., 1975.

Wentz, F. J., 1992.

Wentz, F. J., 1997. 

Wilheit T. T., and M. G. Fowler, 1977.

Wilheit, T. T., and A. T. C. Chang, 1980.

AMSR-E Sea Surface Wind Speed Summary

Coverage: Ocean surface, clear and cloudy 

skies except in the presence of rainfall

Spatial/Temporal Characteristics: 

 Level 2: swath pixels at 24-km resolution

 Level 3: 0.25° latitude-longitude grid/daily, 

weekly, and monthly

Key Science Applications: Climate variability, 

air-sea interaction, hydrological cycle, 

ocean circulation

Key Geophysical Parameters: Sea surface wind 

speed

Processing Level: 2, 3

Product Type: Standard, post-launch

Maximum File Size: 10 MB

File Frequency: 29/day

Primary Data Format: HDF-EOS

Browse Available: Accompanies data product

Additional Product Information: 

http://wwwghcc.msfc.nasa.gov/AMSR/

html/amsr_products.html

DAAC: National Snow and Ice Data Center

Science Team Contact:

 F. J. Wentz

AMSR-E Sea Surface Wind 

Speed

Product Description

The Sea Surface Wind Speed derived from the 

AMSR-E microwave radiances is in terms of a neutral stability wind at 10 m above the surface. The 

spatial resolution of this product is 24 km, and its 

expected rms accuracy at this resolution is 1.0 m/s. 

Only the scalar wind speed is retrievable, not wind 

direction. This product is available for both clear and 

cloudy conditions, but not for areas containing rain. 

The higher AMSR-E frequencies are used to identify 

those cells that contain rain, for which the wind speed 

will not be reported. The product will be produced for 

individual pixels across the AMSR-E swath (Level 2) 

as well as an Earth-gridded product (0.25° in latitude 

and longitude) averaged daily, weekly, and monthly 

(Level 3).

Research and Applications

Surface winds drive ocean currents by transferring 

momentum from the atmosphere to the surface and 

strongly influence the surface evaporative flux of water vapor and consequent thermal coupling between 

the atmosphere and the ocean. They are a critical 

component in the redistribution of water vapor and 

cloud water, facilitating the poleward transport of 

energy and contributing to patterns of precipitation. 

Equatorial trade winds are also intimately tied to the 

El Niño cycle, in which slackening of these winds 

presages the migration of the west Pacific warm pool 

toward South America, with dramatic consequences 

for global weather. 

Data Set Evolution

The wind-speed retrieval algorithm is based on experience gained from previous microwave radiometers, 

including SMMR, SSM/I, and TMI. In particular, the 

SSM/I has demonstrated the ability of a microwave 

radiometer to measure wind speed to an accuracy of 

about 1 m/s. For 250,000 SSM/I overflights of buoys 

(1987-1999), the root-mean-squared (rms) difference 

between the SSM/I and buoy wind is 1.3 m/s. About 

half this difference (in a root sum squared [rss] sense) 

is due to the spatial-temporal mismatch between the 

point buoy observation and the 25-km SSM/I footprint. The AMSR-E wind speed retrievals should 

be more accurate than those from SSM/I because 

AMSR-E has two additional sets of dual-polarization 
Winds • Jason-1 / Poseidon-2 Data Products Handbook - Volume 2 143

Poseidon-2 Normalized Radar 

Backscatter Coefficient and 

Wind Speed

Product Description

This Level 2 product provides the measurement of 

the normalized radar backscatter coefficient and its 

associated wind speed at a resolution of 6.2 km along 

the ground tracks of the satellite. The measurement 

is repeated every 9.9 days along a set of fixed ground 

tracks. The satellite makes 127 revolutions of the Earth 

every 9.9 days. The coverage of the measurement is 

limited from 66° S to 66° N due to the 66° inclination 

of the satelliteʼs orbit plane. The longitudinal distance 

between the ground tracks is [315·cos(latitude)] km, 

i.e., 315 km at the equator, 223 km at 45°, and 128 km 

at 66°. The normalized radar backscatter coefficient 

is measured at two frequencies (Ku band at 13.6 GHz 

and C band at 5.3 GHz). Three kinds of product are 

available: 1) the Operational Sensor Data Record 

(OSDR), which is available three hours after data 

collection; 2) the Interim Geophysical Data Record 

(IGDR), which is available three days after the data 

collection; and 3) the GDR, which is available 30 

days after the data collection. The accuracy of the 

normalized radar backscatter coefficient is 0.7 dB 

for absolute value and 0.2 dB for relative value. The 

accuracy of wind speed is 2 m/s for OSDR and 1.7 

m/s for IGDR and GDR.

Research and Applications

The global distribution and variability of wind speed 

is used to study the dynamics of ocean waves and 

monitor the sea state for ship routing and other maritime applications. The normalized radar backscatter coefficient is related to the air-sea gas-transfer 

coefficient through its relation with the sea-surface 

mean- square slope. The Jason-1 dual-frequency 

measurement of the backscatter makes this relation 

robust for estimation of the gas-transfer coefficient. 

The global distribution and variability of the gastransfer coefficient is used to study the global CO2

flux and the carbon cycle.

Data Set Evolution

The power of the radar return signal is controlled by 

an automatic gain control (AGC) loop to ensure that 

the radar altimeter electronics operate properly. This 

AGC reading is used to calculate the normalized radar 

backscatter coefficient. This coefficient is corrected 

for radar pointing angle as well as sea-state effects 

based on information obtained from the radar-return 

waveform. Further correction is made for the effects 

of atmospheric attenuation caused by liquid water 

and water vapor, which are obtained from the onboard microwave radiometer. The normalized radar 

backscatter coefficient is then used to compute wind 

speed using empirically derived algorithms.

Suggested Reading

Brown, G. S., 1979.

Chelton, D. B., and P. J. McCabe, 1985.

Freilich, M. H., and P. G. Challenor, 1994.

Hara, T. et al., 1995.

Poseidon-2 Normalized Radar Backscatter 

Coefficient and Wind Speed Summary

Coverage: Ocean areas from 66°S to 66°N

Spatial/Temporal Characteristics: 6.2-km 

resolution along-track every 9.9 days; 

[315·cos(latitude)]-km longitudinal track 

spacing

Key Science Applications: Ocean-wave dynamics (wind speed); air-sea gas transfer 

and CO2 flux

Key Geophysical Parameters: Wind speed; normalized radar backscatter coefficient

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 1.5 MB

File Frequency: 25/day

Primary Data Format: Binary

Browse Available: Yes

Additional Product Information: 

http://www-aviso.cnes.fr

DAAC: Jet Propulsion Laboratory

Science Team Contact:

 L.-L. Fu
144 Data Products Handbook - Volume 2 QuikScat / SeaWinds • Winds 

SeaWinds – Normalized Radar 

Cross-Section and Ancillary 

Data

Product Description

This Level 1B data set contains Earth-located, timeordered SeaWinds normalized radar cross-section 

measurements and ancillary data from a single 

orbit, corresponding to all SeaWinds measurements 

acquired during the orbital period known as a “rev.” 

By convention “revs” start at the southernmost orbital latitude and contain only the data from a single 

orbit.

 Data include instrument state; spacecraft position, 

attitude, and velocity; pulse locations on Earth, complete pulse backscatter cross-section, uncertainty and 

quality, as well as dynamic data for individual terms 

in the radar equation; antenna azimuth; 13.4 GHz 

brightness temperatures calculated from SeaWinds 

noise estimates for each complete pulse; backscatter cross-section, uncertainty, quality, and Earth 

location for each of the central eight, high-resolution (~4 × 25 km) slices generated from the Linear 

Frequency Modulation Chirp.

 The data are sorted in time order by telemetry 

frame. Each telemetry frame contains data from 100 

scatterometer pulses. For nominal SeaWinds operating parameters, each Level 1B rev file contains 

information on more than 1-million full backscatter 

measurements (pulses) and more than 8-million highresolution-slice backscatter measurements. Internal 

calibration data are recorded in flag-indicated calibration pulses.

SeaWinds Normalized Radar Cross-Section 

and Ancillary Data Summary

Coverage: Global (land, ocean, ice) for each 

rev 1800-km-wide swath (v-pol); 1400-kmwide swath (h-pol)

Spatial/Temporal Characteristics: ~25 × 35 

km resolution for pulse IFOV; ~4 × 25 km 

resolution for slices

Wavelength: 13.4 GHz

Processing Level: 1B

Product Type: Standard, at-launch

Maximum File Size: 175 MB

File Frequency: 14.25/day

Primary Data Format: NCSA HDF

Browse Available: No

Additional Product Information:

http://podaac.jpl.nasa.gov/quikscat/

DAAC: Jet Propulsion Laboratory

Science Team Contact:

 D. G. Long
Winds • QuikScat / SeaWinds Data Products Handbook - Volume 2 145

SeaWinds – Ocean Wind Vectors 

in 25 km Swath Grid (Level 2B) 

and Ocean Wind Vectors in 

25 km Grid (Level 3)

Product Description

The Level 2B SeaWinds Ocean Wind Vectors in 

25 km Swath Grid data set contains Earth-located, 

open-ocean near-surface vector wind solutions and 

ancillary quality flags and values from a single SeaWinds rev, organized by wind vector cell. By convention, revs are orbital periods that start and end at 

the southernmost orbital latitude.

 Wind vector cells are defined as 25 × 25-km areas 

in a locally rectangular along-track, across-track coordinate system aligned with the satellite subtrack.

 Vector wind solutions are given as (speed, direction) pairs. The wind-speed value corresponds to 

equivalent neutral stability winds at a height of 10 

m above the mean sea surface. Wind directions are 

provided in the oceanographic convention: winds 

flowing toward the North have direction 0°, with 

positive angles increasing clockwise.

 Each wind vector cell may contain up to 4 potential 

wind vector solutions resulting from the maximum 

likelihood wind-retrieval algorithm applied to the 

Level 2A backscatter data from that wind vector cell. 

Multiple solutions are provided in order of decreasing 

likelihood. A flag in the Level 2B product indicates 

the specific wind vector solution chosen by the standard median filter ambiguity removal algorithm. A 

rain flag is provided to indicate wind vector cells for 

which atmospheric effects may significantly degrade 

the accuracy of the wind solution.

 The Level 3 Ocean Wind Vectors in 25 km Grid 

data set consists of gridded values of scalar wind 

speed, meridional and zonal components of wind 

velocity, wind speed squared, and time given in 

fractions of a day. Rain probability determined using the Multidimensional Histogram (MUDH) Rain 

Flagging technique is also included as an indicator of 

wind values that may have degraded accuracy due to 

the presence of rain. Data are currently available in 

Hierarchical Data Format (HDF) and exist from 19 

July 1999 to the present.

 The Level 3 data are obtained from the Direction 

Interval Retrieval with Threshold Nudging (DIRTH) 

wind vector solutions contained in the QuikScat Level 

2B data and are provided on an approximately 0.25° 

× 0.25° global grid. Separate maps are provided for 

both the ascending pass (6 a.m. equator crossing) 

and descending pass (6 p.m. equator crossing). By 

maintaining the data at nearly the original Level 2B 

sampling resolution and separating the ascending and 

SeaWinds – Grouped and 

Surface-Flagged Backscatter and 

Attenuations in 25 km Swath 

Grid

Product Description

This Level 2A data set contains Earth-located and 

grouped SeaWinds normalized radar cross-section 

measurements, quality and uncertainty flags, and 

autonomous rain flags and atmospheric corrections 

from a single orbital period known as a “rev”, corresponding to all SeaWinds backscatter measurements 

acquired during that period. By convention, revs start 

and end at the southernmost orbital latitude.

 Each composite backscatter measurement combines all of the high-resolution slices from a single 

scatterometer pulse that are located within a given 

wind vector cell. All ancillary values required for 

rain flagging, atmospheric absorption correction 

(including processed AMSR measurements from 

ADEOS II), and surface flagging are included.

 Wind vector cells are defined as 25 × 25-km areas 

in a locally rectangular along-track, across-track coordinate system aligned with the satellite subtrack.

SeaWinds Backscatter and Attenuations 

Summary

Coverage: Global (land, ocean, ice) for each rev 

1800-km-wide swath

Spatial/Temporal Characteristics: Slice composites in 25-km wind vector cells

Wavelength: 13.4 GHz

Processing Level: 2A

Product Type: Standard, at-launch

Maximum File Size: 14.4 MB (compressed),

40 MB (uncompressed)

File Frequency: 14.25/day

Primary Data Format: NCSA HDF

Browse Available: No

Additional Product Information: 

http://podaac.jpl.nasa.gov/quikscat/

DAAC: Jet Propulsion Laboratory

Science Team Contact:

 M. H. Freilich
146 Data Products Handbook - Volume 2 QuikScat / SeaWinds • Winds

descending passes, very little overlap occurs in one 

day. However, when overlap between subsequent 

swaths does occur, the values are over-written, not 

averaged. Therefore, a QuikScat SeaWinds Level 3 

file contains only the latest measurement for each 

day.

 In addition to the above mentioned Level 3 Product, which was created by the SeaWinds on QuikScat 

Project, several additional Level 3 products have been 

produced by members of the SeaWinds on QuikScat 

Science Working Team. Many of these products provide the QuikScat data on coarser grids (0.5° or 1°) 

and at smaller time intervals (6 or 12 hour maps) and 

use advanced interpolation techniques to fill gaps in 

the wind fields. A list of the publicly available QuikScat Level 3 products produced by members of the 

SeaWinds on QuikScat Science Working Team can 

be found on the PO.DAAC SeaWinds on QuikScat 

web site, http://podaac.jpl.nasa.gov/quikscat.

Suggested Reading

Freilich, M. H., and R. S. Dunbar, 1999.

Naderi, F. M. et al., 1991.

Wentz, F. J., and D. K. Smith, 1999.

(SeaWinds – Ocean Wind Vectors, continued) SeaWinds Ocean Wind Vectors Summary

Coverage: Global ice-free open ocean for 

each rev 1800-km-wide swath (Level 2B); 

Global ice-free open ocean (Level 3)

Spatial/Temporal Characteristics: 25-km wind 

vector cells within the swath (Level 2B); 

0.25° × 0.25° grid/daily (Level 3)

Wavelength: 13.4 GHz

Processing Level: 2B, 3

Product Type: Standard, at-launch

Maximum File Size: 8.25 MB (compressed 

Level 2B), 32.66 MB (uncompressed Level 

2B); 35 MB (Level 3)

File Frequency: 14.25/day (Level 2B); 

1/day (Level 3)

Primary Data Format: NCSA HDF

Browse Available: Daily ascending and 

descending images available at http:

//podaac.jpl.nasa.gov/quikscat/

(Level 3)

Additional Product Information:

http://podaac.jpl.nasa.gov/quikscat/

DAAC: Jet Propulsion Laboratory

Science Team Contact:

 M. H. Freilich
147

Sea Surface Height

and

Ocean Wave Dynamics

Jason-1 • DORIS

• JMR

• Poseidon-2
148 Data Products Handbook - Volume 2 • Sea Surface Height and Ocean Wave Dynamics 

Sea Surface Height and Ocean 

Wave Dynamics – An Overview

Relationship to Global Change Issues

The ocean covers 70% of the Earthʼs surface and 

is responsible for regulating the Earthʼs climate and 

sustaining life. The upper three meters of the ocean 

stores the same amount of heat as resides in the entire atmosphere. The circulation of the oceanʼs water 

mass carries half of the poleward transport of the heat 

gained in the Earthʼs tropics. The ocean and its circulation patterns thus play a key role in determining 

the state of the Earthʼs climate and its evolution. 

However, observing the global oceans from surface 

level is difficult, to say the least. It takes months 

for a research ship to cross an ocean basin to make 

measurements, and during the course of the cruise 

the ocean will have already changed in many ways, 

thereby greatly hindering the attempt to make global 

and systematic ocean observations to comprehend its 

totality and variability. 

 The development of satellite radar altimetry in the 

1970s for measuring sea surface height from space 

has opened the door to global observation of a key 

aspect of the dynamics of the ocean. The deviation 

of the sea surface from the equilibrium sea surface 

in balance with the Earthʼs gravity, the geoid, is a 

measure of the surface dynamic pressure of the ocean 

caused by ocean currents. This height relative to the 

geoid is called ocean topography. The gradient of 

ocean topography allows scientists to calculate the 

speed and direction of the surface geostrophic current 

velocity; and the temporal change of the topography 

allows the determination of the current variability. 

Due to the thermal expansion of sea water, the ocean 

topography is also related to the heat content of the 

ocean. Satellite altimetric observation of the global 

ocean topography thus provides important information about ocean circulation and its heat transport. 

This information has been used to constrain ocean 

circulation models for making prediction of the state 

of the deep ocean, where direct global observations 

are difficult and expensive. Altimetry data have also 

been used to initialize coupled ocean-atmosphere 

models for improved forecasting of seasonal-to-interannual climate variability phenomena such as El 

Niño and La Niña. Long-term measurement of the 

ocean topography is thus a key element of a global 

observing system for building a record of long-term 

climate change and its prediction.

 In addition to sea surface height, the radar return 

waveform from the Poseidon-2 instrument is also 

used to produce measurements of the ocean wave 

Overview 

height and radar backscatter cross-section, which 

are then used to determine ocean wind speed. The 

global wave-height field undergoes changes due to 

changes in the wind field. In addition to responding 

to atmospheric storms, the wave-height field also 

reflects seasonal-to-interannual changes of the wind 

field in relation to climate change. 

 The radar backscatter cross-section can be used to 

estimate the air-sea gas-exchange rate, which is important for the study of the global CO2

 flux and thus 

the carbon cycle. The wind speed measurements can 

be used to complement scatterometer observations 

for studying global ocean dynamics.

Product Overview

The Jason-1 Poseidon-2/JMR/DORIS sea surface 

height data and Poseidon-2 significant wave height 

data are organized as sequential records along the 

ground track of the satellite. Each record contains all 

the information regarding the instrumentʼs measurements averaged over a period close to one second of 

flight time, including the measurements of sea-surface 

height, wave height, radar backscatter cross-section, 

and wind speed, along with a host of auxiliary data, 

including the atmospheric, geophysical, and instrumental corrections, plus other relevant information 

such as ocean-tide-model products and atmospheric 

pressure products. 

Product Interdependency and Continuity 

with Heritage Data

The heritage of satellite-based altimetric data products traces back to the short-lived SeaSat project in 

June-October 1978. The European Space Agencyʼs 

Remote Sensing Satellite (ERS-1), launched in 1991, 

carried a radar altimeter amongst its complement of 

five instruments, to obtain further data on sea surface 

height. The following year saw the launch of the joint 

U.S.-French TOPEX/Poseidon mission, with a central 

goal of accurate sea surface height measurement. The 

Jason-1 Poseidon-2 is a follow-on to the highly successful TOPEX/Poseidon mission. 

Suggested Reading

Fu, L.-L. et al., 1994.

Ménard, Y. et al., 2000.

Wunsch, C., and E. M. Gaposchkin, 1980.

Wunsch, C., and D. Stammer, 1998.
Sea Surface Height and Ocean Wave Dynamics • Jason-1 / Poseidon-2/JMR/DORIS Data Products Handbook - Volume 2 149

Poseidon-2/JMR/DORIS Sea 

Surface Height (SSH)

Product Description

This Level 2 product provides the measurement of the 

sea surface height relative to a model of the ellipsoidal 

Earth (the ellipsoid) at a resolution of 6.2 km along 

the ground tracks of the satellite. The measurement 

is repeated every 9.9 days along a set of fixed ground 

tracks. The Jason-1 satellite makes 127 revolutions 

of the Earth every 9.9 days. The coverage of the 

measurement is limited to the latitude range 66° S 

to 66° N due to the 66° inclination of the satelliteʼs 

orbit plane. The longitudinal distance between the 

ground tracks is [315· cos(latitude)] km, i.e., 315 km 

at the equator, 223 km at 45o, and 128 km at 66o. This 

product is used to generate gridded Level 3 products 

by various Jason-1 Science Working Team members. 

Two kinds of product are available. The Interim Geophysical Data Record (IGDR) is available three days 

after the data collection, while the GDR, based on a 

more precisely determined orbit, is available 30 days 

after the data collection. The root-mean-square error 

is 5 cm for the IGDR and 4.2 cm for the GDR.

 Poseidon-2 is the key instrument for measuring 

sea surface heights, with DORIS providing precise 

orbit determinations for these measurements and JMR 

providing range corrections. 

Research and Applications

The global distribution and variability of sea surface 

height provides key inputs to the study of the ocean 

circulation and its heat transport and hence the 

oceanʼs role in climate change such as El Niño. Sea 

surface height is a dynamic upper boundary condition 

of the ocean and hence a powerful constraint for the 

simulation of the interior of the ocean by numerical 

models via data assimilation. In addition, sea surface 

height data are also useful for studying the ocean 

tides, monitoring the global mean sea level change, 

and providing information for marine navigation, 

fisheries, and offshore operations, e.g., oil drilling.

Data Set Evolution

Sea surface height is determined from the difference 

between the range measurement made by a nadirlooking radar altimeter and the altitude (relative to 

the ellipsoid) of the spacecraft determined by the 

precision orbit determination technique. The radar 

range measurement is corrected for the path delay of 

the radar signals caused by the ionospheric free electrons (via simultaneous radar measurements in the Ku 

and C bands), the tropospheric water vapor (via an 

on-board 3-frequency microwave radiometer), and 

the dry air mass in the atmosphere (via the products 

from operational meteorological centers). The range 

measurement is further corrected for the effects of 

ocean gravity waves that create a bias in the measurement depending on the sea state (wave height, wind 

speed, etc.) The sea surface height determined from 

the corrected radar range is then subject to further 

corrections for geophysical effects other than ocean 

circulation dynamics and thermodynamics such as 

ocean tides, and atmospheric pressure loading. 

Suggested Reading

Fu, L.-L. et al., 1994.

Ménard, Y. et al., 2000.

Wunsch, C., and E. M. Gaposchkin, 1980.

Wunsch, C., and D. Stammer, 1998.

Poseidon-2/JMR/DORIS Sea Surface Height 

Summary

Coverage: Ocean areas from 66° S to 66° N.

Spatial/Temporal Characteristics: 6.2-km 

resolution along-track every 9.9 days; 

[315·cos(latitude)] km longitudinal track 

spacing

Key Science Applications: Ocean circulation 

and heat transport, ocean data assimilation

Key Geophysical Parameters: Sea surface 

height

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 1.5 MB

File Frequency: 25/day

Primary Data Format: Binary

Browse Available: Yes

Additional Product Information:

http://www-aviso.cnes.fr

DAAC: Jet Propulsion Laboratory

Science Team Contacts:

 L.-L. Fu

Y. Ménard
150 Data Products Handbook - Volume 2 • Sea Surface Height and Ocean Wave Dynamics

Poseidon-2 Significant Wave 

Height

Product Description

This Level 2 product provides the measurement of 

the significant wave height at a resolution of 6.2 km 

along the ground tracks of the satellite. The measurement is repeated every 9.9 days along a set of 

fixed ground tracks. The satellite makes 127 revolutions of the Earth every 9.9 days. The coverage of the 

measurement is limited from 66o S to 66o N due to 

the 66o inclination of the satelliteʼs orbit plane. The 

longitudinal distance between the ground tracks is 

[315· cos(latitude)] km, i.e., 315 km at the equator, 

223 km at 45o and 128 km at 66o. This product is used 

to generate gridded Level 3 products by various Jason-1 Science Working Team members. Three kinds 

of product are available. The Operational Sensor 

Data Record (OSDR) is available three hours after 

data collection. The accuracy of the measurement 

is the greater of the following: 0.5 m or 10% of the 

truth. The Interim Geophysical Data Record (IGDR) 

is available three days after data collection; and the 

GDR is available 30 days after data collection. 

Research and Applications

The global distribution and variability of significant wave height are used to study the dynamics of 

ocean-surface waves and global change in the marine 

environment. The data are also used to describe and 

predict the sea state condition for applications to ship 

routing, offshore operations and structure design, and 

coastal monitoring and prediction. 

Data Set Evolution

Significant wave height is determined from the slope 

of the leading edge of the radar return waveform. 

The value is determined from the onboard waveform 

tracking algorithm and corrected on the ground using 

an algorithm based on parameters of the sea state 

and the off-nadir pointing angle of the radar. Both 

parameters are obtained from the altimeter data 

processing stream.

Suggested Reading

Carter, D. J. T. et al., 1992.

Cotton, P. D., and D. J. T. Carter, 1994.

Mognard, N. M., 1984.

Mognard, N. M., and B. Lago, 1988.

Rufenach, C. L., and W. R. Alpers, 1978.

Poseidon-2 Significant Wave Height 

Summary

Coverage: Ocean areas from 66° S to 66° N.

Spatial/Temporal Characteristics: 6.2-km 

resolution along-track every 9.9 days; 

[315·cos(latitude)] km longitudinal track 

spacing

Key Science Applications: Ocean wave dynamics and prediction

Key Geophysical Parameters: Significant wave 

height

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 1.5 MB

File Frequency: 25/day

Primary Data Format: Binary

Browse Available: Yes

Additional Product Information:

http://www-aviso.cnes.fr

DAAC: Jet Propulsion Laboratory

Science Team Contacts:

 L.-L. Fu

Y. Ménard

Jason-1 / Poseidon-2
151

Surface Temperatures

of Land and Oceans,

Fire Occurrence, and 

Volcanic Effects

Aqua

• AIRS

• AMSR-E

• AMSU-A

• HSB

• MODIS
152 Data Products Handbook - Volume 2 • Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects 

Surface Temperatures of Land 

and Oceans, Fire Occurrence, 

and Volcanic Effects – An 

Overview

Relationship to Global Change Issues

Land and ocean surface temperatures have a multitude of impacts on the Earth system. On land, 

soil and canopy temperature are among the main 

determinants of the rate of growth of vegetation, 

and they govern seasonal initiation and termination 

of growth. Hydrologic processes such as evapotranspiration and snow and ice melt are highly sensitive to 

surface temperature fluctuations, as are the rates of 

heat flow and water transport between the surface and 

the atmosphere. In addition, surface temperature is an 

important discriminating factor in the classification 

of land surface types. For the oceans, the surface 

temperature influences the rate of growth of phytoplankton, the rate of absorption of carbon dioxide 

and other gases by sea water, and the rate of flow of 

heat to and from the atmosphere. Thus, temperature 

products are key not only for global temperature mapping and change determination but also as inputs to 

many additional product algorithms.

 Fire changes the surface-cover type and releases 

gases and particulate matter into the atmosphere, 

affecting ecosystems and atmospheric chemistry on 

a rapid and intense scale. Similarly, the burning of 

oil slicks on water is another significant and intense, 

but infrequent, input into the land and atmosphere 

systems.

 Volcanic eruptions can have short- and long-term 

consequences on both the local land surface and the 

regional and global atmosphere, including destruction 

of local crops and, for some eruptions, lowering of 

the global tropospheric temperatures. Higher in the 

atmosphere, in the middle and upper stratosphere, the 

effect of volcanic emissions can be cooling instead, 

from increased reflection of solar radiation caused 

by volcanic aerosols and reduced absorption of ultraviolet light brought on by reduced ozone. Both the 

cooling of the troposphere and the warming of the 

middle and upper stratosphere can impact climate 

change.

