ADEOS-II- Advanced Earth Observing Satellite-II -Reference Handbook

Introduction
Chapter 1 ADEOS-II Science Research Plan
1.
Targets and objectives............................................................................................ 1
1.1 Targets..................................................................................................................... 1
1.2 Objectives ............................................................................................................... 22.
Aeronomy .............................................................................................................. 3
2.1 Aeronomy with ADEOS-II ..................................................................................... 3
2.2 Investigation into water cycle involving clouds ..................................................... 4
2.3 Investigation into influence on aerosols ................................................................. 5
2.4 Investigation into aeronomy in the polar regions ................................................... 5
3.
Oceanography ........................................................................................................ 6
3.1 Oceanography with ADEOS-II............................................................................... 6
3.2 Marine metrology and physical oceanography ....................................................... 7
3.2.1 Sea surface fluxes measurement using the satellite ....................................... 7
3.2.2 Investigation into ocean circulation mechanism............................................ 8
3.3 Marine biophysics................................................................................................. 10
3.3.1 Estimates of biomass and productivity ........................................................ 10
3.3.2 Investigation into mechanism of constituents circulation and food chain in
ocean ............................................................................................................ 11
4
Hydrology ............................................................................................................ 13
4.1 Development of Hydrology with ADEOS-II........................................................ 13
4.2 Estimates of Primary Order of Water Amount ..................................................... 14
4.3 Tasks of Algorithm Development and Validation ................................................ 18
5.
CRYOLOGY ....................................................................................................... 19
5.1 Development of Cryology with ADEOS-II .......................................................... 19
5.2 Change in distribution of Ocean Ice, Glacial and Ice Sheet /Science with
Microwave ............................................................................................................ 20
5.3 Science with Optical Observations ....................................................................... 21
6.
Vegetation ............................................................................................................ 24
6.1 Importance of observing vegetation with ADEOS-II ........................................... 24
6.2 Investigation into water-energy cycle process ...................................................... 24
6.3 Assumption of carbon cycle and plant primary productivity................................ 24
6.4 Monitoring greenhouse effects ............................................................................. 25
6.5 Contribution to vegetation research and practical use .......................................... 25
7.
For Comprehensive understanding ...................................................................... 26
8.
Combination of remote sensing data with numerical value models .................... 28
9.
Field program....................................................................................................... 31
1.
GLI....................................................................................................................... 33
1.1 Scientific objectives .............................................................................................. 33
1.2 Overview of Mission Instruments......................................................................... 35
1.3 Specific ................................................................................................................. 38
1.3.1 Types of Higher-order Products................................................................... 38
1.3.2 Definition of Higher-order Products............................................................ 38
1.3.2.1 Standard Products ................................................................................. 38
1.3.2.2 Near-Real-Time Products ..................................................................... 38i1.3.2.3 Browse Data.......................................................................................... 38
1.3.2.4 Research Product .................................................................................. 39
1.3.3 Scene Definition........................................................................................... 46
1.3.4 Processing Level Definitions ....................................................................... 46
1.3.4.1 Level 1 .................................................................................................. 46
1.3.4.2 Level 2A (GLI-1km)............................................................................. 46
1.3.4.3 Level 2 .................................................................................................. 47
1.3.4.4 Level 2MAP.......................................................................................... 49
1.3.4.5 Level 3 Binned...................................................................................... 51
1.3.4.6 Level 3 STA Map.................................................................................. 56
1.3.5 Image Catalog (GLI 1km)............................................................................ 59
1.3.6 Standard Product Processing Format ........................................................... 60
1.3.7 GAIT ............................................................................................................ 61
1.3.7.1 GAIT Organization ............................................................................... 61
1.3.7.2 GAIT Analysis and Evaluation Functions ............................................ 61
1.3.8 GLI First-light images.................................................................................. 66
1.4 Calibration and Validation.................................................................................... 68
1.4.1 Calibration and Validation Plan................................................................... 68
1.4.1.1 Overview............................................................................................... 68
1.4.1.2 Definitions............................................................................................. 69
1.4.1.3 System................................................................................................... 70
1.4.1.4 Schedule................................................................................................ 72
1.4.1.5 Information Disclosure Policy .............................................................. 73
1.4.2 Calibration Plan ........................................................................................... 76
1.4.2.1 Solar-Ray Calibration and Internal-Lamp Calibration ......................... 76
1.4.2.2 Black-Body Calibration ........................................................................ 78
1.4.2.3 Geometric Calibration........................................................................... 79
1.4.2.4 Evaluation of Sensor Characteristics and Image Quality ..................... 80
1.4.2.5 Vicarious Calibration, Cross Calibration.............................................. 82
1.4.2.6 L1-Process Software ............................................................................. 84
1.4.3 Validation Plan............................................................................................. 85
1.4.3.1 Atmosphere ........................................................................................... 85
1.4.3.2 Ocean .................................................................................................... 87
1.4.3.3 Land ...................................................................................................... 89
1.4.3.4 Cryosphere ............................................................................................ 91
2.
AMSR ................................................................................................................... 93
2.1 Scientific Objectives ............................................................................................. 93
2.2 Overview of the Mission Instruments................................................................... 94
2.3 Specific ................................................................................................................. 96
2.3.1 Products........................................................................................................ 96
2.3.2 Definition of higher-order products ............................................................. 96
2.3.2.1 Standard products.................................................................................. 96
2.3.2.2 Near real time products......................................................................... 96
2.3.2.3 Browse data........................................................................................... 96
2.3.2.4 Research products ................................................................................. 96
2.3.3 Scene definition ........................................................................................... 97ii

2.3.4 Processing level definitions ......................................................................... 97
2.3.4.1 Level 1 .................................................................................................. 98
2.3.4.2 Level 2 .................................................................................................. 98
2.3.4.3 Level 2 Map .......................................................................................... 99
2.3.4.4 Level 3 ................................................................................................ 101
2.3.5 Image Catalog ............................................................................................ 105
2.3.6 Standard Product Processing Format ......................................................... 105
2.3.7 AMSR First-light images........................................................................... 106
2.4 Calibration and Validation.................................................................................. 108
2.4.1 Basic Plans for AMSR Calibration ............................................................ 108
2.4.1.1 Brightness Temperature Calibration ................................................... 108
2.4.1.2 Geometric Calibration......................................................................... 110
2.4.2 Post-launch validation................................................................................ 111
2.4.2.1 Water Vapor........................................................................................ 111
2.4.2.2 Cloud Liquid Water ............................................................................ 113
2.4.2.3 Sea-surface Wind Speed ..................................................................... 114
2.4.2.4 Precipitation ........................................................................................ 115
2.4.2.5 Sea-ice Concentration ......................................................................... 117
2.4.2.6 Sea-surface Temperature .................................................................... 118
2.4.2.7 Snow-Water Equivalent ...................................................................... 119
2.4.2.8 Soil Moisture....................................................................................... 121
2.4.3 schedule...................................................................................................... 124
3.
SeaWinds ............................................................................................................ 125
3.1 Scientific objectives ............................................................................................ 125
3.2 Overview of Mission Instruments....................................................................... 126
3.3 Specific ............................................................................................................... 127
3.3.1 Standard products....................................................................................... 127
3.3.2 Research product........................................................................................ 127
4.
POLDER ............................................................................................................ 128
4.1 Scientific objectives ............................................................................................ 128
4.2 Overview of Mission Instruments....................................................................... 129
4.3 Specific ............................................................................................................... 130
4.3.1 Standard products....................................................................................... 130
4.3.2 Research products ...................................................................................... 130
5.
ILAS-II............................................................................................................... 131
5.1 Science objectives............................................................................................... 131
5.2 Overview of Mission Instruments....................................................................... 133
5.3 Specific ............................................................................................................... 134
5.3.1 Standard products....................................................................................... 134
5.3.2 Research products ...................................................................................... 134
6.
DCS.................................................................................................................... 135
7.
TEDA................................................................................................................. 135
1.
System................................................................................................................ 136
1.1 Overview of the Satellite System........................................................................ 136
1.2 Overview of the Ground System......................................................................... 139
2.
User Services ...................................................................................................... 144

