NASA Technical Memorandum 104566, Vol. 19

SeaWiFS Technical Report Series

Stanford B. Hooker, Elaine R. Firestone, and James G. Acker, Editors

Volume 19, Case Studies for SeaWiFS

Calibration and Validation, Part 2

C.R. McClain, R.S. Fraser, J.T. McLean, M. Darzi, J.K. Firestone, F.S. Patt,

B.D. Schieber, R.H. Woodward, E-n. Yeh, S. Mattoo, S.F. Biggar, P.N. Slater,

K.J. Tome, A.W. Holmes, R.A. Barnes, and K.J. Voss

August 1994
NASA Technical Memorandum 104566, Vol. 19

SeaWiFS Technical Report Series

Stanford B. Hooker, Editor

NASA Goddard Space Flight Center, Greenbelt, Maryland

Elaine R. Firestone, Technical Editor

General Sciences Corporation, Laurel, Maryland

James G. Acker, Technical Editor

Hughes STX, Lanham, Maryland

Volume 19, Case Studies for SeaWiFS

Calibration and Validation, Part 2

C.R. McClain, R.S. Fraser, and J.T. McLean

NASA Goddard Space Flight Center, Greenbelt, Maryland

M. Darzi, J.K. Firestone, F.S. Patt, B.D. Schieber, R.H. Woodward, and E-n. Yeh

General Sciences Corporation, Laurel, Maryland

S. Mattoo

Applied Research Corporation, Landover, Maryland

S.F. Biggar, P.N. Slater, and K.J. Tome

University of Arizona, Tucson, Arizona

A.W. Holmes

Santa Barbara Research Center, Goleta, California

R.A. Barnes

ManTech Environmental Technology, Inc., Wallops, Island, Virginia

K.J. Voss

University of Miami, Coral Gables, Florida

National Aeronautics and

Space Administration

Goddard Space Flight Center

Greenbelt, Maryland 20771

1994
Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Preface

The Calibration and Validation Program for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) instrument,

as outlined in Volume 3 of the SeaWiFS Technical Report Series (McClain et al. 1992) encompasses four primary

functions:

a) bio-optical algorithm development,

b) atmospheric correction algorithm development,

c) sensor calibration and characterization, and

d) product verification and quality control.

In the calibration and validation case studies volumes of the SeaWiFS Technical Report Series, investigations

supported by the SeaWiFS Project that are related to these four categories are documented. Part 1, Vol. 13,

provides an assortment of Coastal Zone Color Scanner (CZCS) algorithm comparisons and sensitivity analyses

which serve as a basis for similar investigations and data processing requirements anticipated for SeaWiFS.

Part 2, this volume, provides a more diverse suite of studies and includes contributions related to atmospheric

correction methodologies, ancillary data sets required for level-2 processing of CZCS and SeaWiFS data, laboratory techniques for instrument calibration relevant to calibration round-robins, and field observations designed

for transferring the prelaunch calibration to orbit and in interpreting the on-orbit lunar calibration data.

The data format chapter is included because the Calibration and Validation Program took the lead in the original

format evaluation study. This study was very time consuming and outlines many of the important issues and

criteria that must be considered in selecting a format. Ultimately, the Hierarchical Data Format (HDF) was

selected in order to ensure compatibility with the Earth Observing System (EOS) and the EOS Project has

provided much assistance to the SeaWiFS Project in the implementation of the HDF over the past two years.

The Calibration and Validation Program has played a major role in the definition of SeaWiFS product and

format specifications and in the development of the HDF input/output (I/O) routines for level-1 to level-3

calibration, browse and ancillary data products. These specifications and I/O routines will be published in a

future volume of the SeaWiFS Technical Report Series.

Greenbelt, Maryland — C. R. McClain

May 1994

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Table of Contents

Prologue ................................................................................................. 1

1. Atmospheric and Glint Corrections ................................................................... 4

1.1 Introduction ......................................................................................... 4

1.2 Theory .............................................................................................. 4

1.3 Look-Up Tables ...................................................................................... 5

1.4 Validation ........................................................................................... 9

1.5 Discussion .......................................................................................... 20

2. Spectral Reflectance of the Moon .................................................................... 21

2.1 Introduction ........................................................................................ 21

2.2 Instrumentation .................................................................................... 21

2.3 Data Acquisition .................................................................................... 21

2.4 Results and Discussion ...............................................................................22

3. Preflight Solar-Based Calibration of SeaWiFS ........................................................ 25

3.1 Introduction ......................................................................................... 25

3.2 Concept ............................................................................................. 26

3.3 Method ..............................................................................................26

3.4 Error Sources ........................................................................................29

3.5 Accuracy ............................................................................................ 30

3.6 Results .............................................................................................. 30

3.7 Conclusions ..........................................................................................32

4. Spectral Radiance of the GSFC Integrating Sphere Using a Trap Detector ............................ 33

4.1 Introduction ......................................................................................... 33

4.2 Experiment Description ..............................................................................33

4.3 Conclusions ..........................................................................................34

5. Evaluation of the Standard Data Formats HDF, netCDF, and CDF

for SeaWiFS Operational Products .................................................................. 36

5.1 Introduction ......................................................................................... 36

5.2 Recommendations ................................................................................... 37

5.3 Technical Considerations .............................................................................37

5.4 Practical Considerations ............................................................................. 40

6. The Generation of CZCS Ancillary Data Sets for Simulated SeaWiFS Processing ..................... 43

6.1 Introduction ......................................................................................... 43

6.2 Meteorological Data ................................................................................. 43

6.3 Ozone Data Set ......................................................................................43

6.4 Data File Generation ................................................................................ 43

7. The Generation of CZCS Near-Real Time Ancillary Data Files ....................................... 47

7.1 Introduction ......................................................................................... 47

7.2 Meteorological Data ................................................................................. 47

7.3 Ozone Data Set ......................................................................................47

7.4 Data File Generation ................................................................................ 47

iii
Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Table of Contents cont.

8. An Evaluation of Surface Wind Products for Use in SeaWiFS ........................................ 50

8.1 Introduction ......................................................................................... 50

8.2 Data Sets ............................................................................................50

8.3 Match Software ......................................................................................51

8.4 Results .............................................................................................. 54

Glossary ...............................................................................................65

Symbols ................................................................................................ 65

References ............................................................................................ 67

The SeaWiFS Technical Report Series ............................................................ 68

iv
Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Abstract

This document provides brief reports, or case studies, on a number of investigations and data set development

activities sponsored by the Calibration and Validation Team (CVT) within the Sea-viewing Wide Field-of-view

Sensor (SeaWiFS) Project. Chapter 1 is a comparison the atmospheric correction of Coastal Zone Color Scanner

(CZCS) data using two independent radiative transfer formulations. Chapter 2 is a study on lunar reflectance

at the SeaWiFS wavelengths which was useful in establishing the SeaWiFS lunar gain. Chapter 3 reports the

results of the first ground-based solar calibration of the SeaWiFS instrument. The experiment was repeated in

the fall of 1993 after the instrument was modified to reduce stray light; the results from the second experiment

will be provided in the next case studies volume. Chapter 4 is a laboratory experiment using trap detectors which

may be useful tools in the calibration round-robin program. Chapter 5 is the original data format evaluation

study conducted in 1992 which outlines the technical criteria used in considering three candidate formats, the

Hierarchical Data Format (HDF), the Common Data Format (CDF) and the network CDF (netCDF). Chapter 6

summarizes the meteorological data sets accumulated during the first three years of CZCS operation which are

being used for initial testing of the operational SeaWiFS algorithms and systems and would be used during a

second global processing of the CZCS data set. Chapter 7 describes how near-real time surface meteorological

and total ozone data required for the atmospheric correction algorithm will be retrieved and processed. Finally,

Chapter 8 is a comparison of surface wind products from various operational meteorological centers and field

observations. Surface winds are used in the atmospheric correction scheme to estimate glint and foam radiances.

Prologue

The purpose of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Project is to obtain valid ocean color data of

the world ocean for a five-year period, to process that data

in conjunction with ancillary data to meaningful biological parameters, and to make that data readily available to

researchers. The National Aeronautics and Space Administration’s (NASA) Goddard Space Flight Center (GSFC)

will develop a data processing and archiving system in conjunction with the Earth Observing System Data and Information System (EOSDIS), which includes a ground receiving system, and will oversee a calibration and validation

effort to ensure the integrity of the final products.

The Calibration and Validation Team (CVT) has three

main tasks: calibration of the SeaWiFS instrument; development and validation of the operational atmospheric correction algorithm; and development and validation of the

derived product algorithms, such as chlorophyll a concentration. Some of this work will be done internally at GSFC

while the remainder will be done externally at other institutions. NASA and the Project place the highest priority

on assuring the accuracy of derived water-leaving radiances

globally, and over the entire mission. If these criteria are

met, development of global and regional biogeochemical

algorithms can proceed on many fronts. These activities

are discussed in detail in The SeaWiFS Calibration and

Validation Plan (McClain et al. 1992a).

Because many of the studies and other works undertaken with the Calibration and Validation Program are

not extensive enough to require dedicated volumes of the

SeaWiFS Technical Report Series, the CVT has decided to

publish volumes composed of brief, but topically specific,

chapters. Volume 19 is the second in a set of such volumes.

Volume 13 was the first, and consists primarily of contributions related to atmospheric correction methodologies, ancillary data sets required for level-2 processing of Coastal

Zone Color Scanner (CZCS) and SeaWiFS data, laboratory techniques for instrument calibration relevant to calibration round-robins, and field observations designed for

transferring the prelaunch calibration to orbit and in interpreting the on-orbit lunar calibration data. A short

synopsis of each chapter is given below.

1. Atmosphericand Glint Corrections

This chapter explains an algorithm for correcting satellite measurements of ocean color for atmospheric and surface reflection effects. The algorithm depends on taking

the difference between measured and tabulated radiances

for deriving water-leaving radiances. The tabulated radiances are related to the measured radiance where the

water-leaving radiance is negligible (670 nm). The tabulated radiances are calculated for rough surface reflection,

polarization of the scattered light, and multiple scattering.

The accuracy of the tables is discussed. The method is validated by simulating the effects of various wind speeds that

differ from the single wind speed for which the look-up table was calculated. Another validation exercise employs

aerosol models that are different from the maritime aerosol model for which the table was computed. The derived

water-leaving radiances are accurate enough to compute

the chlorophyll concentration with an error of less than

or equal to 15% for light wind speeds, and an urban atmosphere with an aerosol optical thickness of 0.2. The

1
Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

accuracy was unacceptably poor for a model with aeolian

dust. On the other hand, this algorithm and the CZCS

operational algorithm produced values of chlorophyll concentration that agreed closely.

2. Spectral Reflectance of the Moon

This report details the results of a field trip to Mauna

Loa, Hawaii, the objective of which was to obtain data on

the spectral reflectance of a full moon. The end use of

this data was to determine the relative levels of spectral

reflectance for use by the SeaWiFS instrument during its

in-flight calibration. For the moon to be a good calibration device, the reflectance of the lunar surface needs to

be consistently predicted. The Mauna Loa measurements

indicate: a) lunar reflectance varies with day, and thus perhaps with the lunar phase; and b) the lunar surface might

exhibit a so-called back gloss, that is, an enhancement of

the reflectance in the direction of the illumination. Because of the latter, the reflectance of the moon may change

rapidly during the course of a measurement, and it may

be better to make use of the moon as a calibration source

when the moon is not full.

3. Preflight Solar-Based Calibration of SeaWiFS

A new method for performing a preflight calibration of

an optical remote sensing instrument with an onboard solar diffuser calibration system is presented. The rationale,

method, advantages, disadvantages, error sources, and expected accuracies are discussed. The method was applied

to the SeaWiFS instrument, which is to be flown on the

SeaStar satellite.

4. Spectral Radiance of the GSFC Integrating Sphere

Using a Trap Detector

The GSFC 42 in (107 cm) diameter spherical integrator

source is used to provide traceability in the optical in situ

measurements made by different researchers who support

the SeaWiFS program. The sphere’s spectral radiance calibration is based on the irradiance standard lamp scale.

The purpose of this study is to verify the radiance calibration of the sphere by using an absolute detector based

scale which would be independent of the irradiance lamp

scale. The experiment described herein shows that filtered

absolute silicon photodetectors in a trap configuration can

be used to verify the scale of spectral radiance of an integration sphere source.

5. Evaluation of the Standard Data Formats

HDF, netCDF, and CDF for SeaWiFS

Operational Products

Three standard data formats have been evaluated for

possible use in archiving and distributing SeaWiFS operational products. The three formats are the Hierarchical

Data Format (HDF), the network Common Data Format

(netCDF), and the Common Data Format (CDF). Major technical criteria that were considered include machine

independence, platforms supported, self description, subsampling, high-level specification language, and computer

language interfaces. Practical considerations included acceptance by the scientific community, user support, cost,

available tools, and availability of SeaWiFS related data

and software. Although each format has certain strengths

and deficiencies, CDF was found to hold significant overall advantages over the other two formats for SeaWiFS.

Specific reasons and tradeoffs are discussed in this report

which was submitted to the SeaWiFS Project in September 1992.

6. The Generation of CZCS Ancillary Data Sets for

Simulated SeaWiFS Processing

The SeaWiFS development effort includes simulated

data processing using CZCS data files for the three-year

time period of November 1978 through December 1981.

Ancillary meteorological data products of total ozone, and

surface values of zonal and meridional wind speed, atmospheric pressure, and relative humidity will be used for producing simulated level-2 CZCS (derived ocean color) products as a test of the SeaWiFS processing system. These

data files are provided from the GSFC Distributed Active

Archive Center (DAAC) and Total Ozone Mapping Spectrometer (TOMS) Project archives and converted to the

SeaWiFS data format for subsequent processing. The calibration and validation element has developed procedures

and software to process the CZCS ancillary data in a manner that simulates the processing that will occur once SeaWiFS is operational. The data are stored as HDF files for

each of the ancillary parameters. This chapter describes

the methods used for the generation of ancillary files from

the CZCS era for use in simulating the SeaWiFS Data Processing System (SDPS).

7. The Generation of SeaWiFS Near-Real Time

Ancillary Data Files

In the SDPS, near-real time (NRT) ancillary data files

will be used for producing level-2, i.e., derived ocean color,

products. In addition to a data file for total column ozone,

separate files will contain surface value data for: zonal and

meridional wind speeds, atmospheric pressure, and relative humidity. These ancillary data files are obtained from

several sources, with additional backup sources identified

for each data type should the primary data source become

unavailable. The ancillary data files will be an integral

part of the SDPS. The Calibration and Validation element

for SeaWiFS has developed procedures and software to acquire and format the NRT data. The ancillary parameters

will be stored as HDF files. This chapter describes the

methods used for the generation of NRT files.

8. An Evaluation of Surface Wind Products

for Use in SeaWiFS

This study statistically compared remotely sensed and

modelled surface wind speeds from several sources with

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

available ground truth, in order to provide a recommendation for which source to use in the SDPS. Model wind field

sources included the US Navy Fleet Numerical Oceanography Center (FNOC), the European Center for Medium

Range Weather Forecasts (ECMWF), and the National

Meteorological Center (NMC). Remotely sensed wind

speed fields came from the Department of Defense (DoD)

Special Sensor for Microwave/Imaging (SSM/I), and

ground truth sources included seven fixed moorings provided by the National Oceanic and Atmospheric Administration (NOAA) National Data Buoy Center (NDBC), and

the Tropical Ocean-Global Atmosphere (TOGA) Thermal

Array for the Ocean (TAO) Project. A temporal and spatial match between the large scale and in situ winds was

completed, using input data for the period 1982–90. An

additional matchup was run to compare mooring winds

with 1,000 mb and boundary layer winds from NMC, for

1990. A suite of software applications for reading and

merging the various data sets used in the analysis was developed as part of the Laboratory for Hydrospheric Processes’ VAX SEAPAK package. Scatterplots comparing

each of the large scale wind sources to the seven moorings

were generated, using commercial personal computer (PC)

software, and square of the correlation coefficient (R2) values, slopes and number of points for each plot were noted.

This analysis indicated that SSM/I had the highest R2

values of any of the large scale wind fields. Since they are

gridded products, NMC and FNOC are the suggested wind

field sources, and mechanisms are in place to transfer them

to GSFC automatically. The new versions of these models

incorporate SSM/I data in their production.

3
Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Chapter 1

Atmospheric and Glint Corrections

Robert S. Fraser

Goddard Space Flight Center

Greenbelt, Maryland

Shana Mattoo

Applied Research Corporation, Landover, Maryland

Abstract

This document explains an algorithm for correcting satellite measurements of ocean color for atmospheric and

surface reflection effects. The algorithm depends on taking the difference between measured and tabulated

radiances for deriving water-leaving radiances. The tabulated radiances are related to the measured radiance

where the water-leaving radiance is negligible (670 nm). The tabulated radiances are calculated for rough surface

reflection, polarization of the scattered light, and multiple scattering. The accuracy of the tables is discussed.

The method is validated by simulating the effects of various wind speeds that differ from the single wind speed

for which the look-up table was calculated. Another validation exercise employs aerosol models that are different

from the maritime aerosol model for which the table was computed. The derived water-leaving radiances are

accurate enough to compute the chlorophyll concentration with an error of less than or equal to 15% for light

wind speeds, and an urban atmosphere with an aerosol optical thickness of 0.2. The accuracy was unacceptably

poor for a model with aeolian dust. On the other hand, this algorithm and the CZCS operational algorithm

produced values of chlorophyll concentration that agreed closely.

1.1 INTRODUCTION

The current CZCS algorithm for making atmospheric

corrections is not generally applied when the aerosol optical thickness exceeds 0.2, or when near the sun glint.

The aerosol optical thickness frequently exceeds 0.2 off the

east coasts of Asia and the United States due to either

urban pollution, outflows of dust from desert regions in

Asia and Africa, and smoke from biomass (forest) burning.

Evans and Gordon (1994) eliminate regions of surface glint

for estimates of chlorophyll concentration where Lg/F0 is

greater than 0.005. Lg is the radiance of the surface glint at

a sea surface and F0 is the solar spectral irradiance above

the Earth’s atmosphere. If the wind speed were 10 m s−1,

for example, a region within 40◦ of the nadir would be

excluded when the sun is at the zenith; and for a scan azimuth of 90◦ between vectors from a pixel to the sun and

to the satellite, glint regions would be excluded until the

solar zenith angle reached 40◦.

C. McClain suggested developing an algorithm to overcome these difficulties. A method had been developed for

making atmospheric corrections over land utilizing lookup tables (Fraser et al. 1992). Also, a radiative transfer code already existed for ocean-atmosphere models that

accounted for rough surface reflection, an arbitrary atmosphere, degree of polarization of the light, and multiple

scattering (Ahmad and Fraser 1982). In this report, the

theory, the ocean-atmosphere model, and the formulation

and use of the look-up tables are described. Simulations

are made to derive the water-leaving radiance in the glint

region, and when dust or urban aerosols are present. Finally, a comparison is made of water-leaving radiances and

chlorophyll concentrations derived with the look-up tables

and the CZCS algorithm.

1.2 THEORY

The radiance of the ocean-atmosphere system measured

at a satellite (Lm) can be expressed as

Lm = L0(λ; θ, φ; θ0, φ0; τa)

+ Lsfc(λ; θ, φ; θ0, φ0; v; τa)t(λ; θ; τa)

+ LW (λ; θ, φ; θ0, φ0; W; τa; C)t



(λ; θ; τa),

(1)

where L0 is the radiance of the atmosphere, if the radiance just above the sea surface were zero; Lsfc is the radiance of the light reflected from the surface; and LW is the

water-leaving radiance of light scattered from beneath the

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

surface and penetrating it. All four radiances are vectors

representing the four Stokes parameters, in order to account for the polarization properties of the scattered light.

LW contains the information about the sea particles and

absorbing species, e.g., chlorophyll and pigments; t and t



are the transmissions through the atmosphere of Lsfc and

LW , respectively. The independent parameters in (1) are

defined as follows:

λ wavelength,

θ, φ polar and azimuth angles of the line-of-sight at

a spacecraft,

θ0,φ0 polar and azimuth angles of the direct sunlight,

W surface wind speed,

τa aerosol optical thickness, and

C chlorophyll concentration.

It is convenient to express the measured radiance by

Lm = Lsa + t



LW , (2)

where Lsa is the sum of the first two terms on the right

side of (1).