Product Overview

Land- and ocean-surface temperatures will be obtained by five instruments on Aqua, the MODIS, 

AMSR-E, and AIRS/AMSU-A/HSB, each with 

complementary advantages. Fire products will be 

obtained from MODIS.

 Regarding the surface temperatures, temperature 

algorithms require calibrated radiances as a basic 

input. Since the radiation received by satellites has 

passed through the atmosphere, corrections must be 

made for atmospheric attenuation and scattering. 

Hence meteorological data on temperature and water-vapor profiles, as well as chemical constituents, 

are needed. Also, the radiation emitted by the surface 

depends on surface emissivity as well as temperature. 

Further complicating the situation, land-surface pixels generally contain a mixture of materials having 

different emissivities, geometric characteristics, and 

topographic characteristics that must be known prior 

to computing the temperature. Land-material emissivities also can vary significantly as a function of 

wavelength, and, for the mid-wavelength IR bands, 

sufficient reflectance can exist that solar reflected 

energy must be predicted and removed from the 

measured radiance. At microwave wavelengths, the 

surface emissivity can vary significantly as a function of the soil moisture and vegetation biomass. 

Estimates of these parameters can be obtained using 

multichannel microwave data, e.g., from AMSR-E, 

and ancillary data, e.g., from external sources and 

precomputed databases keyed on surface material 

provided by the MODIS land-cover-type product 

and the global ocean masks.

 At microwave frequencies, the emissivity of the sea 

surface depends not only on sea surface temperature 

(SST) but also on roughness, the angle of incidence, 

and polarization. Surface waves mix the horizontal 

and vertical polarizations and change local incidence 

angles. Capillary waves result in diffraction of the 

radiation. Sea foam increases emissivity for both 

horizontal and vertical polarizations. Relationships 

between these effects and wind vectors have been 

determined by previous studies. The wind-vector relationships in conjunction with a tilt+foam+diffraction 

model provide the means to compute the sea surface 

emissivity. At infrared frequencies, the sea surface 

emissivity is usually assumed to be unity. 

 The AIRS/AMSU-A/HSB surface temperature 

measurements are retrieved iteratively, with updated 

cloud-cleared radiances and temperature-moisture 

profiles. Both microwave and infrared channels 

are used except in those cases where cloud cover 

prevents the successful use of the infrared channels, 

in which cases a microwave-only retrieval is made. 

SSTs for the combined microwave/infrared retrievals are expected to have an rms accuracy of better 

than 0.3 K under conditions of ≤80% cloud cover. 

AIRS/AMSU-A/HSB land surface temperatures are 

expected to have an accuracy of about 1 K.

 AMSR-E will provide SST measurements through 

clouds at a spatial resolution of 50 km. The primary 

 Overview
Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects • Data Products Handbook - Volume 2 153

AMSR-E channels for sensing SST are the 6.9 and 

10.7 GHz channels. At these frequencies, non-raining clouds are nearly transparent, with 97% of the 

surface radiation reaching the top of the atmosphere. 

In addition, the micrometer-sized atmospheric aerosols have no effect in the microwave spectrum. The 

microwave radiation penetrates about 1 cm into the 

sea water. Thus the skin temperature sensed by microwaves is considerably deeper than that for the infrared 

measurements (1 cm compared to several micrometers). A physically based algorithm is used to retrieve 

the surface temperature. Computer simulations and 

comparisons of TRMM microwave retrievals (see 

Volume 1) with buoy measurements indicate that the 

accuracy of the retrievals is about 0.5 K at a spatial 

resolution of 50 km. Retrievals will not be possible 

for a limited number of pixels due to contamination 

by sun glitter or significant rain. These are relatively 

rare occurrences (5% of all observations), and almost 

complete global coverage will be available on a daily 

basis. 

 AMSR-E land-surface temperature measurements 

are retrieved as part of the AMSR-E surface soilmoisture product. Soil moisture and vegetation water content are retrieved simultaneously with surface 

temperature in a multi-channel retrieval algorithm. 

The retrieved surface temperature is a spatial average 

over the heterogeneous land cover within the footprint 

and represents the temperature within a skin depth 

of a few centimeters below the surface. (The skin 

depth varies with the moisture content of the soil and 

vegetation.) The accuracy of the surface temperature 

measurements, taking into account the variability in 

emissivity and other complications, is estimated as 

approximately 2-3 K.

 The MODIS land-surface temperature product 

(MOD 11) provides the composite temperature of 

each pixel and the estimated emissivity from database information plus adjustments made possible 

by the multi-window algorithm. The temperature 

accuracy is better than 1 K for materials with known 

emissivities. MODIS SST (MOD 28), derived from 

radiance measurements, is an estimate of the skin 

temperature (top micrometers) of the 1-km pixel 

viewed by MODIS. The algorithm is similar to the 

land-surface temperature algorithm in that it uses 

multiple atmospheric window techniques to estimate 

the atmospheric parameters required to compensate 

for absorption and scattering. The bulk temperature of 

the near-surface ocean is the temperature of the upper 

10-20 cm to one meter as measured by conventional 

thermometers on buoys and ships. Extensive analysis 

has been done of satellite and in situ data to enable 

the algorithm to estimate bulk temperature as well as 

skin temperature.

 The MODIS thermal anomaly product (MOD 14) 

contains information unique to understanding the 

characteristics of fires, including energy emitted, size, 

temperature, and the ratio of smoldering to flaming 

area, and is available for both day and night periods. 

The composites produced include a monthly day-andnight fire-occurrence aggregation and a summary of 

the number of fires in classes related to the strength of 

the fire. The fire product also includes monitoring of 

the burning of oil slicks. Separately, MOD 40 detects 

burn scars.

Product Interdependency and Continuity 

with Heritage Data

The AIRS/AMSU-A/HSB surface temperatures build 

on the heritage of the HIRS3/AMSU-A, although 

should obtain improved accuracies because of the 

much higher spectral resolution of the AIRS. The 

AIRS/AMSU-A/HSB retrievals will take advantage 

of both infrared and microwave channels, then be 

compared with the infrared-only retrievals of the 

MODIS and the microwave-only retrievals of the 

AMSR-E.

 The AMSR-E SST is a stand-alone product, not 

requiring inputs from other sensors or ancillary data. 

However, blending of the AMSR-E microwave retrievals with the infrared retrievals from AIRS and 

MODIS offers the potential for a significant synergism in that the microwave retrievals are not affected 

by clouds or aerosols while the infrared retrievals 

offer superior spatial resolution. 

 The MODIS land-surface temperature product 

requires MODIS calibrated radiances and the cloudmask product as inputs. Also, atmospheric temperature and water-vapor profiles are needed along with 

a database of emissivities, the use of which is driven 

by the land-cover-classification products, which enable surface material to be specified for each pixel. 

The land-surface temperature product is an input to 

other land products including vegetation indices, 

evapotranspiration, and net primary production, and 

as a background temperature estimate for the MODIS 

fire product.

 Similarly, the MODIS SST product uses MODIS 

calibrated radiances and the cloud-mask product, but 

does not require the complex emissivity estimation 

databases and algorithms that the MODIS landsurface temperature product requires. Sun glint is a 

significant source of error in the mid-wavelength IR 

bands, so the mask product is critical for identifying 

glint pixels as are sea surface wind ancillary data, 

which are used to estimate the magnitude of the glintarea radiance.

Overview
154 Data Products Handbook - Volume 2 Overview • Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects 

(Surface Temperatures, etc., continued)

 The MODIS fire product uses calibrated MODIS 

radiances, the MODIS cloud mask, vegetation 

indices, land surface temperature, and land cover 

type.

 In situ surface temperatures are measured at 

numerous locations around the world. These locations include meteorological stations on land and 

ship- and buoy-based instruments at sea. However, 

these measurements are not adequate in terms of the 

radiative phenomena or spatial coverage. Land- and 

sea-surface temperature data taken by instruments 

on Aqua will add to the valuable input already provided by AVHRR, HIRS, Landsat, CZCS, and the 

Terra instruments, contributing to a large, long-term 

global land- and sea-surface temperature data set that 

should lead to a more complete understanding of the 

effects of the various Earth system cycles on surface 

temperature.

Suggested Reading

Becker, F., 1987.

Chahine, M. T., 1980.

Deepak, A., 1980.

Justice, C. et al., 1998.

Kaufman, Y. J. et al., 1998a,b.

Njoku, E., 1994.

Njoku, E. G., and O. B. Brown, 1993.

Parker, D. E. et al., 1995.

Price, J. C., 1983.

Price, J. C., 1984.

Reynolds, R. W., and T. M. Smith, 1994.

Robinson, J. M., 1991.

Smith, W. L. et al., 1996.

Susskind, J. et al., 1998.
Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects • Data Products Handbook - Volume 2 155

AIRS/AMSU-A/HSB Surface 

Analysis Product (AIR 06)

Product Description

The AIRS Surface Analysis Product consists of seaand land-surface skin temperature and infrared and 

microwave spectral surface emissivity. One sounding 

of each parameter is produced in a given AMSU-A 

footprint.

 The skin temperature is representative of the 

temperature in the clear portion of the scene. Objective rejection criteria indicate those cases in which 

a successful AIRS/AMSU-A/HSB retrieval cannot 

be produced, generally because of excessive cloud 

cover in the field of view. No infrared spectral surface 

emissivity is produced under these conditions.

 All retrieved values are given an error estimate 

and a flag indicative of whether they are combined 

microwave/infrared (MW/IR) retrievals or primarily MW retrievals. Successful MW/IR retrievals are 

expected about two thirds of the time.

 Sea surface temperatures for the combined MW/IR 

retrieval are expected to have an rms accuracy of 

better than 0.3 K for a single spot retrieval under 

conditions of up to 80% cloudiness. Land surface 

temperatures are expected to have an accuracy of 

about 1 K. The degradation compared to sea surface 

temperature is due to non-homogeneity in the scene 

with regard to both surface skin temperature and 

spectral emissivity.

Research and Applications

Sea- and land-surface temperatures play a strong role 

in the exchange of energy from the surface to the atmosphere and also strongly affect longwave radiation 

lost to space. They are very important for studying 

interannual variability and the effects of phenomena 

such as El Niño on the global circulation.

 The surface temperatures and emissivities are 

considerably better than what is achievable from 

HIRS3/AMSU-A data because the high spectral 

resolution of AIRS allows for very clean windows 

in the 8-12- μm and 4.0-3.7-μm regions, which better discriminate surface from atmospheric emission 

and allow for the determination of surface spectral 

emissivity.

Data Set Evolution

The surface parameters are retrieved iteratively in 

a number of passes. Each pass is a fully non-linear 

retrieval using the latest set of cloud-cleared radiances 

and temperature-moisture profiles determined from 

the sounding channels. Selected AIRS channels are 

used in the surface parameter retrieval in the spectral 

ranges 758-980 cm-1, 1072-1234 cm-1, 2170 cm-1- 

2177 cm-1, and 2393-2668 cm-1.

 The surface skin temperature is determined as well 

as coefficients of eight spectral-emissivity functions 

and three functions of the spectral bi-directional reflectance of solar radiation. The surface-parameter 

retrieval step also uses six microwave channels 

between 23.8 GHz and 150 GHz that provide information about the microwave spectral emissivity and 

determine the surface skin temperature in those cases 

when a successful combined MW/IR retrieval could 

not be produced.

 The AMSU-A data are critical for accounting for 

cloud effects on the AIRS observations. Successful 

AIRS/AMSU-A soundings of all atmospheric 

parameters can generally be produced in cases of 

multilayer clouds with up to 80% fractional cloud 

cover in the AMSU-A footprint.

 Data validation will be accomplished through 

comparison of the retrieved surface temperatures and 

emissivities with simultaneous in situ measurements 

at DoE ARM/CART sites and MODIS and ASTER 

dedicated validation sites. Vicarious validation will 

be accomplished through comparisons with MODIS 

and ASTER-retrieved land surface temperatures and 

AVHRR-retrieved sea surface temperatures.

Suggested Reading

Anyamba, E., and J. Susskind, 1998.

Aumann, H. H., and C. Miller, 1995.

Chahine, M. T. et al., 1987.

Chahine, M. T. et al., 1997.

Janssen, M. A., 1993.

Lakshmi, V. et al., 1998.

Lambrigtsen, B., 1996.

Rosenkranz, P. W., 1995.

Smith, W. L., and H. M. Woolf, 1976.

Susskind, J., and D. Reuter, 1985.

Susskind, J. et al., 1993.

Susskind, J. et al., 1998.

Aqua / AIRS/AMSU-A/HSB
156 Data Products Handbook - Volume 2 Aqua / AIRS/AMSU-A/HSB • Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects 

AIRS/AMSU-A/HSB Surface Analysis Product 

Summary

Coverage: Global

Spatial/Temporal Characteristics: Resolution of 

AMSU-A FOV (40.6 km at nadir)/twice daily 

(daytime and nighttime)

Key Science Applications: Exchange of energy 

from surface to atmosphere, interannual 

variability, TOA longwave radiation

Key Geophysical Parameters: Land and sea 

surface skin temperature, infrared spectral 

surface emissivity, microwave spectral surface emissivity

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 5 MB

File Frequency: 240/day

Primary Data Format: HDF-EOS

Browse Available: Yes

Additional Product Information:

http://www-airs.jpl.nasa.gov/

DAAC: NASA Goddard Space Flight Center

Science Team Contacts: 

 J. Susskind

 H. Revercomb

 L. L. Strow

(AIRS/AMSU-A/HSB Surface Analysis Product, continued)
Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects • Aqua / AMSR-E Data Products Handbook - Volume 2 157

AMSR-E Sea Surface 

Temperature

Product Description

The sea surface temperature (TS) derived from the 

AMSR-E microwave radiances is indicative of the 

water temperature at a depth of about one millimeter. 

The spatial resolutions of this product are 38 km and 

56 km, and its expected rms accuracy at these resolutions is 0.5 K. At microwave frequencies below 11 

GHz, non-raining clouds are nearly transparent, and 

hence this product is available even when clouds are 

present. However, retrievals in rainy areas, which can 

be identified using higher AMSR-E frequencies, are 

not reliable and will not be reported. The product will 

be produced for individual pixels across the AMSR-E 

swath (Level 2) as well as an Earth-gridded product 

(0.25° in latitude and longitude) averaged daily, 

weekly, and monthly (Level 3).

Research and Applications

Sea surface temperature reflects the storage of thermal energy in the upper mixed layer of the oceans. It 

is relevant as a potential indicator of global climate 

change, responding to long-term variations in total 

heat flux. Shorter term variability, particularly that associated with the onset of El Niño and La Niña phases, 

also has significant ramifications. For example, the 

normally productive fisheries in the upwelling zones 

of the eastern Pacific experience dramatic declines in 

stocks during the warm El Niño phase. Furthermore, 

this cycle strongly influences global precipitation and 

weather patterns, leading to severe flooding in some 

areas and severe droughts and associated fires in 

others. The value of improvements in the ability to 

anticipate the occurrence and magnitude of El Niño 

events based on combined analysis of remote sensing data and general circulation models was clearly 

demonstrated before and during the 1997/98 El Niño 

and the 1998/99 La Niña. By having advance warning of impending shifts in weather patterns, regions 

that are most strongly affected by these phenomena 

were able to act to mitigate those effects. Accurate 

SST measurements are also a principal component 

of atmospheric general circulation models, defining the surface boundary conditions, and serve as a 

validation reference for more-sophisticated coupled 

models, which attempt to calculate both atmospheric 

and oceanic state self-consistently. Other areas of 

interest which benefit from accurate measurements 

of sea surface temperature include studies of coastal 

flow and boundary-current dynamics, which are relevant both to the research community and to users 

such as those in the shipping and fishing industries, 

as well as to navies, all of which rely heavily on the 

ability to effect safe and efficient ocean transit. 

Data Set Evolution

The TS retrieval algorithm is based on experience 

gained from previous microwave radiometers, including SMMR, SSM/I, and the TRMM microwave 

imager (TMI). In particular, recent analysis of the 

TMI data has provided the means to characterize the 

sea-surface emission precisely as a function of TS

and wind speed. In preparation for AMSR-E, a TS

algorithm was developed for TMI. The performance 

of the TMI algorithm was assessed by comparison 

with the Reynoldsʼ Optimum Interpolation (OI) 

SST product, which is a blend of AVHRR retrievals 

and buoy/ship reports. The rms difference between 

the TMI and OI TS retrievals is 0.8°C. Some of this 

difference is due to the spatial-temporal mismatch between the two types of observations as well as errors 

in the OI product. The AMSR-E TS retrievals should 

be more accurate than the TMI retrievals because 

AMSR-E has an additional set of dual-polarization 

channels at 6.9 GHz, which is ideally suited for TS

retrievals. Also, TMI is experiencing some calibration 

problems due to the heating and cooling of the radiometer over the course of an orbit. This problem 

should be less severe for AMSR-E, which will be in 

a sun-synchronous orbit. 

Suggested Reading

Hofer, R. E. et al., 1981.

Lipes, R. G. et al., 1979.

Parker, D. E. et al., 1995.

Reynolds, R. W., and T. M. Smith, 1994.

Wentz, F. J., 1983.
158 Data Products Handbook - Volume 2 • Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects 

AMSR-E Sea Surface Temperature Summary

Coverage: Global ocean surface, clear and 

cloudy skies except in the presence of 

rainfall

Spatial/Temporal Characteristics: 

Level 2: Swath pixels at 38-km and 56-km 

resolution

Level 3: 0.25° latitude-longitude grid/daily, 

weekly, and monthly

Key Science Applications: Climate variability, 

air-sea interaction, radiation balance, hydrological cycle, ocean circulation

Key Geophysical Parameters: Sea surface 

temperature

Processing Level: 2, 3

Product Type: Standard, post-launch

Maximum File Size: 10 MB

File Frequency: 29/day

Primary Data Format: HDF-EOS

Browse Available: Accompanies data product

Additional Product Information:

http://wwwghcc.msfc.nasa.gov/AMSR/

html/amsr_products.html

DAAC: National Snow and Ice Data Center

Science Team Contact:

F. J. Wentz

 Aqua / AMSR-E

(AMSR-E Sea Surface Temperature, continued)
Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects • Data Products Handbook - Volume 2 159

Data Set Evolution

The Aqua MODIS LST products build and improve 

upon the experience of LST retrieval from MODIS 

Airborne Simulator (MAS) data and Terra MODIS 

data.

Suggested Reading

Justice, C. et al., 1998. 

Running, S. W. et al., 1994.

Salisbury, J. W., and D. M. DʼAria, 1992.

Snyder, W., and Z. Wan, 1996.

Wan, Z., and J. Dozier, 1996.

Wan, Z., and Z.-L. Li, 1997.

Sample MOD 11 Level-2 LST Image generated from Terra MODIS data in 

bands 31 and 32 on April 4, 2000 (19:15 UTC) in California and Nevada. 

The dark area in the lower left corner is the Pacific Ocean. The sites to 

the left of symbols A, B, C, and D are Lake Tahoe, Mono Lake, Railroad 

Valley, and Death Valley. The coincident IR radiometer measurement data 

over Mono Lake agree with the MODIS LST value within 1.2K. (Image 

developer: MODIS Science Team / MODLAND / Z. Wan, UCSB).

MODIS Land Surface 

Temperature (LST) and 

Emissivity (MOD 11, MOD 

11comb, MOD 11adv)

Product Description

The Aqua MOD11 product is similar to the Terra 

MOD11 product (see p. 191 in Volume 1). It contains Level 2 and 3 LST and emissivity retrieved 

from Aqua MODIS data at spatial resolutions of 1 

km and 5 km over global land surfaces under clearsky conditions. The generalized split-window LST 

algorithm will be used to retrieve LST for MODIS 

pixels with known emissivities in bands 31 and 32. 

The physics-based day/night LST algorithm will be 

used to simultaneously retrieve surface band emissivities and temperatures from a pair of daytime and 

nighttime MODIS observations in bands 20, 22, 23, 

29, and 31-33 over all types of land cover.

 The MOD11comb (combination) product contains Level 2 and 3 land-surface emissivities and 

temperatures retrieved through the combination of Aqua and Terra MODIS data. The 

MOD11comb surface temperature, with an 

enhanced diurnal feature, will be more suitable for various applications than an Aqua or 

Terra product alone.

 In the early post-launch period, the Aqua 

MOD11adv (advanced) product will include 

Level 2 and 3 land surface emissivities and 

temperatures retrieved by an advanced LST 

algorithm that also corrects the effects of thin 

cirrus clouds and aerosols with inputs from 

MODIS atmospheric products.

Research and Applications

Land-surface temperature is a good indicator of both the energy balance at the Earthʼs 

surface and the greenhouse effect because it is 

one of the key parameters in the physics of the 

land-surface processes. It is required for a wide 

variety of climate, hydrological, ecological, and 

biogeochemical studies. This product will be 

used in generating other MODIS products and 

in a variety of EOS interdisciplinary studies.

Aqua / MODIS
160 Data Products Handbook - Volume 2 • Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects 

MODIS Land Surface Temperature and 

Emissivity Summary

Coverage: Global land surface

Spatial/Temporal Characteristics: 1 km, 5 km, and 

0.5°/8-day and monthly

Key Science Applications: Inputs to climate, 

hydrological, ecological modeling

Key Geophysical Parameters: Land surface temperature, land surface emissivity

Processing Level: 2, 3

Product Type: Standard, at-launch and postlaunch

Maximum File Size: 24 MB

File Frequency: 288 /day

Primary Data Format: HDF-EOS

Additional Product Information:

http://modis-land.gsfc.nasa.gov/products/

products.asp?ProdFamID=8

DAAC: EROS Data Center

Science Team Contact:

 Z. Wan

(MOD 11, MOD 11comb, MOD 11adv, continued)

 Aqua / MODIS
Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects • Data Products Handbook - Volume 2 161

MODIS Thermal Anomalies 

– Fires (MOD 14); MODIS Burn 

Scars (MOD 40)

Product Description

The MODIS Thermal Anomalies product includes 

fire occurrence (day/night), fire location, the logical criteria used for the fire selection, and an energy 

calculation for each fire. The product also includes 

composite 8-day-and-night fire occurrence (full resolution), composite monthly day-and-night fire occurrence (full resolution), gridded 10-km summary 

per fire class (daily/8-day/monthly), and a gridded 

0.5° summary of fire counts per class (daily/8-day/

monthly). The Level 2 product includes various 

fire-related parameters including the occurrence of 

day and nighttime thermal anomalies, flagged and 

grouped into different temperature classes with 

emitted energy from the fire. These parameters are 

retrieved daily at 1-km resolution. The fire product 

uses the special fire channel at 3.9 μm that saturates 

at 500 K and the high-saturation level of the 11-μm 

channel. During the night, the fire product will also 

use the 1.65- and 2.15-μm channels. The standard 

products will include the area burned.

Research and Applications

Fire is an important process within a significant number of terrestrial biomes, and the release of gases 

and particulate matter during biomass burning is an 

important contributor to the chemical reactions and 

physical processes taking place in the atmosphere. 

Aqua / MODIS

NOAA/AVHRR-Detected High-Temperature Sources for June 25, 1992, derived from the IGBP-DIS global 1-km set provided by 

the USGS Eros Data Center.

Fire is a significant and continuous factor in the 

ecology of savannas, boreal forests, and tundra, and 

plays a central role in deforestation. Fire information will be used to drive regional emissions models, 

trace-gas transport models, and mesoscale models 

of atmospheric chemistry. Important impacts of fires 

include:

• changes of physical state of vegetation and release 

of greenhouse gases;

• release of chemically reactive gases during 

biomass burning;

• release of soot and other particulate matter during 

fires;

• changes in the exchange of energy and water between land surfaces and the atmosphere; and

• changes in plant community development and soil 

nutrient, temperature, and moisture, and cloud development and reflectivity.

Data Set Evolution

The MODIS fire products build and improve upon 

the experience of fire assessment primarily using the 

NOAA AVHRR and GOES systems. Currently, no 

one sensing system provides the instrument characteristics needed for an effective global fire-monitoring 

program. The MODIS sensor has been designed to 

include characteristics specifically for fire detection 

and will provide a unique capability over existing 

sensors in terms of fire monitoring. The locational 

accuracy and improved instrument characterization 

and calibration will enable unprecedented fire-monitoring data sets. The combination of Terra and Aqua 

data will provide four observations per day, giving 
162 Data Products Handbook - Volume 2 • Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects 

MODIS Sea Surface 

Temperature (SST) (MOD 28)

Product Description

This Level 2 and 3 product provides sea surface temperature at 1-km (Level 2) and 4.6 km, 36 km, and 

1° (Level 3) resolutions over the global oceans. In 

addition, a quality-assessment parameter is included 

for each pixel. The Level 2 product is produced daily 

and consists of global day and night coverage every 

24 hours. It is used to generate the gridded Level 3 

products daily, 8-day weekly, monthly, and yearly 

for day and night conditions. A quality parameter is 

provided for each data set.

Research and Applications

The global distribution and variability of sea surface temperature are key inputs to Earth energy and 

hydrological balance studies and long-term climatechange studies. In addition, sea surface temperature is 

required by a number of MODIS algorithms including 

those for precipitable water, lifted index, water-leaving radiance, productivity, oceanic aerosol properties, 

and temperature and water-vapor profiles. MODIS 

sea surface temperature retrievals will be incorporated 

into a match-up database with radiance and buoy sea 

surface temperature observations (see MOD 32).

Data Set Evolution

Sea surface temperature determination is based on 

MODIS-calibrated mid- and far-IR radiances (Bands 

20, 22, 23, 31, and 32 from MOD 02), using an algorithm that exploits the differences in atmospheric 

transmissivity in the different IR bands to enable 

highly accurate estimation of the atmospheric effects, thereby enabling ancillary input to the algorithm along with a land mask, which is used to mark 

non-water pixels while an ice-extent mask limits polar 

sea coverage. A sequence of spatial and temporal homogeneity tests is applied to validate the quality of 

the cloud-free observations. The AIRS SST estimate 

will be used as a near-real-time quality assessment 

of skin temperature. Visible and near-IR radiances 

(Bands 3, 4, 5, 6) will be used as a secondary cloud 

flag in the event that the cloud-screening product is 

not available.

 Aqua / MODIS

a good sampling of the diurnal cycle of fire activity. 

MODIS will also offer unique spatial and radiometric 

capabilities for burn-scar detection. Automatic procedures for burn-scar detection will be developed to 

provide a standard burn-scar product.

Suggested Reading

Andreae, M. O. et al., 1994.

Justice C. O. et al., 1993.

Justice C. O. et al., 1998.

Kaufman, Y. J. et al., 1990.

Kaufman, Y. J. et al., 1998a.

Levine, J. S., 1991.

Penner, J. E. et al., 1992.