iii

Data Policy.......................................................................................................... 144
Operation Phase .................................................................................................. 145
User Definition.................................................................................................... 146
User Services ...................................................................................................... 147
2.4.1 Overview of Services................................................................................. 147
2.4.2 Services According to Operation Phase..................................................... 148
2.4.2.1 Calibration and Validation Phase........................................................ 148
2.4.2.2 Regular Use Phase .............................................................................. 150
2.5 Data Comprehensive Management and Provision Services ............................... 154
2.5.1 Overview of Data Provision Service.......................................................... 154
2.5.2 Catalog Information Service ...................................................................... 155
2.5.2.1 Inventory Information......................................................................... 155
2.5.2.2 Image Catalog ..................................................................................... 156
2.5.2.3 Guide Information............................................................................... 157
2.5.3 Data Provision............................................................................................ 157
2.5.3.1 Ordering Data...................................................................................... 157
2.5.3.2 Data provision flow............................................................................. 157
2.5.3.3 Provision Media .................................................................................. 157
2.5.3.4 Online Data Provision......................................................................... 158
2.5.4 Uses of On-Line Services .......................................................................... 159
2.5.4.1 WWW Browser................................................................................... 159
2.5.4.2 EUS/GUI............................................................................................. 162
2.5.4.3 Service Limitations ............................................................................. 164
2.6 Data Analysis Research System (EORC) ........................................................... 165
2.6.1 Overview of the System............................................................................. 165
2.6.1.1 System Details .................................................................................... 166
1.
Abbreviation ....................................................................................................... 167
2.1
2.2
2.3
2.4

iv

Introduction
The Advanced Earth Observation Satellite-II (ADEOS-II) is an Earth environmental
observation satellite launched on December 14, 2002, to take over the missions of its
predecessor ADEOS, which was launched in August 1996 but stopped operating in June
1997. It is eventually to complement the GPM missions as well as the mission of satellite
GOSAT, which is planned to be launched in 2007 or later. The missions of ADEOS-II
are, therefore, not only to serve as the ADEOS successor but also to continue theadvancement of the Earth’s observation mission pioneered by Japan ADEOS and lay asolid foundation for the uses of satellite observation in research on the Earth’s
environmental problems as well as in applications to social and other problems.
ADEOS-II is designed to observe the distribution of chlorophyll, distributions ofwater vapor and seawater, sea-surface temperature, and other indices critical to the main
objective of the satellite—to study the processes of water, energy, and carbon circulation.
These data will then be used for the Global Energy Water Circulation Experiment Plan
(GEWEX) and the Climate Variation Research Plan (CLIVER) of the World Climate
Joint Research Plan (WCRP) as well as for the International Global and Biological Joint
Research Plan (IGBP); contribution to global climate change research is expected.
ADEOS-II is equipped with a global imager (GLI) and a high-performancemicrowave radiometer (AMSR) developed by NASDA as the core sensor, an improved
atmospheric infrared spectrometer (ILAS-II), a wind observation instrument for the sea(Sea Winds), and a land-surface reflective light observation instrument (POLDER).
Combined use of these observation sensors is expected contribute to the discovery of new
information about the Earth’s environment and the development of new application fields
using satellite data. Along with large platform satellites of other countries already inoperation, such as Terra and Aqua (NASA, the United States) and ENVISAT (ESA,
Europe), it is important that attempts be made to pioneer new research areas and enhance
the field of applications through international harmony and cooperation.
This handbook has been designed to provide basic information on ADEOS-II with thehope that it will promote the actual use of the data by researchers and a variety of remotesensing users interested in global environmental problems. We sincerely hope that,through the use of this reference handbook, not only will new information be gained
concerning global environmental problems, but also the application of satellite remotesensing will be promoted.

Chapter 1 ADEOS-II Science Research Plan

1.

Targets and objectives

1.1

Targets

This chapter describes scientific objectives based on observed parametersprovided by the sensors aboard ADEOS-II. The scientific objectives of ADEOS-II areto use various sensors comprehensively and to contribute to clarifying Earthenvironmental changes. Such results won’t come out or exist without the sciencebased on each of the sensors. Thus, one scientific objective may overlap with ascientific objective of the sensors mentioned above.
ADEOS-II is an Earth environment observing satellite, so the related sciences are
Aeronomy, Oceanography, Hydrology, Cryology, and Vegetation. Basically it isscience related to water on the Earth’s surface. Above all, it is important to obtain new
knowledge about clouds, water vapor, soil moisture, snow runoff, snow ice, plant andorganic materials in oceans that used to be difficult to estimate.
The next most important thing is to establish the point of view to make thatknowledge comprehensive. For this, we must combine numerical models or groundvalidation data, and develop techniques and systematization that mobilizes all theknowledge and information we have. In other words, ADEOS-II science seeks toclarify climatic mechanisms of the Earth’s environment based on establishing of thiscomprehensive technique.

1

1.2

Objectives
The ADEOS-II scientific objectives can be summarized into three parts.

(1) Understand the fixed quantity of the water-energy cycle in the climate system.
(2) Estimate the quantity of biomass and primal production in relation to the carbon
cycle, which is responsible for global warming.
(3) Detect signal changes in long-term climatic changes, through continuous
observations by ADEOS.
The investigation into “(1) water-energy cycle” is a distinguishing characteristicof the ADEOS-II mission. To support this objective, GLI will estimate clouds, water
vapor, and aerosols; AMSR will estimate hydrology parameters like water vapor,
precipitation, soil moisture, and snow runoff; Sea Winds will estimate ocean stress;
POLDER will estimate aerosols; and ILAS-II will estimate ozone and the vertical
distribution of very small amounts of gaseous components in the polar regions. Suchdata will be useful in understanding the fixed quantity of water-energy cycle on global
scale. Continuous observations of ocean stress by SeaWinds, successor to NSCATand QuickScat, will greatly contribute to explaining ocean dynamics.
At the same time, these estimates of parameters using satellites will never be
accurate without precise and accurate ground validation programs. The Validation
Plan must clearly describe the scientific objectives, implementation plan, data
management, and major researchers, and it must be internationally open. Such a
research plan cannot be prepared in a day or a night but will take a long time and will
require international coordination. In this sense, we are planning to acquire the groundvalidation sites developed for ADEOS and GEWEX/GAME, expand them, andprepare and implement a comprehensive validation program. Fortunately, aninternational program called the Coordinated Enhanced Observing Period (CEOP)
held in 2001 and 2002 proposed that the Japanese researchers serve as the leaders. We
also need to consider joining forces with these projects.
Estimates of yield and chlorophyll a in relation to the carbon cycle are otherdistinguishing characteristics of ADEOS-II. GLI, which was improved from OCTS
that flew aboard ADEOS, has multiple channels for receiving data and a highresolution of 250 meters. GLI continues from the successful OCTS and will work
effectively to estimate ocean biomass, ocean primary productivity, land biomass, land
primary productivity, and their fluctuations. ILAS-II, a follow-on satellite of ILAS, iscapable of observing ozone in the polar regions and the vertical distribution of verysmall amounts of gaseous components. As such it will greatly contribute todeveloping stratospheric chemistry.
In addition, POLDER, that will fly aboard ADEOS-II and previously flew aboard
ADEOS, will continuously tell us the global distribution of aerosol, which will beessential information for improving global warming predictions.