The water-leaving radiance is:

LW = Lm − Lsa

t . (3)

A normalized water-leaving radiance, LWN (λ), is used in

the algorithm and is defined by

LWN (λ)

F0

= LW

Fd/(1 − sρ) = LWF

d

, (4)

and

Fd = t0F0cos θ0, (5)

where s is the reflectance of the atmosphere for isotropic

radiance incident at its base; ρ is the average reflectance

of the sea; Fd is the total flux incident on the surface if

it did not reflect light; F0 is the solar spectral irradiance

at the top of the atmosphere; F

d = Fd/(1 − sρ); and t0 is

the sum of the direct and diffuse transmission of sunlight

through the atmosphere. Note (4) is slightly different than

the expression given by Gordon and Casta ̃no (1987).

The water-leaving radiance (actually LW divided by

F0) transmitted to the top of the atmosphere is given by

rearranging the factors in (4) and multiplying by t/F0:

tLW

F0

= LWN (λ)

F0

= tFd

(1 − sρ)F0

.

(6)

Equation (6) is computed by the subroutine CLWTAU, where

the equivalencies between the four variables in (4) and

CLWTAU are as follows: LWN (λ) is represented by CONLW,

t by TVALUE, Fd by TFLUX, and F0 by FFLUX.

The radiance calculated for the look-up tables (LU),

LLU , has the same equation as Lsa; by comparing (1) and

(2),

LLU = Lsa = L0 + tLsfc. (7)

In (2), it may be assumed that the transmission t

 is approximately equal to t with negligible error. The atmospheric correction depends on computing the radiation parameters in (7), and subtracting LLU from the measured

radiance, as in (4). LLU is calculated as a Stokes vector,

but only the scalar radiance is stored in the look-up tables: LLU (λ; θ, φ; θ0, φ0 = 0; W = 6; τa). The transmission

t(λ;θ;τa) is computed by a scalar radiative transfer code

D, SPD (Dave 1972b).

1.3 LOOK-UP TABLES

The radiances in the look-up tables are computed for a

fine mesh of angles (θ, φ−φ0; θ0), a surface wind speed (W)

equal to 6 m s−1, and 8 aerosol optical thicknesses. The radiative properties requiring correction are those for the atmospheric gas, aerosols, and surface reflection. The radiative properties of the atmospheric gases can be calculated

accurately. The surface reflectance and aerosol properties

are variable. The radiance due to them is either comparable, or larger, than the water-leaving radiance. The aerosol

optical thicknesses for the four spectral bands of the CZCS

are the only parameters that determine the correction to

the measured radiance to estimate the water-leaving radiance.

The estimated aerosol optical thickness is generally not

the actual value of the optical thickness. It accounts not

only for the optical thickness (τa), but also for the surface

reflection and the aerosol albedo of single scattering (ωa)

and the aerosol albedo of the scattering phase function

(pa/4π). To show how the optical thickness represents the

aerosol optical properties, consider the equation for the

radiance, La, caused by single scattering of direct sunlight

from just the aerosols:

La

F0

= ωapaτa

4π cos θ

, (8)

An aerosol optical thickness in the algorithm accounts

for ωapa/4πτa, plus other effects of surface reflection, and

multiple scattering. The albedo of single scattering and

scattering phase function are also unknown. Due to these

factors, the choice of an accurate aerosol model is difficult,

if not impossible, but the aerosol model chosen is not critical to the look-up table computation. For this reason, a

maritime aerosol model is chosen, since it is believed that

it would be most representative of prevailing aerosols.

1.3.1 Aerosol Model

Aerosol optical properties over the world ocean vary

considerably. These properties mostly depend on the relative concentrations of continental, dust, and oceanic aerosols (Weller and Leiterer 1988). The model adopted here

5
Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

is based on a generalized maritime aerosol model given by

Jursa (1985). The size distribution is given by

d

dr N(r) = 

2

i=1

Ni

ln(10)rσi

√2π e

−1

2σ2

i



log r−log ri

2

(9)

where N is the total number density, Ni is the number

density of either the first or second aerosol model, ri is the

geometric mean radii, and σ2

i = (log r − log ri)2. Equation (9) in terms of natural logarithms becomes

d

dr N(r) = 

2

i=1

Ni

ln(10)rσi

√2π e

−1

2σ2

i

 ln r−ln ri ln(10) 2

. (10)

The geometric mean radii and standard deviations are

given for the two models in Table 1. The albedo of single

scattering is 0.99 for a maritime aerosol in the spectral

range 0.4 ≤ l ≤ 0.7 μm (Jursa 1985).

Table 1. Log-normal maritime aerosol model (Jursa 1985).

Aerosol Ni ri σi log10(σi)

[μm] Decadic Natural

Continental 0.99 0.03 0.35 0.8059

Oceanic 0.01 0.30 0.40 0.9210

The optical properties of this aerosol model are calculated with the Mie equations in scalar radiative transfer

code B, SPB (Dave 1972b). The calculations are made separately for each log-normal size distribution. The extinction coefficient (β) and phase function for the combined

distribution are combined additively. The extinction (or

scattering) cross section is given by

Nβ = N1β1 + N2β2; (11)

and the scattering phase function by

Nβρ = N1β1ρ1 + N2β2ρ2 (12)

where N = N1 + N2. The extinction coefficient and radiance of scattered light (La) have an approximate power

law dependence on wavelength where a and α are numerical values:

β = β0λ−α, (13)

and

La = Laoλ−a. (14)

These model optical data can be compared with measured data. This maritime aerosol model has a very large

number of accumulation mode particles (N1 = 0.99) compared to the number of coarse particles (0.01). For this

model, the extinction is nearly independent of wavelength

(α = 0.19). The measured values are somewhat larger:

0.7 <α< 1.7 (Hoppel et al. 1990). The measured and

model wavelength dependence of the radiance can be compared at a scattering angle of 120◦, which is in the range

of measurement angles. The dependence of the radiance of

light scattered by the model aerosol is somewhat smaller

(a = 0.3) than the function derived from CZCS observations.

An aerosol model that almost satisfies these measured

constraints has the relative number of accumulation mode

particles (N1 = 0.998 and N = 1.00 = N1+N2). The aerosol albedo of single scattering for this new model (with an

index of refraction expressed by 1.43 − 0.0035i) is smaller,

however, (ωa = 0.93) than that given by Jursa (1985).

Even when N1 = 0.998, the radiance of light scattered is

dominated by the coarse particles, and is 1.4 to 2.7 times as

strong as if the model contained only accumulation mode

particles. Hence, the index of refraction should be adjusted

to represent that of the coarse particles (n = 1.38−0.001i).

Optical data are computed for the same aerosol model

that has just been discussed, with the index of refraction

n = 1.38 − 0.001i, and are given in Table 2.

Table 2. Optical data for the same bimodal maritime aerosol model that is specified in Table 1, except for a different number of particles in the two

modes. The index of refraction n = 1.38 − 0.001i.

N1 2 3 4 5 6 7 8

1.000 1.76 0.92 0.99 0.99 1.0 1.0 1.0

0.999 0.97 0.65 0.98 0.98 1.3 2.0 2.5

0.998 0.72 0.46 0.98 0.98 1.6 3.0 4.0

0.996 0.46 0.22 0.97 0.97 2.2 5.1 7.0

0.994 0.32 0.07 0.96 0.97 2.8 7.1 10.0

0.992 0.24 −0.04 0.96 0.97 3.4 9.1 13.0

0.990 0.18 −0.11 0.96 0.97 4.1 11.2 16.0

The number of particles in the accumulation mode,

N1, is given in column 1. The number in the coarse particle mode, N2, is equal to 1 − N1. Columns 2 and 3

give the wavelength dependence of extinction and radiance at a scattering angle of 120◦, (α and a), respectively.

Columns 4 and 5 give the aerosol albedo of single scattering

for λ = 441 and 670 nm, respectively. Columns 6, 7, and 8

give the ratio of radiance to that of just the accumulation

mode alone, for scattering angles of 120◦, 160◦, and 180◦,

but only for λ = 670 nm. An aerosol model in Table 2 that

almost satisfies the measured constraints has the relative

number of accumulation mode particles N1 = 0.998, and

n = 1.38 − 0.001i.

The wavelength dependence of the extinction and radiance at a scattering angle of 120◦ is 0.7 and 0.5, respectively, which is close to measured values. The aerosol

albedo of single scattering (ωa = 0.98, columns 4 and 5)

is the same as that given by Jursa (1985). If N1 = 0.998,

the radiance of light scattered is dominated by the coarse

particles. Columns 6, 7, and 8 of Table 2 show that the

6
Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

radiance scattered by this model is 1.6–4.0 times as strong

as if this model contained only accumulation mode particles. The aerosol model in Table 2 with N1 = 0.998 and

n = 1.38 − 0.001i is used to construct the look-up tables.

1.3.2 Gaseous Optical Properties

The optical thickness due to scattering by the standard

molecular atmosphere (τr) and for a surface pressure of

1,013 mb is given in Table 3. The optical thickness refers

to attenuation expressed in terms of base e rather than

base 10. The Rayleigh scattering phase matrix is used for

computing the molecular scattering.

Table 3. The scattering optical thickness, τr, of the

standard atmospheric gases and absorption optical

thickness of water vapor, τwv (Kneizys et al. 1983).

λ [nm] τr τwv

443 0.2394 0.000

520 0.1240 0.000

550 0.0986 0.000

670 0.0442 0.0043

The optical thickness of ozone, τoz, is defined as

τoz(λ) = δ(λ)Ω, (15)

where δ is the absorption coefficient and the amount of

ozone, Ω, is given in Dobson units (DU). The optical thickness for an average amount of ozone (313 DU) is given in

Table 4.

Table 4. Ozone optical thickness for an average

amount of ozone (313 DU).

λ [nm] α Source Average τoz

443 3 × 10−6 1 0.00094

520 51 × 10−6 2 0.016

550 85 × 10−6 2 0.027

670 46 × 10−6 2 0.014

Source 1: Inn and Tanaka (1953).

Source 2: Griggs (1968).

1.3.3 Surface Reflection

The sea surface is assumed to be rough. The probability distribution of surface slopes is given by Cox and

Munk (1955). The radiative transfer code is described by

Ahmad and Fraser (1982). This code accounted for the polarization characteristics of light reflected from the surface

and scattered by the atmosphere, plus multiple scattering. The water-leaving radiance (LW ) is assumed to be

isotropic and unpolarized. The radiances outside of the

glint pattern computed by this algorithm, and SPD, developed by Dave (1972b), generally agree within 1%.

The index of refraction of sea water is less than 0.01

greater than that of pure water (Jerlov 1976). Because

the reflection coefficient of sea water exceeds that of pure

water by only 0.004, the index of refraction of pure water

(n = 1.331 − 1.1 × 10−8i) is used in the computations of

the look-up tables.

1.3.4 Computations

The tables are computed for the following values of the

independent variables:

λ 443, 520, 550, 670 nm;

θ 0–84◦ in 6◦ increments;

φ 0–180◦ in 4◦ increments (φ is measured from the

direction of the solar beam and the radiances

are symmetric with respect to principal plane

through the sun);

θ0 0–66◦ in 6◦ increments;

φ0 0◦;

W 6ms−1; and

τa 0–0.7 in increments of 0.01 for λ = 443 nm.

A model computation is made for each combination of

the 4 wavelengths, 12 solar zenith angles, and 8 aerosol

optical thicknesses, for a total of 384 models. As a result,

265,000 numerical values are stored in the look-up tables.

1.3.4.1 Interpolations

The ultimate accuracy required for CZCS atmospheric

corrections is limited by the digitization of the radiance

voltages. The radiances, L, are digitized to 8 bits. The

radiances per count for the lowest and highest gains are

given in the next to last column of Table 5. The reflectance

per count is given in the last column. [The reflectance

equals L/F0, where F0 is the solar spectral irradiance when

the distance between the Earth and sun is 1 astronomical

unit (AU).] The error in the look-up table radiances is 1%,

which at saturation is 0.01×255=3 counts. If the radiances

are only one-third of the saturation values, then the lookup table error is only 1 count. The goal is to make the

error in the atmospheric corrections less than 1 count.

1.3.4.2 Radiance Gradients

Additional errors occur because of interpolations in the

look-up tables. In order to match the measured and lookup table radiances, the measured radiance has to be interpolated for 4 parameters: the look-up radiance and three

geometry parameters, i.e., the solar and observation zenith

angles and the azimuth difference (φ−φ0). The largest interpolation errors are sought in the tests made here. These

are expected to occur where the magnitude of the radiance

gradient is largest or nonlinear. Since the radiance, as a

function of aerosol optical thickness, is nearly linear for

each of the CZCS channels, the largest nonlinear radiance

gradients occur near the horizon and near the glint pattern.

In order to determine the maximum radiance gradients, they are computed with the RADMAIN2 code, which accounts for rough ocean reflection. The approximate CZCS

7
Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Table 5. CZCS saturation radiances, radiance increment per count, and reflectance increment per count.

The radiances are digitized to 8 bits. The irradiance units are mW cm−2 μm−1 and the radiance units are

mW cm−2 μm−1 sr−1. Data are taken from Hovis et al. (1980).

Wavelength Solar Gain Saturation Radiance Reflectance per

[nm] Irradiance (F0) Radiance (Lmax) per Count Count  L

F0 / count

443 187.0 3 5.41 0.021 1.1 × 10−4

443 187.0 0 11.46 0.045 2.4 × 10−4

520 187.0 3 3.50 0.014 0.7 × 10−4

520 187.0 0 7.64 0.030 1.6 × 10−4

550 186.8 3 2.86 0.011 0.6 × 10−4

550 186.8 0 6.21 0.024 1.3 × 10−4

670 153.1 3 1.34 0.005 0.4 × 10−4

670 153.1 0 2.88 0.011 0.7 × 10−4

geometry is used. Its scan through the nadir occurs between azimuths of 80◦ and 100◦ from the principal plane

through the sun for most of the CZCS orbit; the exact

azimuth is not important, since the radiance gradients are

similar for nearby azimuths. It is assumed that useful measurements can be made only when the zenith angle of the

radiance is less than 60◦. At still larger zenith angles, the

atmospheric correction rapidly increases and becomes less

accurate. Also, the horizontal extent of the instantaneous

field-of-view (IFOV) becomes larger, increasing the probability of clouds being present.

The maximum radiance gradients occur when scanning

through the ocean glint and near the horizon. The maximum glint encountered by the CZCS scans occurred when

the solar zenith angle was near 20◦. The reflectance gradient, with respect to viewing nadir angle, is computed for

channel one (443 nm) and is given in Table 6.

Table 6. Reflectance gradients with respect to the

zenith angle at the top of the atmosphere for a rough

ocean surface, where W = 6ms−1; aerosol optical

thickness τa = 0.2; λ = 443 nm; solar zenith angle

equals 18◦; azimuth from the principal plane is φ =

80◦; and the gain is 0.

θ L/F0 θ d

dθ  LF0

 Counts

(φ = 80◦) (φ = 80◦) per Deg.

0 0.046 3 −3.3 × 10−5 −0.1

6 0.046 9 −3.0 × 10−4 −1.3

12 0.044 15 −4.5 × 10−4 −1.9

18 0.041 21 −4.7 × 10−4 −1.9

24 0.038 27 −3.7 × 10−4 −1.5

30 0.036 33 −1.8 × 10−4 −0.8

36 0.035 39 0.0 0.0

42 0.035 45 2.0 × 10−4 0.8

48 0.036 51 3.7 × 10−4 1.5

54 0.038 57 6.0 × 10−4 2.5

60 0.042 63 8.6 × 10−4 3.6

66 0.047 69 1.2 × 10−3 5.1

72 0.054

If the observation zenith angle θ is less than 60◦, then

the absolute gradient of reflectance is less than 0.0006 per

degree, or 2.5 counts per degree. In order for the atmospheric correction error to be less than 1 count, the CZCS

scan angle would have to be specified to tenths of a degree.

The reflectance gradient with respect to azimuth is

given in Table 7. The maximum absolute gradient is less

than 0.00015 per degree, or 0.2 counts per degree. The azimuth gradients are very weak. The reflectance gradients

are similar for the other CZCS wavelengths.

Table 7. Reflectance gradients with respect to azimuth for the same conditions as for Table 6.

θ L/F0 L/F0

d

dφ LF0

 Counts

(φ = 80◦) (φ = 84◦) (φ = 82◦) per Deg.

0 0.046 0.046 0.0 0.00

6 0.046 0.045 −1.0 × 10−4 −0.06

12 0.044 0.043 −1.5 × 10−4 −0.09

18 0.041 0.041 −1.5 × 10−4 −0.09

1.3.4.3 Interpolation Errors

To find the atmospheric correction, a one-dimensional

cubic spline is used to interpolate for the radiance, first

for the azimuth between a vector from the sun to a pixel,

and a vector from the pixel to CZCS, second for the zenith

angle of that vector (θ), third for the zenith angle of the

sun (θ0), and finally for the total radiance. A severe test

is made with λ = 443 nm and a model with a large aerosol

optical thickness (τa = 0.65) and a large solar zenith angle

(56◦). The interpolations are made for four variables:

the observation azimuth angle is 82◦;

the zenith angles of the line-of-sight are 4, 16,

28, 40, 52, and 64◦;

the sun zenith angle is (56◦); and

the aerosol optical thickness is 0.65.

The simulated look-up tables are computed for τa equals

0.6 and 0.7; θ0 equals 54, 60, and 66◦; θ from 0–90◦ in

6◦increments; and φ from 0–180◦ in 4◦ increments. The

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

interpolation errors for radiance at the top of an atmosphere are given in Table 8, columns 7 and 8. The errors

are only a fraction of a count, even for a large scan angle

where the radiances approach the saturation value of 0.06.

Radiances emerging from the base of the model atmosphere, where the gradients are strong in the aureole, are

given in Table 9 for another test of the interpolation accuracy. The errors are less than one count. The interpolation

errors are also small for an azimuth of 78◦.

Interpolation errors for a model with a smaller optical

thickness where the reflectance gradients are different, are

given in Table 10. The interpolation errors are negligible,

as are the errors for the downward reflectance at the base

of the atmosphere (not shown).

The interpolated reflectances at the top of the atmosphere are less than 0.3% or 0.3 counts, when the aerosol

optical thickness is still smaller (τa = 0.05) and also for

a smaller solar zenith angle (θ0=20◦). The errors increase

to 2 counts for the downward reflectances at the base of

the atmosphere (Table 11), because the interpolation is infringing on the bright aureole where the reflectance gradients are larger. Also, the θ0 interval was mistakenly made

twice as large, namely 12◦.

All the tests made for interpolation of the upward reflectance at the top of the atmosphere show errors less than

1 count in Tables 6 and 7, where the mesh size of θ and θ0 is

6◦, for the azimuth 4◦, and aerosol optical thickness is varied in increments of 0.1 from 0.0 to 0.7. The tests attain 2

counts of error for the downward reflectance at the base of

the atmosphere near the bright aureole region (Table 11)

where the reflectance gradients are stronger. Hence, the

interpolation errors for the reflectances at the top of the

atmosphere may attain errors of a few counts where the

reflectance gradients are strong in the solar glint region. A

method does not exist for making such interpolation tests

without using hours of computer time.

1.3.4.4 Application of Look-Up Tables

The look-up tables that are described for deriving the

water-leaving radiance are based on some of the procedures developed for the operational CZCS algorithm. The

variable reflectance from the ocean and atmosphere is parameterized by only the aerosol optical thickness. The flow

chart (Fig. 1) indicates that a clear, cloud-free region with

chlorophyll concentration less than 0.25 mg m−3 is selected

for pixels where the water-leaving radiance is assumed for

the three longest wavelengths. The radiation parameters

in the table are interpolated with a one-dimensional spline

algorithm for the geometrical independent variables, which

are the solar zenith angle, the solar azimuth, the view nadir

angle, and the view azimuth.

The aerosol optical thickness for each of the three longest wavelengths (520, 550, and 670 nm) is chosen where the

look-up table radiance (L/F0) matches the measured radiance. These optical thicknesses are assumed to be proportional to the wavelength raised to a power; the logarithm

of the optical thickness, as a function of the logarithm of

the wavelength, is extrapolated to the shortest wavelength

(443 nm). A gamma function for each of the three shortest

wavelengths is computed as the ratio of the corresponding

optical thickness to the optical thickness at 670 nm:

γ(λ) = τa(λ)

τa(670). (16)

This gamma function is applied elsewhere in an image until

a new gamma function can be computed. At other pixels,

the aerosol optical thickness at 670 nm is computed by assuming the water-leaving radiance, LW (670), equals zero.

The optical thickness at 670 nm is derived, whereas, the

optical thicknesses at the three shorter wavelengths are

computed from the gamma function. The optical thicknesses specify the ocean-atmosphere model and the corresponding look-up table radiances for comparison with the

measured radiances, to obtain the water-leaving radiances

for the three shortest wavelengths (3). Finally, the chlorophyll concentration is computed by means of the following

equation (Gordon et al. 1983):

C = 1.053

LW (550)

LW (443)1.71

. (17)

1.4 VALIDATION

The algorithm is tested for accuracy in deriving LW /F0

and chlorophyll concentration by simulating the radiances

of sunlight scattered from the ocean-atmosphere system.