MODIS Thermal Anomalies – Fires and Burn 

Scars Summary

Coverage: Global, daytime/nighttime

Spatial/Temporal Characteristics: 1 km, 10 km, 

and 0.5°/1-day (MOD 14 only), 8-day, 

16-day (MOD 40 only), monthly

Key Geophysical Parameters: Fire occurrence 

and class, fire selection criteria, fire location, 

smoldering and flaming ratio, burned area

Processing Level: 2, 3 for MOD 14; 4 for 

MOD 40

Product Type: Standard, at-launch and postlaunch

Maximum File Size: 81 MB (MOD 14), 92 MB

(MOD 40)

File Frequency: 1/day (Daily, MOD 14 only), 

1/8-day (8-day), 1/16-day (16-day, MOD 40 

only),1/month (Monthly)

Primary Data Format: HDF-EOS

Additional Product Information:

http://modis-land.gsfc.nasa.gov/products/

products.asp?ProdFamID=1

DAAC: EROS Data Center

Science Team Contacts: 

 Y. J. Kaufman

 C. O. Justice

(MODIS Thermal Anomalies, continued)
Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects • Data Products Handbook - Volume 2 163

Changes in Pacific Sea Surface Temperature (SST) due to El Niño. SST from the NOAA AVHRR, showing patterns before (left) and 

during (right) the major 1992 El Niño. The improved accuracy of IR SST products expected from MODIS will enable investigation of 

the relatively small changes in SST hypothesized to be responsible for triggering El Niño cycles in the Pacific and their ramifications 

globally. (Note: temperatures range from 20° to 30°C.) (From Brown, O., and G. Feldman, “Reports to the Nation: El Niño and 

Climate Prediction,” Spring 1994, No. 3, University Corporation for Atmospheric Research, Boulder, CO.)

Aqua / MODIS

Suggested Reading

Abbott, M. R., and D. B. Chelton, 1991.

Barton, I. J. et al., 1989.

Brown, O. B., and R. E. Cheney, 1983.

Deschamps, P., and T. Phulpin, 1980.

Edwards, T. et al., 1990.

Llewellyn-Jones, D. T. et al., 1984.

McClain, E. P. et al., 1985.

Minnett, P. J., 1991.

Minnett, P. J., 1995.

Schluessel, P. et al., 1990.

Smith, A. H. et al., 1994.

Smith, W. L. et al., 1996.

Strong, A. E., and E. P. McClain, 1984.

MODIS Sea Surface Temperature Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 1 km/daily 

(Level 2); 4.6 km, 36 km, 1°/daily, 8-day, 

monthly, yearly (Level 3)

Key Science Applications: Energy and hydrological balance, climate-change models

Key Geophysical Parameters: Sea surface 

temperature

Processing Level: 2, 3

Product Type: Standard, at-launch

Maximum File Size: 33 MB (Level 2); 640 MB 

binned, 134 MB mapped (Level 3)

File Frequency: 288/day (Daily Level 2), 4/day 

(Daily Level 3), 4/8-day (8-day Level 3),

4/month (Monthly Level 3), 4/year (Yearly 

Level 3)

Primary Data Format: HDF-EOS

Browse Available: 36 km sample imagery available at the Goddard DAAC (Level 3 only)

Additional Product Information: 

http://modis-ocean.gsfc.nasa.gov/

dataprod.html

DAAC: NASA Goddard Space Flight Center

Science Team Contact:

 O. Brown
164 Data Products Handbook - Volume 2 Aqua / MODIS • Surface Temperatures of Land and Oceans, Fire Occurrence, and Volcanic Effects

An Example of an 8-day Global Sea Surface Temperature Map generated using data acquired in 

March 2000 from the MODIS instrument on Terra. 

(MODIS Sea Surface Temperature, continued)

Sea Surface Temperature off the East Coast of North America, May 8, 2000, derived 

from data of the MODIS instrument on Terra. This is a Level 2, 1-km resolution product and shows entrainment of Gulf Stream, shelf, and Sargasso Sea waters into cold 

and warm core rings.
165

Vegetation Dynamics,

Land Cover, and 

Land Cover Change

Aqua

• AMSR-E

• MODIS

Landsat 7* • ETM+

VCL

• MBLA

* See Overview Product Description immediately following, and also see chapter on Radiance/Reflectance and Irradiance Products.
166 Data Products Handbook - Volume 2 • Vegetation Dynamics, Land Cover, and Land Cover Change 

Vegetation Dynamics, Land 

Cover, and Land Cover Change 

– An Overview

Relationship to Global Change Issues

In spite of the severe inroads of human constructions, 

much of the Earthʼs land surface remains vegetated, 

and this vegetation remains critical to the life-support 

systems of humans and all other land-based terrestrial 

animals. Knowledge of vegetation, land cover and 

land-use change provides the basis for understanding 

land and water resource management and a range of 

research and global monitoring objectives, including global carbon modeling, greenhouse gas emission 

inventories, regional environment and development 

assessments, and forest resource management. Ecologists and climatologists have developed models that 

describe the matter and energy exchanges, but lack of 

long-term global data limits their use in Earth system 

models. Additionally, we lack good global data on 

land-surface albedos, on the distribution of soils and 

important properties of vegetation morphology and 

physiology, and on water storage in soils and snow 

cover and its relationship to runoff. Incorporation of 

this information in the land-surface component of 

climate models will improve estimates of exchanges 

of water and energy with the atmosphere and lead to 

more realistic climate simulations.

Product Overview – Vegetation

The MODIS land-vegetation products provide information on terrestrial systems that have important links 

to climate and atmospheric composition. These links 

are needed for development and application of fully 

coupled models of the Earthʼs system. They involve 

exchanges of energy and moisture, radiatively active 

trace gases such as carbon dioxide and methane, and 

photochemically active species such as nitric oxide 

and non-methane hydrocarbons, released by soils, 

plants, and biomass burning.

 The MODIS products provide the basic measurements of the photosynthetic engines that drive carbon 

transport on land. For land areas, vegetation density 

and vegetation-canopy factors are produced, and 

these are used to generate land primary productivity, 

the amount of carbon fixed by vegetation growth per 

day or per a longer unit of time such as a year. The 

MODIS vegetation measurements are provided by the 

Vegetation Indices (MOD 13), Leaf Area Index (LAI) 

and Fraction of Photosynthetically Active Radiation 

(FPAR)(MOD 15), and Vegetation Cover Conversion 

(MOD 44) products. 

 Vegetation indices are radiometric measures of the 

amount of vegetation present on the ground in a particular pixel. The MODIS indices are obtained from 

measurements of reflected visible and near-infrared 

energy from vegetation and are called normalized 

difference vegetation indices (NDVIs). These indices 

have values between -1 and 1 and increase with increasing amounts of vegetation. Several new indices 

have been developed which compensate for soil and 

atmospheric effects, and these are combined into the 

modified vegetation index (MVI), also to be produced 

by MODIS. The indices will be produced at different 

resolutions and time-averaging intervals to provide 

measures of vegetation density and productivity required by global-change models.

 The LAI product provides information on the structural property of a plant canopy such as the number 

of equivalent layers of leaves the vegetation displays 

relative to a unit ground area. It is derived from the 

vegetation index. This parameter is an input to the 

Photosynthesis and Net Primary Production algorithms that produce the global model inputs for land. 

The FPAR product provides another key input to the 

production algorithm. It measures the proportion of 

available radiation in the specific photosynthetically 

active wavelengths of the spectrum that a canopy 

absorbs. It is also derived from the vegetation index. 

These two products provide the vegetation measurements that are combined with surface temperature 

and radiation inputs in the vegetation-production 

algorithm.

 The two primary vegetation products from the VCL 

mission and its MBLA instrument are geolocated 

canopy-top height and geolocated vertical distribution 

of intercepted surfaces (VDIS). These are both Level 

2 products obtained with a spatial resolution of approximately 25 km. The canopy-top height provides a 

unique measure of vegetative heights at global scales 

and also an indication of above-ground biomass. The 

VDIS provides a new means to classify vegetation 

architecture and functions as a predictor of the successional state of a forest.

 The ETM+ instrument on Landsat 7 does not directly provide vegetation products in the sense that 

the MODIS instrument on Aqua or the MBLA on 

VCL do. Rather, calibrated radiances from ETM+ 

are made widely available to the scientific community concerned with the global distribution and 

characterization of vegetation at higher resolution 

than provided by MODIS. It is left to the scientific 

community to develop specific ETM+ products, 

which will include maps that quantify land use, land 

cover, deforestation, plant biomass, urban sprawl, and 

ice-sheet dynamics. 

 Overview
Vegetation Dynamics, Land Cover, and Land Cover Change • Data Products Handbook - Volume 2 167

Product Overview – Land Cover and Land 

Cover Change 

The AMSR-E, MODIS, MBLA, and ETM+ instruments will all obtain information of value to studies 

of land cover and land cover change, AMSR-E for soil 

moisture, MBLA for ground elevations, and MODIS 

and ETM+ for land cover types.

 The AMSR-E soil moisture product obtains a measure of the mean volumetric moisture content in the 

top few millimeters of soil, at a 56-km spatial resolution. The product is restricted to non-mountainous 

regions with no snow, low vegetation, and no ongoing 

precipitation. However, in spite of the limitations, 

AMSR-E is considered the best instrument to date 

for satellite sensing of soil moisture. The anticipated 

accuracy of the AMSR-E soil moisture estimates in 

the restricted regions of soil moisture retrievals is 

about 0.06 g cm-3.

 The MBLA ground-elevation measurements will 

obtain billions of sub-canopy surface elevation points, 

determined by assuming that the laser waveformʼs 

last return exceeding a set threshold level is the 

ground return. These measurements will provide 

a significant advance over current remote sensing 

topographic techniques, none of which is able reliably to penetrate a canopy-covered surface to obtain 

accurate surface heights.

 The MODIS Land Cover Type product (MOD 12) 

maps global terrestrial land cover at 1-km resolution. 

The land cover change product will detect areas of 

change and identify types of change processes as they 

occur. Land cover, as well as both human and natural 

alteration of land cover, plays a major role in globalscale patterns of climate and biogeochemistry.

 The MODIS land cover product will recognize 

18 basic classes of land cover types, independent of 

climate. Vegetation classes will separate needleleaf, 

broadleaf, and graminoid leaf structures; annually 

deciduous and evergreen canopy habitats; and sparseto-dense-cover fractions following the International 

Geosphere-Biosphere Programme (IGBP) scheme. 

Also, major classes of agricultural activities will be 

recognized as well as several categories of land surfaces with little or no plant cover, such as bare sand, 

soil, or rock.

 The MODIS land cover product will detect and 

categorize changes in terrestrial features and processes on a global scale. Independent of the land 

cover product, the land-cover-change algorithm will 

compare, pixel-by-pixel, the temporal development 

curve of a set of biophysical and spatial indicators 

derived from MODIS data.

Overview

 As indicated above, the ETM+ instrument on Landsat 7 will not directly be the source of land-cover and 

land-cover-change products, but rather will be the 

source of calibrated radiances at 30-m resolution and 

at seven spectral bands that will make it possible for 

members of the scientific community worldwide to 

apply their own algorithms to develop products that 

define land cover and land cover change on a global 

basis.

Product Interdependency and Continuity 

with Heritage Data

The MODIS land products flow in a chain from the 

Level 2 reflectance and land cover type parameters to 

biophysical- and biochemical-derived products used 

as inputs to Earth system models. Specific product 

dependencies for the land vegetation indices are MODIS radiance and imagery products, including Level 

1B calibrated radiance, surface-leaving radiance, and 

aerosol optical depth. The vegetation index is the key 

input to Leaf Area Index and FPAR products, which 

are the key inputs along with land cover type into the 

biomass-product algorithm.

 Each MODIS product has its own science requirements and objectives and builds on the heritage of 

products produced for many years from AVHRR and 

other data sets. MODIS will produce a “continuity” 

NDVI to maintain the AVHRR NDVI time series 

using an algorithm that closely approximates the 

heritage index. A worldwide database has been created with the AVHRR data, and EOS will continue 

this series.

 The products that will come into being as a result 

of analysis of the calibrated radiances from ETM+ 

will continue the long heritage of measurements that 

extend back to the Landsat 1 satellite, launched in 

1972. Despite improvements in Landsat through the 

development of ETM+, continuity has been maintained with the original measurements from Landsat 1 

and with the spectral enhancements added to Landsats 

4 and 5. Improvements incorporated in the Landsat 7 

sensor include a 15-m panchromatic band, superior 

spatial resolution for the thermal band (60 versus 120 

m), and two solar calibrators.

 Landsats 2 and 3, launched in 1975 and 1978 

respectively, were configured similarly. In 1984, 

Landsat 4 was launched with the MSS and a new 

instrument called the Thematic Mapper (TM). Instrument upgrades included improved ground resolution (30 m) and 3 new channels, or bands. In addition 

to using an updated instrument, Landsat 4 made use 

of the multimission modular spacecraft (MMS), 
168 Data Products Handbook - Volume 2 Overview • Vegetation Dynamics, Land Cover, and Land Cover Change 

which replaced the Nimbus-based spacecraft design 

employed for Landsats 1-3. Landsat 5, a duplicate 

of Landsat 4, was launched in 1984 and continued 

returning useful data in the late 1990s, far beyond its 

5-year design life. Landsat 6, equipped with a 15-m 

panchromatic band, was lost immediately after launch 

in 1993.

Suggested Reading

Asrar, G., 1989.

Lambin, E. F., and A. H. Strahler, 1994a,b.

Los, S. O. et al., 1994. 

Moody, A., and C. E. Woodcock, 1995.

Nemani, R. R., and S. W. Running, 1995.

Nyoku, E., and L. Li, 1999.

Oliver, C. D., and B. C. Larson, 1990.

Rencz, A., 1999.

Running, S. W. et al., 1994.

Sellers, P. J., 1987.

Ustin, S. L. et al., 1993.

van Leeuwen, W. J. D. et al., 1999.

Vonder, O. W., and J. P. G. W. Clevers, 1998.

Vorosmarty, C. et al., 1991.

(Vegetation Dynamics, Land Cover, and Land Cover Change 

– An Overview, continued)
Vegetation Dynamics, Land Cover, and Land Cover Change • Data Products Handbook - Volume 2 169

AMSR-E Surface Soil Moisture

Product Description

This Level 2 and 3 product provides surface soil 

moisture at 56-km resolution on a nominal 25-km 

Earth-registered grid. Gridded brightness temperatures are also provided at Level 3. Quality-control 

indices and surface-type classification are included. 

The product spatial resolution is determined by the 

sensor resolution at the lowest frequency used in 

the retrievals (6.9 GHz). Surface soil moisture is 

defined as the mean volumetric moisture content (g 

cm-3) in the top few millimeters of soil, or skin depth, 

averaged over the retrieval resolution. In regions of 

moderate-to-dense vegetation cover, snow, frozen 

ground, precipitation, open water, or mountainous 

terrain, soil moisture retrievals are not made. The 

accuracy of the soil-moisture retrievals is best for 

bare soils, and worsens as the vegetation cover increases. Surface temperature and vegetation water 

content are retrieved simultaneously with the soil 

moisture and are included in the product. The Level 

2 product is provided in ascending and descending 

swath segments. The Level 3 product is generated by 

compositing the ascending and descending Level 2 

products into daily global maps.

Research and Applications

Water and energy fluxes at the land-atmosphere 

interface are influenced to a large extent by the surface soil moisture. Satellite-derived information on 

soil moisture and its variability is thus important for 

modeling surface hydrologic processes, for enabling 

improved weather and climate prediction, and for 

monitoring floods, droughts, and vegetation status 

on a global basis. 

Data Set Evolution

Soil-moisture remote sensing requires measurements 

at long wavelengths to penetrate vegetation cover, and 

large antennas to provide adequate spatial resolution. 

The AMSR-E is the best instrument to date for this 

purpose, with a lowest frequency of 6.9 GHz and a 

spatial resolution of 56 km at this frequency. The 

AMSR-E goal is to obtain soil-moisture estimates 

with an accuracy of ~0.06 g cm-3 in regions with 

vegetation water content of less than ~1.5 kg m-2. No 

adequate satellite soil-moisture data sets currently exist. The AMSR-E soil-moisture data set is produced 

using algorithms developed from field-experiment 

data using airborne and ground-based instrumentation 

and from historical Nimbus 7 SMMR data analysis.

Suggested Reading

Njoku, E. G., and D. Entekhabi, 1996.

Njoku, E., and L. Li, 1999.

Wang, J. R., and B. J. Choudhury, 1995.

AMSR-E Surface Soil Moisture Summary

Coverage: Global land surface, although only 

under snow-free and low-vegetation conditions

Spatial/Temporal Characteristics: 56-km spatial 

resolution on a nominal 25-km Earth-fixed 

grid; swath (Level 2) and daily (Level 3) 

ascending and descending

Key Science Applications: Hydrological processes, 

climate modeling and monitoring

Key Geophysical Parameters: Surface soil moisture

Processing Level: 2, 3

Product Type: Standard, at-launch

Maximum File Size: 0.5 MB

File Frequency: 29/day

Primary Data Format: HDF-EOS

Browse Available: Accompanies data product

Additional Product Information: 

http://wwwghcc.msfc.nasa.gov/AMSR/

html/amsr_products.html

DAAC: National Snow and Ice Data Center

Science Team Contact:

 E. G. Njoku

Aqua / AMSR-E
170 Data Products Handbook - Volume 2 Aqua / MODIS • Vegetation Dynamics, Land Cover, and Land Cover Change 

MODIS Surface Reflectance; Atmospheric 

Correction Algorithm Products Summary

Coverage: Global land surface (Level 2)

Spatial/Temporal Characteristics: Bands 1 and 

2, 250 m; bands 3-7, 500 m; daylight data 

only

Key Science Applications: Global climate 

modeling, regional climate modeling, surface-energy-balance modeling, land cover 

characterization

Key Geophysical Parameters: Surface reflectance

Processing Level: 2

Product Type: Standard, at-launch

Maximum File Size: 424 MB

File Frequency: 288/day

Primary Data Format: HDF-EOS

Additional Product Information: 

http://modis-land.gsfc.nasa.gov/mod09/

DAAC: EROS Data Center

Science Team Contact:

 E. Vermote

MODIS Surface Reflectance; 

Atmospheric Correction 

Algorithm Products (also 

called Spectral Reflectance) 

(MOD 09)

Product Description

The MODIS Surface-Reflectance Product (MOD 

09) is computed from the MODIS Level 1B land 

bands 1, 2, 3, 4, 5, 6, and 7 (centered at 648 nm, 858 

nm, 470 nm, 555 nm, 1240 nm, 1640 nm, and 2130 

nm, respectively). The product is an estimate of the 

surface spectral reflectance for each band as it would 

have been measured at ground level if there were no 

atmospheric scattering or absorption. 

 The correction scheme includes corrections for 

the effect of atmospheric gases, aerosols, and thin 

cirrus clouds; it is applied to all noncloudy MOD 35 

Level 1B pixels that pass the Level 1B quality control. 

The correction uses band 26 to detect cirrus cloud, 

water vapor from MOD 05, aerosol from MOD 04, 

and ozone from MOD 07; best-available climatology 

is used if the MODIS water vapor, aerosol, or ozone 

products are unavailable. Also, the correction uses 

MOD 43, BRDF without topography, from the previous 16-day time period for the atmosphere-BRDF 

coupling term.

Research and Applications

The surface-reflectance product is the input for product generation for several land products: Vegetation 

Indices (VIs), BRDF, thermal anomaly, snow/ice, and 

Fraction of Photosynthetically Active Radiation/Leaf 

Area Index (FPAR/LAI). It is, therefore, an important 

and essential product. The at-launch version will be 

fully operational.

Data Set Evolution

The Aqua MODIS product will be based upon the 

latest version of the Terra MODIS product.

Suggested Reading

Vermote, E. F. et al., 1997.
Vegetation Dynamics, Land Cover, and Land Cover Change • Data Products Handbook - Volume 2 171

Research and Applications

This product is used for biophysical and biogeochemical parameterization of land cover for input 

to global- and regional-scale models of climate, 

hydrologic processes, and biogeochemical cycling. 

Examples of biogeophysical parameters keyed to land 

cover include leaf-area index, vegetation density, and 

FPAR. Other parameters are biomass permanence and 

energy-transfer characteristics of the land surface.

Data Set Evolution

Recent attempts to produce regional-scale land cover 

datasets use coarse spatial resolution and high-temporal-frequency data from the AVHRR instrument 

aboard the NOAA series of meteorological satellites. 

Most of these efforts have used the Normalized Difference Vegetation Index (NDVI) parameter derived 

from these data. The NDVI generally quantifies the 

biophysical activity of the land surface and as such 

does not provide land cover directly. The MODIS land 

cover algorithm draws from information domains 

well beyond those used in these efforts including 

directional surface reflectance, texture, vegetation 

index, acquisition geometry, land-surface temperature, and snow/ice cover derived from the MODIS 

instruments on both Terra and Aqua. The algorithm 

will use a hybrid decision-tree neural-network classifier. Product validation will be based on a network 

of about 400 test sites. High-resolution imagery (e.g., 

Landsat) will be used to establish truth for the sites, 

MODIS Land Cover Type

(MOD 12)

Product Description

This Level 3 product contains land cover type 

and land cover change parameters, which will be 

produced at 1-km resolution on a quarterly basis 

beginning 18 months following launch of the Terra 

and Aqua platforms. In the period 6-18 months after 

launch, prototype products will be made available. 

The land cover parameter identifies 17 categories 

of land cover following the IGBP global vegetation 

database, which defines nine classes of natural vegetation, three classes of developed lands, two classes of 

mosaic lands, and three classes of nonvegetated lands 

(snow/ice, bare soil/rocks, water). The land cover 

change parameter quantifies subtle and progressive 

land-surface transformations as well as major rapid 

changes. As such, it is not a conventional change 

product that only compares changes in land cover 

type at two times but combines analyses of change in 

multispectral-multitemporal data vectors with models 

of vegetation-change mechanisms to recognize both 

the type of change and its intensity. It will highlight 

interannual variability in particular. In addition to 

the basic 1-km product, summary products containing proportions of land covers and change characteristics will be available at one-quarter, one-half, 

and 1-degree resolutions. These products are prepared 

independently from the 250-m Land Cover Change 

Product and use a different algorithm.

Aqua / MODIS

1. Evergreen Needleleaf Forest

2. Evergreen Broadleaf Forest

4. Deciduous Broadleaf Forest

5. Mixed Forests

8. Woody Savannahs

9. Savannahs

10. Grasslands

12. Croplands

13. Urban and Built

14. Cropland/Natural Vegetation Mosaic

17. Water Bodies

Scale 1:6,000,000

0 200

Boston University Center for Remote Sensing

Land Cover Classes in a Mid-Atlantic Subset of the North American Prototype of the MODIS Land Cover 

Product, September 1999.

km
172 Data Products Handbook - Volume 2 Aqua / MODIS • Vegetation Dynamics, Land Cover, and Land Cover Change 

(MODIS Land Cover Type, continued)

which will be used to train the land-cover classifier 

and validate the products. The validation procedure 

will characterize the accuracy of the product as well 

as provide information that can be used in spatial aggregation to provide land cover data and land cover 

change data at coarser resolutions.

Suggested Reading

Justice, C. et al., 1998. 

Running, S. W. et al., 1994.

Running, S. W. et al., 1995.

Salisbury, J. W., and D. M. DʼAria, 1992.

Snyder, W., and Z. Wan, 1996.

Strahler, A. et al., 1995.

Wan, Z., and J. Dozier, 1996.

Wan, Z., and Z.-L. Li, 1997.

MODIS Land Cover Type Summary

Coverage: Global, clear-sky only

Spatial/Temporal Characteristics: 1 km, 1/4°, 

1/2°, 1°/seasonal (96-day)

Key Science Applications: Biogeochemical 

cycles, land cover change

Key Geophysical Parameters: Land cover type, 

land cover change

Processing Level: 3

Product Type: Standard, post-launch

Maximum File Size: 16 MB

File Frequency: 289/96-day

Primary Data Format: HDF-EOS

Additional Product Information: 

http://modis-land.gsfc.nasa.gov/products/

products.asp?ProdFamID=10

DAAC: EROS Data Center

Science Team Contact

 A. H. Strahler
Vegetation Dynamics, Land Cover, and Land Cover Change • Data Products Handbook - Volume 2 173

MODIS Vegetation Indices 

(MOD 13)

Product Description

The MODIS Vegetation-Index (VI) products will 

provide consistent spatial and temporal comparisons 

of global vegetation conditions that will be used to 

monitor the Earthʼs terrestrial photosynthetic vegetation activity in support of change detection and 

phenologic and biophysical interpretations. Gridded 

vegetation-index maps depicting spatial and temporal 

variations in vegetation activity are derived at 8-day, 

16-day, and monthly intervals for precise seasonal 

and interannual monitoring of the Earthʼs vegetation. 

The MODIS VI products will improve upon currently 

available indices and will more accurately monitor 

and detect changes in the state and condition of the 

Earthʼs vegetative cover. The vegetation-index products are made globally robust with enhanced vegetation sensitivity and minimal variations associated 

with external influences (atmosphere, view and sun 

angles, clouds) and inherent, non-vegetation influences (canopy background, litter), in order to serve 

more effectively as a “precise” measure of spatial and 

temporal vegetation change.

 Two vegetation-index (VI) products are to be produced globally for land at launch. One is the standard 

normalized difference vegetation index (NDVI), 

which is referred to as the “continuity index” to the 

existing NOAA-AVHRR-derived NDVI. The other is 

an enhanced vegetation index (EVI) with improved 

sensitivity in high biomass regions and improved 

vegetation monitoring through a decoupling of the 

canopy background signal and a reduction in atmosphere influences. The two VIs complement each 

other in global vegetation studies and improve upon 

the extraction of canopy biophysical parameters. A 

new compositing scheme that reduces angular, suntarget-sensor variations is also utilized. The gridded 

vegetation-index maps will use as input MODIS 

Terra and Aqua surface reflectances, corrected for 

molecular scattering, ozone absorption, and aerosols, 

and adjusted to nadir and standard sun angles with use 

of BRDF models. The gridded vegetation indices will 

include quality-assurance (QA) flags with statistical 

data, indicating the quality of the VI product and 

input data. The MODIS vegetation-index products 

will include:

• 250-m NDVI and QA as 8-day, 16-day, and 

monthly products (high resolution)

• 1-km NDVI, EVI, and QA as 8-day, 16-day, and 

monthly products (standard resolution)

• 25-km NDVI, EVI, and QA as 8-day, 16-day, and 

monthly products (coarse resolution)

Research and Applications

Due to their simplicity, ease of application, and widespread familiarity, vegetation indices are widely used 

by the broader user community from global circulation climate modelers and EOS instrument teams 

and interdisciplinary projects in hydrology, ecology, 

and biogeochemistry to those making regional- and 

global-based applications involving natural-resource 

inventories, land-use planning, agricultural monitoring and forecasting, and drought forecasting. Some 

of the more common applications of the vegetation 

index concern:

• Global warming/climate 

• Global biogeochemical and hydrologic modeling

• Agriculture; precision agriculture; crop stress, crop 

mapping

• Rangelands; water supply forecasting; grazing 

capacities; fuel supply

• Forestry, deforestation, and net primary production 

studies

• Pollution/health issues (Rift valley fever, mosquitoproducing rice fields)

• Desertification

• Anthropogenic-change detection and landscape 

disturbances.

Data Set Evolution

At the time of the launch of Aqua, there will be a 

20-year NDVI global data set (1981-2000) from the 

NOAA- AVHRR series, which could be extended by 

MODIS Terra and Aqua data to provide a long-term 

data record for use in operational monitoring studies. 