22.

Aeronomy

2.1

Aeronomy with ADEOS-II

The ADEOS-II is a follow-on satellite of the ADEOS mission to monitor
greenhouse effects and ozone depletion in the stratosphere. ADEOS ceased operatingin the middle of its mission, so ADEOS-II basic objective in Aeronomy is long-termmonitoring by continuing the ADEOS observations.
ADEOS will observe carbon circulation and the energy cycle to monitor
greenhouse effects. Observation targets for energy cycle research include clouds,
aerosols, air-ocean fluxes, and the Earth’s surface (snow ice) albedo. For ADEOS-II
observations, it is important to research the distribution and changes in properties ofthese parameters. Such a long-term monitoring mission will contribute to GCOS.
In addition, ADEOS-II will enable microwave observations and investigating thehydrologic cycle on a global scale as a research subject in the field of Agronomy. The
water-energy cycle on global scale literally means global climatic changes. Our Earthis known as “the planet of water,” and its environment is greatly influenced by waterin a liquid state as ocean, inland water, soil moisture, and cloud water; in solid state assnow ice, ocean ice and ice crystal cloud grain, and in gaseous state as water vapor in
the atmosphere. Hydrogical transfer among atmosphere, water, and ground involves
transformation among these three states. It is thus an important phenomenon thatgenerates short-term atmospheric changes and long-term climatic changes due to theinteraction of sensible and latent heat, exchange of heat between the atmosphere andocean, and radiation properties. It also influences ecosystems and geographicalfeatures. Observing changes in clouds and water vapor (cloud water amount andpossible rain off amount) using GLI and AMSR (over the ocean) will be an importantresearch theme. Also, observing ocean stress will rapidly progress in accuracy of
estimating ocean surface fluxes.
Even though ADEOS-II was planned in Japan, it is necessary to promote global
analysis of the three dimensional distribution of ocean near-surface wind and climatic
value and changes in water vapor, clouds and precipitation, and to participate inrelated international projects such as GEWEX and CLIVAR. In recent years,
researchers have pointed out the importance of observing aerosols. This is also an
important research theme for ADEOS-II. General progress, including algorithm
development, is desired.
The ADEOS-II is expected to contribute significantly to atmospheric chemistry inthe stratosphere as well. Observing ozone and other atmospheric trace elements using
ILAS-II will clarify various aspects of atmospheric chemistry for the stratosphere in
the polar regions, which can be called natural laboratories.

3

2.2

Investigation into water cycle involving clouds

In climate models, it is difficult to qualitatively assess clouds. On the whole,
ADEOS-II seeks to investigate the water-energy cycle, and both GLI and AMSR are
ideal for investigating the water cycle involving clouds.
Lately, two new theories about the influences of clouds on the climate systemhave been proposed. One is “the thermostat theory” proposed by Ramanusam. It firstnotes that the maximum tropical sea-surface temperature has hardly changed
throughout Earth’s history. It then explains a mechanism by which the rising seasurface temperature increases the cumulonimbus cloud top height. This subsequently
reduces the ice grain size of the expanding anvil cloud, causing more sunlight to thereflected and thus reducing the sea surface temperature. It is doubtful whether the seasurface temperature is stable. Even if it is stable, it is doubtful whether the sea-surface
temperature was stable because of the thermostat effect. However, this proposal issignificant at least in that it reminds us that cloud grain-size distribution cansignificantly affect radiation properties. Fortunately, GLI has channels that canestimate the cloud grain-size distribution. If the global distribution of cloud grain size
were known, it would contribute to understanding our climate system.
The second new theory concerns cloud absorption of solar radiation. It says that,compared with the former theory, real clouds absorb more solar radiation than wethought. Similar results have been reported based on observations using satellites andaircraft. This theory has not been proven right or wrong. If is right, it would benecessary to rewrite the radiant codes for atmospheric models. Likewise, we would
not able be to ignore the effects on atmospheric circulation. This problem should be
resolved immediately. ADEOS-II is considered capable of solving this kind ofproblem.
The important global water-energy-cycle parameters are the quantity of water
vapor transport and rainfall. Spatial fluctuation of water vapor and precipitation issmall in the horizontal plane, and it is difficult to analyze the parameters using only
existing data. Thus, there is need to develop a method of analyzing parameters byusing microwave data. Precipitation is the primary theme of TRMM, and results ofprecipitation distribution estimated by the developed TMI will be continued by
AMSR. As a result, ADEOS will continue TRMM’s global observations ofprecipitation. In this regard, joint observation by TMI and AMSR is indispensable.

4

2.3

Investigation into influence on aerosols

In cloud physics, aerosols have been of scientific interest as an atmosphericcomponent and as a factor in the cloud condensation nucleus and in the circulation ofmatter. Recently, the influence of aerosols on the radiation process, however, has beengetting more attention from scientists. One reason is that it has been reported that thesolar incidence onto the sea surface used for global numerical prediction models tends
to exceed actual observations. The fact is that we are unable to verify it because of thelack of observations indicating how much aerosol actually exits in the atmosphere.
Another reason is that a recent experiment has shown that the simulation of global
warming agrees with actual data if it takes into account that aerosols have been
increasing due to development of economic activities. (For this experiment, refer tothe second report of the Intergovernmental Panel on Climate Change (IPCC).)
Aerosols affect and restrain global warming by reflecting solar radiation due both tothe increase of aerosols and to the increase of lower-layer clouds caused by emitted
aerosols. However, aerosols do not produce a “soot-covered effect.” If aerosols did
create black clouds, they would absorb solar radiation, which is far from restraining
the global warming.
For this reason, ADESO-II will carry a sensor that builds upon the ADEOS sensor
to observe aerosols. If the global aerosol distribution and its seasonal change were
provided for the data set, it would contribute significantly to solving the globalwarming problem.