The simulations account for foam. The look-up tables for

the surface radiance accounts only for the Cox-Munk reflection (Cox and Munk 1955) from a rough sea surface:

Lsfc(θ, φ) = 	

2π

0

π

	2

0

R(θ, φ; θ, φ)Ld(θ, φ) sin θdθdφ (18)

where R is the reflection matrix†, Lsfc and Ld are columnar

matrices of the four Stokes parameters, and Ld is the radiance of light incident on the surface. In the simulations,

the upward radiance (Lu) just above the surface is modified to account for foam reflection and underwater light:

Lup = (1 − f)Lsfc +

1

π fAfF

d + (1 − fAf )Lw, (19)

where f is the fraction of the surface covered by foam;

(1 − f)Lsfc is the matrix of the radiance of light reflected

from a rough surface, but minus the coverage by foam;

Af is the foam reflectance; F

d is the total downward flux;

and (1 − f)Lw is the water-leaving radiance. Both Lw

† R, L, and F designate matrices;more detailed variable definitions appear in the Symbols list at the end of this document.

9
Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Table 8. Interpolation accuracy for upward radiance (L/F0) at the top of an atmosphere with no surface reflection,

where λ = 443 nm.

Interpolated Validation Percent Error in

τa θ0 θ φ L/F0 L/F0 Error Counts

0.65 56 4 82 0.0267 0.0266 0.1 0.1

0.65 56 16 82 0.0273 0.0278 0.0 0.0

0.65 56 28 82 0.0294 0.0294 0.0 0.0

0.65 56 40 82 0.0335 0.0335 0.0 0.0

0.65 56 52 82 0.0406 0.0406 0.0 −0.1

0.65 56 64 82 0.0526 0.0526 −0.1 −0.2

Table 9. Errors for downward L/F0 at the base of the atmosphere.

Interpolated Validation Percent Error in

τa θ0 θ φ L/F0 L/F0 Error Counts

0.65 56 4 82 0.0446 0.0444 0.4 0.7

0.65 56 16 82 0.0463 0.0463 0.2 0.3

0.65 56 28 82 0.0484 0.0484 0.1 0.1

0.65 56 40 82 0.0511 0.0511 0.0 −0.1

0.65 56 52 82 0.0550 0.0551 −0.1 −0.2

0.65 56 64 82 0.0602 0.0603 −0.2 −0.4

Table 10. Same as Table 8 except that the aerosol optical thickness is reduced to 0.25.

Interpolated Validation Percent Error in

τa θ0 θ φ L/F0 L/F0 Error Counts

0.25 56 4 82 0.0216 0.0216 0.01 0.1

0.25 56 16 82 0.0220 0.0220 0.0 0.0

0.25 56 28 82 0.0233 0.0233 0.0 0.0

0.25 56 40 82 0.0260 0.0260 0.0 0.0

0.25 56 52 82 0.0312 0.0312 0.0 0.0

0.25 56 64 82 0.0408 0.0408 −0.1 −0.1

Table 11. The downward L/F0 at the base of the atmosphere for the following parameters: λ = 443 nm; θ0 = 14◦,

26◦, 32◦; θ = 0◦ to 90◦ in 6◦ increments; φ = 0◦ to 180◦ in 4◦ increments; and τa = 0.0, 0.1, 0.2.

Interpolated Validation Percent Error in

τa θ0 θ φ L/F0 L/F0 Error Counts

0.05 20 4 82 0.0454 0.0448 1.1 2.1

0.05 20 10 82 0.0441 0.0439 0.4 0.8

0.05 20 16 82 0.0417 0.0419 −0.3 −0.6

0.05 20 22 82 0.0393 0.0395 −0.5 −0.9

0.05 20 28 82 0.0373 0.0375 −0.5 −0.8

0.05 20 34 82 0.0359 0.0361 −0.4 −0.6

0.05 20 40 82 0.0352 0.0354 −0.3 −0.5

0.05 20 46 82 0.0353 0.0354 −0.2 −0.4

0.05 20 52 82 0.0361 0.0362 −0.2 −0.3

0.05 20 64 82 0.0410 0.0411 −0.2 −0.3

10
Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Fig. 1. Flow chart for making sun glint and atmospheric corrections to satellite measurements of ocean color.

11
Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

and the foam reflectance are assumed to be isotropic and

unpolarized.

All radiances and fluxes are normalized to F0 (Lup,

Ld, Fd, and Lw.) The RADTRAN radiance parameters are

modified as follows:

Lup,1 =



(1 − f)Lu,1 + fAf F 

d

π + (1 − fAf )LW

2



, (20)

Lup,2 =



(1 − f)Lu,2 + fAf F 

d

π + (1 − fAf )LW

2



, (21)

Lup,3 = 

(1 − f)Lu,3



, (22)

and

Lup,4 = 

(1 − f)Lu,4



. (23)

The fractional coverage by foam and its reflectance are

given by Koepke (1985):

f = 2.95 × 10−6 W3.52, (24)

where W is in units of m s−1, and Af=0.22.

The simulations are made for moderate operational conditions. The algorithm is first tested with the same maritime aerosol used for calculating the look-up tables. Then

the wind speed is increased from 6 m s−1 for the look-up tables to 10 m s−1 for a simulation. The simulations continue

for an urban atmosphere that would appear off the US

east coast, and Saharan dust off the west coast of Africa.

Finally, a comparison is made of the chlorophyll concentrations derived with this algorithm and the CZCS operational algorithm by applying it to measured radiances.

1.4.1 Sea Glint

The effectiveness of the algorithm in retrieving waterleaving radiances is tested for a case where they are important but cannot be retrieved from CZCS measurements

with the operational atmospheric correction algorithm because of sea glint. The strongest glint that can occur where

useful ocean color data might be retrieved occurs at 30◦ N

latitude (M. Darzi pers. comm.). On such occurrences,

the solar zenith angle at a pixel is assumed to be 30◦. The

sensor tilt is 20◦, but a somewhat more severe test will be

made by neglecting the tilt, and by having the simulated

scan pass through a brighter part of the glint pattern.

The accuracy of the derived water-leaving radiance in

the presence of glint is computed first. The model atmosphere (maritime) and wind speed (6 m s−1) for the simulation are the same as those used for the look-up tables. The

surface reflectance in the simulation is modified slightly by

adding foam to the water, whose radiance is only 0.0001.

The aerosol optical thickness at 443 nm equals 0.2. A maritime atmosphere with large optical thickness will not be

tested, since such conditions are not common.

The water-leaving radiances are those observed where

the chlorophyll concentration equals 0.12 mg m−3 (Morel

1980). The values of the water-leaving radiances (LW /F0)

are 0.00668, 0.00315, 0.00180, and 0.00000 for the respective wavelengths: 443, 520, 550, and 670 nm. No noise is

added to the simulated radiances. The observation zenith

angles range from 0–60◦ at an azimuth angle equal to 88◦,

which is nearly orthogonal to the principal plane through

the sun. Interpolations are not made for the geometric

variables, because a code is not available for simulating geometries that differ from those of the look-up tables. The

derived water-leaving radiances are quite accurate, being

within 2% of the simulated values. The assumption that

the water-leaving radiance, LW (670), is negligible leads to

errors in the derived water-leaving radiances at the shorter

wavelengths. The look-up table itself can be used to estimate this error.

To simplify the notation, a reflectance, ρ, is defined as

ρi(λ) = Li(λ)

F0(λ)

, (25)

where i may represent any of the following:

m for measured,

LU for look-up table,

0 for light scattered by the atmosphere,

sfc for reflection from the sea surface, and

w for water-leaving radiance.

The measured and look-up table ρi are:

ρm = ρ0 + tρsfc + t



ρw, (26)

ρLU = ρ0 + tρsfc, (27)

ρw = ρm − ρLU

t , (28)

and

∆ρw(λ) = −∆ρLU (λ)

t . (29)

Assuming the only error in the algorithm derives from error in the water-leaving radiance (LW ) or in the reflectance

(ρw) for the red channel, ∆LW (670) or ∆ρw(670), respectively, then the error in the look-up radiance is

∆ρLU (670) = t



(670)∆ρw(670). (30)

This error shall be traced to the water-leaving radiance

errors at the shorter wavelengths.

The water-leaving radiance error causes errors in identifying the correction model by means of the aerosol optical

thickness. The aerosol optical thickness after substitution

of (30) is

∆τa(λ) = ∂τa(λ)

∂ρLU

∆ρLU (λ) = ∂τa(λ)

∂ρLU

t



∆ρw(λ), (31)

and after substitution of (16) is

∆τa(λ) = γ(λ, 670)∂τa(670)

∂ρLU

t



∆ρw(670). (32)

12
Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

The error in the water-leaving radiance, (33), is found by substituting (32) in (29):

∆ρw(λ) = −1

t

∂ρLU (λ)

∂τa

∆τa(λ)

= −1

t(λ)

∂ρLU (λ)

∂τa

γ(λ, 670)∂τa(670)

∂ρLU

t



(670)∆ρw(670).

(33)

The look-up table can be used to estimate the errors

in the water-leaving radiances. Table 12 shows that for

a modest water-leaving radiance error of ∆(LW /F0(670))

equal to −0.00015, the error in the water-leaving radiances

for the other wavelengths are acceptably small.

A simulation is made with the wind speed increased to

10 m s−1, but with the same maritime model atmosphere.

Figure 2 compares the radiances at the top of the atmosphere for models with wind speeds of 6 and 10 m s−1.

The higher wind speed causes the glint pattern to expand.

The algorithm estimates nearly the correct aerosol optical thickness at nadir of 0.21 (443 nm), compared with

a simulation value of 0.2. The derived water-leaving radiances (LW ) are compared with the simulated values of

LW = 0.00688, 0.00315, and 0.00180 for the respective

wavelengths of 443, 520, and 550 nm. Figure 3 shows the

deviation or error in digital counts.

The errors lie within the range of ±2 counts for a nadir

angle less than 45◦. It is important that the errors are well

correlated for the channels, since the chlorophyll concentration is computed by a ratio of the water-leaving radiances at 443 and 550 nm (17). The deviation in the estimated chlorophyll concentration from the model value of

0.12, lies within the range of ±10% .

1.4.2 Urban Atmosphere

The westerly winds of middle latitudes carry continental and urban aerosols over the western Atlantic and Pacific

Oceans. Representative physical characteristics of such an

aerosol can be described by a log-normal size distribution

with a geometric mean radius of 40 nm and a standard

deviation of ln r = 0.69. Their index of refraction is assumed to be 1.43 − 0.0035i, which results in an albedo of

single scattering of 0.98. This model with an aerosol optical thickness of 0.4 (443 nm), combined with surface winds

of 10 m s−1, results in radiances saturating, even though

the look-up optical thicknesses extend to 0.7. In this simulation, the wavelength dependence of the aerosol optical

thickness is approximated by λ−1.7. In contrast, for the

maritime aerosol used to compute the look-up tables, the

aerosol optical thickness is nearly independent of wavelength. Figure 4 shows that the radiances of the urban

and maritime models of the look-up tables do not differ

strongly. As a result, the error in the derived water-leaving

radiances is less than one count.

Another simulation is considered using the same urban

aerosol, but the wind speed is increased to 10 m s−1. Figure 5 shows that the total radiance of this model differs

more strongly from that of the look-up tables than in the

previous simulation with a wind speed of 6 m s−1. The

water-leaving radiance error is rather large: ±3 counts for

nadir angles less than 42◦ (Fig. 6). Because of the strong

correlation of the errors with respect to wavelength, the

relative error in the chlorophyll concentration is within an

acceptable range of ±15% when the nadir angle is less than

48◦ (Fig. 7). These errors are small enough to expect satisfactory estimates of chlorophyll concentration from CZCS

measurements off of the eastern coasts of mid-latitude continents, assuming moderate amounts of haze and wind

speed.

1.4.3 Aeolian Dust

The following simulation is made for a dust aerosol.

The dust size distribution is log-normal with the standard

deviation (ln r) equals 0.875 and the geometric mean radius rm equals 0.176 μm. The total aerosol optical thickness equals 0.5, and the optical properties of the dust

aerosol are given in Table 13. The geometric conditions

for the simulation set the solar zenith angle equal to 60◦.

The scattering angle varies between 100◦ and 120◦, where

the relative azimuth angle between the principal and scan

planes equals 88◦. The difference between the dust and

look-up phase functions is small for scattering angles less

than 145◦, but the ratio of the dust to the look-up phase

functions increases to 3 for a scattering angle equal to 180◦.

The surface wind speed equals 6 m s−1.

The simulation of the water-leaving radiance, LW , is

equal to zero in all spectral bands. Figure 8 shows the ratio of the derived water-leaving radiance to the total radiance. The errors are acceptably small for 520 and 550 nm,

but not for the shortest wavelength. The reason for the

large error is the moderate aerosol absorption at the shortest wavelength. Optical thicknesses derived for the three

longest wavelengths, when extrapolated to a wavelength of

443 nm, overestimate the optical thickness at the shortest

wavelength. As a consequence, the look-up table radiance

is too large—even larger than the simulated measured radiance. This algorithm, as it stands, fails for this particular aerosol model. This problem for dust may possibly be

overcome by using a separate look-up table computed for

dust.

1.4.4 Comparison with CZCS Algorithm

In order to gain additional confidence in the algorithm,

values of water-leaving radiances are compared with values

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Fig. 2. Comparison of the radiances at the top of the atmosphere for two models with wind speeds of 6

and 10 m s−1. The atmospheric model is the same as the maritime model used to compute the look-up

tables. The solar zenith angle equals 30◦.

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Fig. 3. Water-leaving radiance error for a model with the same atmosphere as the look-up tables, but

with a higher wind speed of 10 m s−1.

Fig. 4. Comparison of the total radiance at the top of the atmosphere with that of the look-up tables.

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Fig. 5. Total radiance at the top of the atmosphere of an urban model atmosphere and wind speed of

10 m s−1 with the look-up table radiance.

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Fig. 6. Water-leaving radiance error for urban model aerosol and wind speed of 10 m s−1—Gain 0.

Fig. 7. Relative error in chlorophyll concentration for a model with an urban aerosol and wind speed

equalling 10 m s−1.

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Table 12. The water-leaving radiance relative error is calculated by utilizing the look-up table. The relative

error, ∆ρw(λ)/ρw(λ), is given in the last column for a value of the water-leaving radiance error in channel 4,

∆ρw(670), equal to −1.5 × 10−4. The relative error is computed with (25). The solar zenith angle equals 30◦;

view zenith angle equals 42◦; the azimuth angle is 88◦; and the aerosol optical thickness is τa(443) = 0.2.

λ Transmission ∆ρLU /∆τa γ ρw(λ)/F0 ∆(ρw(λ)/F0) Relative

[nm] (t



) Error

443 0.832 0.0205 1.426 6.70 × 10−3 −2.28 × 10−4 −0.034

550 0.875 0.0205 1.184 1.80 × 10−3 −1.79 × 10−4 −0.100

670 0.921 0.0214 1.000 1.50 × 10−4 −1.50 × 10−4 −1.000

Table 13. Optical properties of dust (Fraser 1993). The real and imaginary indices of refraction are given in

the second and third columns. The last column gives the average total extinction cross section of a dust particle.

λ Real Imaginary Albedo of Cext

[nm] Single Scattering [cm2/particle]

443 1.575 0.011 0.80 1.113 × 10−8

520 1.561 0.0062 0.86 1.136 × 10−8

550 1.559 0.0050 0.88 1.131 × 10−8

670 1.548 0.0032 0.94 1.163 × 10−8

Fig. 8. Percent error in water-leaving radiance for dust simulation.

derived with the CZCS algorithm. This effort required the

close cooperation and support given by C. McClain, M.

Darzi, and E-n. Yeh of the SeaWiFS CVT. They provided

the calibrated radiances for the four spectral bands and the

ephemeris data. The CZCS radiances were measured over

the Gulf of Mexico in December 1978. The solar zenith

angle was 48–49◦. The scattering angle between the direct

sunlight and the line-of-sight ranged between 121–131◦.

The relative contribution of the radiance of light reflected

from rough surface reflection to the total radiance at the

top of a Rayleigh atmosphere is given in Fig. 9.

As the CZCS channel 1 scans from nadir to the Earth’s

limb, surface reflection decreases from 9% to 5% of the

total measured radiance. This contribution to the total

radiance is about the same as either the water-leaving

or aerosol radiance. The look-up water-leaving radiances

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Fig. 9. Relative contribution of the radiance of light reflected from the surface to total radiance at the

top of the atmosphere.

Fig. 10. Water-leaving radiance, LW , difference in counts: LW from the look-up table minus that from

the CZCS algorithm. Gain was 0.

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Fig. 11. Comparison of chlorophyll concentrations (mg m−3) as derived from the look-up tables and

the CZCS operational algorithm.

are generally a few counts higher than the CZCS waterleaving radiances (Fig. 10). A generally positive difference of two counts and increasing difference with decreasing wavelength suggests the possibility of calibration errors

in the CZCS data. The goal is to get the water-leaving

radiances derived from the two methods to differ by less

than one count. Because the differences are well correlated among the three wavelengths, the errors tend to cancel when the chlorophyll concentration is computed with a

ratio of radiances (17).

The chlorophyll concentrations for both the look-up table and operational algorithms are in good agreement (see

Fig. 11). The look-up and operational concentrations are

strongly correlated; but there is a bias in the line, since the

slope is not equal to one. The look-up tables overestimate

the CZCS chlorophyll concentration for small amounts of

chlorophyll.

1.5 DISCUSSION

This preliminary study shows that estimates of waterleaving radiance cannot be expected with errors less than

10%, attributable to 1% errors in the radiances tabulated

in these look-up tables. The simulations for modest surface

reflection and aerosol perturbations, resulting in calculated

chlorophyll concentrations, could be derived with an error

in the range of ±15%, because of the strong correlation of

the water-leaving radiance errors for 443 and 550 nm. Simulations for stronger perturbation produced radiances that

exceeded the look-up table values and are not discussed

herein. Before the stronger perturbations can be simulated, the look-up tables have to be extended for aerosol

optical thicknesses of 0.8, 0.9, and 1.0. Still higher optical

thicknesses are not required, since the look-up table radiances would exceed the CZCS saturation radiances. The

errors for an urban aerosol are small, but are unacceptably

large for aeolian dust. In principal, a separate look-up table for dust could be calculated, but its reliability would

be uncertain because the optical properties of aeolian dust

are poorly known. There are at least three different research efforts to determine the optical properties of dust.

A comparison of chlorophyll concentrations derived with

this algorithm and the CZCS operational algorithm agree

closely.

Comparisons with the CZCS algorithm can give confidence that the two algorithms are essentially correct; but

the check is not absolute, since both algorithms may cause

similar errors. An absolute validation requires simultaneous measurements of water-leaving radiance and atmospheric optical properties at the same time remote measurements of ocean color are being made.

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Chapter 2

Spectral Reflectance of the Moon

Kenneth J. Voss

University of Miami, Coral Gables, Florida

Abstract

This report details the results of a field trip to Mauna Loa, Hawaii, the objective of which was to obtain data on

the spectral reflectance of a full moon. The end use of this data was to determine the relative levels of spectral

reflectance for use by SeaWiFS during its in-flight calibration. For the moon to be a good calibration source,

the reflectance of the lunar surface needs to be consistently predicted. The Mauna Loa measurements indicate:

1) lunar reflectance varies with day, and thus perhaps with the lunar phase; and 2) the lunar surface might

exhibit a so-called back gloss, that is, an enhancement of the reflectance in the direction of the illumination.

Because of the latter, the reflectance of the moon may change rapidly during the course of a measurement, and

it may be better to make use of the moon as a calibration source when the moon is not full.

2.1 INTRODUCTION

The following is a short report detailing the results of

a field trip to Mauna Loa, Hawaii. The objective of the

trip was to obtain data on the spectral reflectance of a

full moon. The end use of this data was to determine the

relative levels of spectral reflectance for use by SeaWiFS

during its in-flight calibration.

2.2 INSTRUMENTATION

There were two instruments used in this study. The

first was a 10 channel solar photometer designed to measure both the solar transmittance and the sky radiance.

Because of its use in measuring the sky radiance, it was

designed and built using a photomultiplier as the photodetector. Most sun photometers are built with silicon

photodetectors, which while more stable for their primary

use, would not be sensitive enough for measurement of

the lunar irradiance. The drawback of the photomultiplier is the temperature sensitivity, which is wavelength

dependent. The instrument is temperature stabilized for

its primary use as a sun photometer, which entails cooling the instrument with thermoelectric peltier pads. On

Mauna Loa, however, temperatures were often down to

40◦ F; hence, heating, not cooling, was required. It was

known previously that this might be a problem, but due

to the short lead time and other factors, the steps taken

to alleviate this problem were insufficient. Thus, upon return from the field, a special calibration was performed to

determine the temperature sensitivity of the instrument.