The MODIS Terra and Aqua data set can readily be 

composited to provide 16-day, cloud-free time-series 

maps of vegetation activity. When both Terra and 

Aqua data are combined, higher frequency, 8-day, 

cloud-free time-series data will be readily made 

available.

Aqua / MODIS
174 Data Products Handbook - Volume 2 • Vegetation Dynamics, Land Cover, and Land Cover Change 

(MODIS Vegetation Indices, continued)

Suggested Reading

Cihlar, J. C. et al., 1997.

Huete, A. R. et al., 1994.

Huete, A. R. et al., 1997.

Huete, A. R. et al., 1999.

Myneni, R. B. et al., 1997a,b.

van Leeuwen, W. J. D. et al., 1999.

MODIS Vegetation Indices Summary

 

Coverage: Global land surface

Spatial/Temporal Characteristics: 250 m, 1

km, and 0.25° resolutions/16-day and 

monthly products for Terra and Aqua 

data separately, plus an 8-day product for 

combined Terra and Aqua data.

Key Science Applications: Global vegetation 

monitoring, biogeochemical and hydrologic modeling, health and food security, 

range and forestry monitoring, agriculture management

Key Geophysical Parameters: Vegetation indices (NDVI & EVI)

Processing Level: 3

Product Type: Standard, at-launch

Maximum File Size: 277 MB

File Frequency: 289/16-day (16-day), 

289/month (Monthly)

Primary Data Format: HDF-EOS

Additional Product Information:

http://modis-land.gsfc.nasa.gov/products/

products.asp?ProdFamID=6

DAAC: EROS Data Center

Science Team Contact:

 A. R. Huete

Aqua / MODIS
Vegetation Dynamics, Land Cover, and Land Cover Change • Data Products Handbook - Volume 2 175

High Resolution (1 km) Composite of MODIS Enhanced Vegetation Index (top) and Normalized Difference Vegetation Index 

(bottom) for North America, from data of the Terra MODIS 

instrument for March 5 - March 20, 2000.

High Resolution (500 m) Composite of the Normalized 

Difference Vegetation Index for North and South 

America, from data of the Terra MODIS instrument 

for April 22 - May 7, 2000.

Global Composite of MODIS Enhanced Vegetation 

Index (top) and Normalized 

Difference Vegetation Index 

(bottom), both from data of 

the Terra MODIS for March 

17 - April 2, 2000.

 Aqua / MODIS
176 Data Products Handbook - Volume 2 • Vegetation Dynamics, Land Cover, and Land Cover Change 

MODIS Leaf Area Index 

(LAI) and Fraction of 

Photosynthetically Active 

Radiation (FPAR) – Moderate 

Resolution (MOD 15)

Product Description

The MOD 15 Leaf Area Index (LAI) and Fraction 

of Photosynthetically Active Radiation absorbed 

by vegetation (FPAR) are 1-km at-launch products 

provided on a daily and 8-day basis. LAI defines 

an important structural property of a plant canopy, 

namely the one-sided leaf area per unit ground area. 

FPAR measures the proportion of available radiation 

in the photosynthetically active wavelengths (400 to 

700 nm) that a canopy absorbs. The LAI product will 

be a LAI value between 0 and 8 of the global gridded 

database at the corresponding modified vegetation 

index (MVI) compositing interval. The FPAR product 

will be an FPAR value between 0.0 and 1.0 assigned 

to each 1-km cell of the global gridded database at 

the corresponding MVI compositing interval.

Research and Applications

LAI and FPAR are biophysical variables that describe 

canopy structure and are related to functional process 

rates of energy and mass exchange. Both LAI and 

FPAR have been used extensively as satellite- derived 

parameters for calculation of surface photosynthesis, 

evapotranspiration, and NPP. These products are 

essential in calculating terrestrial energy, carbon, 

water-cycle processes, and biogeochemistry of vegetation. The LAI product is an input to Biome-BGC 

(Biogeochemical) models to produce conversionefficiency coefficients, which are combined with 

the FPAR product to produce daily terrestrial PSN 

(photosynthesis) and annual NPP.

Data Set Evolution

This product is derived from the Surface Reflectance 

Product (MOD 09), the Land Cover Type product 

(MOD 12), and ancillary information on surface characteristics such as land cover type and background. 

The retrievals are performed by comparing observed 

and modeled surface reflectances for a suite of canopy 

structures and soil patterns that covers a range of 

expected natural conditions. All canopy/soil patterns 

for which the magnitude of the residuals in the comparison does not exceed uncertainties in observed and 

modeled surface reflectances are treated as acceptable 

solutions. For each acceptable solution, a value of 

FPAR is also evaluated. The mean and dispersion values of the LAI solution distribution function are taken 

as the retrieved LAI accuracy; likewise for FPAR. A 

three-dimensional formulation of the radiative transfer is used to derive spectral and angular biome-specific signatures of vegetation canopies. Should this 

main algorithm fail, a back-up algorithm is triggered 

to estimate LAI and FPAR using a Normalized Difference Vegetation Index (NDVI).

Suggested Reading

Knyazikhin, Y. et al., 1998a,b.

Myneni, R. B. et al., 1997b.

Tian, Y. et al., 2000.

Zhang, Y. et al., 2000.

MODIS LAI and FPAR – Moderate Resolution 

Summary

Coverage: Global

Spatial/Temporal Characteristics: 1 km/daily,

 8-day

Key Science Applications: Biogeochemical 

cycle modeling, NPP estimation

Key Geophysical Parameters: Leaf area index, 

fraction of photosynthetically active radiation absorbed by vegetation

Processing Level: 4

Product Type: Standard, at-launch 

Maximum File Size: 5.8 MB

File Frequency: 289/day (Daily Level 4),

289/8-day (8-day Level 4)

Primary Data Format: HDF-EOS

Additional Product Information: 

http://modis-land.gsfc.nasa.gov/products/

products.asp?ProdFamID=5

DAAC: EROS Data Center

Science Team Contacts:

 R. B. Myneni

 S. W. Running

Aqua / MODIS
Vegetation Dynamics, Land Cover, and Land Cover Change • Aqua / MODIS Data Products Handbook - Volume 2 177

40° N

30° N

120° W 100° W 80° W

40° N

30° N

20° N

100° W 80° W

40° N

30° N

120° W 100° W 80° W

40° N

30° N

20° N

100° W 80° W

The First Complete Continental Computation of Two MODIS Land Products, Leaf Area Index (LAI) and Fraction 

of Absorbed Photosynthetically Active Radiation (FPAR). Both of these variables quantify green vegetation but in 

different ways. LAI defines canopy leaf area, while FPAR defines the amount of incoming solar radiation absorbed 

by the plant canopies. These variables are used to parameterize terrestrial vegetation for global carbon, hydrologic, 

and climate models, and also are used for a number of land management applications. This image is a time composite 

from March 24 to April 9, 2000, using MODIS Spectral Channels 1 and 2 at 250 m rescaled to 1 km spatial resolution. The image shows that in this early spring time period, most agricultural land and deciduous forest does not yet 

show spring leaf growth. Continental greening will occur rapidly over the next 4-6 weeks.
178 Data Products Handbook - Volume 2 • Vegetation Dynamics, Land Cover, and Land Cover Change 

MODIS Surface Resistance 

and Evapotranspiration (MOD 

16)

Product Description

This Level 4 product consists of Surface Resistance and Evapotranspiration (ET) parameters and 

has a temporal resolution of eight days at a spatial 

resolution of 1 km over the land surface only. The 

land Surface Resistance is an Aqua product that 

captures sub-optimum conditions produced by soil/

atmospheric moisture deficits for vegetation photosynthesis and transpiration. The spatial resolution of 

these products is 1 km.

Research and Applications

These two parameters are essential to global modeling 

of climate, water balance, and trace gases. In addition, 

they are required in estimating photosynthesis, respiration, and net primary production. The surface-resistance product is an Aqua (post-launch) research 

data product intended for real-time implementations. 

Practical applications of this product are for monitoring wildfire danger and crop/range drought.

Data Set Evolution

The Surface Resistance product will be calculated 

using the MODIS Land Surface Temperature (MOD 

11) and the MODIS modified vegetation index (MOD 

13), and will be used along with incident radiation 

for computing the ET.

Suggested Reading

Dickinson, R. E., 1987.

Goward, S. N., and A. S. Hope, 1989.

Nemani, R. R., and S. W. Running, 1989.

Nemani, R. R. et al., 1993.

Running, S. W. et al., 1989.

Running, S. W. et al., 1994.

MODIS Surface Resistance and 

Evapotranspiration Summary

Coverage: Global

Spatial/Temporal Characteristics: 1 km/8-day, 

yearly

Key Science Applications: Global water balance, 

net primary production

Key Geophysical Parameters: Surface resistance, evapotranspiration

Processing Level: 4

Product Type: Research, post-launch

Maximum File Size: 5.8 MB

File Frequency: 289/8-day

Primary Data Format: HDF-EOS

Additional Product Information:

http://modis-land.gsfc.nasa.gov/products/

products.asp?ProdFamID=3

DAAC: EROS Data Center

Science Team Contact:

 S. W. Running

Aqua / MODIS
Vegetation Dynamics, Land Cover, and Land Cover Change • Aqua / MODIS Data Products Handbook - Volume 2 179

MODIS Vegetation Production 

and Net Primary Production 

(MOD 17)

Product Description

MOD 17 is a Level 4 product consisting of 8-day Net 

Photosynthesis (PSN) and Net Primary Production 

(NPP). Annual NPP is the time integral of the PSN 

product over a year.

Research and Applications

This product provides an accurate measure of terrestrial vegetation growth and production activity. The 

theoretical use is to define the seasonally dynamic 

flux of terrestrial-surface carbon dioxide for climate 

modeling. Fluxes will be computed specific to each 

vegetation type. The practical utility is to measure 

crop yield and forest production and any other socially significant products of vegetation growth. As 

this global NPP product becomes regularly available, 

a wide variety of derived products is expected to be 

developed making regionally specific estimates of 

crop production. The value of an unbiased, regular 

source of crop and forest production estimates for 

global political and economic decision making is 

immense.

Data Set Evolution

The NPP parameter is the yearly integral of the PSN 

which is obtained from the product of PAR (Photosynthetically Active Radiation), FPAR (Fraction of 

Photosynthetically Active Radiation) and conversionefficiency coefficients obtained from other MODIS 

products and other sensors and ancillary data. The 

algorithms for these products are based on the original 

logic of Monteith (1972), which relates PSN and NPP 

to the amount of Absorbed Photosynthetically Active 

Radiation (APAR). The MODIS modified vegetation 

indices (MVI) along with climate variables and the 

land cover product are used to estimate APAR.

Suggested Reading

Field, C. B. et al., 1995.

Monteith, J. L., 1972.

Prince, S. D., and S. N. Goward, 1995.

Ruimy, A. et al., 1994.

Running, S. W., 1990.

Running, S. W. et al., 1994.

MODIS Vegetation Production and Net 

Primary Production Summary

Coverage: Global

Spatial/Temporal Characteristics: 0.5, 1, 10 

km/8-day, yearly

Key Science Applications: Interannual variability of vegetation

Key Geophysical Parameters: NPP, photosynthesis, respiration

Processing Level: 4

Product Type: Standard, at-launch

Maximum File Size: 4.3 MB

File Frequency: 289/8-day

Primary Data Format: HDF-EOS

Additional Product Information:

http://modis-land.gsfc.nasa.gov/products/

products.asp?ProdFamID=3

DAAC: EROS Data Center

Science Team Contact:

 S. W. Running
180 Data Products Handbook - Volume 2 • Vegetation Dynamics, Land Cover, and Land Cover Change 

<0.1

0.1-0.5

0.5-1.0

1.0-1.5

1.5-2.0

2.0-2.5

>2.5

(kg/m2/yr)

Resolution 1°×1° Lat/Lon

neman/hunt/running/NTSG

Global Net Primary Production (NPP) during 1987. This data set is produced using 1° × 1° gridded climate data from 7000 weather 

stations distributed globally, Mathews land cover classes collapsed into six classes, biome-specific NDVI-LAI relations used to estimate 

LAI from NOAA/NASA Pathfinder data, and an ecosystem simulation model, BIOME-BGC.

Aqua / MODIS

quantify the directional information in the remotelysensed signal, which is related to ground-cover type; 

and 3) provide a surface-radiation-scattering model 

for boundary-layer parameterization in regional and 

global climate modeling. The BRDF can also be used 

in extraction of surface reflectances at Level 2. Two 

albedo measures are provided: “black-sky” albedo 

(directional-hemispherical reflectance) and “whitesky” albedo (bihemispherical reflectance). These 

are intrinsic surface properties, independent of atmospheric state. They describe the upward scattering 

of the solar beam and of uniform diffuse irradiance, 

respectively, and may be used as input to global and 

regional climate models.

Data Set Evolution

The BRDF/Albedo algorithm combines gridded, multidate, multiband surface-reflectance data from EOS 

MODIS and MISR (on Terra) instruments to produce 

BRDF functions and derived albedo measures. For 

each grid cell, all cloud-free observations in a 16-day 

period are assembled and fit to the Ross-Thick/LiSparse semiempirical model that describes the BRDF 

as a linear function of two basic BRDF shapes. In addition, a single empirical model (modified Walthall) is 

currently also fitted. The algorithm outputs include: 1) 

Ross-Thick/Li-Sparse BRDF parameters that best fit 

MODIS Surface Reflectance 

BRDF/Albedo Parameter

(MOD 43)

Product Description

The Bidirectional Reflectance Distribution Function 

(BRDF)/Albedo parameter provides: 1) coefficients 

for mathematical functions that describe the BRDF of 

each pixel in the seven MODIS “Land” bands (1-7); 

and 2) albedo measures derived simultaneously from 

the BRDF for bands 1-7 as well as three broad bands 

(0.4-0.7, 0.7-3.0, and 0.4-3.0 micrometers).

 Because deriving BRDF and albedo requires merging multiple looks at each pixel, the BRDF/Albedo 

parameter is provided every 16 days. Its spatial resolution is 1 km, gridded to Level 3. A 32-day summary albedo product at 0.25° spatial resolution is also 

provided. Both Terra and Aqua data are merged as 

available.

Research and Applications

The BRDF functions provided by the BRDF/Albedo 

parameter: 1) allow normalization of MODIS data to 

standard viewing and illumination angles, thus removing geometric effects from multidate images; 2) 
Vegetation Dynamics, Land Cover, and Land Cover Change • Data Products Handbook - Volume 2 181

the observations, 2) modified Walthall parameters, 3) 

black-sky and white-sky albedos in three broad bands, 

and 4) nadir BRDF-adjusted reflectances (NBARs).

Suggested Reading

Barnsley, M. J., and J.-P. Muller, 1991.

dʼEntremont, R. E. et al., 1999.

Hu, B. et al., 1997.

Hu, B. et al., 1999.

Hu, B. et al., 2000.

Justice, C. et al., 1998.

Li, X. et al., 1995.

Liang, S. et al., 1999

Lucht, W., 1998.

Lucht, W., and P. Lewis, 2000.

Lucht, W. et al., 2000.

Wanner, W. et al., 1995.

Wanner, W. et al., 1997.

 Aqua / MODIS

MODIS Surface Reflectance BRDF/Albedo 

Parameter Summary

Coverage: Global land surface

Spatial/Temporal Characteristics: 1 km, 

16 km, 0.5°/16-day; 0.25°/32-day

Key Science Applications: Biogeochemicalcycle modeling, net primary productivity 

estimation, global climate models

Key Geophysical Parameters: Bidirectional 

reflectance, spectral albedo

Processing Level: 3

Product Type: Standard, at-launch

Maximum File Size: 329 MB (16-day), 

946 MB (32-day)

File Frequency: 289/16-day (16-day), 

1/month (32-day)

Primary Data Format: HDF-EOS

Additional Product Information: 

http://modis-land.gsfc.nasa.gov/products/

products.asp?ProdFamID=2

DAAC: EROS Data Center

Science Team Contacts:

A. H. Strahler

J.-P. Muller

Bihemispherical Albedo (White-Sky Albedo) for North America for the 

period April 22 - May 7, 2000, calculated from the MODIS BRDF/Albedo 

product algorithm using data from the Terra MODIS near-infrared channel (Band 2). In this image, cloud cover has obscured some regions in the 

central U.S. as well as other smaller areas shown in white. Light tones in the 

southeastern U.S. indicate strong reflectance from leaf cover, while much of 

the more northern regions remains leafless.
182 Data Products Handbook - Volume 2 • Vegetation Dynamics, Land Cover, and Land Cover Change 

MODIS Vegetation Cover 

Conversion (MOD 44)

Product Description

Using MODIS data from the two 250-m bands, the 

vegetation cover conversion product will show the 

global distribution of the occurrence of vegetation 

cover change. Where there is sufficient evidence, the 

type of change will be labeled (e.g., forest conversion 

to agricultural fields or grassland or bare surface). 

The distribution of these changes will be represented 

at a resolution of 250 meters and as gridded 10-km 

summaries. These will be generated at 3-month intervals. An interannual product is also being produced, 

displaying the global distribution of vegetation-cover 

change during the previous year.

Research and Applications

Vegetation-cover change is an important driver of 

many important biogeochemical, hydrological, and 

climate processes. It also represents the integrated 

response to several biophysical and anthropogenic 

impacts. Among the important influences of vegetation-cover change are the following:

• It strongly affects changes in many biophysical 

factors such as surface roughness and albedo;

• It has a major effect on changes in sensible heat 

flux, because it affects global albedo and surface 

roughness, which in turn affects atmospheric 

drag;

• It is of crucial importance for determining the 

biogeochemical cycling of carbon, nitrogen, and 

other elements at regional-to-global scales;

• It has a major impact on the runoff characteristics 

of catchments through its effects on evapotranspiration and partitioning of precipitation into overland 

flow, interflow, and groundwater accretion; 

• It gives a direct insight into ecosystem response 

related to climate change and anthropogenic influences;

• It affects biodiversity through direct impacts on 

habitat;

• It provides increasingly important information for 

natural-resource managers.

This product will be combined with data obtained 

from finer spatial resolution data from sensors such 

as ETM+ on Landsat 7 and ASTER on Terra (see 

Volume 1) to assist in the identification of the types 

of vegetation conversion occurring. The product 

also provides information to assist the acquisition 

strategy of finer resolution systems since it helps 

flag areas where significant changes are likely to be 

occurring.

Data Set Evolution

Previous work has shown that data with a resolution 

of 1 km and coarser are sufficient for the mapping of 

the distribution of vegetation cover and for the monitoring of those changes in vegetation cover caused 

by seasonal-to-interannual climate change. However, 

such relatively coarse resolution data are often inadequate to detect changes caused by anthropogenic 

factors. Analyses of many types of vegetation-cover 

change indicate that they are relatively small in size 

largely due to the inherently local nature of anthropogenic vegetation-cover conversions. Consequently 

a very large proportion of changes is only detectable 

at fine spatial resolutions. For this reason it was decided to use the two 250-m bands for the identification 

and mapping of this type of conversion.

Suggested Reading

Townshend, J. R. G., and C. O. Justice, 1988. 

Townshend, J. R. G. et al., 1991.

Zhan, X. et al., 1999.

Aqua / MODIS 
Vegetation Dynamics, Land Cover, and Land Cover Change • Data Products Handbook - Volume 2 183

MODIS Vegetation Cover Conversion 

Summary

Coverage: Global, daytime

Spatial/Temporal Characteristics: 250 m, 

10 km/3-month, yearly

Key Geophysical Parameters: Vegetation-cover change occurrence and type

Processing Level: 4

Product Type: Research, at-launch and postlaunch

Maximum File Size: 1 GB/tile (land tiles only)

File Frequency: 1/month (Monthly Level 4),

1/year (Yearly Level 4)

Primary Data Format: HDF

Browse Available:

http://www.geog.umd.edu/landcover/

modis

Additional Product Information:

http://modis-land.gsfc.nasa.gov/products/

products.asp?ProdFamID=2

DAAC: EROS Data Center

Science Team Contact: 

 J. R. G. Townshend

 Aqua / MODIS
184 Data Products Handbook - Volume 2 VCL / MBLA • Vegetation Dynamics, Land Cover, and Land Cover Change 

onstrated that even within the densest canopies, a 

majority of laser pulses return waveforms with valid 

ground reflections. 

VCL Geolocated Ground Elevations Summary

Coverage: 67°N - 67°S

Spatial/Temporal Characteristics: ~25 m 

resolution along track, ~2 km across 

track

Key Science Applications: Global data set 

of topographic elevations in consistent 

reference frame, topographic control 

points for other applications

Key Geophysical Parameters: Topographic 

elevations

Processing Level: 2

Product Type: Standard, at-launch

Science Team Contact:

 R. O. Dubayah 

VCL (MBLA): Geolocated 

Ground Elevations

Product Description

The strong scientific need for accurate, global topographic databases has led to recent progress in limited 

release of portions of the Defense Mapping Agency 

Digital Terrain Elevation Data (DTED) Level 1 data 

that have 90-m horizontal pixel resolution and 16-m 

vertical accuracy. Despite this progress the scientific 

benefit for global studies is not nearly realized. The 

existing DTED Level 1 data will not be released for 

the entire Earth, and space-based imaging sensors 

now in orbit and planned in the EOS Terra/Aqua era 

(1999-2005) require global topography at DTED 

Level 2 resolution (30 m spatial, 16 m vertical) for full 

realization of their science potential in land-cover/

global productivity, short-term climate modeling, and 

natural hazard studies. The Shuttle Radar Topography 

Mission (SRTM) has flown to address these needs. 

Since Interferometric Synthetic Aperture Radar 

(IfSAR) data, or the conventional photogrammetric 

data sets, are only relative in their measurement of 

surface elevation, direct measurements are needed to 

“control” the vertical dimension of the topographic 

image. Estimates of topographic control points 

(TCPs) needed for a global DTED Level 2 are well 

in excess of 100,000,000. Only a limited number 

of these TCPs can be provided by ground-based 

radar targets and GPS receivers; the remainder will 

have to be estimated from existing maps and digital 

elevation models that do not routinely achieve the 

1-meter level of vertical accuracy and do not have a 

common reference frame. Of equal importance, none 

of the existing remote sensing techniques listed above 

has the ability to reliably penetrate a canopy-covered 

surface to provide accurate measurements of surface 

topographic height. Such measurements are needed 

for a variety of geophysical, hydrological, and landuse purposes.

 The VCL surface-elevation measurements using 

the MBLA instrument will have a common, global 

reference frame and are “direct” rather than inferred. 

Furthermore, only VCL will address the vegetationcover issues that limit all present mapping techniques 

to tens-of-meters rms in forested areas. The VCL 

mission, by virtue of its primary vegetation-canopy 

measurements, will provide billions of sub-canopy 

surface elevation points. The surface-height measurement is obtained when processing the returned 

laser waveform. The last return that is captured that 

exceeds a given threshold is the ground return. Calibration and validation experiments using the aircraft 

simulator of the VCL instrument, LVIS, have dem-
Vegetation Dynamics, Land Cover, and Land Cover Change • VCL / MBLA Data Products Handbook - Volume 2 185

VCL (MBLA): Geolocated 

Canopy-Top Heights

Product Description

Within the returned waveform from the MBLA instrument, the first return above a threshold represents 

the top of the canopy, and the last return in the waveform represents the ground return. Canopy height is 

calculated by subtracting the elevations of the first and 

last returns. Vegetation height is a function of species 

composition, climate, and site quality, and can be used 

for land cover classification alone or in conjunction 

with vegetation indices, e.g., NDVI. Leaf-on versus 

leaf-off conditions will also affect this measurement 

for certain species. Along-track measurements, i.e., 

footprint-to-footprint, of VCL-derived height variation provides additional information such as fractal 

(Palmer, 1988) or autocorrelative (Cohen et al., 1990) 

properties of the canopy that further may be used 

to differentiate among natural and anthropogenically disturbed land cover patterns (Krummel et al.,

1987). When coupled with species composition and 

site-quality information (e.g., edaphic and climatic 

variables), height serves as an estimate of stand age or 

successional state, which can be correlated to carbon 

flux rates (Ustin et al., 1993). 

 In addition to providing a unique metric, i.e., 

the vertical dimension, to classify vegetative cover 

at global scales, height is highly correlated with 

above-ground biomass (Oliver and Larson, 1990; 

Avery and Burkhart, 1994; Nilsson, 1996). Biomass 

in forests represents the major reservoir of carbon in 

terrestrial ecosystems that can be quickly mobilized 

by disturbance or land-use change (e.g., Houghton et 

al., 1987; Dixon et al., 1994). 

Suggested Reading

Avery, T. E., and H. E. Burkhart, 1994.

Cohen, W. B. et al., 1990.

Dixon, R. K. et al., 1994.

Houghton, R. A. et al., 1987.

Krummel, J. R. et al., 1987.

Nilsson, M., 1996.

Oliver, C. D., and B. C. Larson, 1990.

Palmer, M. W., 1988.

Ustin, S. L. et al., 1993.

VCL Geolocated Canopy-Top Heights 

Product Summary

Coverage: 67°N - 67°S

Spatial/Temporal Characteristics: ~25 m resolution along track, ~2 km across track

Key Science Applications: Global climate 

modeling, terrestrial ecosystem modeling, 

above-ground biomass estimation, biodiversity studies, aerodynamic roughness.

Key Geophysical Parameters: Canopy height

Processing Level: 2

Product Type: Standard, at-launch 

Science Team Contact:

R. O. Dubayah
186 Data Products Handbook - Volume 2 • Vegetation Dynamics, Land Cover, and Land Cover Change

VCL (MBLA): Geolocated 

Vertical Distribution of 

Intercepted Surfaces (VDIS)

Product Description

By recording the complete time-varying amplitude 

of the return signal of the laser pulse from the 

MBLA instrument between the first and last returns 

(representing the canopy top and the ground), VCL 

captures a waveform that is related to canopy architecture. This architecture is specifically related 

to the nadir-projected vertical distribution of the 

surface area of canopy components (foliage, trunk, 

twigs, and branches) that reflect the incoming laser 

pulse. Like the simple height estimate, the vertical 

distribution of laser return provides a new means to 

classify vegetation and functions as a predictor of 

the successional state of a forest. Trees in younger 

stands tend to exhibit a more leptokurtic vertical distribution of phytomass concentrated near the ground. 

As a stand ages and grows, the vertical distribution 

of canopy components becomes more platykurtic. 

Bimodal distributions are associated with the presence of an understory that may occur in more mature 

stands. Older stands characterized by canopy gaps 

and trees of multiple ages and sizes exhibit a more 

even distribution of canopy components (e.g., Aber, 

1979; Brown and Parker, 1994). 

 When combined with greenness measures from 

other sensors, such as ETM+, MODIS, or AVHRR, 

VCL observations may be used to determine whether 

the greenness signal is the result solely of low-lying 

vegetation (via the height distribution). Many areas of 

the world have ground covers with greenness indices 

comparable to those of forests, making land cover 

discrimination based on greenness measures alone 

difficult. Measures of the vertical organization of canopy components are also critical for modeling factors 

that relate to biophysical and micrometeorological 

processes at the atmospheric-vegetation boundary 

layer such as radiative transfer, evapotranspiration, 

and trace gas flux.