2.4

Investigation into aeronomy in the polar regions

The stratospheric ozone depletion problem has been severe, particularly in thepolar region. As concern about the trend increased all over the world, an international
cooperative outline was developed. The Vienna protocol of 1995 was drafted toprotect the ozone layer, and the Montreal protocol was concluded in 1987. In the fieldof observations, member countries of the treaty are required to promote organizedobservations, including use of satellites, to monitor the ozone layer condition.
ADEOS-II will carry the ILAS-II sensor that builds upon the ILAS sensor flown on
ADEOS. The scientific objective of ILAS-II is to keep continuous watch over theozone layer fluctuation and to resolve chemical and physical processes of the ozone
layer. It will significantly contribute to organized ozone-layer observations from space.
In addition to the ozone layer, this sensor also monitors nitrogen oxide, CFC, andother atmospheric trace elements. Those data will give us important information,
particularly for understanding atmospheric chemical reactions in the polar regions,such as the ozone hole. Improving ozone hole detection and prediction andinvestigating the role of polar stratosphere clouds (PSC) will contribute to theatmospheric science of polar regions.

5

3.

Oceanography

3.1

Oceanography with ADEOS-II

Although ADEOS-II is an ADEOS follow-on satellite, it builds up thefundamental scientific objectives even more clearly. The scientific objectives are (1)to investigate the water-energy cycle, and (2) to investigate chlorophyll a /vegetation
distribution that plays an important role in carbon circulation. The ocean clearly plays
a very important role in both research subjects, and the most important tasks assigned
to ADEOS-II is to investigate the role of the ocean in the circulation of water-energy
and carbon. Both of those research subjects are important task that correspond tointernational research projects such as the WCRP/GEWX&CLIVAR, IGBP (JGOFS,
LOICZ), and GOOS. In the water-energy cycle, marine meteorology and physicaloceanography that involve momentum fluxes and heat fluxes in the seawater surface
will take a leading part in the research on carbon circulation. Marine biophysics will
have a major role in the research. Together, they are two pillars in oceanography
research supported by ADEOS-II.
Frequent abnormal weather phenomena have increased social concern aboutclimate change problems. To solve such climate change problems, it is necessary tounderstand the climate system. In recent years, we have rapidly come to understandthat the ocean plays very important roles in the global climate system. The climate
system is fundamentally considered as a water-energy cycle. The roles of the ocean in
the process can be categorized into two parts: water-energy transfer between oceans
and the atmosphere, and water-energy transfer below the ocean surface. Clarifying
the role of oceans in both processes is necessary for understanding the global climate
system. Satellite use may make this possible for the first time.
Global warming caused by increased carbon dioxide gas in the atmosphere has
unveiled important implications for earth environmental problems in recent years. As
a result, investigations into the carbon circulation process on a global scale have been
accelerated. The ocean accounts for about a half of the biogeochemical cycle ofcarbon compounds. Serious efforts to clarify the role of the ocean in global carbon
cycle processes should thus be promoted. However, we are now at a turning point
concerning how we look at the ocean as a source of food production for human beings.
Most major marine resources have already been developed, and it is the necessary toplan more effective and lasting use of sea productivity. Research on the food chain
cycle based on sea primary productivity is related to the research on the carbon cycle.
Humans must conduct research on ocean primary productivity on a global scale fromthe viewpoints of both environmental and food resources concerns.

6

3.2

Marine metrology and physical oceanography

3.2.1 Sea surface fluxes measurement using the satellite
The first problem we encounter when thinking of the water-energy cycle is thesurface fluxes between the atmosphere and the ocean. Surface fluxes set up theboundary conditions of circulation of the ocean or the atmosphere. Understanding sea
fluxes is necessary for understanding atmosphere-ocean interactions. Heat flux fromthe ocean to the atmosphere is a dynamic in the atmosphere cycle. If its value can be
estimated precisely on a global scale, more accurate and precise weather forecasting
and longer period forecasting will be possible. However, it is logically impossible toobserve regions below the ocean from a satellite. Basically, only information about
the sea surface can be observed, so accurate and precise estimates of sea-surface
fluxes are important objectives for ADEOS-II. There are two types of seawater
surface fluxes. One is a parameter of first-order that can be estimated directly from thesensors aboard ADEOS-II. The other is a parameter of second–order that can be
estimated from the several sensors together.
(1) First-order Parameter Calculation Algorithm Development
The near sea surface velocity considered a first-order surface flux is parameter.
Here we will omit the specific explanation about the near sea surface velocity
since we have already explained it in GLI chapter. Although the momentum fluxes
are important dynamic factors for the ocean, we suppose they can be easily
calculated from first-order parameters. The most desired first-order parameter isthe sea-surface temperature that can be provided by AMSR even under cloud
cover. Estimating sea-surface temperature under cloudy cover was impossible
with a thermal infrared radiometer. This used to be a major impediment to theprogress of oceanography. AMSR is expected to provide continuous space-time
sea surface temperature observations from space, greatly reducing system errors inprevious turbulent heat flux calculations.
Moreover, we expect that simultaneous observation of sea surface temperature
with other data will lead to highly accurate evaluation of turbulent heat fluxes.
These points are considered as remarkable, and the ADEOS-II can make itpossible for the first time(2) Second-order Parameter Algorithm Development
Not all composites of higher-order parameters can be computed for surface
fluxes as second-order parameters. Also, it is necessary to note that surface fluxes
are computed generally by bulk methods. For instance, estimating latent heat
fluxes requires saturation specific humidity, sea surface specific humidity, windvelocity, etc. However sea-surface specific humidity is computed empiricallyusing the rainfall amount because it is not a first-order parameter. Consequently it
is also necessary to develop a method of estimating parameters like this one that
require the bulk method.
Sea-surface fluxes expected to be estimated by using the sensors aboard the
ADEOS-II are turbulent heat fluxes (sensible heat/latent heat fluxes), momentum
fluxes, fresh-water fluxes, radiation fluxes, carbon dioxide fluxes, and others.
Most flux data were supposed to be estimated using ADEOS, but this was
impossible due to ADEOS’s failure. Real acquisition of flux data will thus bemade possible by ADEOS-II for the first time7

(3) Development of Techniques for Combining and Assimilating Remote Sensing
Data with Numerical Models
We will reach a limitation if we only improve algorithms in order to estimate
each atmosphere-ocean flux over greater areas and with higher accuracy, which isthe fundamental purpose of ADEOS-II. Combination with or assimilation into
numerical models like an air circulation model is surely needed. Development oftechniques for such combination or assimilation is an important research subject.
Each weather organization has already assimilated satellite data and atmosphericcirculation models or has started discussing this, and the results are very
promising. Such activities will advance the assimilation method itself, and it ispredicted that this field will be expanded.
(4) In Situ Observations, or Evaluation of Outcome by using Numerical Model
Estimated sea surface fluxes must be evaluated by using in situ data acquired bybuoys or vessels. However, buoy and vessel observations cannot evaluate fluxesthat are estimated in very limited locations. It is thus necessary to evaluate fluxes
using the results from the models of the oceanic or atmospheric circulation withestimated flux inputs.
3.2.2

Investigation into ocean circulation mechanism

What the scientific objectives and results to can be achieved using each flux? As
we mentioned previously, the most important scientific objective of ADEOS-II is toinvestigate the role of the ocean in the water-energy and matter cycle. If we perfectly
and precisely understand the quantity of each flux over the ocean, we will understandthe static oceanography. However, this won’t solve the essential part of the role of theocean in the water-energy and matter cycle. We need to understand the sea-surface
fluxes or ocean circulation mechanisms, involved in distribution in the ocean. A
number of researchers input or assimilate surface fluxes obtained from the satellite
into ocean circulation models to understand and predict these phenomena. These
researchers perform monitoring. We emphasize here that developing a numericalmodel is essential for both research endeavors.
(1) Quantitative Estimates of Oceanic Heat Transfer
Much research has been conducted on oceanic heat transfer, primarily using
satellite data. However, because of the low space-time density of observations
data, the research results have had very large average scales. Since the errors werevery large, the results obtained differed considerably. If we cloud understandocean circulation accurately, we could improve the accuracy of estimating heatflow.
(2) Understanding Ocean Circulation in Tropical Regions
A large amount of thermal energy is accumulated in tropical regions, andchanges beneath the tropical ocean like El Niño greatly affect global climaticchange. Thus, understanding the ocean circulation is very important, not only intropical but also in global climate systems. The problem of the Indonesianthrough-flow is very important from the viewpoint of ocean heat flow.