For all but one of the channels, temperature sensitivity was on the order of a few percent. For channel 10

(at 861 nm), however, the temperature sensitivity was not

correctable. Since this wavelength is near the long wavelength cutoff of the photomultiplier tube, the temperature

sensitivity is extreme. In fact, the sensitivity of the tube

at this wavelength varies by a factor of 2 in the range of

40–70◦ F. Thus, measurements at this wavelength were not

obtained. For the other channels, data were reduced using

both the raw data and data corrected for the temperature

sensitivity, and the difference between the results was less

than 5%.

The temperature corrected data are used throughout

this report. The second instrument used was an electrooptic camera system originally designed for use in measuring the point spread function in the ocean. The only

modification was removal of the housing window and replacement of the wide angle lens (28 mm) with a zoom telephoto lens (185 mm). This allowed images of the moon to

be obtained at more than 13 times the resolution expected

by SeaWiFS (1.56 mrad). This system was outfitted with

a 20 nm interference filter at 500 nm. This allowed measurement of the lunar features in a relatively small spectral bandwidth. These measurements were combined with

Hand Held Contrast Reduction Meter (HHCRM) data to

obtain reflectance values expected in the SeaWiFS pixels.

2.3 DATA ACQUISITION

The technique of a Langley plot, normally used to measure solar radiance through the atmosphere, was employed

to determine the lunar radiance in the presence of the

Earth’s atmosphere. This technique consists of multiple

measurements of the moon (or sun) through varied air

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

masses, i.e., as the moon (or sun) rises or sets. The logarithm of the moon irradiance values measured is then regressed against 1/cos(zenith angle). The y intercept obtained in this manner is the extraterrestrial value of the

lunar (or solar) irradiance. This method depends on a stable, clear atmosphere. For this reason, the Mauna Loa

observatory on the island of Hawaii was chosen as a suitable location.

The Mauna Loa observatory is located at an altitude of

11,500 ft and is usually above the marine boundary layer.

Its location in the middle of the Pacific Ocean also removes it from most anthropogenic sources of atmospheric

particulates. Thus, it should be ideal for these types of

measurements. However, the technique does depend on

the absence of high level cirrus or other clouds.

Since measurements during the full moon were desired,

it was decided to attempt measurements on the two days

before the full moon, during the full moon, and if necessary, on the day after the full moon. Fortunately, this

last day was unnecessary. On the days before the measurements were to begin, there was a fair amount of cirrus

present. On 25 May, the weather was quite clear until

approximately 2130 Hawaii Standard Time (HST), when

an upslope event caused low level clouds to obscure the

observatory. On 26 May, there was significant high level

cirrus throughout the day. The cirrus cleared at approximately 1700 HST, which allowed a solar Langley measurement to take place. Skies remained clear until approximately 2030 HST, when the return of light cirrus was recognized by the formation of a halo around the moon. At

approximately 2057 HST, the weather cleared for a final

lunar observation. Soon after, an upslope event obscured

the observatory with low level clouds. On the night of

the full moon, high level cirrus obscured the moon during

moonrise.

During moonset on the night of the full moon, the momentary occurrence of a clear lunar view allowed lunar

measurements to take place. If it were not for the importance of obtaining measurements during the full moon,

these measurements may not have been attempted. (When

the weather bureau was consulted, full cirrus coverage for

the island of Hawaii throughout the night was predicted.)

However, as will be shown in the next section, the results

obtained appear to be acceptable. No measurements were

performed on 28 May, the night after the full moon in

Hawaii, as the weather showed no sign of improvement.

2.4 RESULTS AND DISCUSSION

The desired quantity is reflectance, ρ, which is defined

as:

ρ = πLM

ES

, (34)

where ES is the irradiance of the sun and LM is the radiance of the moon. In terms of experimentally measured

quantities, the latter is:

LM = HM

ΦM

= CF

ΦD

ΦMVM

,

(35)

where CF is a calibration factor, HM is the measured moon

irradiance, ΦM is the solid angle subtended by the moon at

the measuring instrument, ΦD is the detector solid angle,

and VM is the voltage read on the detector. The extraterrestrial solar irradiance, ES, can be determined from a table (Neckel and Labs 1984), or it can be determined with

a technique similar to that used in measuring the moon.

For the latter, ES is given by:

ES = CF ΦDVSND, (36)

where the ND factor compensates for the approximately

4 log neutral density filter, which is inserted in the detector for solar measurements but not lunar measurements.

This neutral density filter was previously measured in two

different ways to derive a consistent spectral dependence

of the true density factor.

When written out as above, LM and ES can be combined to give:

ρ = πLM

ES

= π VM

NDVSΦM

.

(37)

Note that in (36) the calibration factor, CF , and ΦD cancel

and the measurement only depends on ND, the ratio of

the voltages measured, and the solid angle of the moon (a

factor which is well known). This is the equation used in

this report to obtain ρ.

Two factors which enter into the above equation are neglected in this presentation. The first factor is the distance

variation between the sun and the moon, and the second

is the distance variation between the Earth and the sun.

The first factor is important when measuring the LM, as

this distance determines the solar irradiance falling on the

moon. The second factor is important when measuring

the ES, as this distance determines the solar irradiance

measured on the surface of the Earth. Both of these factors varied by only 0.05% during the measurement period.

Also, these factors were approximately equal (to within

0.3%). In this case, they were assumed to cancel. If the

lunar measurements and solar measurements were to take

place at different periods in the year, these factors would

become important.

The Earth-moon distance varied during the measurement period and this affects the above equations through

the ΦM factor. The mean angular subtense of the moon is

0.518◦. The distance from the moon to the Earth, normalized by the mean Earth-moon distance, varied from 1.0326

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Table 14. Lunar and solar radiances as a function of wavelength (units of mW cm−2 μm−1 sr−1).

Wavelength 25 May 26 May 27 May

[nm] VM τ VM τ VS τ VM τ

381 1.23 0.387 1.48 0.304 179.0 0.280 2.11 0.302

410 1.81 0.314 2.10 0.245 237.0 0.224 3.00 0.246

440 3.61 0.281 3.99 0.185 407.0 0.167 5.65 0.181

488 2.33 0.175 2.53 0.128 232.0 0.122 3.55 0.124

522 1.40 0.202 1.67 0.124 137.0 0.098 2.24 0.103

558 4.10 0.175 4.48 0.110 369.0 0.069 6.19 0.103

611 3.00 0.202 0.320 0.080 26.4 0.041 0.439 0.079

672 4.81 0.147 0.516 0.056 39.1 0.036 0.707 0.053

779 1.24 0.121 1.318 0.036 125.0 0.015 1.75 0.025

(25 May) to 1.0506 (28 May). This variation was taken

into account in ΦM.

Langley plots for each day were generated. The intercept of the fit in these plots is important, since this is

ln(VM), or in the case of the solar measurement, ln(VS).

The slope represents the atmospheric attenuation, τ , which

is the sum of Rayleigh, aerosols, and other gas extinctions.

In most sun photometry studies, τ is the desired quantity.

Of note in these Langley plots are the very good regression

coefficients obtained on 25 and 26 May, and apparently

spurious values for 27 May.

The problem for 27 May appears to be caused by two

points. However, no mention of cloud problems is noted in

the experimental log. As previously discussed, these observations took place on a difficult night with many clouds

moving in and out, so these points could still be suspect.

However, neglecting these points makes little difference

in the intercept or slope. Their inclusion only seems to

strongly effect the R2 value, so the points were not removed.

Table 14 summarizes the results for the three nights of

lunar HHCRM measurements and one day of solar measurements. The interesting thing to note on the values are

the consistency of the values for the evenings of 26 and

27 May 1991, and how close these are to the values in the

afternoon of 26 May. This is to be contrasted with 25

May, for which the attenuation values obtained were much

higher. The regression coefficients for this day were very

good which would indicate that these values should be reliable, also the LM values obtained on this day were fairly

consistent with the other days.

Values of ρ were analyzed from the data in Table 14 using a power law. The results of this analysis are presented

in Table 15. Interestingly, the exponent of the relationship

seems to vary regularly with day, and thus perhaps with

the lunar phase. Another measurement, obtained in Miami previous to this set, at a different phase angle (much

smaller) showed a power law relation exponent of 1.03. The

trend appears to be toward a flattening of the spectral dependence of ρ with increasing phase angle, i.e., toward a

full moon.

Table 15. Values of ρ analyzed from the data in

Table 14 using a power law.

Wavelength ρ

[nm] 25 May 26 May 27 May

381 0.059 0.068 0.098

410 0.059 0.070 0.101

440 0.067 0.074 0.106

488 0.073 0.080 0.113

522 0.073 0.088 0.119

558 0.080 0.088 0.123

611 0.084 0.090 0.125

672 0.097 0.105 0.146

779 0.097 0.104 0.139

The above discussion deals with the average ρ of the

lunar surface. The lunar surface has a varied reflectance

pattern. To characterize this variation, each night higher

resolution imagery was obtained using a charge coupled

device (CCD) array camera system. The first data reduction step taken with these data was to integrate the image

over the lunar surface. While the camera system could

be calibrated absolutely, it was not practical to perform

a Langley-type measurement scheme with it. Thus, the

atmospheric attenuation was not be determined using the

CCDcamera system.

The integral values of the lunar surface were used in

combination with the HHCRM measurements to perform

an empirical calibration. In this manner, the camera counts

could be related to ρ easily, without considering the atmosphere. The next step in the image analysis was to

subsample the lunar surface at the angular resolution of

the SeaWiFS instrument. This step was done by moving

a 13×13 pixel grid, or super pixel, over the image of the

moon. Each super pixel completely on the lunar surface

was integrated, and the resulting values were then used to

compile histograms. It is important to note that this is the

complete set of possible reflectances seen at the SeaWiFS

angular resolution. The histograms show that the moon

has a bimodal distribution of reflectances in all cases. Also

note the wide spread in possible values that can occur.

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

For the moon to be a good calibration device, it must

be possible to consistently predict the ρ of the surface. To

test one possibility, the moon was divided into these super

pixels and then the maximum super pixel value was determined. The squares were shifted slightly and the maximum determined again. In this way, the variability in

the maximum reflectance could be determined. The result

of this test is that the average maximum ρ (27 May) was

0.171 ± 0.00623 where the latter number is the standard

deviation. It can be shown by analyzing the distribution

of maximum values that the distribution of ρ values on

27 May is far from a normal distribution. This is probably

caused by a strongly varying bidirectional reflectance in

the near normal direction. Many materials exhibit a back

gloss, an enhancement of the reflectance in the direction of

the illumination, i.e., looking at a surface with the illuminating source behind you. For this reason, the reflectance

of the lunar surface may be changing rapidly. Because

of this phenomenon, it may be better to make use of the

moon as a calibration source when the moon is not full.

For example, the mean and standard deviations for the

26th and 25th are 0.114 ± 0.00381 and 0.108 ± 0.00242. Not

only are the standard deviations lower, but the shape of

the function is more normally distributed.

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Chapter 3

Preflight Solar-Based Calibration of SeaWiFS

Stuart F. Biggar

Philip N. Slater

Kurtis J. Thome

University of Arizona, Tucson, Arizona

Alan W. Holmes

Santa Barbara Research Center, Goleta, California

Robert A. Barnes

ManTech Environmental Technology, Inc.

Wallops Island, Virginia

Abstract

A new method for performing a preflight calibration of an optical remote sensing instrument with an onboard

solar diffuser calibration system is presented. The rationale, method, advantages, disadvantages, error sources,

and expected accuracies are discussed. The method was applied to the SeaWiFS instrument to be flown on the

SeaStar satellite.

3.1 INTRODUCTION

Integrating sphere sources are commonly used for the

preflight calibration of sensors† in the solar reflective spectral range. They have the advantage of providing a full

aperture, full field, end to end calibration at several different points over the dynamic range of the sensor. However,

integrating sphere sources have several disadvantages related to the lamp sources used and the reflective coatings

of the spheres.

Tungsten quartz halogen lamps, operated at a color

temperature of about 3,000 K, are usually used to illuminate the sphere interior. The output of these lamps peaks

at about one micrometer and falls off rapidly at shorter

wavelengths. Thus, it is difficult to match the radiance

level of solar radiation reflected from the Earth’s atmosphere and surface at short wavelengths using an integrating sphere source. Also, the rapid fall-off of the spectral

radiance is both difficult to measure, and to account for

accurately, in the calibration of the sensor’s short wavelength spectral bands. The measurements made by the

sensor are of reflected solar radiation; the sun’s spectrum

contains Fraunhofer lines, whereas lamps do not contain

† This chapter originally appeared as an article in an SPIE

publication (Biggar et al. 1993) and is being included in

this volume with permission of the authors. Minor editorial

changes have been made to reflect the style of The SeaWiFS

Technical Report Series.

these absorption features. For sensors with narrow spectral bandpasses, e.g., less than 10 nm, the presence of a

Fraunhofer line within a bandpass can change the response

of the sensor by several percent with respect to its integrating sphere source calibration (Flittner and Slater 1991).

The interior of a large integrating sphere source is usually coated with barium sulfate (BaSO4) paint. The spectral reflectance of the paint is reasonably flat in the visible

and near infrared, but exhibits rapid changes in the shortwave infrared that are difficult to account for accurately

in a calibration. Integrating sphere sources have some instability associated with them. There are several causes:

the instability of the power supplies, the warm-up time required for the lamps to become stable, and the warm-up

time required for the BaSO4 paint in the sphere to lose

any water vapor it has absorbed. In addition, there are

the uncertainties associated with the calibration of the irradiance standard used to calibrate the output radiance

of the sphere, the transfer of the lamp scale to the sphere

output, and the aging of the sphere lamps and coating.

The first is a function of wavelength, being approximately

±1% in the visible, but greater than ±2% in the shortwave infrared. (All uncertainties given in this paper are

one standard deviation, or one σ.) When all these sources

of uncertainty are considered, it is obvious that to achieve

an uncertainty as low as, say, ±3% for the output of an

integrating sphere source is very difficult, time consuming,

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

and costly. Furthermore, in practice, it will be misleading for spectral bands of the sensor that fall over strong

Fraunhofer lines.

The best form of onboard calibration is provided by a

solar diffuser, because it is end to end, full aperture and

full field, and has the appropriate spectral distribution and

radiance levels at all wavelengths. However, one of the

main shortcomings of solar diffusers is that no method has

been found to calibrate the sensor preflight via the solar

diffuser without using the aforementioned tungsten lamp.

Thus, an assumption has to be made that the in-flight calibration of the sensor via the solar diffuser is not subject to

systematic errors. This assumption implies that the bidirectional reflectance distribution function (BRDF) of the

diffuser, and the irradiance on the diffuser, are both accurately known. Without the use, ideally, of a ratio taking

radiometer (Slater and Palmer 1991), or an alternative solar diffuser monitor (Pagano and Durham 1993, Bruegge

et al. 1993), questionable assumptions must be made concerning freedom from stray light on, and contamination of,

the diffuser. In any case, the results of an onboard diffuserbased calibration can only be approximately related to a

preflight integrating sphere source calibration, partly for

the reasons given earlier, and partly because of the uncertainty in our knowledge of solar exoatmospheric spectral

irradiance.

This paper describes what is believed to be the first

attempt at a preflight solar based calibration of a sensor

and diffuser system, conducted using the same illumination and viewing geometry as will be used during in-flight

calibration. The preflight solar based calibration can be

directly compared to the preflight integrating sphere and

source calibration, thus providing a desirable link between

in-flight solar based calibration, which is simply in digital

counts (DC), and the preflight absolute calibration in DCs

per unit in-band spectral radiance. The link then is directly to national laboratory standards in physical units.

In respect to national standards, it is worth emphasizing

that the sun’s output is the same anywhere on earth, while

there are differences in the irradiance and radiance scales

between different national standards laboratories.

There are various risks and disadvantages associated

with this method of calibrating a sensor. First and foremost is the risk to the sensor associated with the move

to an appropriate site outside. There is also a chance of

contamination of the optics when actually doing the experiment. Clear sky conditions are required, and a very low

aerosol loading is best. Accurate transmittance measurements are required. High accuracy solar exoatmospheric

spectral data are needed to provide an accurate comparison with national laboratory standards. The currently

available solar data are not sufficiently accurate, nor of

high enough spectral resolution, for such a comparison.

The calibration procedure necessitates illuminating the

diffuser on the flight sensor with direct solar irradiation.

Heath (pers. comm.) has described a similar procedure,

not for sensor calibration purposes, but to provide a measure of the relative ozone columnar concentration from the

various Solar Backscatter Ultraviolet (SBUV) instrument

spectral bands.

In order to calibrate SeaWiFS via the diffuser, the unit

had to be taken outside. (In the case of the SBUV instrument, the laboratory roof was opened.) The possibility of

taking a flight unit sensor outside was not anticipated by

the University of Arizona authors until, at the first SeaWiFS Science Team meeting in January 1993, during a

Santa Barbara Research Center (SBRC) presentation, A.

Holmes remarked that this had been done to check that

the appropriate gain settings had been selected for diffuser

and lunar viewing. This prompted P. Slater to propose to

the Science Team that a solar based preflight calibration be

attempted. Such an approach was approved by the Science

Team and the Project Managers from SBRC and GSFC.

The hour-long calibration, described here, was conducted

at SBRC on 8 March 1993 by S. Biggar, K. Thome, A.

Holmes, and other SBRC staff.

3.2 CONCEPT

The concept is simple. An attempt is made to duplicate, on the ground, the solar illumination conditions seen

by the sensor in orbit. This simulation requires just the

direct solar beam, and it must illuminate the diffuser at

the incidence angle used during in-flight calibrations. The

requirement for just the direct solar beam is the difficult

one. At ground level, there is substantial scattering of

the direct beam by the molecules of the atmosphere, as

well as the aerosols distributed in the lower part of the

atmosphere. This scattering is strongly wavelength dependent, and causes a variable amount of diffuse light, which

illuminates the diffuser from angles other than the solar

incidence angle.

The atmosphere also attenuates the direct beam, as

compared to the beam at the top of the atmosphere. Therefore, the simulation must compensate for the lower-thanunity atmospheric transmittance. Part of the transmittance loss is caused by absorption by gases such as ozone,

oxygen, and water vapor. Another cause of lower transmittance is the loss due to scattering of light out of the direct

beam into the diffuse field. In order to make the calibration, corrections are made both for the transmittance and

for the diffuse light that illuminates the diffuser.

3.3 METHOD

The method described in this section is generic to any

sensor with a solar diffuser. The procedure is to situate

the sensor such that the solar diffuser is illuminated in

the same geometry as the in orbit geometry during calibration. The mount for the instrument must have several angular degrees of freedom in order to correctly align

the diffuser and instrument and, hopefully, allow continual

26
Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

tracking of the sun. When the diffuser is aligned correctly,

the digital counts from the sensor while it is viewing the

diffuser are recorded for each spectral band. Then the diffuser is shadowed by a small disk that blocks the direct

beam and the digital counts are again recorded for each

band. The difference, corrected for the atmospheric transmittance and the small component of diffuse light blocked

by the disk, corresponds to the in-orbit illumination of the

diffuser. The transmittance is measured during the period

of diffuser measurements with a solar radiometer. The optimal choice for the solar radiometer wavelengths would be

the actual sensor bands if they are sufficiently narrow, but

in general this may not be possible. Schematic diagrams of

the illumination geometry for the experiment, both fully

illuminated (Fig. 12) and shadowed by the disk (Fig. 13)

are provided. Figure 14 is a photograph of the experiment

as done at SBRC on 8 March 1993.

3.4 ERROR SOURCES

In this experiment, we want to determine the digital

counts (DC) that will be measured at the top of the atmosphere (TOA):

DCTOA(λ) = DCmeas(λ)

T(λ) − DCscat(λ) (38)

where T(λ) is the transmittance along the slant path to the

sun, DCmeas are the digital counts measured unshadowed,

and DCscat are the digital counts due to scattered sunlight

for wavelength λ.

There are several error sources that must be carefully

considered when doing the experiment. The first error

relates to the measured transmittance. The atmospheric

transmittance along the slant path from the sun to the diffuser must be known accurately. The transmittance T(λ)

is defined as

T(λ) = e−σ(λ)/μs (39)

where σ(λ) is the spectral optical depth, μs ≈ cos θs, and

θs is the solar zenith angle.