Suggested Reading

Aber, J. D., 1979.

Brown, M. J., and G. G. Parker, 1994.

VCL (MBLA) Level 2: Geolocated Vertical 

Distribution of Intercepted Surfaces (VDIS) 

Summary

Coverage: 67°N - 67°S

Spatial/Temporal Characteristics: ~25 m resolution along track, ~2 km across track

Key Science Applications: Global climate 

modeling, terrestrial ecosystem modeling, 

above-ground biomass estimation, biodiversity studies, aerodynamic roughness.

Key Geophysical Parameters: Above-ground 

vertical distribution of intercepted surfaces

Processing Level: 2

Product Type: Standard, at-launch 

Science Team Contact:

R. O. Dubayah

VCL / MBLA
187

Phytoplankton 

and

Dissolved Organic

Matter

Aqua

• MODIS
188 Data Products Handbook - Volume 2 • Phytoplankton and Dissolved Organic Matter 

Phytoplankton and Dissolved 

Organic Matter – An Overview

Relationship to Global Change Issues

The global oceans, covering 70% of the Earthʼs 

surface and extending in some locations to depths 

exceeding 10 km, contain a varied abundance of life 

forms; and central to the ocean ecosystem are the unicellular plants called phytoplankton. Phytoplankton 

take dissolved carbon dioxide from sea water and use 

it for phytoplankton photosynthesis, enabling phytoplankton growth and releasing oxygen. This process 

captures approximately the same amount of carbon 

as does photosynthesis by land vegetation.

 Phytoplankton photosynthesis has been a primary 

sink for the increased carbon dioxide injected into 

the atmosphere over the past century through human activities such as biomass burning and fossilfuel burning. The amount and distribution of carbon 

dioxide consumed by the phytoplankton depends 

heavily on ocean circulation, which supplies nutrients 

to the upper layers of the ocean where sunlight is 

abundant. Some fraction of carbon produced by the 

phytoplankton sinks to the ocean floor—a long-term 

sink for atmospheric carbon dioxide. The fraction is 

not well known and is a source of a large uncertainty 

in the global carbon budget.

 Changes in the amount of phytoplankton indicate 

changes in productivity of the oceans and provide a 

key ocean link for the monitoring of global climate 

change. Ocean and land biological productivity, heatexchange processes, and water-exchange processes 

are tightly coupled, and it is important for globalchange models to take such interactions into account 

in global climate-change prediction. The hope is that 

the phytoplankton and dissolved organic matter data 

products will help scientists to determine whether 

ocean productivity is changing and, if so, how such 

changes relate to the global carbon cycle and other 

global changes.

Product Overview

The ten products discussed in this chapter are all from 

the MODIS instrument, on both the Terra and Aqua 

satellites. They are all related, in one way or another, 

to oceanic life forms.

 The Ocean Primary Productivity product (MOD 

27) provides the time rate of change of phytoplankton 

biomass, which governs the fixing of carbon and influences CO2

 in the atmosphere and ultimately global 

climate. MOD 27 is a Level 4 product that provides 

an estimate of the ocean primary productivity on a 

weekly and an annual basis. It is analogous to the Net 

Primary Production (NPP) product over land (MOD 

04). The Pigment Concentration product (MOD 19) 

and three associated products, Suspended Solids Concentration (MOD 23), Organic Matter Concentration 

(MOD 24), and Ocean Water Attenuation Coefficient 

(MOD 26), are used to support development and validation of the algorithms for primary productivity. 

 The Coccolith Concentration product (MOD 25) 

provides five parameters describing the concentration 

of coccoliths, calcium carbonate scales produced by 

the phytoplankton group named coccolithophores. 

These scales eventually sink to the ocean floor, accounting for about 75% of the deposition of carbon 

on the sea floor. Similarly, the Phycoerythrin Concentration product (MOD 31) provides three parameters 

regarding phycoerythrin, one of the major algal pigment groups in ocean water. Experimental evidence 

suggests that water-leaving radiance includes backscattering and absorption effects of phycoerythrin 

pigments that should be taken into account in spacebased estimations of primary productivity.

 The primary phytoplankton indicator product used 

as input to the primary ocean productivity algorithm 

is Chlorophyll a Pigment Concentration (MOD 21). 

This product uses the Normalized Water-leaving 

Radiance (MOD 18) and Match-up Database (MOD 

32) to calibrate semi-analytical models using the remote-sensing reflectances in the MODIS ocean bands 

and has a companion, intermediate product termed 

Absorption Coefficients (MOD 36). The second phytoplankton indicator used as input to the productivity 

algorithm is the Chlorophyll Fluorescence product 

(MOD 20), which provides another measure of 

chlorophyll concentration and hence phytoplankton 

concentration.

Product Interdependency and Continuity 

with Heritage Data

The phytoplankton and dissolved organic matter 

products require water-leaving radiances (MOD 18) 

and the MODIS match-up data base (MOD 32), which 

provides remote sensing and in situ sea-surface-radiance measurements. Some individual products 

require additional parameters, such as Chlorophyll 

a Case I for Fluorescence and downwelling seasurface radiance for Chlorophyll a. The MODIS 

pigment algorithms were developed from existing 

CZCS pigment algorithms to provide a direct link 

to the series of measurements made by CZCS over a 

period of about 7.5 years. The MODIS measurements 

will continue the ocean color data sets from the CZCS 

 Overview
Phytoplankton and Dissolved Organic Matter • Data Products Handbook - Volume 2 189

and SeaWiFS, overlapping with the latter, and will 

benefit from other simultaneous data sets and the 

further global context that they can provide.

Suggested Reading

Esaias, W. E. et al., 1999.

Gordon, H. R., 1978.

Gordon, H. R., 1997.

Goyet, C., and P. G. Brewer, 1993.

Iverson, R. L. et al., 2000.

Letelier, R. M. et al., 1997.

Sikes, S., and V. Fabry, 1994.

Yoder, J. A. et al., 1993.

Overview
190 Data Products Handbook - Volume 2 • Phytoplankton and Dissolved Organic Matter 

MODIS Pigment Concentration 

(MOD19), Suspended Solids 

Concentration (MOD 23), 

Organic Matter Concentration 

(MOD 24), and Ocean Water 

Attenuation Coefficient

(MOD 26)

Product Description

This set of products provides particle concentrations 

in Case 1 sea water, which have optical properties 

dominated by chlorophyll and associated covarying 

detrital pigments. (Case 2 waters contain substances 

that affect optical properties that may not covary with 

chlorophyll, such as gelbstoff, suspended sediments, 

coccolithophores, detritus, and bacteria.) Product 

MOD 19 is total Pigment Concentration; Product 

MOD 23 is Suspended Solids Concentration; Product MOD 24 is Organic Matter Concentration in two 

parameters, Particulate and Dissolved; and Product 

26 gives the Ocean Water Attenuation Coefficient at 

two wavelengths, 490 nm and 530 nm. The products 

are available at Level 2 daily and at Level 3 daily, 

8-day weekly, monthly, and yearly.

Research and Applications

This set of ocean-substance concentrations is needed 

for input to the ocean productivity algorithm, which 

is a key element in global biogeochemical models 

and ultimately global climate models. The pigment parameter is the sum of the chlorophyll a and 

phaeopigment concentration in Case 1 waters. The 

suspended-solids parameter is a measure of oceansuspended sediments, which is used in the analysis 

of complex bio-optical properties of coastal and 

estuarine regions/environments and helps to map 

the extent of terrestrial changes. The organic-matter 

concentration relates to the composite of carbon and 

nitrogen substances. The ocean water attenuation coefficient is derived using MODIS bands 10 and 11 and 

describes penetration of sunlight in the sea.

Data Set Evolution

The algorithm is based primarily on methods and algorithms developed for the CZCS program described 

by Gordon and Clark (1980) and refined and adapted 

to the MODIS bands. The recasting of the CZCS 

forms of the phytoplankton pigment algorithms in 

terms that are more representative for MODIS has 

resulted in minor changes. Of particular significance 

is the fact that the multiple band ratios will provide 

a robustness not possible with the CZCSʼs limited 

spectral coverage.

Suggested Reading

Gordon, H. R., and D. K. Clark, 1980.

Gordon, H. R. et al., 1980.

Gordon, H. R., and A. Y. Morel, 1983.

Lorenzen, C. J., and S. W. Jeffrey, 1980.

Smith, R. C., and K. S. Baker, 1977.

 Aqua / MODIS
Phytoplankton and Dissolved Organic Matter • Data Products Handbook - Volume 2 191

MOD 19, MOD 23, MOD 24, and MOD 26 

Data Product Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 1 km/daily 

(Level 2); 4.6 km, 36 km, 1°/daily, 8-day, 

monthly, yearly (Level 3)

Key Science Applications: Ocean productivity, 

biogeochemical models

Key Geophysical Parameters: Total ocean pigment, suspended solids, organic-matter 

concentration, attenuation coefficient

Processing Level: 2, 3

Product Type: Standard, at-launch

Maximum File Size: 

 MOD 19: 102 MB (Level 2); 620 MB 

(Level 3)

 MOD 23: 102 MB (Level 2); 640 MB 

binned, 134 MB mapped (Level 3)

 MOD 24: 83 MB (Level 2); 640 MB binned, 

134 MB mapped (Level 3)

 MOD 26: 102 MB (Level 2);

640 MB binned, 134 MB mapped

(Level 3)

File Frequency: 

MOD 19: 144/day (Daily Level 2); 3/day 

(Daily Level 3), 3/8-day (8-day Level 3), 

3/month (Monthly Level 3), 3/year (Yearly 

Level 3)

 MOD 23, MOD 24, MOD 26: 144/day 

(Daily Level 2); 1/day (Daily Level 3), 

1/8-day (8-day Level 3), 1/month (Monthly 

Level 3), 1/year (Yearly Level 3)

Primary Data Format: HDF-EOS

Browse Available: 36 km sample imagery available at the Goddard DAAC (Level 3 only)

Additional Product Information:

http://modis-ocean.gsfc.nasa.gov/

dataprod.html

DAAC: NASA Goddard Space Flight Center

Science Team Contact:

 D. Clark

 Aqua / MODIS
192 Data Products Handbook - Volume 2 • Phytoplankton and Dissolved Organic Matter 

U.S. East Coast Ocean-Color Image. This daily Coastal Zone Color Scanner (CZCS) product for the northeastern coast of the United 

States reveals the high pigment concentrations along the coast and the influence of the Gulf Stream. Phytoplankton concentrations, 

and additional ocean-carbon-system parameters from MODIS, will be considerably more accurate than was possible with the CZCS 

because of improvements in spectral bands, calibration, and algorithms. (From NASA GSFC.)

.1

.2

.4

.6

.8

1

10

Phytoplankton

Pigment

Concentration

(mg/m3)

Aqua / MODIS
Phytoplankton and Dissolved Organic Matter • Data Products Handbook - Volume 2 193

MODIS Chlorophyll 

Fluorescence (MOD 20)

Product Description

This Level 2 product contains several parameters describing ocean chlorophyll fluorescence properties. 

Fluorescence line height is a relative measure of the 

amount of radiance leaving the sea surface at the fluorescence wavelength of 683 nm. Fluorescence Efficiency provides a relative measure of the absorption 

of PAR and its emission as chlorophyll fluorescence. 

The third parameter is Fluorescence Line Curvature. 

The spatial resolution will be 1 km for chlorophyll 

levels greater than 1.5 mg/m3 and 5 × 5 km for values 

less than 1.5. The Level 2 product is produced daily, 

and Level 3 is gridded and produced daily, 8-day 

weekly, monthly, and yearly.

Research and Applications

Solar-stimulated chlorophyll fluorescence is a measure of the current photophysiology of phytoplankton, 

in contrast to the biomass estimate provided by chlorophyll. The product quantifies the level of photosynthesis by phytoplankton in the ocean. Historically, 

the coupling between fluorescence and chlorophyll 

has been studied extensively, and recent research has 

focused on the use of Sun-stimulated fluorescence to 

estimate primary productivity (Kiefer and Reynolds, 

1992). Basic fluorometric measurements are made 

using an instrument described by Holm-Hansen et al.

(1965) that uses blue-light stimulation; this method 

has been used unchanged for 30 years. Gower and 

Borstad (1990) was among the first to attempt to use 

sun-stimulated radiance at 683 nm to estimate chlorophyll concentrations from aircraft and satellites.

Data Set Evolution

Inputs to the algorithm are chlorophyll concentration (within MOD 19), Absorbed Radiation by 

Phytoplankton (ARP) (within MOD 22), and WaterLeaving Radiance (MOD 18). Water-leaving radiance 

for MODIS bands 13 (667 nm), 14 (678 nm), and 15 

(748 nm) are used in the algorithm. The algorithm is 

applied to the daily input standard-product data sets 

and is remapped into standard Level 3 grids. The 

validation approach will be to compare the fluorescence results with other MODIS data products (e.g., 

Chlorophyll a), with surface measurements, and with 

other satellite-based estimates of the same products. 

The products are produced only for non-cloud, glintfree ocean pixels during daylight hours.

Suggested Reading

Abbott, M. R. et al., 1982.

Abbott, M. R., and R. M. Letelier, 1998.

Chamberlin, W. S., and J. Marra, 1992.

Gower, F. J. R., and G. A. Borstad, 1990.

Holm-Hansen, O. et al., 1965.

Kiefer, D. A., and R. A. Reynolds, 1992.

Letelier, R. M. et al., 1997.

Topliss, B. J., and T. Platt, 1986.

MODIS Chlorophyll Fluorescence Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 1 km for 

chlorophyll levels greater than 2.0 mg/m3/

daily, (Level 2); 4.6 km, 36 km, 1°/8-day, 

monthly, yearly (Level 3)

Key Science Applications: Ocean chlorophyll, 

ocean productivity

Key Geophysical Parameters: Chlorophyll 

fluorescence (fluorescence line height, 

fluorescence efficiency, fluorescence line 

curvature)

Processing Level: 2, 3

Product Type: Standard, at-launch

Maximum File Size: 102 MB (Level 2); 640 MB 

binned, 134 MB mapped (Level 3) 

File Frequency: 144/day (Daily Level 2); 3/day 

(Daily Level 3), 3/week (Weekly Level 3), 

3/month (Monthly Level 3), 3/year (Yearly 

Level 3)

Primary Data Format: HDF-EOS

Additional Product Information:

http://modis-ocean.gsfc.nasa.gov/

dataprod.html

DAAC: NASA Goddard Space Flight Center

Science Team Contact: 

 M. Abbott

 Aqua / MODIS
194 Data Products Handbook - Volume 2 Aqua / MODIS • Phytoplankton and Dissolved Organic Matter 

Chlorophyll a Concentrations (MOD 21) as measured by MODIS 

in the Indian Ocean on March 1, 2000. Chlorophyll, the green 

pigment in plants and phytoplankton, absorbs sunlight for use 

in photosynthesis. Note the high concentrations (light grays) in 

the Arabian Sea and relatively low concentrations (dark grays) in 

the Bay of Bengal. The black color in the image represents land 

areas and gaps between successive satellite orbits, and the gray 

“stripes” indicate where clouds or sun glint prevented accurate 

measurements.

Chlorophyll a (Chl)

Chlorophyll Fluorescence (MOD 20) as measured from the Terra 

MODIS instrument on March 1, 2000. Phytoplankton re-emit 

some of the light that is captured by chlorophyll as fluorescence. 

The amount of fluorescence is roughly proportional to the amount 

of chlorophyll, so in this image, for example, high fluorescence 

can be found in the Arabian Sea.

Fluorescence Line Height (FLH)

Ratio of Chlorophyll Fluorescence (MOD 20) to Chlorophyll a

Concentration (MOD 21), illustrating the considerable variability 

in the amount of fluorescence per unit of chlorophyll. This variability is largely a function of the health of the phytoplankton. 

A high ratio implies lower growth rates, as the light captured by 

phytoplankton is being re-emitted as fluorescence rather than 

being used for photosynthesis. Phytoplankton in the Arabian Sea 

are probably growing more rapidly than elsewhere, perhaps in 

response to dust inputs (which are rich in iron) from the Arabian 

Peninsula.

FLH/Chl Ratio

(MODIS Chlorophyll Fluorescence, continued)
Phytoplankton and Dissolved Organic Matter • Aqua / MODIS Data Products Handbook - Volume 2 195

MODIS Chlorophyll a Pigment 

Concentration (MOD 21)

and Absorption Coefficients 

(MOD 36)

Product Description

This is a Level 2 and 3 product which contains ocean 

chlorophyll a pigment concentration for Case 1 and 

Case 2 waters at 1-km resolution. It is produced daily 

at Level 2 and daily, 8-day weekly, monthly, and 

yearly at Level 3. Absorption parameters generated 

by the chlorophyll algorithm are also provided as 

the intermediate product MOD 36, Absorption Coefficients, which includes absorption due to water, 

phytoplankton, detritus, and gelbstoff, and gelbstoff 

absorption only. Valid data exist only for ocean 

cloud-free pixels and the weekly composite will be 

an average of cloud-free acquisitions for each ocean 

pixel.

Research and Applications

Chlorophyll a concentration is a key input to the 

Ocean Primary Productivity product (MOD 27) and 

is used to trace oceanographic currents, jets, and 

plumes. The product provides the concentration of 

chlorophyll in Case 1 sea water (water that has optical properties that are dominated by chlorophyll and 

associated covarying detrital pigments) and in Case 

2 waters (waters that contain substances that affect 

optical properties but do not covary with chlorophyll, 

such as gelbstoff (marine chromomorphic dissolved 

organic matter [CDOM] substance that absorbs at 

400 nm), suspended sediments, coccolithophores, 

detritus, and bacteria). The algorithm derives from 

extensive research using CZCS data in which good 

performance was obtained for Case 1 waters and has 

evolved to perform successfully for the Case 2 waters 

for the MODIS algorithm. The 1-km resolution and 

nearly daily coverage will allow the observation of 

mesoscale oceanographic features in coastal and 

estuarine environments, which are of increasing 

importance in marine science studies.

Data Set Evolution

The product algorithm is based on a semi-analytical, bio-optical model of remote sensing reflectance 

which uses Water-Leaving Radiance (MOD 18), PAR 

(MOD 22), and Absorption Coefficients (MOD 36). 

The model is inverted to obtain the absorption coefficient due to phytoplankton at 675 nm, and chlorophyll a concentration is derived from this coefficient. 

The algorithm will be thoroughly tested during the 

SeaWiFS project, and post-launch validation will be 

conducted using data from instrumented collection 

cruises through ocean test sites including the nine 

used for algorithm development. Also, hyperspectral 

data will be used to simulate the 10-nm bands and 

produce comparison results.

Suggested Reading

Austin, R. W., 1974.

Carder, K. L. et al., 1986.

Carder, K. L. et al., 1991a,b.

Holm-Hansen, O., and B. Riemann, 1978.

Lee, Z. P. et al., 1996.

Smith, R. C., and K. S. Baker, 1982.
196 Data Products Handbook - Volume 2 Aqua / MODIS • Phytoplankton and Dissolved Organic Matter 

(MODIS Chlorophyll a Pigment Concentration and Absorption 

Coefficients, continued)

MODIS Chlorophyll a Pigment Concentration 

and Absorption Coefficients Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 1 km/daily 

(Level 2); 4.6 km, 36 km, 1°/daily, 8-day, 

monthly, yearly (Level 3)

Key Science Applications: Ocean productivity, 

bio-optical properties

Key Geophysical Parameters: Case 1 and 2 

chlorophyll a concentration, absorption 

coefficients

Processing Level: 2, 3

Product Type: Standard, at-launch

Maximum File Size: 

MOD 21: 83 MB (Level 2); 640 MB binned, 

134 MB mapped (Level 3)

 MOD 36: 102 MB (Level 2); 865 MB 

binned, 134 MB mapped (Level 3)

File Frequency: 144/day (Daily Level 2); 2/day 

(Daily Level 3), 2/8-day (8-day Level 3), 

2/month (Monthly Level 3), 2/year (Yearly 

Level 3)

Primary Data Format: HDF-EOS

Browse Available: 36 km sample imagery available at the Goddard DAAC (Level 3 only)

Additional Product Information:

http://modis-ocean.gsfc.nasa.gov/

dataprod.html

DAAC: NASA Goddard Space Flight Center

Science Team Contact: 

 K. Carder
Phytoplankton and Dissolved Organic Matter • Aqua / MODIS Data Products Handbook - Volume 2 197

Coastal Zone Color Scanner Images of the Gulf of Maine and the Gulf of St. Lawrence. Satellite ocean-color imagery, 

in addition to allowing the concentration of single-celled plants to be measured remotely, permits the detection and 

identification of certain algal blooms. The white-to-light-grey waters in both regions are blooms of a single type of algae 

(called coccolithophores), that affects regional climate and fisheries.

MODIS Coccolith 

Concentration (MOD 25)

Product Description

This Level 2 and 3 product provides five parameters describing the concentration of coccoliths in 

sea water: the detached coccolith concentration in 

number/m3; the estimated calcite concentration due to 

the coccoliths in mg-CaCO3/m3; the pigment concentration in the coccolithophore biomass; a descriptor 

for the particular look-up table used; and a quality 

measure. The product is produced at 1-km spatial 

resolution daily for Level 2 and at 4.6-km, 36-km, 

and 1° resolution daily, 8-day weekly, monthly, and 

yearly for Level 3.

Research and Applications

Coccolithophores are small marine phytoplankton 

which form external calcium carbonate (CaCO3) 

scales (called coccoliths) having diameters of a few 

mm and a thickness of 250 to 750 nm. Coccolithophores are the largest source of calcium carbonate 

on Earth. Thus, coccolith production is an important 

part of the biogenic carbon cycle. The observed 

characteristics of coccolithophores, including their 

ubiquitous nature, possible role in climate, and intense scattering property, make a global-scale study of 

their distribution an important application for MODIS 

imagery. Specifically, it is important to estimate the 

rate at which CaCO3 is formed by phytoplankton and 

to look for long-term changes in that rate.

Data Set Evolution

The algorithm for extracting the detached coccolith 

concentration from surface waters is based on the 

semianalytic model of ocean color of Gordon (1988). 

The model relates the normalized water-leaving radiance to the absorption and scattering properties of 

the constituents of the water using radiative-transfer 

theory. The absorption and scattering properties are 

then related to the constituent concentrations through 

statistical analysis of direct measurements. The model 

is validated by comparison with a set of water-leaving 

radiances independent of the measurements used to 

establish the statistical relationships between constituents and optical properties.

Suggested Reading

Balch, W. M. et al., 1991.

Gordon, H. R. et al., 1988.

Groom, S. B., and P. M. Holligan, 1987.

Holligan, P. M. et al., 1983.

Sarmiento, J. L. et al., 1988.

Sikes, S., and V. Fabry, 1994.
198 Data Products Handbook - Volume 2 Aqua / MODIS • Phytoplankton and Dissolved Organic Matter 

MODIS Ocean Primary 

Productivity (MOD 27)

Product Description

This Level 4 product provides an estimate of the 

Ocean Primary Productivity on an 8-day and an 

annual basis at spatial resolutions of 4.6 km and 

36 km.

Research and Applications

The objective of the product is to quantify the magnitude and interannual variability (for decadal trends) in 

the oceanic primary productivity and phytoplankton 

carbon fixation. Primary productivity is the time rate 

of change of phytoplankton biomass, and, with allowance for excreted soluble carbon compounds, reflects 

the daily integrated photosynthesis within the water 

column. The integral of the values over the year is the 

annual primary productivity. The annual productivity 

product will be used for global- and regional-scale 

studies of interannual variability of ocean productivity, for comparisons with annual summations of 

short-term analytic estimates, and for comparison 

with global biogeochemical models.

Data Set Evolution

Ocean primary-productivity algorithms fall into 

two general classes, termed empirical and analytic 

algorithms. The empirical approach is based on 

simple correlation between time-averaged in situ

estimates of productivity and satellite-derived estimates of surface chlorophyll concentration. The 

analytic approach is based on models of the general 

photosynthetic response of the algal biomass as a 

function of major environmental variables such as 

light, temperature, and nutrient concentration. The 

overall methodologies differ significantly in the way 

various parameters are estimated and in the way they 

are assigned spatially and temporally across ocean 

basins. The approach taken for the MODIS algorithm 

is to begin implementation of an annual, global, empirical algorithm for at-launch product generation, 

while pursuing a vigorous research program within 

the SeaWiFS Science Team, to develop a consensus 

analytic algorithm for short-term (daily to weekly) 

global productivity. Cloudiness prevents deriving 

chlorophyll a concentrations over about 60% of the 

ocean on daily basis, excluding that already lost due to 

high sun glint. Chlorophyll a concentrations derived 

from all available sensors, including the Terra and 

(MODIS Coccolith Concentration, continued)

MODIS Coccolith Concentration Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 1 km/daily 

(Level 2); 4.6 km, 36 km, 1°/daily, 8-day, 

monthly, yearly (Level 3)

Key Science Applications: Input to global biogeochemical cycle models

Key Geophysical Parameters: Coccolith and calcite concentration, pigment concentration 

in coccolithophore blooms

Processing Level: 2, 3

Product Type: Standard, at-launch

Maximum File Size: 102 MB (Level 2); 

640 MB binned, 134 MB mapped 

(Level 3)

File Frequency: 144/day (Daily Level 2); 3/day 

(Daily Level 3), 3/8-day (8-day Level 3), 

3/month (Monthly Level 3), 3/year (Yearly 

Level 3)

Primary Data Format: HDF-EOS

Browse Available: 36 km sample imagery 

available at the Goddard DAAC (Level 3 

only)

Additional Product Information:

http://modis-ocean.gsfc.nasa.gov/

dataprod.html

DAAC: NASA Goddard Space Flight Center

Science Team Contacts:

 H. R. Gordon

 W. B. Balch
Phytoplankton and Dissolved Organic Matter • Aqua / MODIS Data Products Handbook - Volume 2 199

Global Ocean Phytoplankton Biomass. A pre-MODIS, global ocean-color product from the Coastal Zone Color Scanner 

shows the algal pigment concentrations within the upper layers of the ocean and indicates the biomass and productivity 

distribution within the oceans. While this is an annual product, MODIS will provide comparable global coverage on a 

weekly-to-monthly basis to enable scientists to study large-scale changes in marine ecosystems over weeks to decades. 

(From NASA GSFC.)

Aqua MODIS instruments, will be used to increase 

sampling frequency since these plankton processes 

vary rapidly over time and space.

Suggested Reading

Eppley, R. W. et al., 1985.

Esaias, W. E. et al., 1999.

Fitzwater, S.E. et al., 1982.

Iverson, R.L. et al., 2000.

Morel, A., and J. M. Andre, 1991.

Platt, T. C. et al., 1991.