8(3) Ocean Circulation in Subtropical Regions including Kuroshio Current and OtherWest Coast Boundary Layer Currents
Kuroshio Current is one of the west-coast boundary currents that forms theocean circulation in the subtropical regions and greatly affects the climate andfisheries industry in Japan. Thus, understanding the ocean circulation in thesubtropical regions including Kuroshio Current by using the satellite data will bevery socially significant. Also, the development of data assimilation systems will
enable us to predict changes in the Kuroshio Current. Highly accurate
observations of sea-surface height using TOPEX/POSEIDON have rapidly
advanced the data assimilation of the ocean circulation models in recent years.
Moreover, JASON 1, the follow-on to TOPEX/POSEIDON, will continuously
conduct highly accurate observations on the sea surface and will greatly affect theobservational data of ADEOS-II.
(4) Investigation into Water Mass Formation and Transfer Process
The water masses with distinguishing characteristics are North Pacificmesothermal layer, the Sub-tropical mode water, and other. Although theexistence of the water mass already have been known, its formation, transfer, andtransform process have not been understood well. We can easily imagine thatinteraction between the sea surface and the atmosphere is essentially important for
the water mass formation. Thus, the sea-surface fluxes estimated by satellites will
be important information for water mass formation of ocean surface layer and willplay the important role in solving its mechanism.
(5) Research on ocean pollutants and floating wreckage
The problems of tanker oil spills and the spread of illegally dumped pollutants
have raised severe public criticism. Those are very significant problems for
mankind. These subjects are directly connected to the ocean circulation anddefinitely need information about sea-surface fluxes. Thus, it is urgently necessary
to solve problems like those using satellite data and ocean circulation models.
(6) Development of Techniques to Forecast Water Condition in Fisheries Zones
We know that the ocean circulation greatly influences the fisheries industry.
Thus, it is important to accurately forecast water conditions in the fisheries zone
by using information obtained from sea-surface fluxes or by combining thesefluxes with information obtained from ocean circulation models. Also, this subject
is strongly by related to research on marine biophysics.

9

3.3

Marine biophysics

3.3.1

Estimates of biomass and productivity

Carbon-containing material is transformed from inorganic to organic elements byphotosynthesis by phytoplankton in seawater. This is called primary production, and it
is an important food chain process supporting fishery resources and helps account forcirculation beneath the ocean. Photosynthesis by phytoplankton beneath the ocean isthought to account for 30% or more of the total photosynthesis on the Earth.
Techniques for creating maps of chlorophyll a concentration, an indicator ofphytoplankton near the ocean surface, were almost completed using OCTS andPOLDER aboard ADEOS and in the SeaWiFS project. We have been acquiringknowledge on the seasonal fluctuation in the chlorophyll a distribution. GLI will
replace OCTS and SeaWiFS in playing an important role in mapping the chlorophyll aconcentration during the next decade. In addition to conducting follow-on
observations of chlorophyll a, we expect to gain an understanding of phytoplankton
pigments along the coastal region, the material originating from land, pigments in
phytoplankton, and primary production and export production measurements and CO2
fluxes between the atmosphere and the oceans.
(1) Distribution of Chlorophyll a Concentration under Oceans
The in-situ chlorophyll a concentration near the ocean surface can be measured
globally from OCTS. Moreover, the chlorophyll a concentration has beenmeasured similarly by the US SeaWiFS even after the failure of ADEOS. The
ADEOS-II mission will take over data collection from OCTS, POLDER, and
SeaWIFS and clearly reveal global changes in chlorophyll a distribution in ocean
regions over about a decade. To compare the data of these different sensors, GLI
will improve its accuracy of chlorophyll a concentration measurement by a factorof 1.5. We will then need to clarify the accuracy of the standard upward radiationbrightness, which serves as a basis of the chlorophyll a concentration
measurement.
(2) Estimates of Chlorophyll a Concentration and Dissolved Colored Organic
material/Weight of Suspended Minerals
A technique to measure chlorophyll a in open oceans where opticalcharacteristic are determined by phytoplankton (called water I) was establishedusing OCTS. However, the technique still had difficulties measuring chlorophyll ain coastal areas where dissolved colored organic material and/or suspended
minerals and matter other than phytoplankton might influence optical properties(called water II). GLI aboard ADEOS-II is expected to estimate dissolved colored,suspended minerals, and other matter and is also expected to measure the exact
chlorophyll a concentration in mixed waters.
(3) Estimates of Other Phytoplankton Pigments
Different oceans have different formations of taxon of chlorophyll aphytoplankton, and they are categorized by their copigments. GLI has observation
bands that can estimate important pigments and copigments of phytoplankton,
enabling GLI to distinguish different water masses for formation of taxon. Since
these taxons each play different roles in matter circulation or the food chain,categorizing taxons will be a major advancement.