The errors in transmittance must be understood to allow differentiation of the transmittance defined in (39) as a

function of the various components, and to determine the

fractional error in T(λ) as

∆T(λ)

T(λ) = −∆σ(λ)μs

+ σ(λ) sin θs∆θs

μ2

s

(40)

where ∆σ(λ) is the absolute error in optical depth (σ(λ)),

and ∆θs is the error (in radians) in the knowledge of θs.

Note that for a well designed experiment, the second

term in the fractional error equation, (39), will be quite

small as compared to the first. The absolute error in optical depth is normally wavelength dependent, being greater

at shorter wavelengths where the optical depth is normally

greater. A second order error term, not dependent on the

transmittance, is caused by the unavoidable blockage of

a small part of the diffuse field by the small disk. This

error term is expressed as the second term in (38). This

blocked diffuse light is the central part of the solar aureole and is the strongest part of the diffuse field, as almost

all of this light consists of forward scatter from the direct

solar beam. We can make a first order correction for the

blocked aureole with very small uncertainty. Another small

error source derives from the non-Lambertian characteristic of any real diffuser. These last two errors are difficult

to quantify without detailed specifications for the diffuser,

diffuser screen (if present, as in SeaWiFS), apertures, and

any baffles.

The experiment can be conducted to best minimize the

errors. The first and most important goal is to reduce

the optical depth uncertainties and hence the uncertainty

in the transmittance computations. The solar radiometer

used to measure the transmittance should be linear and

well calibrated. Filter radiometers based on silicon detectors are linear and stable, and can be calibrated to an

uncertainty of a few percent. Reliance on the calibration

of the radiometer can be reduced by taking data over an

extended period, corresponding to a large variation in solar zenith angle from 75◦ to as low a value as is available

on the calibration day (Biggar et al. 1990). If the atmosphere is stable over that period, a Langley plot can be

made from the data for each band. The slope of each plot

is equal to the optical depth in that band of the radiometer. A Langley plot requires only a linear instrument and

a stable atmosphere.

Another strategy for reducing transmittance errors is to

make the measurements on a very clear day with low aerosol loading and low humidity. This strategy will reduce

the scattering due to aerosols, and therefore the uncertainty in the aerosol scattering. The low humidity reduces

the absorption in any water vapor bands. Similarly, measurements could be best made at high altitude sites with

low aerosol loadings, such as good astronomical observatory sites. The high altitude reduces both Rayleigh and

aerosol scattering and reduces absorption due to all gasses

present in the atmospheric boundary layer.

Another method for reducing the transmittance uncertainty is to make the actual solar diffuser measurements

at the lowest possible solar zenith angle. The slant path is

then the shortest, and the fractional error equation, (39),

shows a secant of solar zenith angle dependence. Hence,

weather permitting, the measurements should be made at

solar noon. Every effort should be made to ensure that all

angles are known and set to the appropriate values.

In order to reduce the uncertainties in the correction for

the diffuse light, several precautions should be taken. The

shadower disk should be as small as possible to reduce the

blocked aureole. As the disk will probably be at the end

of a 5 m long pole, however, the disk must be large enough

to still completely cover the full diffuser aperture while allowing for small errors in positioning. The disk used in the

27
Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Fig. 12. Experiment illumination geometry—unshadowed.

Fig. 13. Experiment illumination geometry—shadowed.

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Fig. 14. SeaWiFS solar based calibration experiment, 8 March 1993 at SBRC in the “Rock Garden.”

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Table 16. Solar-based calibration results.

Band Gain Transmit- Unshaded Shaded Forward Predicted In- SphereNo. Setting tance DCs DCs Scatter DCs Flight DCs Derived DCs

1 2 0.567 420 21 1 701 708

2 3 0.634 355 17 1 532 546

3 3 0.708 484 19 1 655 670

4 4 0.728 737 28 2 972 1,004

5 1 0.763 618 23 1 778 808

6 4 0.836 625 22 1 720 730

7 4 0.799 581 21 1 699 729

8 4 0.895 664 26 1 712 772

SeaWiFS experiment was 30 cm in diameter and was at the

end of a 5 pole. The diffuser aperture, about 80 mm in diameter, was about 1.7 m above the ground. The blocked

solid angle was small, but a correction was still necessary.

Knowing the precise geometry of the disk/diffuser assembly and the sun also reduces uncertainty in the correction

computation. In a similar vein, the measurements are best

made on a very clear day, as the scattering is reduced, the

diffuse signal is smaller, and the uncertainty in the direct

beam computation is then reduced.

3.5 ACCURACY

In order to simulated the signals expected in this type

of calibration, a nominal 23 km visibility US standard atmosphere was used. The extinction optical depth, which

is measured during the experiment, is comprised of various components: those due to Rayleigh scattering, aerosol

scattering, ozone absorption, water vapor absorption, and

also including absorption due to other gases such as oxygen, carbon dioxide, and nitrogen dioxide. The Rayleigh

optical depth can be known accurately from the barometric pressure, which is measured for each optical depth data

point.

The error in the optical depth is due to the aerosol

optical depth and extinction due to absorption. This error should be less than 3% of the difference between the

extinction and Rayleigh optical depths. For a standard atmosphere, this corresponds to a transmittance uncertainty

of ±2.7% to ±1.1% for the shortest to longest wavelength

SeaWiFS bands, at a solar zenith angle of about 60◦. This

uncertainty could be reduced by making the measurements

at a higher solar elevation (the error would be lower by a

factor of 1.65 if the measurements were taken at noon in

early March with a solar zenith angle of 39◦).

The errors in the calibration which are attributable to

the small disk are smaller than the transmittance uncertainties, yet are much more difficult to quantify. The uncertainty in the global minus diffuse measurement was estimated to be about ±0.8%, after correction for the blocked

component of the aureole. There is another 0.4% uncertainty due to the inaccuracies in measuring the various

angles. The total uncertainty is therefore estimated to be

from ±2.8% to ±1.4%, depending on wavelength, for a solar zenith angle of 60◦. The uncertainty could be as low as

±1.9% to ±1.1% for a noon measurement in early March

at SBRC. These uncertainties are one σ with respect to

the solar output.

3.6 RESULTS

The calibration experiment was done in early March

1993. In conjunction with the 8 March 1993 experiment,

Kurtis Thome measured the atmospheric transmittance

during the afternoon of 7 March 1993. This measurement was to be used to infer the transmittance during

an attempt to view the full moon during moon rise that

evening. The conditions were good during the afternoon.

An example of the Langley plots is given in Fig. 15. The

zero airmass intercepts from these data agree well (within

2%) to historical and other data taken later in March in

Arizona. The good intercept agreement allows the use of

the radiometer to make instantaneous optical depth measurements if the atmosphere is changing during the measurement period (Biggar et al. 1990).

On 8 March, the atmospheric conditions were not as

good as the day before. There is some instability, which

is shown in Fig. 16. The SeaWiFS calibration data were

collected around 11:30 am Pacific Standard Time (PST).

The error bars are the uncertainty in transmittance due to

the uncertainty in the zero airmass intercept.

The measurements of transmittance during the data

collection period were successful. The optical depth due

to Rayleigh scattering, aerosol scattering, and ozone absorption in the solar radiometer bands were determined.

The optical depths for the same components in the SeaWiFS bands were then computed. A correction was also

made for oxygen absorption in band 7 using MODTRAN (Berk

et al. 1989). The results are presented in Table 16. The

unshaded and shaded DC columns are the data collected

by SeaWiFS while viewing the solar diffuser. The forward

scatter DC is the correction needed due to the blocking

of the forward scatter part of the aureole. The predicted

in-flight DCs column is the calibration result expected in

space from the solar diffuser.

30
Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Fig. 15. Langley plot for 7 March 1993 at SBRC.

Fig. 16. Aerosol transmittance.

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Fig. 17. Calibration comparison.

A preliminary attempt has been made to compare the

solar diffuser calibration done with the sun to the laboratory calibration using the SBRC 100 cm integrating sphere.

The solar spectrum from Iqbal (1983) was numerically integrated over the SeaWiFS bands, followed by application

of the transmittance correction. The solar diffuser bidirectional reflectance distribution function and the laboratory

counts per unit radiance calibration was then used to compute the last column in Table 16, the sphere-derived DCs.

Figure 17 shows plots of the two calibrations where the

sphere derived data have been corrected to correspond to

the Earth-to-sun distance on 8 March 1993. The results

are quite encouraging, as the difference is less than 4%

for all bands except for band 8. The 7.7% difference in

band 8 requires further analysis in order to determine the

cause of this difference. (The presence of two Fraunhofer

calcium lines are accounted for in the Iqbal (1983) data

and residual water vapor is estimated to be less than a 1%

effect.)

In reviewing these results, it should be realized that

there are many error sources associated with both the predicted in-flight DCs and the sphere derived DCs. The former were addressed under error sources (Section 3.4) and

the latter were addressed in the introduction. The two uncertainties that are the hardest to define are those of the

solar exo-atmospheric spectral irradiance, as published by

Iqbal (1983) from Neckel and Labs (1984) data, and those

of the reference lamp used to calibrate the sphere. The

results also depend strongly on the calibration of the diffuser and screen assembly—any uncertainties increase the

uncertainty in the sphere derived results. In addition, the

results presented here are from a single set of measurements. Time and weather restrictions prevented repeated

measurements that would have provided a useful indication

of the precision of the predicted in-flight DC data.

3.7 CONCLUSIONS

In this experiment, the SeaWiFS solar diffuser was calibrated using the sun. The results agree well with calibration results from the 100 cm spherical integrating source

at SBRC. The sum of the uncertainties from each method

is less than the difference in results for all bands except

for band 8. The method appears promising for any sensor which uses a solar diffuser for calibration. The most

interesting result, the actual DCs when the diffuser is illuminated by the sun while in orbit, will have to wait until

after launch.

Acknowledgments

The authors from the University of Arizona wish to thank the

SeaWiFS Project Office for support of this calibration work. R.

Barnes was supported under National Aeronautics and Space

Administration contract number NAS5-31540. The SeaWiFS

instrument is being built by SBRC for Orbital Sciences Corporation in Virginia for a 1994 launch on the Pegasus booster.

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Chapter 4

Spectral Radiance of the GSFC Integrating Sphere

Using a TrapDetector

James T. McLean

NASA Goddard Space Flight Center, Greenbelt, Maryland

Abstract

The GSFC 42 in (107 cm) diameter spherical integrator source is used to provide traceability in the optical in

situ measurements made by different researchers who support the SeaWiFS program. The sphere’s spectral

radiance calibration is based on the irradiance standard lamp scale. The purpose of this study is to verify the

radiance calibration of the sphere by using an absolute detector based scale which would be independent of the

irradiance lamp scale. The experiment described herein shows that filtered absolute silicon photodetectors in a

trap configuration can be used to verify the scale of spectral radiance of an integration sphere source.

4.1 INTRODUCTION

GSFC maintains a 42 in (107 cm) diameter spherical

integrator source as a reference for the SeaWiFS calibration and validation radiation scale. This source is used

to provide traceability in the optical in situ measurements

made by different researchers who support the SeaWiFS

program. This integrating sphere’s spectral radiance calibration is based on the irradiance standard lamp scale.

The procedure used to transfer the irradiance standard to

the sphere output in terms of spectral radiance is described

elsewhere (McLean and Guenther 1989). The purpose of

this study is to verify the radiance calibration of the sphere

by using an absolute detector based scale, which would be

independent of the irradiance lamp scale.

A number of investigators have used filtered radiometers for radiometric measurements of lamps (Hughes 1982,

Walker et al. 1991, and Corredera et al. 1990). Bruening

(1987) used filtered absolute silicon photodetectors, the socalled quantum efficient device (QED), in a trap configuration (Zalewski and Duda 1983) to realize a scale of spectral irradiance. Techniques similar to the ones described

by Bruening are followed.

4.2 EXPERIMENT DESCRIPTION

The laboratory setup is shown in Fig. 18. When making measurements with the equipment as shown, the signal

produced by the detector package is a function of the filter

transmittance, the response of the detector, and the optical power of the source (Bruening 1987). That is, I(λ) is

proportional to the power incident on the detector, or

d



I(λ)

 = Ad E(λ) T(λ) R(λ) d(λ). (41)

The irradiance, E(λ), from the sphere is given by

E(λ) = πr2

1

r2

1 + r22 + d2 L(λ), (42)

Using these conventions, the detector current may be expressed as

I(λ) = Adπr2

1

r2

1 + r22 + d2

	 λ2

λ1

T(λ)R(λ) dλ, (43)

which may be rewritten as,

L(λm) = I(λm)

κ

	 λ2

λ1

L(λ)

L(λm)

T(λ)R(λ) dλ

, (44)

where L(λm) is the radiance of the sphere at the nominal

peak wavelength of the filter. And,

κ = Adπr2

1

r2

1 + r22 + d2 (45)

where Ad is the detector aperture area (0.1 cm2), r1 is the

radius of the sphere aperture, r2 is the radius of the receiving aperture (same as the detector aperture), d is the distance between the sphere aperture and the detector aperture, and λ1 to λ2 is the wavelength interval over which

the filter transmits.

The ratio L(λ)/L(λm) is a weighting function to account for the spectral variation of the source over the filter

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Fig. 18. Laboratory setup for making spectral radiance measurements of the GSFC integrating sphere.

passband where λm represent the nominal center wavelength of the filter. The effective center wavelength of each

filter was calculated using:

Eeff =


 T(λ) dλ


 λT(λ) dλ. (46)

However, the nominal values as specified by the manufacturer were used for this experiment.

The experimental procedure consisted of using an obscuration baffle to block the direct light from the sphere

source and then record a background reading for a given

filter-detector combination. The obscuration baffle was removed and the detector signal was recorded. This procedure was repeated for all 13 filters spanning the spectral

range from 400–1,100 nm. The nominal passband for each

filter is 25 nm. The data obtained is given in Table 17.

Note, the integrated values in Table 14 are computed using

the following formulation: L(λ)/L(λm)



T(λ)R(λ)∆λ.

Table 17. The experimental procedure data obtained by using an obscuration baffle.

Filter Detector Integrated Radiance

[nm] Signal Value Calculated Measured

400 0.0026 4.63846 2.404 2.89

450 0.0090 6.07838 6.392 6.83

500 0.0179 6.33059 12.166 12.03

550 0.0277 6.51905 18.221 18.31

600 0.0521 9.01097 24.793 24.63

650 0.0458 6.39953 30.689 30.53

700 0.0537 6.44112 35.751 35.70

750 0.0805 8.62166 40.038 39.86

800 0.0977 10.00564 41.872 42.70

850 0.0850 8.23934 44.238 44.20

900 0.1280 12.48152 43.976 44.65

950 0.0855 8.47733 43.249 43.92

1000 0.0595 5.80379 43.962 43.76

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

4.3 CONCLUSIONS

The measured values in Table 17 are one of the data sets

obtained for the sphere during the comparisons at SBRC.

The data in Table 17 shows agreement better than 2% at

all filter wavelengths except 400 nm and 450 nm. Further

evaluation of the measurements at these two wavelengths

showed that a rotation of the filters about an optical axis

normal to the geometric center produced detector readings

that varied as much as 6%. This effect, attributable to filter orientation, was discernible in all of the filters used

in this experiment. However, the effect was much less

than 1% in filters with wavelengths greater than 500 nm.

Polarization of the sphere source was ruled out by the

introduction of a polarizing sheet in the optical path. Rotation of the polarizer produced minimum effect on the

detector output. Non-uniformity of the filter is thought

to contribute to this effect. This experiment shows that

filtered absolute silicon photodetectors in a trap configuration can be used to verify the scale of spectral radiance

of an integration sphere source.

The filters used were measured in October 1992 on a

Perkin Elmer model Lambda 19 spectrophotometer.

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Chapter 5

Evaluation of the Standard Data Formats HDF, netCDF, and CDF

for SeaWiFS Operational Products

Michael Darzi

Frederick S. Patt

James K. Firestone

Brian D. Schieber

General Sciences Corporation, Laurel, Maryland

Charles R. McClain

NASA Goddard Space Flight Center, Greenbelt, Maryland

Abstract

Three standard data formats have been evaluated for possible use in archiving and distributing SeaWiFS operational products. The three formats are HDF, netCDF, and CDF. Major technical criteria that were considered

include machine independence, platforms supported, self description, subsampling, high-level specification language, and computer language interfaces. Practical considerations included acceptance by the scientific community, user support, cost, available tools, and availability of SeaWiFS related data and software. Although each

format has certain strengths and deficiencies, CDF was found to hold significant overall advantages over the

other two formats for SeaWiFS. Specific reasons and tradeoffs are discussed in this report which was submitted

to the SeaWiFS Project in September 1992.

5.1 INTRODUCTION

The SeaWiFS Project would like to define a standard

data format for its operational distribution products. The

following report, submitted to the SeaWiFS Project in

September 1992, describes the three formats that have

been evaluated. A standard format permits the applications programmers and the data users to use the data

without requiring knowledge of the actual physical storage

format—a set of software routines associated with the format used to read or write the data in the standard’s form.

The technical and practical considerations of such a format

include:

1. Technical considerations:

a) machine independence,

b) support of platform-native (i.e., a platform’s

internal data representation) formats,

c) translation of foreign formats on input and

output (I/O),

d) platforms supported,

e) self-describing,

f) subsampling on input,

g) support of multi-dimensional data,

h) high-level specification of data structure,

i) computer language interfaces,

j) storage requirements, and

k) availability of convenient library routines.

2. Practical considerations:

a) acceptance by the scientific community,

b) development and user support,

c) documentation,

d) cost,

e) accompanying tools, and

f) available data and software for SeaWiFS.

It should be noted that none of these items are absolutely

required to permit distribution of data, but they are important in ensuring a certain level of convenience for the

users of SeaWiFS products and in facilitating the Project’s

efforts in software development and data validation.

Three formats have been considered: HDF, developed

by the University of Illinois’s National Center for Supercomputer Applications (NCSA); netCDF, developed by

the University Consortium for Atmospheric Research

(UCAR) Unidata Project; and CDF, developed at GSFC.

NetCDF is a derivative of an earlier version of CDF and

therefore, these two formats possess many similarities.

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

5.2 RECOMMENDATIONS

Of the three formats evaluated, CDF offers the clearest

advantages and no technical disadvantages with respect to

its application to SeaWiFS products. Important advantages of CDF include:

1) translation of native formats across platforms on

I/O,

2) current or planned availability on all platforms

of interest to SeaWiFS,

3) capability to subsample on input,

4) data structure specification language,

5) good support and documentation, and

6) large amounts of data and software available for

use by the Project.

The only negative with respect to CDF is the fact that it is

not currently planned to be supported by the EOS Project

directly, as for HDF, or indirectly, as for netCDF (Section

5.4.1).

HDF, on the other hand, suffers from serious disadvantages including:

a) no translation of native formats across platforms

on I/O,

b) no port to NeXT computers,

c) awkward in terms of programming and suitability for SeaWiFS product contents,

d) no capability to subsample on input,

e) no data structure specification language, and

f) no integrated documentation manual for current

release,

Because of these disadvantages, its use would entail a significant additional burden to the Project in terms of development effort, its ability to coordinate non-GSFC HighResolution Picture Transmission (HRPT) stations’ data,

and, ultimately, in convenience to its end users. The only

advantage of HDF over CDF is its designation by the EOS

Project as the standard prototype format for DAAC products (Section 5.4.1).

NetCDF also suffers from serious disadvantages relative to CDF with respect to its applicability to SeaWiFS

products. These disadvantages include:

no support of native formats,

no recognition of native formats across platforms,

no port to NeXT computers.

no capability to subsample on input,

small development group, and

poor tools.

However, netCDF shares some advantages with CDF over

HDF, such as a greater suitability to SeaWiFS product

contents (Sections 5.3.5 and 5.3.10) and a similar data

structure specification language (5.3.8). Its only advantage over CDF is the fact that the EOS Project is funding

a netCDF and HDF merger (Section 5.4.1), currently in

progress. Such a merger, of course, does not eliminate the

shared disadvantages from either of the merged formats.

5.3 TECHNICAL CONSIDERATIONS

5.3.1 Machine Independence

Machine independence implies that a data file created

and used on one platform may be copied and used directly

on another platform whose binary storage format is different. HDF, netCDF, and CDF all provide this capability

by using a binary format based on External Data Representation (XDR). Some platforms, e.g., Sun, use XDR as

their native format. However, on platforms whose binary

format is not this machine independent form, the software

automatically converts the data into the native form during read operations for processing; and it converts the data

into the machine independent form during write operations

for storage. The applications programmer and the user

need not concern themselves with the binary format of the

data. The extent of independence is defined by the platforms for which the format software has been ported (see

Section 5.3.4).