MODIS Ocean Primary Productivity 

Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 4.6 km and 

36 km/8-day and a running annual average 

Key Science Applications: Ocean productivity, 

biogeochemical models

Key Geophysical Parameters: Annual and 

weekly ocean productivity

Processing Level: 4

Product Type: Standard, at-launch

Maximum File Size: 1200 MB binned (8-day), 

1360 MB binned (Yearly); 32 MB mapped 

(8-day and Yearly)

File Frequency: 4/8-day

Primary Data Format: HDF-EOS

Additional Product Information: 

http://modis-ocean.gsfc.nasa.gov/

dataprod.html

DAAC: NASA Goddard Space Flight Center

Science Team Contact:

 W. Esaias
200 Data Products Handbook - Volume 2 Aqua / MODIS • Phytoplankton and Dissolved Organic Matter 

MODIS Phycoerythrin 

Concentration (MOD 31)

Product Description

This product consists of three parameters which give 

the concentration of one of the major algal pigment 

groups in ocean water, Phycoerythrin. The three 

parameters are Phycoerythrobilin Concentration, 

Phycourobilin Concentration, and Constituent Inherent Optical Properties (CDOM absorption, chlorophyllous absorption, and particulate backscatter). 

These quantities are provided for Level 2 at 1-km 

spatial resolution daily and for Level 3 daily, 8-day 

weekly, monthly, and yearly at 4.6-km, 36-km, and 

1° resolution. The product is valid only for clear-sky 

ocean pixels.

 Phycoerythrin is one of three major algal pigment groups found in marine phytoplankton and 

bacteria (Bidigare, 1990). The phycoerythrins are 

further subdivided into phycourobilin-rich (PUB) 

and phycoerythrobilin-rich (PEB) phycoerythrins. 

This algorithm retrieves both PUB- and PEB- rich 

cases. Phycoerythrin is a chlorophyll accessory pigment and serves to receive photosynthetically usable 

light in the 480-505-nm and 540-560-nm ranges. It 

is used to infer the global extent of phycoerythrinbearing phytoplankton such as cyanobacteria, which 

are nitrogen-fixing and thus provide information on 

the nitrogen cycle. Used in conjunction with phytoplankton chlorophyllous pigment, the apparent 

species diversity of the oceans can be inferred.

Research and Applications

One of the intended uses of the phycoerythrin data 

product is to allow scientific investigators to study 

the global distributions of the phycoerythrin pigment 

and, in so doing, to allow definition of the diversity of 

phycoerythrin-bearing species such as cyanobacteria. 

When used in conjunction with chlorophyll distribution, phycoerythrin determination allows global 

phytoplankton species variability studies.

Data Set Evolution

The phycoerythrin retrieval algorithm requires waterleaving radiances generated from the incident solar 

irradiance, the total backscatter, and the total absorption of sea water. The PUB and PEB parameters 

are retrieved by a sequential-convergent-iteration 

method (Gordon et al., 1988) that uses five independent bands. MODIS band 10 (488 nm) and band 

12 (551 nm) correspond to the peaks of the PUB and 

PEB phycoerythrins. The major assumption for the 

algorithm is that the pigment-specific absorptioncoefficient spectral model used is applicable for 

the oceanic province where the satellite image was 

acquired. The algorithm will be validated by ship and 

airborne laser-induced and water Raman-normalized 

fluorescence measurements.

Suggested Reading

Bidigare, R. R. et al., 1990.

Culver, M. E., and M. J. Perry, 1994.

Gordon, H. R. et al., 1988.

Hoge, F. E., and R. N. Swift, 1986.

Hoge, F. E., and R. N. Swift, 1990.

Hoge, F. E. et al., 1999a.

Hoge, F. E. et al., 1999b.
Phytoplankton and Dissolved Organic Matter • Data Products Handbook - Volume 2 201

MODIS Phycoerythrin Concentration 

Summary

Coverage: Global ocean surface, clear-sky 

only

Spatial/Temporal Characteristics: 1 km/daily 

(Level 2); 4.6 km, 36 km, 1°/daily, 8-day, 

monthly, yearly (Level 3)

Key Science Applications: Global phytoplankton species studies, ocean productivity models

Key Geophysical Parameters: Phycoerythrobilin-rich (PEB) and phycourobulin-rich 

(PUB) phycoerythrins

Processing Level: 2, 3

Product Type: Research, at-launch

Maximum File Size: 102 MB (Level 2); 640 MB 

binned, 134 MB mapped (Level 3)

File Frequency: 144/day (Daily Level 2); 2/day 

(Daily Level 3), 2/8-day (8-day Level 3), 

2/month (Monthly Level 3), 2/year (Yearly 

Level 3)

Primary Data Format: HDF-EOS

Browse Available: 36 km sample imagery available at the Goddard DAAC (Level 3 only)

Additional Product Information: 

http://modis-ocean.gsfc.nasa.gov/

dataprod.html

DAAC: NASA Goddard Space Flight Center

Science Team Contact:

 F. E. Hoge

Aqua / MODIS
203

Snow and Ice Cover

Aqua

• AMSR-E

• MODIS
204 Data Products Handbook - Volume 2 • Snow and Ice Cover 

Snow and Ice Cover – An 

Overview

Relationship to Global Change Issues

Snow and ice cover dramatically influence surface 

albedo, hydrologic properties, the interactions between the atmosphere and the underlying surface, 

and regulation of ecosystem biological activity. For 

instance, when sea ice is formed in the ocean or snow 

is deposited on land, these processes can double the 

degree to which the surface reflects incoming solar 

energy, hence affecting the Earthʼs radiation balance 

and, in turn, its climate. Furthermore, both snow 

and ice are highly variable components of the climate system, changing dramatically from summer 

to winter, and, to a lesser extent, from year to year. 

The combination of high variability and impacts on 

critical climate processes insures the importance of 

monitoring snow and ice to the understanding of 

global change.

 Two key sensors on board Aqua that will provide 

enhanced global sea ice and snow measurements to 

supplement existing data sets are the AMSR-E and 

MODIS. Both will provide information to be used 

in the development of more accurate models to help 

understand and predict Earthʼs climate and the role 

of snow and ice in it.

 AMSR-E data will provide daily, global coverage 

of sea ice and snow regardless of cloud conditions or 

darkness. The derived sea ice and snow parameters 

will be used both operationally and for climate research. By extending the existing time series of sea 

ice and snow parameters, which currently span over 

20 years, scientists expect to obtain a better understanding of global, hemispheric, and regional variabilities on seasonal, interannual, and decadal time 

scales. The improved spatial resolution possible from 

the AMSR-E sensor, relative to previous satellite passive-microwave instruments, will also allow greater 

insight into the processes which define the role of the 

cryosphere in the climate system.

 MODIS data will be used to monitor the dynamics 

of large-area (greater than 10 square kilometers) snow 

and ice cover using daily and 8-day products. Information about snow and ice cover, including surface 

temperature, is important for a number of agricultural, 

industrial, social, and environmental reasons. For example, in some regions snow cover data can be critical 

in successfully predicting water supply, planning for 

agricultural and industrial developments, anticipating 

flooding, forecasting crop yield, and estimating freeze 

damage.

 

 Overview

Product Overview

Global snow-water equivalent and snow depth will 

be mapped daily and every five days over the Earthʼs 

land surfaces at 25-km resolution using the AMSR-E 

snow-water equivalent and snow-depth algorithm. A 

global, daily snow-storage map will be an at-launch 

AMSR-E product. Snow-water equivalent values 

will be inferred based on the resampled brightness 

data. After one day, a daily map will be generated 

by compositing all the data within that day. The 

MODIS snow cover product will be mapped daily 

and as 8-day composites at 500-m resolution over 

the Earthʼs land surfaces, using an algorithm based 

on the normalized difference of a visible and a shortwave-infrared band.

 The sea ice products derived from AMSR-E include sea ice concentration, ice temperature, and 

snow depth over sea ice. Daily sea ice concentrations 

and ice temperatures will be mapped on the standard 

SSM/I grid. Ascending data will also be separated 

from descending data for day/night discrimination. 

Sea ice concentrations will be mapped at both 25-km 

and 12.5-km resolutions while sea ice temperatures 

will be mapped on the 25-km-resolution grid only. 

Snow depth on sea ice will be a five-day product and 

mapped at a grid resolution of 12.5 km. A daily sea ice 

product will also be obtained from MODIS at a much 

finer, 1 km, resolution than the AMSR-E product but 

with use limited largely to cloud-free conditions. The 

MODIS sea ice product is based on the normalized 

difference between a visible and a shortwave-infrared 

band signal.

Product Interdependency and Continuity 

with Heritage Data

There have been several satellite missions that have 

collected snow and ice data for many years. Snow and 

ice are among the variables included in NASAʼs Pathfinder Program, which is providing research-quality 

data sets on global change from past and current satellite instruments. The heritage of both the AMSR-E 

and MODIS sensors is strong. Data sets from these 

sensors will extend those from historical and current 

sensors and provide the continuity needed for longterm global climate-change studies.

 The first detailed data sets of sea ice distributions 

regardless of cloud cover or solar illumination came 

from the data of the Electrically Scanning Microwave Radiometer (ESMR) on the Nimbus 5 satellite 

launched in December 1972. ESMR and its successor, 

the Scanning Multichannel Microwave Radiometer 
Snow and Ice Cover • Data Products Handbook - Volume 2 205

(SMMR), launched on Nimbus 7 in October 1978, 

provided the data for three major sea ice atlases 

describing the seasonal cycles and interannual variability of the Arctic and Antarctic sea ice covers for 

the years 1973-76 and 1978-87. The SMMR has been 

superseded by the Special Sensor Microwave/Imager 

(SSM/I), which has flown as part of the Defense Meteorological Satellite Program (DMSP) since 1987. 

The SSM/I is similar to SMMR, and its data also have 

been analyzed to determine snow cover and sea ice 

conditions. Most recently, time-series analyses of the 

combined SMMR and SSM/I data sets have yielded 

information on global sea ice trends and on regional 

and hemispheric variabilities spanning a 20-year 

period. The AMSR-E data will help to extend this 

20-year sea ice record.

 Other sources of historical satellite data for ice 

and snow are Landsat, which started operation (as 

the Earth Resources Technology Satellite) in 1972, 

the Advanced Very High Resolution Radiometer 

(AVHRR) on the NOAA satellites, which started 

operation in 1978, Seasat, Geosat, and many Japanese, European, and Russian satellite sensors. Landsat 

provides very high resolution (15-80 m in the visible 

and near-infrared bands, and 60-120 m in the thermalinfrared bands), while AVHRR provides moderate 

resolution (1 km) in the visible and infrared channels. 

Because of higher spatial resolution, broader spectral 

range, and extensive calibration and validation programs, both AMSR-E and MODIS are expected to 

provide more accurate data products than is currently 

possible with their primary precursors, the SSM/I and 

AVHRR, respectively.

Suggested Reading

Chang, A. T. C. et al., 1987.

Chang, A. T. C. et al., 1997.

Comiso, J. C. et al., 1997.

Comiso, J. C., 2000.

Foster, J. L. et al., 1984.

Gloersen, P. et al., 1992.

Hall, D. K. et al., 1995.

Klein, A. G. et al., 1998.

Markus, T., and D. J. Cavalieri, 1998.

Markus, T., and D. J. Cavalieri, 2000.

Parkinson, C. L., 1997.

Parkinson, C. L. et al., 1987.

Weeks, W. F., 1981.

Zwally, H. J. et al., 1983.

Overview
206 Data Products Handbook - Volume 2 • Snow and Ice Cover 

Landsat TM Scene of Northern Montana including Glacier National Park, after the application of the SNOMAP algorithm 

(Hall et al., 1995). The TM Scene was acquired on March 14, 1991 (scene i.d. #5256917454). (Note: Snow and non-snow features 

were classified automatically by the SNOMAP algorithm, but cloud features were classified by supervised classification.)

SNOW CLOUDS OTHER

 Overview
Snow and Ice Cover • Data Products Handbook - Volume 2 207

AMSR-E Sea Ice Concentration 

Product Description 

Sea ice concentrations will be produced from modified versions of the NASA Team algorithm (Markus 

and Cavalieri, 2000) and the Bootstrap algorithm 

(Comiso and Zwally, 1997). The NASA Team algorithm will be used as the standard algorithm to process Arctic data, while the Bootstrap algorithm will 

be used as the standard algorithm to process Antarctic 

data. Ice-concentration-difference maps derived from 

the two algorithms for both hemispheres will also 

be archived for comparison. Sea ice concentrations 

from both algorithms and their differences will be 

produced as gridded products (Level 3) and mapped 

onto a 25-km grid, enabling ready continuation of a 

twenty-year historical data set derived from SMMR 

and SSM/I. The ice-concentration products will 

also be gridded at 12.5 km to take advantage of the 

higher spatial resolution of the sensor. The grids at 

both resolutions use a polar stereographic projection 

defined for the SMMR and SSM/I data sets (NSIDC, 

1996). 

Research and Applications 

Polar sea ice is an important component of the 

global climate system. It insulates the atmosphere 

from the oceanic heat below in winter and keeps 

the ocean from absorbing heat from the sun in the 

spring and summer. Because of the high albedo of 

sea ice and its snow cover, it also plays an important 

role in the regional and global energy balance. The 

most important measure of the polar sea ice cover 

is its concentration. This product enables quantification of ice-free water in the leads and polynyas 

within the ice pack as well as the extent of the sea 

ice cover itself. Over twenty years of satellite passive-microwave sea ice data exist and have been used 

to study seasonal, regional, and interannual sea ice 

variabilities of the Arctic and Antarctic (e.g., Gloersen 

et al., 1992; Jacobs and Comiso, 1997; Cavalieri et 

al., 1997). 

Data Set Evolution 

The modified NASA Team and Bootstrap sea ice 

algorithms have considerable heritage. The NASA 

Team algorithm is based on the use of radiance ratios called the polarization and the spectral gradient 

ratio. The technique has been used since the launch 

of the Nimbus 7 SMMR in 1978 (Cavalieri et al., 

1984; Gloersen and Cavalieri, 1986) and continued 

with modifications (Cavalieri et al., 1991) with the 

series of SSM/I sensors first launched in 1987. The 

Bootstrap algorithm makes use of the multifrequency 

capabilities of the sensor to reduce errors due to variations in emissivity and temperature, and, like the 

NASA Team algorithm, has been applied to both the 

SMMR and SSM/I data sets (Comiso, 1986; Comiso, 

1995). The theoretical foundation and details of the 

two original algorithms and a comparative analysis 

of results from a year of SSM/I data are presented in 

Comiso et al. (1997). 

Suggested Reading 

Cavalieri, D. J. et al., 1984.

Cavalieri, D. J. et al., 1991. 

Cavalieri, D. J. et al., 1997. 

Comiso, J. C., 1986. 

Comiso, J. C., 1995. 

Comiso, J. C., and H. J. Zwally, 1997. 

Comiso, J. C. et al., 1997.

Gloersen P., and D. J. Cavalieri, 1986. 

Gloersen, P. et al., 1992. 

Jacobs, S. S., and J. C. Comiso, 1997.

Markus, T., and D. J. Cavalieri, 2000. 

NSIDC, 1996. 

Aqua / AMSR-E
208 Data Products Handbook - Volume 2 • Snow and Ice Cover 

AMSR-E Snow Depth on Sea 

Ice 

Product Description 

Snow depth on sea ice will be mapped every five 

days at a 12.5-km grid resolution. The algorithm, 

described in detail by Markus and Cavalieri (1998), 

makes use of the spectral-gradient ratio, defined using 

the 18- and 37-GHz vertically polarized radiances, 

and the total sea ice concentration measured with the 

modified NASA Team sea ice algorithm (Markus and 

Cavalieri, 2000). Currently, the algorithm is limited 

to the Southern Ocean and to the Arctic seasonal sea 

ice zones. Snow depth is measured in centimeters 

with a precision of 1 cm and an estimated accuracy 

of 5 cm. 

Research and Applications 

The overall importance of snow depth on sea ice to 

the climate system is well documented (e.g., Ledley, 

1991; Eicken et al., 1995). Snow cover on sea ice primarily affects the climate through its effect on surface 

albedo and the thermal insulation between ocean and 

atmosphere. Given that the change in sea ice coverage 

from summer to winter is over 15 million km2 in the 

Southern Ocean and 5 million km2 in the Arctic, the 

effect of snow cover can be substantial. Snow depth 

and accumulation rate are important variables in the 

fresh-water budget of the polar oceans. A quantitative 

knowledge of snow-depth variability could provide 

a useful estimate of precipitation minus evaporation 

(P-E), which is critically needed in state-of-the-art 

coupled ice-ocean models (Häkkinen, 1995). Current applications of the retrieved snow depths over 

the Southern Ocean include the study of interannual 

and regional variabilities of snow depth on sea ice 

(Markus and Cavalieri, 1998) and a study of mixedlayer ocean properties using an ECMWF-SSM/I 

forced mixed-layer model (Markus, 1999). 

Data Set Evolution 

The snow-depth-on-sea-ice algorithm has considerable heritage in Nimbus 7 SMMR and the DMSP 

SSM/I snow-depth algorithms derived for land applications (e.g., Chang et al., 1987). The instrument 

channels used are similar (18/19 GHz and 37 GHz), 

but the functional form is different, making use of 

ratios of radiances in the snow-depth-on-sea-ice 

algorithm instead of linear differences for the land 

algorithm. In contrast to the Equal-Area Scalable 

AMSR-E Sea Ice Concentration Summary

Coverage: Global 

Spatial/Temporal Characteristics: 12.5 km and 

25 km/ascending, descending, and daily 

averages

Key Geophysical Parameters: Sea ice concentration and difference 

Processing Level: 3 

Product Type: Standard, at-launch 

Maximum File Size: 53 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Browse Available: Accompanies data product

Additional Product Information:

http://wwwghcc.msfc.nasa.gov/AMSR/

html/amsr_products.html

DAAC: National Snow and Ice Data Center

Science Team Contacts: 

 D. J. Cavalieri

 J. C. Comiso

(AMSR-E Sea Ice Concentration, continued) 

Aqua / AMSR-E
Snow and Ice Cover • Aqua / AMSR-E Data Products Handbook - Volume 2 209

Earth (EASE) grid used for snow cover on land, the 

Level 3 grid used for snow depth on sea ice will be 

the so-called SSM/I polar stereographic grid, from 

NSIDC, with a grid size of 12.5 km. It matches the 

12.5-km grid used for sea ice concentration. 

Suggested Reading 

Chang, A. T. C. et al., 1997. 

Eicken, H. et al., 1995. 

Häkkinen, S., 1995. 

Ledley, T. S., 1991.

Markus, T., and D. J. Cavalieri, 1998. 

Markus, T., and D. J. Cavalieri, 2000. 

Markus, T., 1999. 

NSIDC, 1996. 

AMSR-E Snow Depth on Sea Ice Summary

Coverage: Southern Ocean and the Arctic 

seasonal sea ice zones

Spatial/Temporal Characteristics: 12.5 km/ 

5-day average 

Key Geophysical Parameters: Snow depth on 

sea ice 

Processing Level: 3 

Product Type: Standard, at-launch 

Maximum File Size: 53 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Browse Available: Accompanies data product

Additional Product Information:

http://wwwghcc.msfc.nasa.gov/AMSR/

html/amsr_products.html

DAAC: National Snow and Ice Data Center

Science Team Contacts: 

 D. J. Cavalieri

 J. C. Comiso 

AMSR-E Sea Ice Temperatures 

Product Description 

Sea ice temperatures will be produced from a modified version of the Bootstrap algorithm and from a 

simple relationship among ice temperature, ice emissivity, and ice concentration. Sea ice temperatures 

will be produced as gridded products (Level 3) and 

mapped onto a 25-km grid defined for the SMMR 

and SSM/I data sets by NSIDC. It is based on a polar 

stereographic projection. 

Research and Applications 

Satellite data provide a quantification of the polar sea 

ice cover in only two dimensions. Thickness, the third 

dimension, is not currently observable with satellite 

systems. While changes in ice temperature are closely 

linked with the temperatures of the ocean and atmosphere, these changes may also reflect changes in ice 

thickness and snow cover. For example, a warming 

of the mixed layer of the Arctic Ocean may lead to 

a thinning of the ice cover, which in turn may result 

in a warming of the ice. Thus, by monitoring the ice 

temperature, the snow depth, and the surface temperature derived, for example, from MODIS and AVHRR 

data (e.g., Comiso, 1999), it may be possible to detect 

changes in ice thickness. 

Data Set Evolution 

Microwave observations of sea ice at 6 GHz represent emissions primarily from the snow/ice interface and below. Since the emissivity for sea ice is 

relatively stable at this frequency, SMMR data have 

been used by Gloersen et al. (1992) to obtain maps 

of ice temperatures from 1979 through 1987. This 

time series was discontinued in 1987 because the 

lowest frequency for SSM/I is 19 GHz, which is not 

suitable for the purpose. The AMSR-E will have a 

6-GHz channel and will provide the opportunity to 

obtain sea ice temperatures once again. The AMSRE algorithm for ice temperature will be part of the 

revised Bootstrap Algorithm (Comiso and Zwally, 

1997) that makes use of 6-GHz information to reduce 

spatial variations in ice temperatures. The algorithm 

is different from that of Gloersen et al. in that the 

initial ice concentration used to calculate the effective emissivity at 6 GHz is derived from a combination of the 6- and 37-GHz data, using the Bootstrap 
210 Data Products Handbook - Volume 2 • Snow and Ice Cover 

AMSR-E Snow-Water 

Equivalent and Snow Depth

Product Description

Snow-water equivalent (SWE) and snow depth will 

be mapped daily and every five days over the Earthʼs 

land surfaces at 25-km resolution using an AMSR-E 

snow-water-equivalent and snow-depth algorithm. A 

global, daily snow-storage map will be an at-launch 

AMSR-E product. Snow-water-equivalent values 

will be inferred, based on the resampled brightness 

data. After one day, a daily map will be generated by 

compositing all the data within one day.

 Snow storage is a key parameter of the global 

energy and water balance. Snow-water-equivalent 

values for the U.S. are currently mapped by NOAA/

NWS/NOHRSC based on gamma radiation data. 

An NOHRSC low-flying aircraft collects data over 

selected flight lines a couple of times per year during the winter season. Snow-water-equivalent values 

between the flight lines are generated by optimum 

interpolation. Qualities of these data are difficult to 

assess. 

Research and Applications

The objective of this product is to quantify the amount 

and interannual variability of snow storage during 

the winter season. Snow is one of the most important 

renewable resources, for instance providing over 70% 

of the water supply in the western U.S. Information on 

the amount of snow storage and timing of discharge 

is critical for resource managers. Untimely melting 

of the snowpack could cause enormous damage to 

properties and loss of human lives. 

Data Set Evolution

The algorithm to retrieve the snow-water equivalent 

from AMSR-E data has a considerable heritage. It 

is based on the observations from multi-frequency 

radiometers. The current algorithm is built on the 

algorithm developed for the SMMR data. Due to the 

effects of grain size, density, stratigraphy, and wetness 

on microwave signals, a statistical retrieval approach 

has been adopted.

 Global snow cover will be mapped using a visible 

sensor at a resolution of 500 m (MOD 10). These 

data could provide snow mapping at basin scale for 

hydrological model applications. The persistent cloud 

technique. Also, a threshold of 80% ice concentration 

used by Gloersen et al., below which the temperature 

is set to 271 K, is not used. 

Suggesting Reading 

Comiso, J. C., 2000. 

Comiso, J. C., and H. J. Zwally, 1997. 

Gloersen, P. et al., 1992.

NSIDC, 1996.

 

AMSR-E Sea Ice Temperatures Summary 

Coverage: Global 

Spatial/Temporal Characteristics: 25 km/

ascending, descending, and daily averages

Key Geophysical Parameters: Sea ice temperature 

Processing Level: 3 

Product Type: Standard, at-launch 

Maximum File Size: 19.5 MB

File Frequency: 1/day

Primary Data Format: HDF-EOS

Browse Available: Accompanies data product

Additional Product Information:

http://wwwghcc.msfc.nasa.gov/AMSR/

html/amsr_products.html

DAAC: National Snow and Ice Data Center

Science Team Contacts: 

 D. J. Cavalieri

 J. C. Comiso 

Aqua / AMSR-E

(AMSR-E Sea Ice Temperatures, continued) 
Snow and Ice Cover • Aqua / AMSR-E and MODIS Data Products Handbook - Volume 2 211

cover over some parts of the world restricts this technique. With a combination of microwave and visible 

data, a better snow cover product can be expected.

AMSR-E Snow-Water Equivalent and Snow 

Depth Summary

Coverage: Global

Spatial/Temporal Characteristics: EASE grid 

25-km resolution/daily, 5-day, monthly

Key Geophysical Parameters: Snow-water 

equivalent, snow depth

Processing Level: 3

Product Type:

Snow-water equivalent: Standard, atlaunch

Snow depth: Research, post-launch

Maximum File Size: 3.2 MB

File Frequency: 1/day (Daily Level 3), 1/5-day 

(5-day Level 3), 1/month (Monthly 

Level 3)

Primary Data Format: HDF-EOS

Browse Available: Accompanies data product

Additional Product Information:

http://wwwghcc.msfc.nasa.gov/AMSR/

html/amsr_products.html

DAAC: National Snow and Ice Data Center

Science Team Contact:

 A. T. Chang

MODIS Snow Cover (MOD 10)

Product Description

Global snow cover (including over ice on large, inland water bodies) will be mapped daily and as 8-day 

composites over the Earthʼs land surfaces at 500-m 

resolution using an algorithm called SNOMAP (Hall 

et al., 1995, Klein et al., 1998). A global, daily snow 

cover map will be an at-launch MODIS product. 

Snow cover, with its high albedo, is a key parameter 

of the global energy balance, reflecting much of the 

incident solar radiation back to space. Snow cover 

of the Northern Hemisphere is currently mapped 

by NOAA on a daily basis, but the accuracy of the 

maps has been difficult to determine, in part because 

a variety of techniques has been used to map snow 

cover over the 34-year period during which the maps 

have been produced.

 Additionally, snow/cloud discrimination is difficult 

and often impossible using the current NOAA sensors 

because of the available bands. MODIS bands will 

allow automatic snow/cloud discrimination, and, in 

conjunction with the MODIS cloud mask (MOD 35), 

will allow automated snow mapping. The MODIS 

8-day-composite snow cover product is designed to 

provide snow-cover persistence statistics for each 

pixel so that users can determine how long the snow 

has been on the ground during the compositing period. This is especially important during the transition 

seasons.

Research and Applications

Large, inland water bodies such as the Great Lakes, 

are often ice-covered during the winter months, and 

navigation during part of the winter is a significant 

problem. NOAA data are currently used to map ice 

cover on the Great Lakes, but discrimination between 

snow/ice and cloud is a problem. Additionally, ice 

cover on lakes can be an important climate indicator, 

as the dates of freeze-up and break-up are influenced 

by meteorological conditions. A trend toward earlier 

break-up, for example, could signify a warming as 

has been observed in some areas (e.g., Comb, 1990). 