10(4) Estimates of Primary Production
An objective of an ocean color sensor is to monitor primary productivity overlarge area. OCTS and SeaWiFS have not been able to create an internationallyagreed primary production algorithm. GLI is expected to produce a series ofspace-time distributions of primary production by completing a primary
production algorithm based on sea-surface temperature, sea-surface chlorophyll a,
and bottom radiance.
(5) Estimates of Export Production/CO2 Fluxes
The carbon transformed from inorganic into organic constituents by ocean
primary production, which transfers from, carbon the sea surface to mesobenthic
sediment, is called export production. Export production is a very important
process in the carbon circulation cycle because it transfers the carbon from thesurface, where it contacts carbon in the atmosphere, to the mesobenthic, where it
does not contact the atmosphere directly. Methods to compute export production
have been studied with OCT, and will continue to be important research with
ADESO-II. Also, SeaWinds ocean near-surface wind data is expected to yield theCO2 molecular concentration due to living creatures in the sea surface and tosupport research to obtain CO2 fluxes between the atmosphere and the oceans.
3.3.2 Investigation into mechanism of constituents circulation and food chain inocean(1) Understanding the Mechanism of Constituent Circulation and Food Chain in
Ocean
ADEOS enabled more accurate measurements of chlorophyll a concentration inopen oceans, which an index of phytoplankton producing primary production. A
time series of chlorophyll a on a global scale extending nearly a decade will beacquired by ADEOS-II, including ADEOS and SeaWiFS. In particular, we expect
to clarify the chlorophyll a fluctuation, as ADEOS has already proven that a
combination with ocean near surface wind is effective. Moreover, estimates ofprimary production and new production based on chlorophyll a concentration havebecome possible to a certain degree, as has observation of the fluctuation in speedof constituent export. Also, separating taxons that play different roles inconstituent circulation is expected to expand our horizons with regard to therelationship of the mechanism of constituent circulation and the ecosystem.
(2) Understanding Constituent Circulation in Continental Shelf Regions
ADEOS enabled measuring chlorophyll a in open oceans where phytoplankton
determines the optical properties. However, use of ADEOS was limited along thecoasts where dissolved colored organic materials, suspended minerals and mattersother than phytoplankton might significantly influence optical properties.
ADEOS-II will enable us to measure a certain amount of chlorophyll a in thesekinds of oceans, so that it will be possible to understand biomass production alongcontinental shelves that play important roles in constituent circulation andproduction of fisheries resource. Moreover, many dissolved colored organic
materials, suspended minerals and others originate from land and have beenplaying important roles in matter circulation. If we could understand the11fluctuations, it might be possible to clarify the mechanism of constituent
circulation and the food chain along continental shelves.
(3) Monitoring Red Tide and Toxic Algae Bloom
Recently, red and toxic algae bloom phenomena and their influence on fisheries
have become serious problems in Southeast Asia. ADEOS-II will enable
monitoring red tides and toxic algae blooms by clarifying the distribution andfluctuation land originating materials like phytoplankton pigments along coast. In
particular, the environment in the South Asia has been heavily impacted by humanactivity, and monitoring is greatly needed.
(4) Research on Fluctuation in Higher Order Production and Use of Maintain ofMarine Organism Resource
Organic materials produced by phytoplankton support marine resources like
fish, which human beings then use. Today, continued availability of marineorganism resources has become doubtful due to over hunting or environmental
destruction. To quantitatively estimate marine organism resources, we havedepended on empirical information, and the relationship between the oceanic
environment and fluctuation in quantitative resources has been getting clear. We
need accurate research on how much of the organic materials produced byphytoplankton are supplied to marine organism resources, how it is processed, andwhat should be done to maintain marine organism resources. Information that isbeing proven by ADEOS-II will be essential for this research.
(5) Development of Efficient Use System of Marine Organism Resource
An efficient system is needed in order to use marine organism resources. Byfurther advancing extraction of fishery information that is being proven by
ADEOS-II data, we expect to develop a system to use marine organism resources
more efficiently.
(6) Development of Technique Combining Satellite Data with Numerical Models ofMatter Circulation and Ocean Ecosystem
Today, circulation models and food chain models have been advanced forvarious ocean areas from global to the coastal regions. These numerical models
are expected to provide a means to predict important items in the near future.
ADEOS-II data is expected to be used as information for the input to thesenumerical models, for verification, and for further assimilation.

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4

Hydrology

4.1

Development of Hydrology with ADEOS-II

Modeling the water-heat cycle on a global scale is important for predicting
climatic change and evaluating its effects. In particular, understanding a fixed quantityof heat and water fluxes of the Earth’s surface on a global scale based on interaction
between the atmosphere and soil surface, which plays a role in climate system, isessential to improving global climatic change prediction accuracy. However, the work
has not been started yet since it is difficult to measure fluxes of the Earth’s surface
directly on a global scale and to collect information of precipitation and inland watercontent (soil moisture and snow cover) to estimate the data over a large region.
ADEOS-II is a global observation satellite that will provide high-resolution
temporal and spectral data of the visible spectrum, near infrared, thermal infrared, andmicrowave regions that are useful for estimating inland primary order water such as
precipitation, vegetation, ground temperature, albedo, radiation balance, soil
moisture/water content, and snow cover. In particular, AMSR will attempt to globallyobserve soil moisture and snow in a fixed quantity for the first time, and to improvethe estimate accuracy by incorporating GLI data. GLI’s 250m channels will provide
global detailed data on vegetation, ground temperature, and albedo for the first timeand acquire information for effective research on the influences of human activities on
the hydrologic cycle as well as evaluate climate influences on human activities.
Moreover, the combination of TRMM products and improved accuracy in estimating
inland precipitation with combed data of AMSR and GLI will be important forhydrology.
To estimate sensible heat fluxes, latent heat fluxes, and river runoff on a global
scale by using these primary order water amounts, we must develop heat-water export
and river runoff models of the land-atmosphere interface based on an understanding
and global analysis of hydrologic processes in various soil surfaces. These subjects
have been studied internationally by WCRP/GEWEX. The hydrologists andmeteorologists in Japan have been cooperating in and promoting GAME, aninternational joint study for the water-heat cycle on the Eurasian Continent in order topredict and understand fluctuations in Asian monsoons. Comprehensive observation
data covering a very wide area that was previously impossible have been
simultaneously acquired with the satellite data, and various analyses have been
conducted. The success in water-energy cycle observation research, based on close
cooperation among international science projects and the satellite observation plans,demonstrated that cooperation with these international projects is more importantwhen considering effective use of the ADEOS-II data as well as effective algorithm
development and validation.

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4.2

Estimates of Primary Order of Water Amount(1) Soil MoistureLand-atmosphere interaction through the hydrologic cycle is very influential on
the climate system. To manage seasonally and yearly water resources and predict
long-term fluctuations in water resources due to climate change, we must establish
a system to observe hydrologic conditions on a global scale, where fluctuations
are spatially and temporally large. Simulations using climate models indicate thatsoil moisture is a major factor in fluctuation in climatic formation and controls
transfer of energy to or from fluxes of infraction radiate, sensible heat, latent heatand underground heat by suppressing evapotranspiration. Soil moisture controls
heat and water export into the atmosphere by controlling the evaporation
efficiency and changing the Bowen ratio in the short term and works as a climate
memory in the intermediate and long term. To calculate the radiation balance, wemust determine sensible heat/latent heat fluxes, soil moisture, and runoff. For thiscalculation, the surface-boundary layer scheme of the ECMWF has been improved
from surface data obtained by FIFE. This research developed into research to
improve the accuracy of precipitation prediction accuracy of the 1993 Mississippi
massive flood period. The importance of global mapping of soil moisture to
improve the accuracy of long-term weather forecasting has been recognized allover the world and was the reason for initiating the Global Soil Wetness Project(GSWP). Seasonal snow melting of permafrost over the Eurasian Continent, theNorth American Continent, and the Tibetan Plateau affects soil moisture results inthe seasonal change of the field capacity (water volume remaining in the soil afterrain is drained by gravity) due to changes in the active layer. Furthermore, thefrozen ground is presumed to influence atmospheric circulation as much as ormore than snow coverage by the weather conditions of the previous year, i.e. theactive layer and soil moisture of summer or autumn are conserved until spring of
the next year.
Ideally, soil moisture observations should be made by instruments installed
uniformly and equally over the Earth’s surface. In reality, however, the density of
instruments already installed as observation points is very low. For instance, soil
moisture has been regularly observed only in Russia, Chiba, and a part of the US.
In recent years, advances in microwave instrument technology have suggested thepossibility of observing land water amounts under all weather conditions. Since
water has a high dielectric constant, microwave remote sensing can observe fixed,
quantitative water amounts by directly measuring the dielectric constant. Also,
microwave remote sensing has the advantage that polarization dependence ofemission effects of microwave transfer can be accommodated in the algorithm.
Research on algorithm development for microwave radiometers that measure soil
moisture considering the effects of vegetation was initiated based on an
experimental study using the microwave radiometers on the ground and in the air.
Furthermore, research on satellite use, employing AMMR and AAM/I, has been
conducted. These research activities are categorized into three parts.