The conversion to and from machine independent form

results in a significant decrease of I/O speed (Section 5.3.2),

but is extremely useful for data meant to be transferred,

often across differing platforms. An alternative to machine

independence is to provide utilities that convert data from

one binary form to another, for use when data files are

transferred to different platform types. This is not a convenient option when data are transferred often, since it is

a time consuming process and may result in serious errors

if the user neglects to do a conversion.

5.3.2 Support of Platform-Native Formats

As stated in the previous section, the conversion to and

from machine independent form results in a significant decrease of I/O speed. This overhead may be avoided if the

format software provides options for creating the data files

in native formats. If the data files are stored on the platform on which they will be primarily used, it would obviously be most efficient to create them in that platform’s

native format. When transferred to a new platform type,

they could then be converted to the machine independent

format or to the native binary format of the new platform.

Thus, this capability permits data to be distributed in machine independent form and be converted to native format

for storage and use on users’ individual machines.

This capability is especially important to the SeaWiFS

Project since it would permit HRPT receiving stations

around the world to rely on various platforms, including

less expensive, but slower, International Business Machines

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Table 18. Platforms and operating systems supported by each data format (as of this review).

Data Operating

Format System Computer Platform

HDF UNIX SGI‡, Sun, DECstation, Cray, Alliant

Other PC, Macintosh, VAX†

netCDF UNIX SGI, Sun, DECstation, IBM RS/6000, HP 9000, Cray

Other VAX, PC, and OS/2, IBM mainframes, Macintosh

CDF UNIX SGI, Sun, DECstation, IBM RS/6000, HP 9000, Cray†

Other VAX, PC, NeXT, Macintosh†

† In progress or planned for next release.

‡ Silicon Graphics, Inc.

Personal Computer (IBM-PC) compatible computers for

data capture and processing. Thus, it is important to

have a standard format that performs reasonably well on

such slower platforms when dealing with SeaWiFS’ very

large image files. The savings (20–50%, depending on data

types, structure, and access speed) of this overhead is significant and may be essential to permit the effective use of

slower platforms.

HDF and CDF support the use of native formats on all

their platforms. The new version of HDF allows native formats for its Science Data Sets (SDS) as well as its Vsets (V

is for vertices) construct. (Vsets allow relationships among

SDS and other data types to be specified.) NetCDF provides no native format capability. As stated above, certain

platforms use a native format based on XDR.

5.3.3 Translation of Foreign Formats

In addition to its native format capabilities, CDF software can also recognize the native formats of platforms

other than the host platform, i.e., foreign formats, and

convert them to that of the host for processing. This capability is again especially useful to the Project, since all

non-GSFC HRPT stations which obtain SeaWiFS research

licenses will be required to deliver data to the SeaWiFS

Project or to other authorized users in the specified machine independent format upon request. Only CDF would

allow them to store and efficiently use their data in native

formats and also deliver those data without having to convert them on those stations’ possibly slower machines. In

essence, this capability makes the native format the machine independent format.

Users receiving those data would also not have to convert them to their respective native formats for storage

unless choosing to do so for the sake of efficiency. The

conversion of large amounts of data can be very time consuming and would not be worthwhile for data that are only

to be used occasionally.

HDF software recognizes when data are stored in a different native format. The software issues an error in such

cases but does not allow translation of the data. Since netCDF uses only XDR, even detection of foreign formats is

not necessary.

5.3.4 Platforms Supported

The extent to which the software of each standard is

supported at any one time on different platforms is difficult

to ascertain. In theory, the software should be tested, and

possibly modified, for each new version of each operating

system. Moreover, different models from the same computer manufacturer may not be completely binary compatible, so that the inclusion of a manufacturer should not

imply that the software has been validated on all its platform models. Given these caveats, the platforms claimed

to be supported by the formats are listed in Table 18.

In Table 18, VAX for HDF, and Cray and Macintosh for

CDF, are in parentheses to indicate that those ports are

in progress or are planned for the next release.

Platforms that are of special interest to the SeaWiFS

project should be noted:

SGI is used by various elements of the Project and

for the operational generation of SeaWiFS products;

NeXT is used by the field data processing system

and by the CVT;

VAX is used by the field data processing system, the

University of Miami group, the GSFC Laboratory

for Hydrospheric Processes Ocean Color Group,

and, to a lesser extent, by the SDPS element;

PCs are used by the field data processing system

and by individual Project investigators, and are supported by SEAPAK, the oceanographic satellite and

environmental data analysis package developed by

the Ocean Color Group;

Macintosh is used by the field data processing system and by individual Project investigators; and

Cray is used by the Mission Operations and CVT

elements.

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In addition, personal computers, workstations, and minicomputers are all potential platforms for the HRPT receiving stations and therefore, are also of interest (see Sections

5.3.2 and 5.3.3).

Support for a variety of platforms, as is indicated for

platform-independent formats, requires almost continual

attention by the development group. Because of problems

often encountered when using software with new operating system versions, the availability of development support (see Section 5.4.2 below) will be very important for

SeaWiFS programmers over the life of the Project.

5.3.5 Self-Describing Data Formats

Data formats are considered self-describing when the

files contain explicit information which allows their contents to be interpreted. This information includes details,

such as the number of variables, the size of arrays, and

data types, which permit the software to correctly access

the data values. In addition, self-describing information

includes metadata or information about the data contents

that is more useful to the user. Metadata includes descriptive names, units, and comments.

Although all three candidate formats provide this selfdescription capability, the HDF provisions for such information are not nearly as convenient as those of netCDF or

CDF. SeaWiFS products will require metadata in the form

of characters, integers, real numbers, scalars, and arrays, as

well as on a per-record basis. HDF requires attributes to be

associated with their variables via pointers. Array metadata would have to be declared as separate SDS associated

implicitly with their corresponding variables in a SDS file,

or use Vsets to make this association explicit, since they

allow additional relational information to be defined. (See

also Sections 5.3.2 and 5.3.10). Of course, metadata can

always be written as American Standard Code for Information Interchange (ASCII) strings within HDF annotation

fields. However, this is not a satisfactory solution since

ingest software would have to parse the string in order to

find the labels and read in the values.

5.3.6 Subsampling on Input

CDF is the only format that allows a programmer to

specify a pixel subsampling rate upon input. This capability is particularly useful for SeaWiFS’ very large image files

(100 Mbytes or more) in that it allows, for example, the direct display of an entire image at lower resolution. The less

convenient, and also less efficient, alternative would be to

read the file in small sections in order to accommodate

a computer’s memory constraints while subsampling into

smaller arrays to effect the lower resolution.

5.3.7 Support of Multidimensional Data

All three formats allow variables of differing dimensions

to be represented in the same data set. That is, HDF,

netCDF, and CDF support this type of multi-dimensional

data representation.

5.3.8 High-Level Data Structure

CDF and netCDF support a high-level language that

can describe a data set’s structure and list its metadata.

CDF and netCDF provide utilities that can generate the

specifications for a given data set and can create a data set

skeleton from a given specification. This capability provides, for example, a convenient way for a user to create a

data set structure and input its metadata by writing the

specifications in the proper syntax, or by editing the specifications of an existing data set. Moreover, it allows the

specifications of a data set (its layout) to be communicated

among users in a concise and unambiguous manner.

HDF does not support this type of data set specification. For a large development effort such as the SDPS, the

lack of this capability is a serious handicap.

5.3.9 Computer Language Interfaces

All three formats support an interface for C to their

library routines. HDF, netCDF, and CDF also support a

FORTRAN interface to their library routines.

5.3.10 Convenient Library Routines

Based on prior programming experience with all three

formats, CDF appears to have the most convenient set of

library routines for programming, followed closely by netCDF. HDF tends to require, for example, many more calls

to various subroutines in order to perform a similar task

than CDF or netCDF. Moreover, HDF requires additional

HDF-specific tags for explicitly defining variable types.

The ease-of-use of HDF suffers from the fact that the

format was originally designed to represent only raster images in 8- and 24-bit pixels. The SDS and Vsets constructs

were added subsequently to support other data types, such

as real numbers, and other data structures. As examples,

the SeaWiFS requirements to store multiple data types in

level-3 products, and multiple bands or parameter fields

within the same data sets for all its products, can best

be handled in HDF via its Vsets construct. This representation is more awkward and tedious than for CDF and

netCDF, in that the user must explicitly specify the relationships of the bands to the underlying grid or the geocoordinate lattice.

The complexity of the various HDF data structures is

of additional concern to SeaWiFS because of the HRPT

stations. These stations may not have the resources, in

terms of expertise in data formats or personnel, for software development. As a result, there may be a reluctance

on their part to use a format that is less directly suited

to their data requirements. Because the SeaWiFS Project

is responsible for providing support to these stations, any

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

additional support required by these stations for development of software to input, output, or transfer data translates into greater demands on the resources available to

the Project.

5.3.11 Storage Requirements

The storage space overhead associated with each of the

formats is essentially the same and is usually trivial. The

amount of overhead depends on the amount of metadata

and the type and amount of actual data. For SeaWiFS

products, this overhead will be about two percent of the

total data storage requirements.

Although none of the formats provide a data compression capability, such a capability could be very useful for

large data set files, such as SeaWiFS products. Of interest,

therefore, are the plans of the HDF and CDF development

teams to implement this capability. Depending on the

exact methodology, data compression would be useful to

SeaWiFS for distribution purposes, and for the added convenience of storing occasionally used data in compressed

form.

5.4 PRACTICAL CONSIDERATIONS

5.4.1 Acceptance of a Standard

The acceptance of a standard data format by the scientific community is important for several reasons:

1. It helps ensure that political and financial support for the format and its development effort

will continue, evolving with new features and

supporting new platforms and operating systems.

2. It increases convenience for users who are familiar with the standard and have obtained or

developed software based on that standard.

3. It allows relevant data using the same format to

be analyzed using essentially the same software,

again increasing user convenience.

4. It results in support of that format by commercial companies for their analysis packages, thus

greatly increasing the availability of useful software to users of that format.

It should be noted that it is very difficult to gauge the

extent to which formats are actively being used by the

outside communities and to judge the level of satisfaction

of the users. Such a survey would obviously be very useful.

HDF has been selected as the standard format by the

EOSDIS for use by the DAACs. The format appears to

be widely used by researchers, especially for raster imagetype data for which it was originally designed. NetCDF

also appears to be widely used, especially by the meteorological community. In addition, NCSA has been funded by

the National Science Foundation (NSF) and EOS to incorporate the netCDF library into the HDF library, allowing

users who normally use HDF to input netCDF data sets

and, presumably, convert them into HDF if desired.

At this time, it is not clear how:

a) all the attributes and other metadata are handled and converted in the merged HDF-netCDF

format;

b) whether or not the netCDF data must follow a

standard structure or, conversely, how generic

the code must be; and

c) if there is any overhead associated with inputting

netCDF versus HDF.

If no overhead is involved, there would be no need to convert them, since there would be no disadvantage to remaining as netCDF data. NCSA is also planning to allow

netCDF software to input HDF data, a much more difficult

task, as the next part of this effort.

It should be noted here that merging different format

standards is not necessarily a good idea, since a desirable

attribute of any format is that it be easy to use. The forcing of formats that differ fundamentally in their structure

into one megaformat may result in a standard that is too

big and complicated to understand and use. Also, a merger

does not eliminate disadvantages shared by both formats

with respect to the SeaWiFS Project, as discussed in this

report. Finally, such a merger adds additional constraints

on each format development group, forcing them to march

in lockstep with respect to their support of new features

or operating systems. Thus, a merger may hinder a format’s ability to overcome its current disadvantages as well

as keep up with the ever changing computing environment.

In this sense, the benefits of merging netCDF with HDF

remain to be seen.

CDF is also widely used by the meteorological community. The CDF exposure to this group is primarily via the

NASA Climate Data System (NCDS), whose users number in the hundreds. Over two thousand compact disk-read

only memory (CD-ROM) diskettes containing meteorological and atmospheric constituent data in CDF form were

produced and distributed by the Goddard DAAC at the recent world conference on the global environment in Rio de

Janeiro, Brazil. CDF has been selected as the standard format for the prototype Tropical Rainfall Measuring Mission

(TRMM) data system and for the data generated from the

International Solar Terrestrial Program (ISTP) satellites.

Although the large ISTP planetary science community is

unlikely to be interested in any Earth science data, their

selection does help ensure that adequate funding to the

CDF group will continue for at least the next decade.

NCSA will also look into the possibility of a merger of

CDF and HDF similar to that of netCDF and HDF. The

CDF development group has expressed an interest in such

an effort although there are no definite plans or funding

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

for this at this time. Because of the design similarities

between CDF and netCDF, the effort required to merge

each with HDF would also be expected to be similar.

5.4.2 Development and User Support

Development support is support for the development

of the format software. This includes:

i) timely correction and notification of errors;

ii) continued testing for ports to new platforms or

new operating systems (see Section 5.3.4);

iii) the continued evolution of the software, especially to incorporate new features found to be

desirable or necessary; and

iv) the improvement and updating of documentation.

User support includes response to queries regarding

programming with the software and regarding the efficient

implementation of unusual data.

A primary indicator of both types of support is the

size of the development group for each format. HDF appears to have the largest group, with 4–8 people (including

student personnel) working on the project. A substantial

portion of this effort involves the development of display,

analysis, and data management tools associated with HDF

(Section 5.4.5), and another part of this effort involves the

merger of netCDF and HDF. The EOS use of the standard ensures that support for DAAC product developers

will be adequate. Furthermore, NCSA has expressed, via

the EOS Project, a desire to accommodate requirements

of such DAAC product developers.

The CDF team is comprised of three to four people.

Members of the SeaWiFS Calibration and Validation element and the Laboratory for Hydrospheric Processes’

Ocean Color Group have worked with the CDF team for

a number of years. The interaction has been the result of

an ongoing collaboration with the NCDS which has been

funded for the past five years. The Ocean Color Group

has found the response to be very fast and the quality of

support to be excellent. Moreover, they have already incorporated features suggested by the Ocean Color Group

in new versions of their software. A major advantage to

the Project of the CDF team in this regard is its physical

location at GSFC, permitting person-to-person discussions

of problems.

Finally, the netCDF group includes the equivalent of

less than one person on their development effort. Of the

three format groups, netCDF seems to rely the most on

outside users performing ports and providing public domain software. Such use of third-party software increases

the likelihood that both the quality of the products and

support for these products will not be uniform. NetCDF

does, however, have a very active electronic mail group

which provides a convenient forum for the exchange of useful information.

5.4.3 Documentation

A comparison of the documentation associated with

each format indicates that the current documentation for

CDF is the best, followed closely by that for netCDF. The

documentation for HDF is outdated, poorly organized, and

difficult to follow and understand. The new release of HDF

was not accompanied with integrated documentation; its

new features are described in a series of updates to the

documentation of the previous release (dated 1990).

5.4.4 Cost

All software packages and documentation for the three

formats are available at little or no cost. That is, there are

minimal charges involved in the acquisition of the HDF,

netCDF, and CDF software libraries and associated documents.

5.4.5 Accompanying Tools

HDF appears to have the most comprehensive set of

software tools for that format, including graphical display

of data, data analysis, and data management. However,

most of these do not support Vsets, which, as discussed

previously, would be useful to SeaWiFS products (see Sections 5.4.2 and 5.4.10). CDF has a beta version of a graphical display tool, as well as a well developed interactive tool

for ASCII examination of the data. The netCDF group has

not developed good comparable tools, but again relies on

outside users to make such tools available to the community.

It should be noted that, for the SeaWiFS Project, the

availability of accompanying tools is not an important consideration since more powerful, customized tools will be developed using the commercial data analysis package called

Interactive Data Language (IDL). The current beta version

of IDL supports netCDF and CDF as input data formats

and will have similar support for HDF within a month.

Therefore, the availability of accompanying tools serves

mainly as a convenience to SeaWiFS’ end users. However,

since IDL and a number of other commercial analysis packages support or plan to support these formats, and since

these packages are becoming quite popular, many end users

are also likely to prefer them for building customized tools.

5.4.6 Availability to the Project

As a result of a March 1992 decision to use netCDF,

some effort has been expended by the Project to implement

the netCDF software, create test data sets on the SGI and

PC platforms, and to develop a simple display program for

the SGI. The extent of the effort was on the order of a few

man-weeks. This earlier decision to use netCDF was based

primarily on the judgement that it was more suitable for

SeaWiFS products than either HDF or CDF. At the time,

for example, HDF did not support the storage of two-byte

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

integers, in addition to not having a data structure specification language (Section 5.3.8). For CDF, the available

version did not directly support variables of differing dimensionality (Section 5.3.9).

Previous efforts by the Ocean Color Group include

about five man-years developing software for using CDF

data and another five man-years for the creation of over

15 Gbytes of ocean related, CDF data sets (see Appendix).

Some of this software and data are directly applicable to

the Calibration and Validation effort. The use of the CDF

format by SeaWiFS would eliminate the very resource consuming task of converting these data into another format

and modifying the software accordingly for use by the

Project.

No effort in development of software or creation of data

sets has been expended by the Project for HDF. However,

some effort (several man-days) has been spent on evaluating HDF documentation, attending demonstrations, and

discussing its capabilities with others who have used the

format.

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Chapter 6

The Generation of CZCS Ancillary Data Sets for

Simulated SeaWiFS Processing

Brian D. Schieber

James K. Firestone

General Sciences Corporation, Laurel, Maryland

Abstract

The SeaWiFS development effort includes simulated data processing using CZCS data files for the three-year

time period of November 1978 through December 1981. Ancillary meteorological data products of total ozone,

and surface values of zonal and meridional wind speed, atmospheric pressure, and relative humidity will be

used for producing simulated level-2 CZCS (derived ocean color) products as a test of the SeaWiFS processing

system. These data files are provided from the Goddard DAAC and TOMS project archives and converted

to the SeaWiFS data format for subsequent processing. The calibration and validation element has developed

procedures and software to process the CZCS ancillary data in a manner that simulates the processing which will

occur once SeaWiFS is operational. The data are stored as HDF files for each of the ancillary parameters. This

chapter describes the methods used for the generation of ancillary files from the CZCS era for use in simulating

the SDPS.

6.1 INTRODUCTION

The processing of CZCS data from level-1 (calibrated

radiances) to level-2 (derived products) requires five ancillary fields:

a) total ozone;

b) surface values of zonal (east-west) wind speed;

c) surface values of meridional (north-south) wind

speed;

d) surface atmospheric pressure; and

e) relative humidity.

All of these fields are incorporated into the atmospheric

correction algorithm. This processing will be used to simulate the creation of SeaWiFS data, and will be run for

the period 1978–81. The ancillary data was compiled from

several sources, all distributed by the Goddard DAAC.

6.2 METEOROLOGICAL DATA

The ancillary meteorological data are currently provided by the DAAC for zonal and meridional wind speed,

surface pressure, and relative humidity data sets. The data

files are products from several different research projects

for the three year time period. The US Navy FNOC provided wind components and surface pressure data. Three

data files were used to generate the relative humidity HDF

data (University of Illinois 1989 and 1993). These files,

obtained from the ECMWF, are described below. For

the period 1978–79, a data set from the First Global Atmospheric Research Program (GARP) Global Experiment

(FGGE) was used. Years 1980–81 used ECMWF 00 GMT

and 12 GMT data files. A description of the data parameters, their ranges, and processing programs is shown in

Fig. 19.

6.3 OZONE DATA SET

Total ozone data were provided by the Ozone Processing Team (OPT) at GSFC available on CD-ROM. The

data consisted of daily ASCII files representing averaged

gridded arrays. The data were derived from the gridded

TOMS (GRIDTOMS) orbital data set, and were global,

with a resolution of 1.0◦ in latitude by 1.25◦ in longitude

(i.e., 180 latitude points by 288 longitude points). Since

the original grid cells were equal in area, the OPT used an

interpolation scheme for latitudes higher than 50◦ latitude,

i.e., poleward, to create an equal-angle grid of constant

1.25◦ longitude resolution.

6.4 DATA FILE GENERATION

All data processing of meteorological and ozone data

were performed on SGI UNIX workstations. NRT meteorological data were downloaded from the DAAC using

the electronic File Transfer Protocol (FTP). TOMS ozone

data, on CD-ROMs, were requested and received from the

TOMS OPT.

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Table 19. Software programs and languages written for storing and displaying ancillary data files.

Name Language Input Output

CDFCVT C Platform specific CDF Network compatible CDF§

WPHCZCS C Network compatible CDF†§ HDF†

RDGRID FORTRAN ASCII TOMS Ozone array for O3NRT

O3NRT C Ozone array from RDGRID Ozone HDF

† Contents are comprised of zonal and meridional wind speed, pressure, and relative humidity.

§ Note that Network compatible CDF is different from netCDF.

Fig. 19. Specifications developed for storage of ancillary data in SeaWiFS.

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Fig. 20. Specifications for the storage of CZCS era and SeaWiFS near-real time ancillary data in HDF.