Thus, it is important to measure ice conditions on 

large lakes over an extended period of time in order 

to detect trends as well as for operational uses over 

the short term.
212 Data Products Handbook - Volume 2 • Snow and Ice Cover 

MODIS Snow Cover Summary

Coverage: Global, daytime

Spatial/Temporal Characteristics: 500 m/daily; 

500 m/8-day composite; 1/4°/daily and 

8-day composite

Key Geophysical Parameters: Snow cover, 

lake-ice cover

Processing Level: 2, 3 (mapped)

Product Type: Standard, at-launch

Maximum File Size: 23 MB (Level 2), 12 MB 

(Level 3) 

File Frequency: 288/day (Daily Level 2); 

333/day (Daily Level 3), 333/8-day (8-day 

Level 3)

Primary Data Format: HDF-EOS

Additional Product Information: 

http://snowmelt.gsfc.nasa.gov/MODIS_

Snow/modis.html

DAAC: National Snow and Ice Data Center

Science Team Contact:

 D. Hall

(MODIS Snow Cover, continued)

MODIS “Browse” Product (5-km resolution) showing the Maximum 

Snow Cover (white) in North America composited for the 8-day period 

March 5-12, 2000. Dark gray indicates clouds, and the black line represents the average March snowline as determined from NOAA/NESDIS 

snow cover maps for the years 1966-1998.

Data Set Evolution

SNOMAP has a considerable heritage. It is based on 

the normalized difference of a visible and a shortwave-infrared band. This technique has been used, 

since at least 1978, to map snow from aircraft (Kyle 

et al., 1978). Since the mid-1980s, it has been used to 

map snow using Landsat data on the drainage-basin 

scale (Dozier, 1989). The SNOMAP algorithm has 

been enhanced by incorporating the normalized difference vegetation index (NDVI), to map the snow 

cover in denser forests than was possible with the 

original algorithm (Klein et al., 1998).

 Global snow cover has also been mapped using 

passive-microwave data at a resolution of about 

50 km (Foster and Chang, 1993). While these data 

allow snow mapping through cloud cover, passivemicrowave data do not provide a resolution that is 

suitable for detailed snow mapping. Basin-scale mapping is required for hydrological modeling, and, in 

particular for snowmelt-runoff calculations, which 

are essential for hydroelectric-power generation and 

for water-supply forecasts in the western U.S. and 

in many other parts of the world. The expected use 

of passive-microwave and MODIS snow cover data 

together should yield information on snow extent 

and snow-water equivalent (Salomonson et al., 

1995). Snow cover data are also needed for general 

circulation modeling.

Suggested Reading

Comb, D. G., 1990.

Dozier, J., 1989.

Foster, J. L., and A. T.C. Chang, 1993.

Hall, D.K. et al., 1995.

Klein et al., 1998.

Kyle, H. L. et al., 1978.

Salomonson, V. V. et al., 1995.

Aqua / MODIS
Snow and Ice Cover • Data Products Handbook - Volume 2 213

MODIS Sea Ice Cover (MOD 

29)

Product Description

Global sea ice cover will be mapped daily and every 

eight days at 1-km resolution using an algorithm 

called ICEMAP. Global sea ice cover will be an 

at-launch MODIS product. Sea ice is present over 

approximately 7% of the Earthʼs ocean surface (Parkinson, 1997). Snow-covered sea ice, with its high 

albedo, is a key parameter of the global energy balance, reflecting much of the incident solar radiation 

back to space. Additionally, the sea ice cover is an 

insulating layer between the ocean and atmosphere; 

heat loss through open water is up to 100 times greater 

than heat loss through thick ice. As a consequence, 

leads and polynyas (linear and nonlinear openings in 

the sea ice, respectively) are significant to the energy 

budget of the ice-covered ocean and to local and regional climatology. Such open-water areas and areas 

of reduced ice concentration are also important for 

shipping in ice-covered seas.

Research and Applications

Sea ice cover is currently mapped by NOAA visible 

and near-infrared sensors, and by microwave sensors, 

both passive and active. Using the NOAA sensors, 

snow/cloud discrimination is a major hindrance in 

identifying sea ice. The passive-microwave sensors 

map sea ice through cloud cover, but at a resolution 

of only about 30 km. Active microwave sensors have 

good spatial resolution, up to about 25 m, but currently do not map sea ice cover globally on a daily 

basis. MODIS will be able to map sea ice globally, 

but with the significant limitation that cloud cover 

will obscure the view of the surface for much of the 

time. Together, the MODIS and microwave sensors 

will provide important information on the presence 

and concentration of sea ice. MODIS data, when 

available, will provide the higher resolution view 

of sea ice that is not obtainable using passive-microwave data.

Data Set Evolution

ICEMAP has a considerable heritage. It is based on 

the normalized difference of a visible and a shortwave 

infrared band. This technique has been used to map 

snow from aircraft and satellites (Kyle et al., 1978; 

 Aqua / MODIS

Dozier, 1984; and Hall et al., 1995) and has been 

shown to be effective for mapping sea ice as well. The 

8-day composited sea-ice-cover product is designed 

to provide sea-ice-cover persistence statistics for each 

pixel so that users can determine how long sea ice has 

been present during the previous 10 days in any given 

location. A cloud mask (MOD 35) will be provided 

by another MODIS investigator.

Suggested Reading

Dozier, J., 1984.

Hall, D. K. et al., 1995.

Kyle, H. L. et al., 1978.

Parkinson, C. L., 1997.

Sea Ice Image of the Bering Sea for May 7, 2000, 

generated from Terra MODIS band 2 (0.85 μm) data 

at 250 m spatial resolution. The Seward Peninsula 

is visible at the top of the image, and St. Lawrence 

Island can be seen in the lower left. Black areas are 

either clouds or open water. Individual snow-covered 

ice floes are visible, as is newly-formed sea ice, which 

has a lower reflectance and can sometimes be difficult 

to distinguish from the surrounding water. The ice flow 

direction is predominantly southward from the Bering 

Strait (upper left).
214 Data Products Handbook - Volume 2 • Snow and Ice Cover 

(MODIS Snow and Sea Ice Albedo, continued)

Aqua / MODIS

MODIS Snow and Sea Ice 

Albedo (MODISALB)

Product Description

As currently envisioned, the MODIS snow and 

sea ice albedo product will provide daily albedo 

estimates of the seasonally snow-covered areas of 

the Earth with a spatial resolution of 1 km, and, for 

the climate-modeling grid product, 1/4° × 1/4° resolution. Once a surface has been identified as covered 

by either snow (MOD 10) or sea ice (MOD 29), the 

snow- albedo algorithm will be used to estimate 

the albedo of these surfaces under clear-sky conditions (Klein and Hall, 2000). 

 In addition to relying on other MODIS snow products to determine whether an area is snow or sea-ice 

covered, the planned MODIS snow-albedo product 

will utilize the MODIS cloud mask and the MODIS 

surface reflectance and global land cover products.

Research and Applications

The albedo of snow and sea ice is among the highest 

of all naturally occurring land surface albedos. The 

high albedo and large areal extent enable snow and 

sea ice to influence strongly the Earthʼs radiation 

budget. At a local scale, the high albedo of these 

surfaces affects energy exchange between the surface 

and the atmosphere.

 Snow and ice albedos will be used in general circulation models to improve the parameterization of 

the Earthʼs albedo on a daily basis. This will enable 

more accurate forecasts of the Earthʼs climate. In 

addition, for sea ice, the differences in sea ice albedo 

will help to classify sea ice type. Determination of 

sea ice type is important for calculating the energy 

exchange between the ocean and the atmosphere.

Data Set Evolution

The MODIS snow albedo product builds on a heritage 

of snow and sea ice albedo algorithms (De Abreu et 

al., 1994; Knap and Oerlemans, 1996; Stroeve et al., 

1997) developed for use with sensors on other environmental satellites, primarily NOAA AVHRR. 

MODIS Sea Ice Cover Summary

Coverage: Global, daytime over nonequatorial ocean

Spatial/Temporal Characteristics: 1 km/daily; 

1 km/8-day composite; 1/4°/daily and 

8-day composite

Key Geophysical Parameters: Sea ice cover

Processing Level: 2, 3 (mapped)

Product Type: Standard, at-launch

Maximum File Size: 19 MB (Level 2); 6 MB 

(Daily Level 3), 12 MB (8-day Level 3)

File Frequency: 288/day (Daily Level 2); 

626/day (Daily Level 3), 626/8-day (8-day 

Level 3)

Primary Data Format: HDF-EOS

Browse Available: No

Additional Product Information: 

http://snowmelt.gsfc.nasa.gov/MODIS_

Snow/modis.html

DAAC: National Snow and Ice Data Center

Science Team Contact:

 D. Hall
Snow and Ice Cover • Data Products Handbook - Volume 2 215

Suggested Reading

De Abreu, R. A. et al., 1994.

Klein, A. G., and D. K. Hall, 2000.

Knap, W.H., and J. Oerlemans, 1996.

Stroeve, J. et al., 1997.

MODIS Snow and Sea Ice Albedo Summary

Coverage: Global

Spatial/Temporal Characteristics: 1 km and 

1/4°/daily and 8-day composite

Key Science Applications: Input to hydrological 

and GCM models

Key Geophysical Parameters: Snow/ice albedo

Processing Level: 3

Product Type: Standard, post-launch

Maximum File Size: 17 MB

File Frequency: 128/day

Primary Data Format: HDF-EOS

Browse Available: No

Additional Product Information: 

http://snowmelt.gsfc.nasa.gov/MODIS_

Snow/modis.html

DAAC: National Snow and Ice Data Center

Science Team Contact:

D. Hall

 Aqua / MODIS
Data Products Handbook - Volume 2 217 Appendix B: Points of Contact Appendix A: EOS DAAC Contact Information 

Appendix A: EOS Distributed Active Archive Centers (DAACs) 

Contact Information DAAC & Discipline Address User Support Office Contact Information Alaska SAR Facility (ASF) Sea Ice, Polar Processes, Synthetic Aperture Radar (SAR) Imagery Land Processes (LP) DAAC Land Processes GSFC Earth Sciences (GES) DAAC Upper Atmosphere, Global Biosphere, Atmo- spheric Dynamics

Physical Oceanography (PO) DAACPhysical OceanographyLangley Research Center (LaRC)Radiation Budget, Clouds, Aerosols, Tropospheric ChemistryNatoinal Snow and Ice Data Center (NSIDC)Snow and Ice, Cryosphere and ClimateOak Ridge National Laboratory (ORNL)Biogeochemical DynamicsSocioeconomic Data and Applications Center (EDAC)Human Impact on Global ChangeGlobal Hydrology Resource Center (GHRC)Lightning, Hydrologic Processes

Alaska SAR Facility, User ServicesPO Box 757320University of AlaskaFairbanks, AK 99775-7320United States Geological SurveyEROS Data CenterSioux Falls, SD 57198NASA/Goddard Space Flight CenterCode 902.2Greenbelt, MD 20771Physical Oceanography DAACM/S Raytheon-2994800 Oak Grove DrivePasadena, CA 91109NASA/Langley Research CenterMail Stop 157D2 South Wright StreetHampton, VA 23681-2199University of ColoradoCampus Box 449Boulder, CO 80309-0449Oak Ridge National Laboratory Environmental Sciences DivisionPO Box 2008, MS 6407, Bldg. 1507Oak Ridge, TN 37831-6407Socioeconomic Data and Applications CenterColumbia Univ./Lamont-Doherty Earth ObservatoryP.O. Box 1000, 61 Rt. 9WPalisades, NY 10964National Space Science and Technology Center320 Sparkman DriveHuntsville, AL 35805

Email: asf@eos.nasa.gov, uso@asf.alaska.eduPhone: 907-474-6166 Fax: 907-474-5195URL: www.asf.alaska.edu/Email: edc@eos.nasa.govPhone: 605-594-6116 Fax: 605-594-6963URL: edcdaac.usgs.govEmail: gsfc@eos.nasa.govPhone: 301-614-5224 Fax: 301-614-5268URL: daac.gsfc.nasa.gov/Email: jpl@eos.nasa.gov, podaac@podaac.jpl.nasa.govPhone: 626-744-5508 Fax: 626-744-5506URL: podaac.jpl.nasa.gov/Email: larc@eos.nasa.govPhone: 757-864-8656 Fax: 757-864-8807URL: eosweb.larc.nasa.gov/Email: nsidc@eos.nasa.gov, nsidc@kryos.colorado.eduPhone: 303-492-6199 Fax: 303-492-2468URL: www-nsidc.colorado.eduEmail: ornl@eos.nasa.gov, ornldaac@ornl.govPhone: 423-241-3952 Fax: 423-574-4665URL: www-eosdis.ornl.govEmail: ciesin.info@ceisin.columbia.eduPhone: 845-365-8920 Fax: 845-365-8922URL: sedac.ciesin.org, www.ciesin.orgEmail: ghrc@eos.nasa.govPhone: 256-961-7932 Fax: 256-961-7859URL: ghrc.msfc.nasa.gov/
218 Data Products Handbook - Volume 2 Appendix B: Points of Contact 

ACRIM III

Richard C. Willson

Columbia University

1001 B Avenue, Suite 200

Coronado, CA 92118

Phone: 619-522-2945 or 619-628-8507

Fax: 619-522-2967 or 619-522-2967

Email: acrim@acrim.com

AIRS/AMSU-A/HSB

Hartmut H. Aumann 

Mail Stop 230-300

Jet Propulsion Laboratory

4800 Oak Grove Drive 

Pasadena, CA 91109-8099 

Phone: 818-354-6865 

Fax: 818-393-4918 

Email: hha@airs1.jpl.nasa.gov 

Moustafa T. Chahine 

Mail Stop 180-904

Jet Propulsion Laboratory

4800 Oak Grove Drive 

Pasadena, CA 91109-8099 

Phone: 818-354-6057 

Fax: 818-393-4218 

Email: moustafa.t.chahine@jpl.nasa.gov 

Catherine Gautier 

ICESS

University of California Santa Barbara

Santa Barbara, CA 93106 

Phone: 805-893-8095 

Fax: 805-893-2578 

Email: gautier@icess.ucsb.edu 

Henry E. Revercomb 

Space Science and Engineering Center

University of Wisconsin Madison

1225 W. Dayton Street 

Madison, WI 53706 

Phone: 608-263-6758 

Fax: 608-263-6738 

Email: hankr@ssec.wisc.edu 

Philip W. Rosenkranz 

Massachusetts Institute of Technology

77 Massachusetts Avenue, Room 26-343 

Cambridge, MA 02139 

Phone: 617-253-3073 

Fax: 617-258-7864 

Email: pwr@mit.edu 

David H. Staelin 

Massachusetts Institute of Technology

77 Massachusetts Avenue,

Room 26-341

Cambridge, MA 02139 

Phone: 617-253-3711 

Fax: 617-258-7864 

Email: staelin@ll.mit.edu 

L. Larrabee Strow 

Department of Physics

University of Maryland Baltimore County

1000 Hilltop Circle

Baltimore, MD 21250 

Phone: 410-455-2528 

Fax: 410-455-1072 

Email: strow@umbc.edu 

Joel Susskind 

Code 910.4

NASA/Goddard Space Flight Center

Greenbelt, MD 20771 

Phone: 301-286-7210 

Fax: 301-286-1761 

Email: f41js@charney.gsfc.nasa.gov 

AMSR-E

Peter Ashcroft 

Remote Sensing Systems

438 First St., Suite 220 

Santa Rosa, CA 95404 

Phone: 707-545-2904 

Fax: 707-545-2906 

Email: ashcroft@remss.com 

Appendix B: Points of Contact
Data Products Handbook - Volume 2 219 Appendix B: Points of Contact

Donald J. Cavalieri 

Code 971

NASA/Goddard Space Flight Center

Greenbelt, MD 20771 

Phone: 301-614-5901 

Fax: 301-614-5644 

Email: don@cavalieri.gsfc.nasa.gov 

Alfred T. Chang 

Code 974

NASA/Goddard Space Flight Center

Greenbelt, MD 20771 

Phone: 301-614-5766 

Fax: 301-614-5808 

Email: achang@rainfall.gsfc.nasa.gov 

Josefino C. Comiso 

Code 971

NASA/Goddard Space Flight Center

Greenbelt, MD 20771 

Phone: 301-614-5708 

Fax: 301-614-5644 

Email: comiso@joey.gsfc.nasa.gov 

Ralph Ferraro 

Code E/RA2, Room 601, WWBG

NOAA/NESDIS/ORA

Atmospheric Research & Applications Division

5200 Auth Road 

Camp Springs, MD 20746-4304 

Phone: 301-763-8251 x147 

Fax: 301-763-8580 

Email: ralph.r.ferraro@noaa.gov 

Christian Kummerow 

Department of Atmospheric Sciences

Colorado State University

Ft. Collins, CO 80524

Phone: 970-491-7473

Fax: 970-491-8449 

Email: kummerow@atmos.colostate.edu

Elena Lobl

Code SD60

NASA/Marshall Space Flight Center

NSSTC 320 Sparkman Drive

Huntsville, AL 35805

Phone: 256-961-7912

Fax: 256-922-5788

Email: Elena.Lobl@msfc.nasa.gov

Eni G. Njoku 

Mail Stop 300-233

Jet Propulsion Laboratory

4800 Oak Grove Drive 

Pasadena, CA 91109-8099 

Phone: 818-354-3693 

Fax: 818-354-9476 

Email: eni.g.njoku@jpl.nasa.gov 

Akira Shibata

National Space Development Agency of Japan

Earth Observing Research Center

1-9-9, Roppongi

Minato-ku, Tokyo, 106

Japan

Phone: 81-33-224-7040

Fax: 81-33-224-7051

Email: Ashibata@eorc.nasda.go.jp

Roy W. Spencer

Code SD60

NASA/Marshall Space Flight Center

NSSTC 320 Sparkman Drive

Huntsville, AL 35805

Phone: 256-961-7960

Fax: 256-922-5788

Email: roy.spencer@msfc.nasa.gov

Frank J. Wentz 

Remote Sensing Systems

1101 College Avenue, Suite 220 

Santa Rosa, CA 95404 

Phone: 707-545-2904 

Fax: 704-545-2906 

Email: wentz@remss.com 

Thomas T. Wilheit 

Department of Meteorology

Texas A&M University

Eller O&M Building, Room 1204 

College Station, TX 77843-3150 

Phone: 979-845-0176 

Fax: 979-862-4466 

Email: wilheit@tamu.edu 

CERES

Bruce R. Barkstrom

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681

Phone: 757-864-5676

Fax: 757-864-7996 

Email: b.r.barkstrom@larc.nasa.gov 
220 Data Products Handbook - Volume 2 Appendix B: Points of Contact 

Thomas P. Charlock 

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681

Phone: 757-864-5687 

Fax: 757-864-7996 

Email: t.p.charlock@larc.nasa.gov 

Richard N. Green 

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681 

Phone: 757-864-5684 

Fax: 757-864-7996 

Email: r.n.green@larc.nasa.gov 

David P. Kratz 

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681

Phone: 757-864-5669 

Fax: 757-864-7996 

Email: d.p.kratz@larc.nasa.gov 

Robert B. Lee III 

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681 

Phone: 757-864-5679 

Fax: 757-864-7996 

Email: r.b.lee@larc.nasa.gov 

Norman G. Loeb

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681 

Phone: 757-864-5688

Fax: 757-864-7996 

Email: n.g.loeb@larc.nasa.gov 

Patrick Minnis 

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681 

Phone: 757-864-5671 

Fax: 757-864-7996 

Email: p.minnis@larc.nasa.gov 

Kory J. Priestley

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681 

Phone: 757-864-8147

Fax: 757-864-7996 

Email: k.j.priestley@larc.nasa.gov 

David F. Young 

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681 

Phone: 757-864-5740 

Fax: 757-864-7996 

Email: d.f.young@larc.nasa.gov 

Bruce A. Wielicki 

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681 

Phone: 757-864-5683 

Fax: 757-864-7996 

Email: b.a.wielicki@larc.nasa.gov 

Takmeng Wong 

Mail Stop 420

NASA/Langley Research Center

Hampton, VA 23681 

Phone: 757-864-5607

Fax: 757-864-7996

Email: takmeng.wong@larc.nasa.gov 

MODIS

Mark Abbott

College of Oceanic & Atmospheric Sciences

Oceanography Administration Building 104

Oregon State University

Corvallis, OR 97331-5503

Phone: 541-737-4045

Fax: 541-737-2064

Email: mark@oce.orst.edu

Steven A. Ackerman 

Department of Atmospheric & Oceanic Sciences

University of Wisconsin Madison

1225 W. Dayton Street 

Madison, WI 53706 

Phone: 608-263-3647 

Fax: 608-262-5974 

Email: stevea@ssec.wisc.edu 

William B. Balch 

Bigelow Laboratory

McKown Point 

W. Boothbay Harbor, ME 04575

Phone: 207-633-9600 

Fax: 207-633-9641 

Email: bbalch@bigelow.org 
Data Products Handbook - Volume 2 221 Appendix B: Points of Contact

William L. Barnes

Code 970

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5675

Fax: 301-614-5666

Email: wbarnes@neptune.gsfc.nasa.gov

Otis Brown

RSMAS/MPO

University of Miami

4600 Rickenbacker Causeway

Miami, FL 33149-1098

Phone: 305-361-4018

Fax: 305-361-4622

Email: obrown@rsmas.miami.edu

Kendall Carder

Department of Marine Science

University of South Florida

140 Seventh Avenue South

St. Petersburg, FL 33701-5016

Phone: 727-553-3952

Fax: 727-553-3918

Email: kcarder@monty.marine.usf.edu

Dennis Clark

Code E/RA28

NOAA/NESDIS

5200 Auth Road, Room 105

Camp Springs, MD 20746-4304

Phone: 301-763-8102

Fax: 301-763-8108

Email: dclark1@nesdis.noaa.gov

Wayne Esaias

Code 971

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5709

Fax: 301-614-5644

Email: wayne.e.esaias.1@gsfc.nasa.gov

Robert H. Evans

RSMAS/MPO

University of Miami

Meteorology & Physical Oceanography

4600 Rickenbacker Causeway

Miami, FL 33149-1098

Phone: 305-361-4799

Fax: 305-361-4622

Email: bob@miami.rsmas.miami.edu

Bo-Cai Gao

Remote Sensing Division, Code 7212

Naval Research Laboratory

4555 Overlook Drive, SW

Washington, DC 20375-5320

Phone: 202-767-8252

Fax: 202-404-8894

Email: gao@rsd.nrl.navy.mil

Howard R. Gordon

Department of Physics

University of Miami

Coral Cables, FL 33124

Phone: 305-284-2323

Fax: 305-284-4222

Email: gordon@phyvax.ir.miami.edu

Bruce W. Guenther

Code 920.1

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5940

Fax: 301-614-5970

Email: bruce.w.guenther.1@gsfc.nasa.gov

Dorothy Hall

Code 974

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5771

Fax: 301-614-5808

Email: dhall@glacier.gsfc.nasa.gov

Frank E. Hoge

Building N-159 (West)

NASA/Wallops Flight Facility

Wallops Island, VA 23337

Phone: 804-824-1567

Fax: 804-824-1036

Email: hoge@osb1.wff.nasa.gov

Alfredo R. Huete

Department of Soil, Water, and Environmental 

 Sciences

University of Arizona

1200 E South Campus Drive

Shantz Building #38

Tuscon, AZ 85721-0038

Phone: 520-621-3228

Fax: 520-621-1647

Email: ahuete@ag.arizona.edu
222 Data Products Handbook - Volume 2 Appendix B: Points of Contact 

Christopher O. Justice

Department of Environmental Sciences

University of Virginia

Clark Hall

Charlottesville, VA 22903

Phone: 804-924-3197

Fax: 804-924-4761

Email: justice@virginia.edu

Yoram J. Kaufman

Code 913

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-6189

Fax: 301-614-6307

Email: kaufman@climate.gsfc.nasa.gov

Michael D. King

Code 900

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5636

Fax: 301-614-5620

Email: king@climate.gsfc.nasa.gov

Edward J. Masuoka

Code 922

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5515

Fax: 301-614-5269

Email: emasuoka@ltpmail.gsfc.nasa.gov

W. Paul Menzel

NOAA/NESDIS

University of Wisconsin Madison

1225 W. Dayton Street

Madison, WI 53706

Phone: 608-264-5325

Fax: 608-262-5974

Email: paulm@ssec.wisc.edu

Harry E. Montgomery

Code 920.1

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5941

Fax: 301-614-5970

Email: hmontgom@mcst.gsfc.nasa.gov

Jan-Peter Muller

Department of Geomatic Engineering

University College London

Gower Street

London WC1E 6BT

United Kingdom

Phone: 44-20-7679-7227

Fax: 44-20-7679-0453

Email: jpmuller@ge.ucl.ac.uk

Ranga B. Myneni

Department of Geography

Boston University

Center for Remote Sensing

675 Commonwealth Avenue

Boston, MA 02215

Phone: 617-353-5742

Fax: 617-353-8399

Email: rmyneni@crsa.bu.edu

Steven W. Running

University of Montana

School of Forestry

Missoula, MT 59812

Phone: 406-243-6311

Fax: 406-243-4510

Email: swr@ntsg.umt.edu

Vincent V. Salomonson

Code 900

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5634

Fax: 301-614-5620

Email: vsalomon@pop900.gsfc.nasa.gov

Alan H. Strahler

Department of Geography

Center for Remote Sensing

Boston University

725 Commonwealth Avenue

Boston, MA 02215

Phone: 617-353-5984

Fax: 617-353-8399

Email: alan@bu.edu

Didier Tanré 

Laboratoire dʼ Optique Atmosphérique

Université des Sciences et Techniques de Lille

Bat P5 

F-59655 Villeneuve dʼAscq Cedex 

France 

Phone: 33-3-20-33-70-33 

Fax: 33-3-20-43-43-42 

Email: tanre@loa.univ-lille1.fr 
Data Products Handbook - Volume 2 223 Appendix B: Points of Contact

Kurtis Thome

Remote Sensing Group/OCS

University of Arizona

1630 E. University Blvd

Tucson, AZ 85721-0094

Phone: 520-621-4535

Fax: 520-621-8292

Email: kurt.thome@opt-sci.arizona.edu

John R.G. Townshend

Department of Geography

University of Maryland at College Park

2181 LeFrak Hall

College Park, MD 20742-8225

Phone: 301-405-4050

Fax: 301-314-9299

Email: jt59@umail.umd.edu

Eric Vermote

Code 923

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5521

Fax: 301-614-5269

Email: eric@kratmos.gsfc.nasa.gov

Zhengming Wan

Institute for Computational Earth Systems Science

University of California

Santa Barbara, CA 93106-3060

Phone: 805-893-4541

Fax: 805-893-2578

Email: wan@icess.ucsb.edu

ETM+

Samuel N. Goward 

Department of Geography

University of Maryland College Park

1113 LeFrak Hall

College Park, MD 20742 

Phone: 301-405-4055 

Fax: 301-314-9299 

Email: sg21@umail.umd.edu

Ralph J. Thompson

EROS Data Center

U.S. Geological Survey

Sioux Falls, SD 57198

Phone: 605-594-6162

Fax: 605-594-6963

Email: rjthompson@edcmail.cr.usgs.gov

Darrel L. Williams 

Code 923

NASA/Goddard Space Flight Center

Greenbelt, MD 20771 

Phone: 301-614-6692 

Fax: 301-614-6695 

Email: darrel@ltpmail.gsfc.nasa.gov 

SAGE III

William P. Chu 

Mail Stop 475

NASA/Langley Research Center

Hampton, VA 23681-2199

Phone: 757-864-2675 

Fax: 757-864-2671 

Email: W.P.Chu@larc.nasa.gov 

Er-Woon Chiou 

Mail Stop 475

NASA/Langley Research Center

Hampton, VA 23681-2199 

Phone: 757-864-6810

Fax: 757-864-2671

Email: E.Chiou@larc.nasa.gov

M. Patrick McCormick

Hampton University

Center for Atmospheric Sciences

23 Tyler Street

Hampton, VA 23668

Phone: 757-728-6867

Fax: 757-727-5090

Email: mcc@hamptonu.edu

Michael C. Pitts

Mail Stop 475

NASA/Langley Research Center

Hampton, VA 23681-2199 

Phone: 757-864-2693

Fax: 757-864-2671

Email: M.C.Pitts@larc.nasa.gov

Larry W. Thomason 

Mail Stop 475

NASA/Langley Research Center

Hampton, VA 23681-2199

Phone: 757-864-6842 

Fax: 757-864-2671 

Email: l.w.thomason@larc.nasa.gov 
224 Data Products Handbook - Volume 2 Appendix B: Points of Contact

Charles Trepte

Mail Stop 475

NASA/Langley Research Center

Hampton, VA 23681-2199

Phone: 757-864-5836 

Fax: 757-864-2671 

Email: c.r.trepte@larc.nasa.gov

Joseph M. Zawodny 

Mail Stop 475

NASA/Langley Research Center

Hampton, VA 23681-2199

Phone: 757-864-2681 

Fax: 757-864-2671 

Email: j.m.zawodny@larc.nasa.gov 

QuikTOMS

Pawan K. Bhartia

Code 916

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-5736

Fax: 301-614-5903 

Email: bhartia@chapman.gsfc.nasa.gov

Jay R. Herman

Code 916

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-6039

Fax: 301-614-5903 

Email: jay.herman@gsfc.nasa.gov

Richard D. McPeters

Code 916

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

Phone: 301-614-6038

Fax: 301-614-5903 

Email: mcpeters@wrabbit.gsfc.nasa.gov

Jason-1

Lee-Lueng Fu 

Mail Stop 300-323

Jet Propulsion Laboratory

4800 Oak Grove Drive 

Pasadena, CA 91109-8099 

Phone: 818-354-8167 

Fax: 818-393-6720 

Email: llf@pacific.jpl.nasa.gov

Yves Ménard

Centre National dʼEtudes Spatiales

DSO/ED/AL

bpi 2002

18 Ave. Edouard Belin

31401 Toulouse, Cedex

France

Phone: 33-56-127-4872

Fax: 33-56-128-2595

Email:Yves.Menard@cnes.fr

SeaWinds

Michael H. Freilich 

College of Oceanic & Atmospheric Sciences

Oregon State University

104 Oceanographic Administration Building

Corvallis, OR 97331-5503 

Phone: 541-737-2748 

Fax: 541-737-2064 

Email: mhf@oce.orst.edu 

David G. Long 

Electrical & Computer Engineering Department

Brigham Young University

459 Clyde Building 

Provo, UT 84602-4099 

Phone: 801-378-4383 

Fax: 801-378-6586 

Email: long@ee.byu.edu

VCL

Ralph O. Dubayah 

Department of Geography

University of Maryland College Park

1113 Lefrak Hall 

College Park, MD 20742 

Phone: 301-405-4069 

Fax: 301-314-9299 

Email: rdubayah@geog.umd.edu
Appendix C: References Data Products Handbook - Volume 2 225

Abbott, M.R., and D.B. Chelton, 1991: Advances in 

passive remote sensing of the ocean, U.S. National 

Report to IUGG. Reviews of Geophysics, Supplement, 

571-583.