14

#1 Soil Moisture Statistic Calculation Method, Taking Account of Vegetation
Effects#2 Estimates of Vegetation by Index#3 Real-Time Estimates of Soil Moisture/Vegetation/Surface Temperature
The first activity category calculates soil moisture with the effect of vegetation
removed by statistical methods, and introduces a vegetation index obtained fromvisible –near infrared spectrum data or vegetation effects estimated from land use
sections. The second category calculates vegetation biomass based on polarization
characteristics of scattering due to vegetation. The third category obtains theparameters corresponding to each hydrologic category by solving simultaneous
equations of radioactive transfer obtained from the observation data with various
frequencies and multiple polarizations. It is a generalized method based on
physics. Based on #3, E. Njoke formulizes problems as three simultaneous
equations and proposes a numerical solution method. Koike invokes method #2,
building his argument on the fact that a numerical solution method produces an
unstable solution and makes it difficult to physically understand the grast
convergence of the solution. He proposes an algorithm that calculates surface
physical temperature, removes the effects of surface physical temperature fromthree unknowns, obtains soil moisture and vegetation water content, and substituteits results into the radioactive transfer equation.
(2) Snow Accumulation
Effects of continental seasonal heating on seasonal or yearly changes in climate
were investigated from the viewpoint of changes in snow accumulation. Hahn andShukula have shown that there is an inverse correlation between the snow coverarea during winter on the Eurasia Continent and precipitation during Indian
monsoons the following summer. Observations have not yet verified observations
that snow cover variations between winter and spring greatly affect Asianmonsoons of the summer and atmospheric circulation. However, this has beenverified with the Global Circulation Models (GCM) that Burnet and Yasunari
worked on. The mechanism is remarkable not only due to the albedo effects ofsnow cover surface but also the influence that moisture due to melting snow has
on the surface heat budget of summer.
Numerous of algorithms have been applied to estimate snow accumulation.
Most of these are basically intended for dried snow, estimating only extinction
when radiation from the ground is transmitted into the of snow-cover layer,
estimating only extinction when emission due to scatters transmits energy into thelayer of snow cover, or adding emission to it. Most of them used 19GHz or
37GHz. If information about grain size is added, snow accumulation water content
could be estimated with high accuracy, and incorporating information about grain
size is key to algorithm development. Tsang combines snow cover models andmicrowave radioactive transfer models and develops snow accumulation
calculation models considering grain size change using neural networks. The
effect vegetation has on the snow accumulation calculation is also important.
Cheung has thus introduced statistical vegetation effect models that correspond tothe vegetation categories into a snow accumulation calculation algorithm. Koike
proposed a method that introduces extinction effects of vegetation estimated by
NDVI into the microwave radioactive transfer equation and estimates snow

15

accumulation water content and snow temperature simultaneously by using
19GHz and 37GHz. If snow cover were deep or grain size long, or extinction due
to the vegetation layer large, high-frequency information would be lost due toextinction effects. Consequently, for snow cover observation in regions withheavy snowfall, frost crust layers, or forests, it is important to develop algorithms
using low-frequency observation data, which AMSR enables for the first time.
(3) Vegetation, Ground Temperature, and Albedo
A number of algorithms have already been proposed for land-surface
information acquisition, and global products based on NOAA/AVHRR data havebeen provided. However, GLI has high spectral resolution and channels of 250m,
the same as LANDSAT/TM. Consequently, it will be possible to develop morequantitatively accurate global products than ever before possible.
Current problems include the lack of observation data about spectrum
reflectance factors and radiation from the visible spectrum to infrared received
from various types of land surfaces. Real-time quantitative observation data forland-surface classification has been acquired. However, parameters such as
surface albedo, absorbed photo synthetically active radiation (APAR), and land
surface physical temperature have rarely been observed with atmosphericparameters such as the atmospheric permeability factor, path radiance, or rate of
directly diffused solar radiation, and this has obstructed algorithm development.
Intensive and comprehensive real-time satellite observations with groundobservations are required, and long-term ground observation systems must be
prepared.
Moreover, methods to measure land surface features even more accurately havebeen advanced in recent years by the improved spatial resolution of the sensors.
These methods enable calculating the area rate of category items for each pixel
and are important research subjects when we consider higher-level land-surface
observation by GLI, which has moderate to high spectrum resolution. To establish
analysis methods and create global data sets, we must perform real-time specificsatellite observations within 1km pixel under various climatic and geographicalconditions and collect basic data for pixel resolutions.
(4) Precipitation
Precipitation is a hydrological process that balances moisture in lands regions.
We really need to perform the actual observations because estimates byatmospheric models have been unreliable. Precipitation phenomena occur in ashorter period and are more local than other hydrological phenomenon. Moreover,there are remarkable regions that show daily fluctuation. With the global
observation of precipitation, monthly data (2.5-degree grids) have been created by
combining ground rain gauge data, infrared data from geostationary satellites, andmicrowave radiometer data for the Global Precipitation Climate Project (GPCP)
under WCRP/GEWEX. Attempts to create one-degree grid data sets have also
been made. However, the relationship between precipitation in land regions andsatellite measurements has remarkable regional and seasonal characteristics. The
observation accuracy is low, especially in high-latitude regions and land regions
where only precipitation observations are performed.

16

In recent years, precipitation estimating algorithms have been developed by
combining microwave radiometers and thermal infrared radiometers. However,this is effective only over the ocean where the surface emission is limited. The
accuracy of estimates over land regions prepared using scatter algorithms is quite
low. The problems in observing precipitation over land are shown below.
#1. Since the land surface has a high rate of emission and is quiet uneven, it isdifficult to acquire signals for precipitation in the atmosphere.
#2. The precipitation system is influenced by topography and hydrologic states ofthe surface, and has complex spatial and timely changes.
It influences the vertical profile of precipitation and greatly affects theaccuracy of precipitation calculations.
#3. Precipitation in high-latitude regions usually is solid, but solid precipitation
calculation algorithms have not been developed yet.
For #1, an algorithm for calculating soil moisture, ground temperature, andvegetation water content for land surfaces using information acquired at low
frequencies (6.9 to 36.5GHz) has been proposed in recent years, and the accuracyof estimating the land surface radiation rate has been improved. Moreover,algorithm to calculate the state of the land surface and precipitation systemsimultaneously has been developed by combing the low-frequency band and highfrequency band (85GHz). However, in #2, the vertical profile of precipitation has
great influence, so active sensors for vertical profile observations and microwave
from low to high frequency require integrated observations using a radiometer. In
particular, the relevance of the vertical profile of solid rain grains in the upper
precipitation system has been pointed out, and integrated observations using highfrequency (35GHz) radar for solid precipitation and low-intensity precipitation are
expected in addition to the TRMM type 14GHz radar. Since it is difficult todistinguish between snow cover and snowfall especially when the land surface iscovered by snow, problem #3 and its related algorithm development are expected
to receive attention in the future.
Combining the precipitation radar, the microwave radiometer, and the visible
thermal infrared radiometer aboard TRMM, which was launched in 1997, hasenabled acquiring the vertical profile of raindrop backscatters as well as
microwave and visible thermal infrared radiation simultaneously for the first time.
Based on these data, development of new algorithms for estimating precipitation
over land will be developed. However, TRMM is limited to observing areas
between 35 degrees north and south latitude. Increased sampling frequency is also
necessary for precipitation data when changes are not remarkable. To solve this,we plan to conduct observations every three hours using microwave radiometers
aboard eight satellites (GPM). Improved accuracy of the microwave radiometerfor global precipitation is also expected.
Combining AMSR and GLI will provide higher resolutions for real-time global
data acquisition in microwave and visible thermal infrared bands. It raises thepossibility of global precipitation mapping in land regions based on algorithms for
estimating precipitation in land regions obtained by the TRMM. Also, by combing