The top panel displays meteorological data and the bottom panel shows ozone data.

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

DAAC archive data sets were provided in NASA CDF,

and were VAX encoded files (National Space Science Data

Center 1991). The UNIX CDF program, CDFCVT, converted the data to a network UNIX compatible format,

and translated the data from FORTRAN column major

order to C row major order. Relative humidity data from

the FGGE research project required regridding, using IDL,

to match the 2.5◦× 2.5◦ (latitude by longitude) gridding in

the rest of the meteorological files.

The C program, WPHCZCS, processed the converted CDF

files, which contained wind, pressure, and humidity data

from 1978–81, to HDF files with the SeaWiFS specifications (Fig. 20 [top]). Metadata to describe the data sets

are also included HDF output products. Similarly, the C

program, O3NRT, produced SeaWiFS HDF files containing

ASCII TOMS ozone data, also from 1978–81 (Fig. 20 [bottom]) Note that the Vdata for humidity differs from the

others when FGGE data is the source (used for 1978–79),

since the grid differs.

The program, O3NRT, will be used for NRT production of ozone HDF files by the SeaWiFS Project, since the

input format is the same as for the 1978–81 period. A

FORTRAN routine, RDGRID, was provided by the TOMS

Project to produce data arrays used within O3NRT. The

software processing routines are summarized in Table 19.

Note that the HDF specification is consistent with the

recommendations of the GSFC EOSDIS Project, while the

HDF file-level annotation conforms to the metadata submission guidelines of the Goddard DAAC (Lynnes et al.

1992).

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Chapter 7

The Generation of SeaWiFS Near-Real Time

Ancillary Data Files

Brian D. Schieber

James K. Firestone

General Sciences Corporation, Laurel, MD

Abstract

In the SDPS, NRT ancillary data files will be used for producing level-2, i.e., derived ocean color, products.

In addition to a data file for total column ozone, separate files will contain surface value data for: zonal and

meridional wind speeds, atmospheric pressure, and relative humidity. These ancillary data files are obtained

from several sources, with additional backup sources identified for each data type should the primary data

source become unavailable. The ancillary data files will be an integral part of the SDPS. The Calibration and

Validation element for SeaWiFS has developed procedures and software to acquire and format the NRT data.

The ancillary parameters will be stored as data in HDF files. This chapter describes the methods used for the

generation of NRT files.

7.1 INTRODUCTION

The processing of SeaWiFS data from level-1 to level-2

requires five ancillary data fields:

a) total column ozone,

b) surface values of zonal (east-west) wind speed,

c) surface values of meridional (north-south) wind

speed,

d) surface atmospheric pressure, and

e) relative humidity.

All five atmospheric variables are incorporated into the atmospheric correction algorithm. Generation of SeaWiFS

refined products involves a process of data acquisition,

storage, and quality control (QC) of NRT data. For generation of the Performance Assessment (quick look) product,

climatological data will be used (Firestone and Schieber

1994).

7.2 METEOROLOGICAL DATA

NRT meteorological data is currently provided by the

NOAA NMC. A secondary source for meteorological data

is FNOC, should the primary NOAA source be unavailable.

The NMC data are provided in 6-hour increments in the

gridded binary (GRIB) format (Stackpole 1990). GRIB

files are unpacked to simple binary arrays for each of the

desired meteorological parameters (zonal and meridional

wind speed, surface pressure, and relative humidity). The

binary arrays are then processed to produce HDF data

products (Fig. 20).

7.3 OZONE DATA SET

Total column ozone is provided by the OPT at GSFC.

The data consists of daily ASCII files representing averaged gridded arrays. The data are derived from the GRIDTOMS orbital data set, and are global with a resolution of

1.0◦× 1.25◦ (latitude by longitude). Since the original grid

cells were equal in area, the OPT used an interpolation

scheme poleward of 50◦ latitude to create an equal-angle

grid of constant 1.25◦ longitude resolution (National Space

Science Data Center 1993). A secondary source for ozone

data is the twice-daily set of soundings from the NOAA

Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) instrument.

7.4 DATA FILE GENERATION

All data processing of meteorological and ozone data

are performed on SGI UNIX workstations. The data are

kept in a central repository in the Data Assimilation Office

(DAO) of the GSFC Laboratory for Atmospheres, as part

of an agreement between NMC and the GSFC Earth Sciences Directorate. NRT meteorological and TOVS ozone

data are downloaded from NOAA into the GSFC repository using the electronic FTP in an automated procedure.

The meteorological data are stored in GRIB format, and

the TOVS ozone data in the National Environmental Satellite Data Information Service (NESDIS) sounding products

format (Kidwell 1991). The DAO performs quality control

on the TOVS data to remove duplicate soundings, then

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Table 20. Software programs and languages written for storing and displaying ancillary data files.

Name Language Input Output

WPHNRT C Unpacked GRIB† HDF†

GRIDTOVS FORTRAN NESDIS TOVS profiles ASCII TOVS grid

O3NRT C ASCII TOMS or TOVS Ozone HDF

† Contents are comprised of zonal and meridional wind speed, pressure, and relative humidity.

Fig. 21. Schematic for the generation of ancillary data used in the SDPS.

rewrites the files twice daily in the NESDIS format. The

repository’s disk is made accessible to the SDPS through

a remote mount, using the Network File System (NFS).

TOMS ozone data are directly downloaded in ASCII format, using FTP, from the OPT.

The FORTRAN program, UNPKAWIPS.X, is provided by

NMC to unpack the GRIB meteorological data files to simple gridded binary arrays. The C program, WPHNRT, processed the unpacked GRIB wind, pressure, and humidity

data files to generate the SeaWiFS HDF file (Table 20).

WPHNRT employed the HDF libraries (University of Illinois 1993) and general specifications (University of Illinois

1989) developed by the NCSA.

The process of unpacking GRIB files, generating HDF

files, and cleaning up temporary files is accomplished using

a simple UNIX C-Shell command file (Table 20). Within

WPHNRT, surface pressure is converted from Pascals to millibars. Metadata describing the data sets are also included

in the HDF output products. Similarly, the program O3NRT

is used to convert TOMS ozone values or TOVS NRT ozone

values from ASCII format to the SeaWiFS HDF format.

Note that for the TOVS data, the ASCII ozone values are

generated by an additional program, GRIDTOVS, which objectively analyzes two consecutive 12-hour TOVS sounding

files to form a representative 24-hour grid every 12 hours.

Figure 21 represents the data types and the processing

steps involved in the production of the final HDF data

products. Table 20 summarizes the inputs and outputs for

the programs used in the processing stream.

All generated HDF files conform to the specification de48
Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

veloped by the Project for storage of these ancillary data

(Fig. 20). Note that these specifications are consistent with

the recommendations of the EOSDIS project, while the

HDF global and SDS-level attributes conform to the metadata submission guidelines of the Goddard DAAC (Lynnes

et al. 1992). These considerations will allow the quality

controlled versions of the NRT data to be made publically available. The HDF files consist of separate Vgroups

for each data parameter. Each Vgroup contains a global

latitude-longitude grid stored in an SDS with its associated descriptive attributes. In addition, there are global

attributes that describe metadata at the file level, and a

geometry Vdata which defines the physical characteristics

of the data grid. Note that Vgroup and Vdata are HDF

terms for data organization in Vsets (University of Illinois

1990).

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Chapter 8

An Evaluation of Surface Wind Products

for Use in SeaWiFS

James K. Firestone

Robert H. Woodward

General Sciences Corporation, Laurel, Maryland

Charles R. McClain

Goddard Space Flight Center

Greenbelt, Maryland

Abstract

This study statistically compared remotely sensed and modelled surface wind speeds from several sources with

available ground truth, in order to provide a recommendation for which source to use in the SDPS. Model wind

field sources included the FNOC, ECMWF, and NMC. Remotely sensed wind speed fields came from the SSM/I,

and ground truth sources included seven fixed moorings provided by NDBC, and the TOGA-TAO Project. A

temporal and spatial match between the large scale and in situ winds was completed, using input data for

the period 1982–90. An additional matchup was run to compare mooring winds with 1,000 mb and boundary

layer winds from NMC, for 1990. A suite of software applications for reading and merging the various data

sets used in the analysis was developed as part of the Laboratory for Hydrospheric Processes’ VAX SEAPAK

package. Scatterplots comparing each of the large scale wind sources to the seven moorings were generated,

using commercial PC software, and square of the correlation coefficient (R2) values, slopes and number of

points for each plot were noted. This analysis indicated that SSM/I had the highest R2 values of any of the

large scale wind fields. Since they are gridded products, NMC and FNOC are the suggested wind field sources,

and mechanisms are in place to automatically transfer them to GSFC automatically. The new versions of these

models incorporate SSM/I data in their production.

8.1 INTRODUCTION

The SDPS requires surface wind speed as one of its

ancillary parameters, to be used by the atmospheric correction algorithms, in both sun glint flag (McClain and

Yeh 1994) and correction schemes, and for surface foam

reflectance corrections. In order to select the most accurate wind product possible, a study comparing the statistical correlations of data from several global wind data sets

with wind speed and direction data from fixed ocean moorings (as ground truth) was made. The study facilitated the

production of software routines for ingesting various types

of ocean mooring data, as well as matching the mooring

and global wind data temporally and spatially (Woodward

et al. 1992). These software routines were included in the

VAX version of SEAPAK, a software package consisting

of over 200 programs for processing ocean color and related ancillary data, developed for the GSFC Laboratory

for Hydrospheric Processes (Darzi et al. 1989, McClain et

al. 1991a and 1991b). The end result of the data analysis portion of this study allows for a recommendation to

the SeaWiFS Project on which wind product would be the

most accurate for incorporation into level-2 algorithms.

8.2 DATA SETS

A combination of four model-generated gridded wind

data sets, one remotely sensed wind data set, and data

from seven fixed ocean moorings were used in this study.

The ocean moorings were located in the northern and equatorial Pacific Ocean, the Gulf of Mexico, and the North

Atlantic Ocean (Fig. 22). The sampling period for the

global wind data sets commenced in 1982, and was tied to

the availability of the NDBC moorings from the National

Oceanographic Data Center (NODC) (National Oceanic

and Atmospheric Administration 1990, Meindl and Hamilton 1992). All of the gridded global wind data sets were

stored in NASA CDF and were originally generated by the

staff of NCDS, which is now the Goddard DAAC (Olsen

and McClain 1992). The CDF data sets are part of a large

archive of ancillary environmental data accessible for analysis by VAX SEAPAK (Firestone et al. 1990).

50
Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Table 21. Characteristics of wind data sets used in analysis.

Spatial Resolution

Name Level Coverage Spatial (Temporal)

FNOC Marine layer Global 2.5◦×2.5◦(12 hr)

ECMWF 1,000 mb Global 2.5◦×2.5◦(12 hr)

TOGA-ECMWF Surface Global 2.5◦×2.5◦(12 hr)

NMC 1,000 mb Global 2.5◦×2.5◦(12 hr)

SSM/I Surface Global Non-gridded†

NDBC Ocean moorings 23◦–57◦ N 5 buoys‡

69◦–180◦ W

TOGA-TAO Ocean moorings 2◦ N, 165◦ E 2 buoys§

2◦ N, 110◦ W

† Satellite swath data

‡ Atlantic Ocean, Gulf of Mexico, Pacific Ocean

§ Equatorial Pacific Ocean

Fig. 22. Temporal coverage of data sets used in the study.

The modeling centers providing the gridded data to

NASA were: FNOC, ECMWF, and NMC. The TOGAECMWF data set represented special runs of the ECMWF

model specifically for the TOGA Experiment (National

Research Council 1990). Data from the SSM/I instrument

was produced by F. Wentz and the group at Remote Sensing Systems, Inc. (RSS), and provided by the NASA Jet

Propulsion Laboratory (JPL) DAAC on 9-track magnetic

tapes. Table 21 summarizes the temporal and spatial characteristics of the various global wind data sets.

The mooring data was provided from two sources—

NODC and the TOGA-TAO Project (Hayes et al. 1991)

at the NOAA Pacific Marine Environmental Laboratory

(PMEL). The first group, labeled “Pacific Moorings” in

Fig. 22, along with those labeled “Atlantic Moorings,”

and “Gulf of Mexico,” were from NDBC, while the second group of “Pacific Moorings” were from TOGA-TAO.

The seven moorings used in the analysis were chosen from

an original group of 72 NDBC and 20 TOGA-TAO moorings in this general region (Fig. 23). The seven moorings

chosen possessed the longest available time series of data

from the clusters of moorings considered. Taken together,

the seven moorings encompass a diverse set of climatological wind regimes including tropical, mid-latitude, high

latitude, coastal, and mid-ocean regimes.

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Fig. 23. Geographical location of oceanic moorings which provided data used in the study (Woodward

et al. 1992).

8.3 MATCHUP SOFTWARE

Figures 24 and 25 illustrate, respectively, the program

logic used to generate wind matchup statistics and the

names of the associated SEAPAK software programs. All

input data sets were archived in the GSFC Laboratory for

Hydrospheric Processes on either 9-track tape (in the case

of SSM/I) or write-once read-many (WORM) optical disk

platters (for gridded model winds, and NDBC or TOGATAO moorings). The data were archived in various binary

forms, including NASA CDF.

All applications software were written as part of the ancillary environmental data module in SEAPAK, using the

VAX FORTRAN language and the NASA Transportable

Applications Executive (TAE) as the user interface. To the

best extent possible, batch processing was used to run the

VAX software on the Ocean Color Group’s two MicroVAX

II systems and the Laboratory’s VAX 11/750 system with

a minimum of user intervention.

In order to generate plots and statistics, separate ASCII

formatted files were generated on the VAX. Each ASCII

file contained the following:

a) wind data at each of the seven moorings (the

program BUOYREAD was used to ingest data for

the NDBC moorings, and the program CDF1D2GM

was used for the TOGA-TAO moorings);

b) the grid point closest to each mooring for each

global model (TIMENV was used); and

c) SSM/I data ranging within 35 km of each mooring (SSMIMTCH was used).

Note that for the portion of the study comparing 1990

NMC 1,000 mb and boundary layer winds to mooring winds,

the bilinear interpolation option in TIMENV was used to create a group of NMC time series interpolated to the exact

mooring locations.

The temporal window used for matchup was ±30 minutes for global wind matches to the NDBC moorings, and

one day for global wind matches to the TOGA-TAO moorings (Table 22). Therefore, the NDBC matchups tended to

be clustered around 0000 Greenwich Mean Time (GMT)

and 1200 GMT when comparisons were done with the gridded models. Likewise, the daily mean TOGA-TAO moorings were arbitrarily assigned a time of 0000 GMT, so that

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Fig. 24. Schematic diagram of the data path used to generate wind statistics and scatterplots.

Fig. 25. Software programs for generating wind statistics and scatterplots. (All VAX programs, other

than those from NCDS DAAC, are included in SEAPAK from the GSFC Laboratory for Hydrospheric

Processes.)

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Table 22. Summary of matchup criteria and ingest software used in the study. Each group of descriptors,

which are delimited by colons (:), indicates the following (reading left to right): the matchup temporal window,

the matchup spatial window, the SEAPAK program used for ingesting the global data, and finally the SEAPAK

program used for ingesting the mooring data (np means nearest grid point). Note that for the portion of the

study which compared NMC and mooring winds for only the year 1990, a model value interpolated to each

mooring location was used rather than the nearest model grid point.

Wind Product Model NDBC Moorings TOGA-TAO Moorings

FNOC, NMC, ECMWF 30min:np:TIMENV:BUOYREAD daily:np:TIMENV:CDF1D2GM

SSM/I 30min:35km:SSMIMTCH:BUOYREAD daily:35km:SSMIMTCH/CDF1D2GM

global gridded winds and SSM/I winds collected from 0000

GMT of that day to 0000 GMT of the next day were included in the matchups. Each data ingest run of BUOYREAD,

CDF1D2GM, TIMENV, or SSMIMTCH was executed over the

time ranges available for the individual data sets, as depicted in Fig. 22.

The following commercial PC software was used to interface with the output of the VAX software: Lotus 1-2-3

for Windows, Lotus Freelance for Windows, Norton editor

(Symantec, Inc.), Grapher (Golden Software, Inc.), and

WordPerfect (WordPerfect Corp.). This approach utilizes

the particular strengths of each software package—namely

their ability to restructure, plot, and generate basic statistics relating to the matchups. Lotus 1-2-3 for Windows

was used to eliminate unnecessary, e.g., non-wind, data

columns in the merged ASCII file so the merged ASCII file

could be easily imported into Freelance for Windows and

Grapher.

After processing by Lotus 1-2-3, the two columns of

data (global and mooring wind) were saved again as an

ASCII file. The file was then edited, using the Norton editor to globally change a numerical code used for denoting

missing wind data, -99, to an entry of M (for missing), so

plotting would proceed correctly. Freelance for Windows

was used to generate statistics, including the square of the

correlation coefficient (R2), slope and intercept, and the

number of observations for the matchups stored in each

edited ASCII file. Grapher was used to generate x-y scatterplots for the matchups in each edited ASCII file, and

to save each plot in Hewlett Packard Graphics Language

(HPGL) format. WordPerfect was used to import the

HPGL files representing the scatterplots for a particular

buoy (five in total) into a document in which the statistics were also written. The documents, with scatterplots

included, were printed on an Apple Laserwriter printer.

8.4 RESULTS

Figures 26–32 show the scatterplots generated for each

of the seven moorings, and the associated R2, slope, and

intercept statistics, based on data for the entire period

1982–90. The NMC and ECMWF models generated winds

at the 1,000 mb pressure level, the TOGA-ECMWF model

generated winds at the surface, while FNOC winds were in

the near surface marine layer (approximately 10 m above

the ocean surface). Figures 33 and 34 represent matchups

using 1990 data only, at the NDBC Gulf of Mexico (No.

42001) and mid-Atlantic US coast (No. 44008) moorings.

The NMC and FNOC models were used for the 1990-only

study, since they were the likeliest candidates for real time

reception in SeaWiFS. To facilitate comparisons with the

FNOC winds, an additional set of NMC winds were used

in the 1990-only study to supplement the 1,000 mb values.

This second set contained NMC boundary layer winds obtained from the National Center for Atmospheric Research

(NCAR), put into hemispheric CDF files by the Goddard

DAAC.

On all of the scatterplots, the solid line represents a linear regression computed for the points plotted, while the

dashed line represents the ideal slope of 1 and intercept

of 0 describing the relationship between the mooring and

global data sets. The date range indicated at the top of

each scatterplot indicates the actual starting and ending

dates for the matches found between a particular global

data set and a mooring. Therefore, the range varied from

mooring to mooring for comparisons with a given global

data set, and across different global data sets for a particular mooring. The number of observations included in

each scatterplot varied widely, from minimally in the hundreds of observations, to a maximum of nearly 4,000 over

the period 1982–90. Note also that the scale for SSM/I

wind speed begins at −10 rather than zero. The reason

for this convention is that the SSM/I winds are retrievals

based on passive microwave data, and the algorithm allows

values slightly less than zero. In addition, removing these

negative values would bias the statistics (F. Wentz pers.

comm.).

The results for the 1982–90 analyses show, in general,

that R2 values ranged from 0.5–0.7 for the five NDBC

moorings (Figs. 28–32) and 0.2–0.3 for the two TOGATAO moorings (Figs. 26 and 27). In addition, of the global

data sets, the SSM/I exhibited the highest R2 values at

four out of five NDBC moorings, with values in the 0.6–

0.7 range. One reason for this difference is the relatively

close proximity between SSM/I passes and moorings when

matched (35 km was used as the upper limit), as compared

54
Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Fig. 26. Scatterplots for ocean mooring 00003, in the west central Pacific Ocean.

55
Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Fig. 27. Scatterplots for ocean mooring 00009, in the east central Pacific Ocean.

56
Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Fig. 28. Scatterplots for ocean mooring 41006, located near the southeastern (Atlantic Ocean) coast of

the United States.

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Fig. 29. Scatterplots for ocean mooring 42001, located in the Gulf of Mexico.

58
Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Fig. 30. Scatterplots for ocean mooring 44008, located off of the mid-Atlantic coast of the United States.

59
Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Fig. 31. Scatterplots for ocean mooring 46035, in the Bering Sea.

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Fig. 32. Scatterplots for ocean mooring 51001, near the Hawaiian islands.

61
Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

Fig. 33. Scatterplots for data collected in the year 1990 from ocean mooring 42001, located in the Gulf

of Mexico.

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Fig. 34. Scatterplots for data collected in the year 1990 from ocean mooring 44008, located off of the

mid-Atlantic coast of the United States.

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

with the distance between gridded model points and moorings when matched (as large as half the distance between

grid points, or 1.25◦, which is approximately 140 km).