Abbott, M.R., and R.M. Letelier, 1998: Decorrelation 

scales of chlorophyll as observed from bio-optical 

drifters in the California Current. Deep-Sea Res., 

45, 1639-1668.

Abbott, M.R., P.J. Richerson, and T.M. Powell, 1982: 

In situ response of phytoplankton fluorescence to rapid variations in light. Limn. Oceanog., 27, 218-225.

Aber, J.D., 1979: A method for estimating foliageheight profiles in broadleaved forests. J. Ecology, 

67, 35-40.

Ackerman, S.A., K.I. Strabala, W.P. Menzel, R.A. 

Frey, C.C. Moeller, and L.E. Gumley, 1998: Discriminating clear sky from clouds with MODIS. J. 

Geophys. Res., 103, 32,141-32,157.

Alishouse, J.C., S. Synder, J. Vongsathorn, and R.R. 

Ferraro, 1990: Determination of oceanic total precipitable water from the SSM/I. IEEE Trans. Geosci. 

Remote Sens., 28, 811-816.

Anderson, J., J.M. Russell III, S. Solomon, and 

L.E. Deaver, 2000: Halogen occultation experiment 

confirmation of stratospheric chlorine decrease in 

accordance with the Montreal Protocol. J. Geophys. 

Res., 105, 4483.

Andreae, M.O., J. Fishman, M. Garstang, J.G. 

Goldammer, C.O. Justice, J.S. Levine, R.J. Sholes, 

B.J. Stocke, A.M. Thompson, B. VanWilgen, and 

STARE/TRACE-A SAFARI-92 Science Team, 

1994: Biomass burning in the global environment. 

Global Atmospheric-Biospheric Chemistry, ed. by 

R.G. Prinn, Plenum Press, New York, 83-101.

Anyamba, E., and J. Susskind, 1998: A comparison 

of TOVS surface skin and air temperature with 

other data sets. J. Geophys. Res., 103 (C5), 10,489-

10,511.

Asrar, G., 1989: Theory and Applications of Optical 

Remote Sensing, John Wiley & Sons, New York, 

734 pp.

Asrar, G., and J. Dozier, 1994: Science Strategy for 

the Earth Observing System. American Institute of 

Physics Press, Woodbury, NY., 119 pp.

Asrar, G., and R. Greenstone, eds., 1995: MTPE/EOS 

Reference Handbook. NASA Pub. NP-215, National 

Aeronautics and Space Administration, Washington, 

D.C., 276 pp.

Aumann, H.H., and C. Miller, 1995: Atmospheric 

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Appendix C: References Data Products Handbook - Volume 2 243

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Appendix C: References Data Products Handbook - Volume 2 245

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246 Data Products Handbook - Volume 2 Appendix D: Acronyms and Abbreviations 

A

ACRIM Active Cavity Radiometer Irradiance 

Monitor

ACRIMSAT ACRIM Satellite

ADEOS Advanced Earth Observing Satellite

ADM Angular Distribution Model

AEM Applications Explorer Mission

AERONET Aerosol Robotic Network

AGC Automatic Gain Control

AIRS Atmospheric Infrared Sounder

AMI Active Microwave Instrument

AMS American Meteorological Society

AMSR Advanced Microwave Scanning 

Radiometer

AMSR-E Advanced Microwave Scanning 

Radiometer for EOS

AMSU Advanced Microwave Sounding Unit

APAR Absorbed Photosynthetically Active 

Radiation

APOLLO AVHRR Processing scheme Over Cloud 

Land and Ocean

AREAS Altimeter Return Echo Analysis System

ARM Atmospheric Radiation Measurement

ARP Absorbed Radiation by Phytoplankton

ASF Alaska SAR Facility

ASTER Advanced Spaceborne Thermal 

Emission and Reflection Radiometer

ASTEX Atlantic Stratocumulus Transition 

Experiment

ATBD Algorithm Theoretical Basis Document

ATSR Along Track Scanning Radiometer

AVG Name used for the CERES average 

Monthly Regional Radiative Fluxes and 

Cloud product

AVHRR Advanced Very High 

Resolution Radiometer

AVIRIS Airborne Visible-Infrared Imaging 

Spectrometer

B

BDS Bi-Directional Scan

BGC Biogeochemical

BIOME Biogeochemical Information Ordering 

Management Environment

BOREAS Boreal Ecosystems Atmosphere Study

BRDF Bidirectional Reflectance Distribution 

Function

BUAN Baseline Upper Air Network

C

CAGEX CERES/ARM/GEWEX Experiment

CART Cloud and Radiation Testbed

CCN Cloud Condensation Nuclei

CDDIS Crustal Dynamics Data Information 

System

CDOM Colored (or Chromomorphic) Dissolved 

Organic Matter

CEES Committee on Earth and Environmental 

Sciences

CERES Clouds and the Earthʼs Radiant Energy 

System

CFC Chlorofluorocarbon

CH4 Methane

CHEM-1 Chemistry Mission-1 (now Aura)

CLAVR Cloud Advanced Very high resolution 

Radiometer

ClOx Oxides of chlorine

CNES Centre National dʼEtudes Spatiales 

(France)

CO Carbon monoxide

CPF Calibration Parameter File

CRS Clouds and Radiative Swath

CZCS Coastal Zone Color Scanner

Appendix D: Acronyms and 

Abbreviations
Appendix D: Acronyms and Abbreviations Data Products Handbook - Volume 2 247

D

DAAC Distributed Active Archive Center

DAO Data Assimilation Office

DEM Digital Elevation Model

DIS Data and Information System

DMSP Defense Meteorological Satellite 

Program

DoE Department of Energy

DOM Dissolved Organic Matter

DORIS Doppler Orbitography and 

Radiopositioning Integrated by Satellite

DPH Data Products Handbook

DTED Digital Terrain Elevation Data

E

EASE Equal-Area Scalable Earth, a grid for 

data mapping

ECMWF European Centre for Medium-Range 

Weather Forecasts

ECS EOSDIS Core System

EDC EROS Data Center

ENSO El Niño-Southern Oscillation

ENVISAT Environmental Satellite, ESA

EO-1 Earth Observing 1

EOS Earth Observing System

EOS PM EOS afternoon-crossing satellite (now 

Aqua)

EOSDIS EOS Data and Information System

EOSPSO EOS Project Science Office

EP Earth Probe

EPGN EOS Polar Ground Network

ERB Earth Radiation Budget

ERBE Earth Radiation Budget Experiment

ERBS Earth Radiation Budget Satellite

EROS Earth Resources Observation System

ERS European Remote Sensing Satellite

ERS-1 European Remote Sensing Satellite-1

ERTS Earth Resources Technology Satellite

ES EOS Science, a product identification 

code for CERES data products

ESA European Space Agency

ESDIS Earth Science Data and Information 

System

ESE Earth Science Enterprise

ESMR Electrically Scanning Microwave 

Radiometer

ESSP Earth System Sciences Pathfinder

ET Evapotranspiration

ETM Enhanced Thematic Mapper

ETM+ Enhanced Thematic Mapper Plus

EVI Enhanced Vegetation Index

F

FIFE First ISLSCP Field Experiment

FIRE First ISCCP Regional Experiment

FOV Field of View

FPAR Fraction of Photosynthetically Active 

Radiation

FSW Name used for the CERES Monthly 

Gridded Radiative Fluxes and Clouds 

product

FTP File Transfer Protocol

G

GCM General Circulation Model (also Global 

Climate Model)

GDR Geophysical Data Record

GEOS Goddard Earth Observing System

GEOSAT Geodetic Satellite

GEWEX Global Energy and Water cycle 

Experiment

GOES Geostationary Operational 

Environmental Satellite

GPCP Global Precipitation Climatology 

Project

GPS Global Positioning System

GRACE Gravity Recovery And Climate 

Experiment

G/S Ground Station

GSFC Goddard Space Flight Center

H

HDF Hierarchical Data Format

HIRDLS High-Resolution Dynamics Limb 

Sounder

HIRS High-Resolution Infrared Radiation 

Sounder

HOX Hydrogen oxides

HRPT High-Resolution Picture Transmission

HSB Humidity Sounder for Brazil
248 Data Products Handbook - Volume 2 Appendix D: Acronyms and Abbreviations 

I

ICEMAP Ice-Mapping Algorithm

ICESat Ice, Clouds, and Land Elevation 

Satellite

ID Identification

IES Instrument Earth Scans

IFOV Instantaneous Field of View

IfSAR Interferometric Synthetic Aperture 

Radar

IGBP International Geosphere-Biosphere 

Programme

IGDR Interim Geophysical Data Record

IGS International Ground Station

IPAR Instantaneous Photosynthetically-Active 

Radiation

IPCC Intergovernmental Panel on Climate 

Change

IR Infrared

IR/MW Infrared/microwave

ISCCP International Satellite Cloud 

Climatology Project

ISLSCP International Satellite Land Surface 

Climatology Project

ITPP International TOVS Processing Package

J

JMR Jason-1 Microwave Radiometer

JPL Jet Propulsion Laboratory

K

Kbps Kilobits per second

L

LAI Leaf Area Index

Landsat Land Remote Sensing Satellite

LaRC Langley Research Center

LASE Laser Atmospheric Sensing Experiment

LI Lifted Index

LIS Lightning Imaging Sensor

LP Level Processors

LST Land Surface Temperature

Ltyp Spectral radiance at typical conditions

LVIS Laser Vegetation Imaging Sensor

LW Longwave

M

MAS MODIS Airborne Simulator

MB Megabyte

MBLA Multi-Beam Laser Altimeter

Met. data Meteorological data

METEOSAT Meteorology Satellite (an ESA 

geosynchronous satellite)

MISR Multi-angle Imaging Spectroradiometer

MIT Massachusetts Institute of Technology

MLR Multiple Linear Regression

MLS Microwave Limb Sounder

MMS Multi-mission Modular Spacecraft

MOA Meteorological Ozone and Aerosol

MOBY Marine Optical Buoy

MODIS Moderate Resolution Imaging 

Spectroradiometer

MODLAND MODIS Land science team

MOLA Mars Orbiter Laser Altimeter

MOPITT Measurements of Pollution in the 

Troposphere

MSE Mean squared error

MSFC Marshall Space Flight Center

MSS Multispectral Scanner

MSU Microwave Sounding Unit

MVI Modified Vegetation Index

MW Microwave

MW/IR Microwave/infrared

MWP Microwave Water Path

N

N/A Not Applicable

NASA National Aeronautics and Space 

Administration

NASDA National Space Development Agency 

(Japan)

NBAR Nadir BRDF-Adjusted Reflectance

NCEP National Centers for Environmental 

Prediction

NCSA National Center for Supercomputing 

Applications (at the University of 

Illinois)

NDVI Normalized Difference Vegetation Index
Appendix D: Acronyms and Abbreviations Data Products Handbook - Volume 2 249

NE Noise Equivalent

NESDIS National Environmental Satellite, Data, 

and Information Service

NIR Near Infrared

NISN NASA Integrated Services Network

NMC National Meteorological Center

NOAA National Oceanic and Atmospheric 

Administration

NOHRSC National Operational Hydrologic 

Remote Sensing Center

NORAD North American Aerospace Defense 

Command

NOX Nitrogen oxides (NO, NO2, NO3)

NPOESS National Polar-orbiting Operational 

Environmental Satellite System

NPP Net Primary Production

NSCAT NASA Scatterometer

NSIDC National Snow and Ice Data Center

NWS National Weather Service

O

OClO Chlorine dioxide

OI Optimum Interpolation

OLR Outgoing Longwave Radiation

OMI Ozone Monitoring Instrument

ORNL Oak Ridge National Laboratory

OSDR Operational Sensor Data Record

OX Oxides

P

PAR Photosynthetically-Active Radiation

PDF Portable Document Format

pdf Probability density function

PEB Phycoerythrobilin

PGE Product Generation Executive

PICASSO Pathfinder Instruments for Cloud and 

Aerosol Spaceborne Observations

PICASSO- Pathfinder Instruments for Cloud and 

CENA Aerosol Spaceborne Observations-

 Climatologie Etendue des Nuages et des 

 Aerosols

POCC Project Operation Control Center

PO.DAAC Physical Oceanography Distributed 

 Active Archive Center (at the Jet 

 Propulsion Laboratory) 

POES Polar Orbiting Environmental Satellite

POLDER Polarization and Directionality of the 

Earthʼs Reflectance

PP Pre-Processor

ppm Parts per million

ppmv Parts per million by volume

PR Precipitation Radar

PSC Polar Stratospheric Cloud

PSN Photosynthesis

PUB Phycourobilin

Q

QA Quality Assurance

QuikScat Quick Scatterometer

QuikTOMS Quick Total Ozone Mapping 

Spectrometer

R

RAOBS Radiosonde Observations

RMS Root Mean Square

RSMAS Rosenstiel School of Marine and 

Atmospheric Science, University of 

Miami

RSS Root Sum Square

S 

SAGE Stratospheric Aerosol and Gas 

Experiment

SAM Stratospheric Aerosol Measurement

SAR Synthetic Aperture Radar

SARB Surface and Atmospheric Radiation 

Budget

SASS SEASAT-A Satellite Scatterometer

SBRS Santa Barbara Remote Sensing

S/C Spacecraft

SCAR-B Smoke, Cloud, and Radiation-Brazil

SCF Scientific Computing Facility

SEASAT Sea Satellite

SeaWiFS Sea-viewing Wide Field-of-view Sensor

SEDAC Socioeconomic Data and Applications 

Center

SERCAA Support of Environmental Requirements 

for Cloud Analysis and Archive

SFC Surface Fluxes and Clouds
250 Data Products Handbook - Volume 2 Appendix D: Acronyms and Abbreviations 

SGDR Sensor Geophysical Data Record

SGP Southern Great Plains

SI Systeme Internationale

SIM Spectral Irradiance Monitor

SIPS Science Investigator-led Processing 

System

SLA Shuttle Laser Altimeter

SLR Satellite Laser Ranging

SMM Solar Maximum Mission

SMMR Scanning Multichannel Microwave 

Radiometer

SNOMAP Snow-Mapping Algorithm 

SNR Signal-to-Noise Ratio

SOHO Solar Heliospheric Observatory

SORCE Solar Radiation and Cloud Experiment

SPOT-2 Systeme Pour lʼObservation de la Terre2

SRB Surface Radiation Budget

SRBAVG Name for CERES Monthly TOA/

Surface Averages product

SRTM Shuttle Radar Topography Mission

SSALTO Segment Sol Multimission Altimetry 

and Orbitography

SSF Single Scanner Footprint*

SSM/I Special Sensor Microwave/Imager

SST Sea Surface Temperature

SW Shortwave

SWE Snow Water Equivalent

SWH Significant Wave Height

SWIR Shortwave Infrared

SWS SeaWinds Standard

SYN Name for CERES Synoptic Radiative 

Fluxes and Clouds product

T

TARFOX Tropospheric Aerosol Radiative Forcing 

Observational Experiment

Tb Brightness temperature

TCP Topographic Control Points

TEC Total Electron Content

TES Tropospheric Emission Spectrometer

TIM Total Irradiance Monitor

TIMED Thermosphere, Ionosphere, Mesosphere, 

Energetics, and Dynamics

TIR Thermal Infrared

TLSCF Team Leader Science Computing 

Facility

TM Thematic Mapper

TMI TRMM Microwave Imager

TMR TOPEX Microwave Radiometer

TOA Top Of the Atmosphere

TOGA Tropical Ocean Global Atmosphere

TOMS Total Ozone Mapping Spectrometer

TOPEX Ocean Topography Experiment

TOPEX/ Ocean Topography Experiment/

Poseidon Poseidon (U.S.-France)

TOVS TIROS Operational Vertical Sounder

T/P TOPEX/Poseidon

TRMM Tropical Rainfall Measuring Mission

Ts Sea surface temperature

TSI Total Solar Irradiance

TSIM Total Solar Irradiance Mission

TT Total Totals

Ttyp Temperature at typical conditions

U

UARS Upper Atmosphere Research Satellite

UMD University of Maryland

UNESCO United Nations Educational, Scientific, 

and Cultural Organization

USGS United States Geological Survey

USNO United States Naval Observatory

UT Universal Time

UTC Universal Time Coordinate

UV Ultraviolet

V

VCL Vegetation Canopy Lidar

VDC VCL Data Center

VDIS Vertical Distribution of Intercepted 

Surfaces

VI Vegetation Index

VIRGO Variability of solar Irradiance and 

Gravity Oscillations

VIRS Visible Infrared Scanner

VIS Visible

VISSR Visible Infrared Spin-Scan Radiometer

VNIR Visible and Near Infrared

VPGS VCL Precision Geolocation System

*SSF is also used in the Product ID for the CERES Single Scanner TOA/Surface Fluxes and Clouds product
Appendix D: Acronyms and Abbreviations Data Products Handbook - Volume 2 251

W

WCRP World Climate Research Program

WINCE Winter Cloud Experiment

WMO World Meteorological Organization

WN Window

Z

ZAVG Name for CERES Monthly Zonal and 

Global Radiative Fluxes and Clouds 

product
252 Data Products Handbook - Volume 2 Appendix E: Data Products Index 

ACRIM III (on ACRIMSAT)

ACRIM III Radiometric Products 51

AIRS (on Aqua)

AIRS Level 1A Radiance Counts 52

AIRS Level 1B Calibrated, Geolocated Radiances 52

AIRS/AMSU-A/HSB (on Aqua)

AIRS/AMSU-A/HSB Atmospheric Temperature 

 Product 134

AIRS/AMSU-A/HSB Cloud Product 91

AIRS/AMSU-A/HSB Flux Product 92

AIRS/AMSU-A/HSB Humidity Product 75

AIRS/AMSU-A/HSB Level 2 Cloud-Cleared

 Radiances 53

AIRS/AMSU-A/HSB Ozone Product 126

AIRS/AMSU-A/HSB Surface Analysis Product 155

AIRS/AMSU-A/HSB Trace Constituent Product 126

AMSR-E (on Aqua)

AMSR-E Columnar Cloud Water 94

AMSR-E Columnar Water Vapor 79

AMSR-E Level 2A Brightness Temperatures 53

AMSR-E Rainfall - Level 2 76

AMSR-E Rainfall - Level 3 78

AMSR-E Sea Ice Concentration 207

AMSR-E Sea Ice Temperatures 209

AMSR-E Sea Surface Temperature 157

AMSR-E Sea Surface Wind Speed 142

AMSR-E Snow Depth on Sea Ice 208

AMSR-E Snow-Water Equivalent and Snow Depth 210

AMSR-E Surface Soil Moisture 169

AMSU-A (on Aqua)

AMSU-A Level 1A Radiance Counts 54

AMSU-A Level 1B Calibrated, 

 Geolocated Radiances 55

CERES (on Aqua)

CERES Bi-Directional Scans Product 55

CERES Clouds and Radiative Swath 101

CERES ERBE-like Instantaneous

 TOA Estimates 96

CERES ERBE-like Monthly

 Regional Averages and ERBE-like

 Monthly Geographical Averages 97

CERES Monthly Gridded Radiative

 Fluxes and Clouds 103

CERES Monthly Gridded TOA/Surface 

 Fluxes and Clouds 107

CERES Monthly Regional Radiative Fluxes

 and Clouds and Monthly Zonal 

 and Global Radiative Fluxes and Clouds 105

CERES Monthly TOA/Surface Averages 108

CERES Single Scanner TOA/

 Surface Fluxes and Clouds 99

CERES Synoptic Radiative Fluxes

 and Clouds 104

ETM+ (on Landsat 7)

ETM+ Calibrated Radiances - 1G 68

ETM+ Calibrated Radiances - 1R 67

ETM+ Raw Digital Numbers 66

HSB (on Aqua)

HSB Level 1A Radiance Counts 57

HSB Level 1B Calibrated, Geolocated Radiances 57

JMR (on Jason-1)

JMR Columnar Water Vapor Content 85

MBLA (on VCL)

VCL (MBLA) Geolocated Canopy-Top Heights 186

VCL (MBLA) Geolocated Ground Elevations 184

VCL (MBLA) Geolocated Vertical Distribution of 

 Intercepted Surfaces 185

MODIS (on Aqua)

MODIS Absorption Coefficients 195

MODIS Aerosol Optical Depth 60

MODIS Aerosol Product 109

MODIS Atmospheric Profiles 82, 136

MODIS Burn Scars 161

MODIS Chlorophyll a Pigment

 Concentration 195

MODIS Chlorophyll Fluorescence 193

MODIS Clear-Water Epsilon 64

Appendix E: Data Products Index
Appendix E: Data Products Index Data Products Handbook - Volume 2 253

MODIS Cloud Mask 118

MODIS Cloud Product 112

MODIS Coccolith Concentration 197

MODIS Geolocation Data Set 60

MODIS Land Cover Type 171

MODIS Land Surface Temperature

 and Emissivity 159

MODIS Leaf Area Index and Fraction

 of Photosynthetically Active Radiation

 - Moderate Resolution 176

MODIS Level 1A Radiance Counts 58

MODIS Level 1B Calibrated, Geolocated Radiances 58

MODIS Level 3 Atmosphere Products 115

MODIS Normalized Water-Leaving Radiance 60

MODIS Ocean Primary Productivity 198

MODIS Ocean Water Attenuation Coefficient 190

MODIS Organic Matter Concentration 190

MODIS Photosynthetically Active Radiation 62

MODIS Phycoerythrin Concentration 200

MODIS Pigment Concentration 190

MODIS Processing Framework and 

 Match-up Database 63

MODIS Sea Ice Cover 213

MODIS Sea Surface Temperature 162

MODIS Snow Cover 211

MODIS Snow and Sea Ice Albedo 214

MODIS Surface Reflectance; Atmospheric

 Correction Algorithm Products 170

MODIS Surface Reflectance BRDF/

 Albedo Parameter 180

MODIS Surface Resistance and 

 Evapotranspiration 178

MODIS Suspended Solids Concentration 190

MODIS Thermal Anomalies – Fires 161

MODIS Total Precipitable Water 80

MODIS Vegetation Cover Conversion 182

MODIS Vegetation Indices 173

MODIS Vegetation Production and

 Net Primary Production 179

Poseidon-2 (on Jason-1)

Poseidon-2 Normalized Radar Backscatter 

 Coefficient and Wind Speed 143

Poseidon-2 Significant Wave Height 150

Poseidon-2 Total Electron Content 127

Poseidon-2/JMR/DORIS (on Jason-1)

Poseidon-2/JMR/DORIS Sea Surface Height 149

SAGE III (on Meteor 3M)

SAGE III Aerosol and Cloud Data Products 120

SAGE III Atmospheric Slant-Path Transmission 

 Product 69

SAGE III NO2, NO3, O3, and OClO

 Data Products 128

SAGE III Temperature and Pressure

 Data Products 138

SAGE III Water Vapor Products 86

SeaWinds (on QuikScat)

SeaWinds Grouped and Surface-Flagged 

 Backscatter and Attenuations in 25 km

 Swath Grid 145

SeaWinds Normalized Radar Cross-Section and

 Ancillary Data 144

SeaWinds Ocean Wind Vectors in 25 km Swath 

 Grid 145

TOMS (on QuikTOMS)

TOMS Aerosol Product 122

TOMS Ozone Product 130
National Aeronautics and

Space Administration NP-2000-5-055-GSFC

ACRIMSAT

• ACRIM III

Aqua

• AIRS

• AMSR-E

• AMSU-A

• CERES

• HSB

• MODIS

Jason-1

• DORIS

• JMR

• Poseidon-2

Landsat 7

• ETM+

Meteor 3M

• SAGE III

QuikScat

• SeaWinds

QuikTOMS

• TOMS

VCL

• MBLA

EOS

http://eos.nasa.gov/

EOSDIS

http://spsosun.gsfc.nasa.gov/New_EOSDIS.html