The slopes of the regression lines generally varied from

0.7–1.2. Most of the slope values were less than 1.0, indicating that in these cases, the global wind speed was an

underestimate of the wind speed measured at the mooring. This is to be expected, since the models tend to be

smooth fields, while the ground truth often represents extremes in the data. The tendency for the models to underestimate wind speeds was especially true at the midAtlantic coast mooring (No. 44008 in Fig. 30). This observation may be due to the inability of weather models

to capture the strength of storms which intensify as they

move off shore. The slopes were the smallest of all at the

TOGA-TAO moorings, perhaps indicative of the influence

of tropical convective elements in strengthening the local

wind field, and the shortcomings in convective parameterizations in the global models. The intercept values for both

the NDBC and TOGA-TAO moorings were nearly always

positive. This shows that the global fields rarely, if ever,

include totally calm winds at a grid point, even though an

individual mooring might measure calm winds.

In looking at the results for just 1990 (Figs. 33 and 34),

it is clear they do not differ significantly from the 1982–

90 results (Figs. 29 and 30) for the two moorings studied.

The only difference is both the R2 and slope values are

slightly higher for 1990. The improvement in R2 may be

attributable to the interpolation of NMC model winds to

the mooring locations, thus reducing scatter about the regression line. However, the models still underestimated

the in situ winds by 15–20%. Differences between the

NMC 1,000 mb wind product and the derived boundary

layer winds are also slight. This result is consistent with

the finding of Trenberth et al. (1990), which is in most

cases the 1,000 mb wind closely approximates the boundary layer wind.

Based on these results, all of the global model products

exhibited very similar levels of skill. Since mechanisms

are already available to the SeaWiFS Project for receiving

both FNOC and NMC data in near-real time, they are the

suggested sources for ancillary wind data.

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Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Glossary

ASCII American Standard Code for Information Interchange

AU Astronomical Unit

BRDF Bidirectional Reflectance Distribution Function

CCD Charge Coupled Device

CD-ROM Compact Disk-Read Only Memory

CDF (NASA) Common Data Format

CVT Calibration and Validation Team

CZCS Coastal Zone Color Scanner

DAAC Distributed Active Archive Center

DAO Data Assimilation Office

DC Digital Counts

DEC Digital Equipment Corporation

DoD Department of Defense

DOS Disk Operating System

DU Dobson Units

ECMWF European Centre for Medium-Range Weather Forecasts

EOS Earth Observing System

EOSDIS EOS Data Information System

FGGE First GARP Global Experiment

FORTRAN Formula Translation (computer language)

FNOC Fleet Numerical Oceanography Center

FTP File Transfer Protocol

GARP Global Atmospheric Research Program

GMT Greenwich Mean Time

GRIB Gridded Binary

GRIDTOMS Gridded TOMS (data)

GSFC Goddard Space Flight Center

HDF Hierarchical Data Format

HHCRM Hand-Held Contrast Reduction Meter

HPGL Hewlett Packard Graphics Language

HRPT High Resolution Picture Transmission

HST Hawaii Standard Time

IBM International Business Machines

IDL Interface Design Language

IFOV Instantaneous Field-of-View

I/O Input/Output

ISTP International Solar Terrestrial Program

JPL Jet Propulsion Laboratory

Level-0 Raw data

Level-1 Calibrated radiances

Level-2 Derived products

Level-3 Gridded and averaged derived products

NASA National Aeronautics and Space Administration

NCAR National Center for Atmospheric Research

NCDS NASA Climate Data System

NCSA National Center for Supercomputing Applications

NDBC National Data Buoy Center

NESDIS National Environmental Satellite Data Information

Service

netCDF (NASA) Network Common Data Format

NFS Network File System

NMC National Meteorological Center

NOAA National Oceanic and Atmospheric Administration

NODC National Oceanographic Data Center

NRT Near-Real Time

NSF National Science Foundation

NSSDC National Space Science Data Center

OPT Ozone Processing Team

PC (IBM) Personal Computer

PMEL Pacific Marine Environmental Laboratory

PST Pacific Standard Time

QC Quality Control

QED Quantum Efficient Device

RSS Remote Sensing Systems (Inc.)

SBRC (Hughes) Santa Barbara Research Center

SBUV Solar Backscatter Ultraviolet Radiometer

SDPS SeaWiFS Data Processing System

SDS Scientific Data Set

SEAPAK Not an acronym, an image display and analysis

package developed at GSFC.

SeaWiFS Sea-viewing Wide Field-of-view Sensor

SGI Silicon Graphics, Inc.

SSM/I Special Sensor for Microwave/Imaging

TAE Transportable Applications Executive

TAO Thermal Array for the Ocean or more recently, Tropical Atmosphere-Ocean

TIROS Television Infrared Observation Satellite

TOA Top of the Atmosphere

TOGA Tropical Ocean Global Atmosphere program

TOMS Total Ozone Mapping Spectrometer

TOVS TIROS Operational Vertical Sounder

TRMM Tropical Rainfall Measuring Mission

UCAR University Consortium for Atmospheric Research

VAX Virtual Address Extension

WORM Write-Once Read-Many (times)

XDR External Data Representation

Symbols

Ad The detector aperture.

Af The foam reflectance.

a Exponential value in the expression relating the radiance of scattered light to wavelength.

C Chlorophyll concentration.

Cext Average total extinction cross-section of a particle.

CF The calibration factor.

d The distance between the sphere aperture and the

detector aperture.

d(I(λ)) An increment in detector current.

dλ An increment in wavelength.

DC Digital counts.

DCmeas The digital counts measured unshadowed.

DCscat The digital counts due to scattered sunlight.

DCTOA The digital counts measured at the top of the atmosphere.

E(λ) Irradiance.

Eeff Effective center wavelength.

ES The irradiance of the sun.

f The fraction of the surface covered by foam.

F0 The solar spectral irradiance at the top of the atmosphere.

F0 The scalar value of the solar spectral irradiance at

the top of the atmosphere, multiplied by a columnar

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Case Studies for SeaWiFS Calibration and Validation, Part 2: McClain, Fraser, McLean, Darzi,

matrix of the four Stokes parameters (1/2, 1/2, 0,

0).

Fd The total flux incident on the surface if it did not

reflect light.

F

d The total flux incident on the surface, corrected for

surface reflection.

F

d The scalar value of the total flux incident on the

surface, corrected for surface reflection, multiplied

by a columnar matrix of the four Stokes parameters.

HM The measured moon irradiance.

I(λ) Detector current.

L(λ) Radiance.

L0 The radiance of the atmosphere.

La The radiance of scattered light.

Ld A matrix of the four Stokes parameters for radiance

incident on the surface.

Li(λ) Radiance, where i may represent any of the following: m for measured; LU for look-up table;0 for

light scattered by the atmosphere;sfc for reflection

from the sea surface;and w for water-leaving radiance.

LLU The radiance calculated for the look-up tables.

Lm The radiance of the ocean-atmosphere system measured at a satellite.

LM The radiance of the moon.

Lsa L0 + Lsfc.

Lsfc The radiance of the light reflected from the sea surface.

Lup The columnar matrix of light leaving the surface

containing the values Lup,1, Lup,2, Lup,3, and Lup,4

[(20), (21), (22), and (23), respectively].

Lup,i The RADTRAN radiance parameters (for i = 1, 4).

Lsfc The columnar matrix of the four Stokes parameters

(Lu,1, Lu,2, Lu,3, Lu,4).

LW The water-leaving radiance of light scattered from

beneath the surface and penetrating it.

Lw The scalar value of the water-leaving radiance multiplied by a columnar matrix of the four Stokes parameters.

LWN () Normalized water-leaving radiance.

L(λm) The radiance of a calibration sphere at the nominal

peak wavelength of a filter.

n The index of refraction.

ND The compensation factor for a 4 log neutral density

filter.

N Total number density.

Ni Total number density of either the first or second

aerosol model when i = 1 or 2, respectively.

pa/(4π) Aerosol albedo of the scattering phase function.

r1 Radius of the sphere aperture.

r2 Radius of the receiving aperture.

R The reflection matrix.

R(λ) Reflectance.

R2 The square of the linear correlation coefficient.

ri The geometric mean radii of either the first or second aerosol model when i = 1 or 2, respectively.

s The reflectance of the atmosphere for isotropic radiance incident at its base.

t The transmission of Lsfc through the atmosphere.

t

 The transmission of LW through the atmosphere.

t0 The sum of the direct and diffuse transmission of

sunlight through the atmosphere.

T(λ) The transmittance along the slant path to the sun.

VM The radiance detector voltage while viewing the moon.

VS The irradiance detector voltage while viewing the

sun.

W Surface wind speed.

α Exponential value in the expression relating the extinction coefficient to wavelength.

βi The extinction coefficient of either the first or second aerosol model when i = 1 or 2, respectively.

δ The absorption coefficient.

∆LW (670) The error in the water-leaving radiance for the red

channel.

∆T(λ) The error in transmittance.

∆θs The error (in radians) in the knowledge of θs.

∆λ A wavelength increment.

∆ρw(λ) The error in the water-leaving reflectance for the

red channel.

∆ρLU (670) The error in the look-up radiance.

∆σ(λ) The absolute error in spectral optical depth.

∆τa The error in the aerosol optical thickness.

γ(λ) The ratio of the aerosol optical thickness at wavelength, λ, to the aerosol optical thickness at 670 nm.

κ An integration constant: κ = Adπr2

1(r21+r22+d2)

−1.

θ Polar angle of the line-of-sight at a spacecraft.

θ0 Polar angle of the direct sunlight.

θs The solar zenith angle.

λ Wavelength of light.

λ1 Starting wavelength.

λ2 Ending wavelength.

λm Nominal center wavelength.

μs The reciprocal of the effective optical length to the

top of the atmosphere, along the line of sight to the

sun.

ρ The average reflectance of the sea.

ρi The reflectance of the sea of either the first or second

aerosol model when i = 1 or 2, respectively.

ρi(λ) The reflectance where i may represent any of the

following: m for measured; LU for look-up table; o

for light scattered by the atmosphere;sfc for reflection from the sea surface;and w for water-leaving

radiance.

σ(λ) The spectral optical depth.

σ2

i σ2i = (log r − log ri)

2.

τa The aerosol optical thickness.

τoz The optical thickness of ozone.

τr The optical thickness due to scattering by the standard molecular atmosphere.

τwv The absorption optical thickness of water vapor.

φ Azimuth angle of the line-of-sight at a spacecraft.

φ0 Azimuth angle of the direct sunlight.

ΦD The detector solid angle.

ΦM The solid angle subtended by the moon at the measuring instrument.

ωa Aerosol albedo of single scattering.

Ω The amount of ozone (in Dobson units).

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data. Remote Sens. Environ., 32, 91–101.

, P.N. Slater, K.J. Thome, A.W. Holmes, and R.A. Barnes,

1993: Preflight solar-based calibration of SeaWiFS. Proc.

SPIE, Vol. 1,939, 233–242.

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The SeaWiFS Technical Report Series

Vol. 1

Hooker, S.B., W.E. Esaias, G.C. Feldman, W.W. Gregg, and

C.R. McClain, 1992: An Overview of SeaWiFS and Ocean

Color. NASA Tech. Memo. 104566, Vol. 1, S.B. Hooker

and E.R. Firestone, Eds., NASA Goddard Space Flight

Center, Greenbelt, Maryland, 24 pp., plus color plates.

Vol. 2

Gregg, W.W., 1992: Analysis of Orbit Selection for SeaWiFS:

Ascending vs. Descending Node. NASA Tech. Memo.

104566, Vol. 2, S.B. Hooker and E.R. Firestone, Eds.,

NASA Goddard Space Flight Center, Greenbelt, Maryland, 16 pp.

68
Firestone, Patt, Schieber, Woodward, Yeh, Mattoo, Biggar, Slater, Thome, Holmes, Barnes, and Voss

Vol. 3

McClain, C.R., W.E. Esaias, W. Barnes, B. Guenther, D. Endres, S. Hooker, G. Mitchell, and R. Barnes, 1992: Calibration and Validation Plan for SeaWiFS. NASA Tech.

Memo. 104566, Vol. 3, S.B. Hooker and E.R. Firestone,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, 41 pp.

Vol. 4

McClain, C.R., E. Yeh, and G. Fu, 1992: An Analysis of GAC

Sampling Algorithms: A Case Study. NASA Tech. Memo.

104566, Vol. 4, S.B. Hooker and E.R. Firestone, Eds.,

NASA Goddard Space Flight Center, Greenbelt, Maryland, 22 pp., plus color plates.

Vol. 5

Mueller, J.L., and R.W. Austin, 1992: Ocean Optics Protocols. NASA Tech. Memo. 104566, Vol. 5, S.B. Hooker and

E.R. Firestone, Eds., NASA Goddard Space Flight Center,

Greenbelt, Maryland, 43 pp.

Vol. 6

Firestone, E.R., and S.B. Hooker, 1992: SeaWiFS Technical

Report Series Summary Index: Volumes 1–5. NASA Tech.

Memo. 104566, Vol. 6, S.B. Hooker and E.R. Firestone,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, 9 pp.

Vol. 7

Darzi, M., 1992: Cloud Screening for Polar Orbiting Visible

and IR Satellite Sensors. NASA Tech. Memo. 104566, Vol.

7, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard

Space Flight Center, Greenbelt, Maryland, 7 pp.

Vol. 8

Hooker, S.B., W.E. Esaias, and L.A. Rexrode, 1993: Proceedings of the First SeaWiFS Science Team Meeting. NASA

Tech. Memo. 104566, Vol. 8, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 61 pp.

Vol. 9

Gregg, W.W., F.C. Chen, A.L. Mezaache, J.D. Chen, J.A.

Whiting, 1993: The Simulated SeaWiFS Data Set, Version 1. NASA Tech. Memo. 104566, Vol. 9, S.B. Hooker

and E.R. Firestone, and A.W. Indest, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 17 pp.

Vol. 10

Woodward, R.H., R.A. Barnes, C.R. McClain, W.E. Esaias,

W.L. Barnes, and A.T. Mecherikunnel, 1993: Modeling of

the SeaWiFS Solar and Lunar Observations. NASA Tech.

Memo. 104566, Vol. 10, S.B. Hooker and E.R. Firestone,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, 26 pp.

Vol. 11

Patt, F.S., C.M. Hoisington, W.W. Gregg, and P.L. Coronado,

1993: Analysis of Selected Orbit Propagation Models for

the SeaWiFS Mission. NASA Tech. Memo. 104566, Vol.

11, S.B. Hooker, E.R. Firestone, and A.W. Indest, Eds.,

NASA Goddard Space Flight Center, Greenbelt, Maryland, 16 pp.

Vol. 12

Firestone, E.R., and S.B. Hooker, 1993: SeaWiFS Technical Report Series Summary Index: Volumes 1–11. NASA Tech.

Memo. 104566, Vol. 12, S.B. Hooker and E.R. Firestone,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, 28 pp.

Vol. 13

McClain, C.R., K.R. Arrigo, J. Comiso, R. Fraser, M. Darzi,

J.K. Firestone, B. Schieber, E-n. Yeh, and C.W. Sullivan, 1994: Case Studies for SeaWiFS Calibration and Validation, Part 1. NASA Tech. Memo. 104566, Vol. 13,

S.B. Hooker and E.R. Firestone, Eds., NASA Goddard

Space Flight Center, Greenbelt, Maryland, 52 pp., plus

color plates.

Vol. 14

Mueller, J.L., 1993: The First SeaWiFS Intercalibration RoundRobin Experiment, SIRREX-1, July 1992. NASA Tech.

Memo. 104566, Vol. 14, S.B. Hooker and E.R. Firestone,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, 60 pp.

Vol. 15

Gregg, W.W., F.S. Patt, R.H. Woodward, 1994: The Simulated SeaWiFS Data Set, Version 2. NASA Tech. Memo.

104566, Vol. 15, S.B. Hooker and E.R. Firestone, Eds.,

NASA Goddard Space Flight Center, Greenbelt, Maryland, 42 pp., plus color plates.

Vol. 16

Mueller, J.L., B.C. Johnson, C.L. Cromer, J.W. Cooper, J.T.

McLean, S.B. Hooker, and T.L. Westphal, 1994: The Second SeaWiFS Intercalibration Round-Robin Experiment,

SIRREX-2, June 1993. NASA Tech. Memo. 104566, Vol.

16, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard

Space Flight Center, Greenbelt, Maryland, 121 pp.

Vol. 17

Abbott, M.R., O.B. Brown, H.R. Gordon, K.L. Carder, R.E.

Evans, F.E. Muller-Karger, and W.E. Esaias, 1994: Ocean

color in the 21st century: a strategy for a 20-year time

series. NASA Tech. Memo. 104566, Vol. 17, S.B. Hooker

and E.R. Firestone, Eds., NASA Goddard Space Flight

Center, Greenbelt, Maryland, 20 pp.

Vol. 18

Firestone, E.R., and S.B. Hooker, 1994: SeaWiFS Technical Report Series Summary Index: Volumes 1–17. NASA Tech.

Memo. 104566, Vol. 18, S.B. Hooker and E.R. Firestone,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, (in press).

Vol. 19

McClain, C.R., R.S. Fraser, J.T. McLean, M. Darzi, J.K. Firestone, F.S. Patt, B.D. Schieber, R.H. Woodward, E-n. Yeh,

S. Mattoo, S.F. Biggar, P.N. Slater, K.J. Thome, A.W.

Holmes, R.A. Barnes, and K.J. Voss, 1994: Case Studies

for SeaWiFS Calibration and Validation, Part 2. NASA

Tech. Memo. 104566, Vol. 19, S.B. Hooker, E.R. Firestone,

and J.G. Acker, Eds., NASA Goddard Space Flight Center,

Greenbelt, Maryland, 73 pp.

69
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 MD; S. Mattoo: Applied Research Corporation, Landover, MD; S.F. Biggar, P.N. Slater, and K.J. Thome: University of Arizona,

 Tucson, AZ; A.W. Holmes: Santa Barbara Research Center, Goleta, CA; R.A. Barnes: ManTech Environmental Technology, Inc.,

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Report is available from the Center for AeroSpace Information (CASI),

7121 Standard Drive, Hanover, MD 21076–1320; (301)621-0390

This document provides brief reports, or case studies, on a number of investigations and data set development activities

sponsored by the Calibration and Validation Team (CVT) within the Sea-viewing Wide Field-of-view Sensor (SeaWiFS)

Project. Chapter 1 is a comparison the atmospheric correction of Coastal Zone Color Scanner (CZCS) data using two

independent radiative transfer formulations. Chapter 2 is a study on lunar reflectance at the SeaWiFS wavelengths which

was useful in establishing the SeaWiFS lunar gain. Chapter 3 reports the results of the first ground-based solar calibration of

the SeaWiFS instrument. The experiment was repeated in the fall of 1993 after the instrument was modified to reduce stray

light; the results from the second experiment will be provided in the next case studies volume. Chapter 4 is a laboratory

experiment using trap detectors which may be useful tools in the calibration round-robin program. Chapter 5 is the original

data format evaluation study conducted in 1992 which outlines the technical criteria used in considering three candidate

formats, the Hierarchical Data Format (HDF), the Common Data Format (CDF) and the network CDF (netCDF).

Chapter 6 summarizes the meteorological data sets accumulated during the first three years of CZCS operation which are

being used for initial testing of the operational SeaWiFS algorithms and systems and would be used during a second global

processing of the CZCS data set. Chapter 7 describes how near-real time surface meteorological and total ozone data

required for the atmospheric correction algorithm will be retrieved and processed. Finally, Chapter 8 is a comparison of

surface wind products from various operational meteorological centers and field observations. Surface winds are used in the

atmospheric correction scheme to estimate glint and foam radiances.

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NSN 7540-01-280-5500 Standard Form 298 (Rev. 2-89) Prescribed by ANSI Std. 239-18, 298-102

 SeaWiFS Technical Report Series

Volume 19–Case Studies for SeaWiFS Calibration and Validation, Part 2 Code 970.2

 C.R. McClain, R.S. Fraser, J.T. McLean, M. Darzi, J.K. Firestone, F.S. Patt,

 B.R. Schieber, R.H. Woodward, E-n. Yeh, S. Mattoo, S.F. Biggar, P.N. Slater,

 K.J. Thome, A.W. Holmes, R.A. Barnes, and K.J. Voss

 Series Editors: Stanford B. Hooker and Elaine R. Firestone

 -

SeaWiFS, Oceanography, Calibration, Validation, CZCS, Atmospheric Correction, 73

Radiative Transfer, Lunar Reflectance, Stray Light, Round-Robin, Hierarchical Data

Format, HDF, Common Data Format, CDF, netCDF, Operational SeaWiFS Algorithms,

Surface Wind Products, Glint, Foam Radiances