17

products derived from ANSR-E and MODIS aboard EOS-PM1, we can expect toimprove of daily change observations and sampling frequency. This will enable
providing basic data sets for global observation by GRM.

4.3

Tasks of Algorithm Development and Validation

The major problem with algorithm development and validation for primary water
quantity is that land is much more uneven than the atmosphere or the sea surface.
Determining how to acquire ground validation data at 50km spatial resolution in
6.9GHz bands of the AMSR and what kind of effects unevenness in pixels has on
antenna brightness are especially important subjects. In this case, we must acquire
data over a large area with a uniform pixel size, consider its spatial distribution
characteristics, and estimate the average scale of spatial resolution of the satellite
sensor. Airborne observations are indispensable for data collection. In recent years,
the average hydrologic estimate method applied to complex land surfaces has beenthoroughly researched as a part of scheme development. This method integrated land
surface-atmosphere interaction into the GCM. It has been called the development of“macro hydrology model” and has become an important subject in the Japan-led
GAME project. “Macro” here does not mean a certain size but a method of converting
small-scale hydrology models to large-scale hydrology models without losing nonlinear properties. GAME seeks to improve the GCM, and research focusing on ameso-scale grid several tens of kilometers in size has been promoted. This is on thesame scale as AMSR’s spatial resolution at 6.9GHz and is a very effective strategy forusing satellite data and developing models to advance research and development.
Basic parameters of electromagnetic transfer needed to determine primary water
quantity must also be acquired for algorithm development. I particular, visibleinfrared spectrum reflectance factor data, satellite real-time observation data of land
surface water quantity, and atmospheric physics are unsatisfactory, and joint researchshould be promoted in hydrology, vegetation, and the atmosphere

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5.

CRYOLOGY

5.1

Development of Cryology with ADEOS-II

The tendency of increasing temperature since the Little Ice Age of the 18th
century and the effects of global warming due to recent increasing green house effectstrongly influence fluctuation in the snow ice sphere, especially in the polar regions.
Continuous observations are required because continuous monitoring of the sea ice
distribution and the ice sheet distribution in the snow and ice cover regions on the
Earth take a long time. Some effects directly caused by global warming directly are
the reduction of sea-ice distribution, the increase in snowfall above the ice sheet, theincreasing of the melting area surrounding the ice sheet, and the increasing outflowsof icebergs. Continuous, long-term observations to investigate the processes of thesecryological phenomena centered in the Polar Regions are very important.
The most important way to continue observing and monitoring the snow ice
sphere and the Polar Regions is to use satellite observations. The combined use ofmultiple sensors covering the visible spectrum to microwave frequencies is importantin understanding fluctuations in the distribution of the snow-ice sphere. However, useof microwaves is more effective since it is usually cloudy and nights are long in the
Polar Regions.
With GLI and the AMSR playing the central roles, ADEOS-II mission will beable to perform observations at higher resolutions than conventional sensors. For
instance, glaciers, ice sheets, and changes in the ice sheet edges can be mapped tomonitor the snow-ice sphere change due to the greenhouse effect. Albedo of ice sheet
and change in the characteristics of surface snow cover can be observed by using
SeaWinds and POLDER.
Earth glaciers have been receding due to rising temperatures since the Little Ice
Age. Although we have gained observational experience and conducted researchusing the models, we still do not know in detail the speed and process by which
glaciers grow due to the severe environment in the polar regions.
GLI has multiple channels and high resolution, and its stereovision might enabledetecting changes in topography and thickness. Moreover, GLI can detect opticalproperties of the snow ice surface as changes in the albedo of glacier, ice sheet surface,and sea that are closely related to temperature increases. In addition, spectrum
information obtained from GLI and POLDER us useful for detecting minerals in thesnow-ice surface and dirt due to bio-bacteria activity based on changes in the albedo
and spectrum. Polarization information provided by POLDER are useful for
estimating atmospheric correction and changes in albedo of the snow-ice surface.
The long-wave multiple polarization information provided by AMSR helps us
understand the interaction of snow-ice areas and the surrounding large ice sheets andthe interaction between the atmosphere and the snow-ice surface. These data areimportant for understanding the ice sheet-atmosphere interaction with matter and theprocess of mass balance in ice.

19

Interactions among sea ice in the polar regions, the ocean, and the biosphere aresubjects for sea-ice research. Monitoring the distribution of the water surface and thin
ice in sea ice and thick sea ice is useful for understanding primary biomass production.
To acquire information needed for these research subjects, we will investigate themass balance of sea ice calculated from sea-ice thickness and flow speed, heat transfer
between the atmosphere and the ocean, fluxes of gas and water vapor, effects of snow
cover on sea-ice growth processes, sea-ice growth rate, brine discharge rate ocean
concentration and circulation, radiation balance and change as reflected in the albedo
of the sea-ice surface, temperature of the sea-ice, and seasonal and spatial changes inprimary biomass production and their processes.
5.2

Change in distribution of Ocean Ice, Glacial and Ice Sheet /Science with
Microwave

AMSR can extract various hydrologic parameters, such as water vapor in theatmosphere, cloud water, precipitation, snowfall on land, soil moisture, sea nearsurface wind velocity, ocean temperature, sea ice, and snowfall on ice sheet in
Antarctica by estimating weak microwave signals emitted by the Earth’s surface.
Since microwave characteristics depend on the state of matter, water (H2O) gas, liquid,or solid phases can be discriminated with high accuracy multi-frequency techniques.
AMSR has a 2m diameter antenna that enables high spatial resolution and dataacquisition in low frequency bands (6 and 10GHz). By emphasizing high spatial
resolution and low-frequency data analysis, we will monitor and investigate
phenomena occurring in the snow ice sphere. Furthermore, microwave radiometerdata obtained from GLI and AMSR will be used together for analysis. Since global
data can be obtained in near real time, we will attempt to demonstrate practical use of
microwave radiometer data for forecasting and other activities.
To accomplish these research objectives, we will develop and improve algorithms
for AMSR using airborne microwave radiometer (AMR) data, for airborne
observation and the microwave radiometer (SSM/I) data of DMSP. Calibration anddata creation methods will be established to guarantee data quantity.
(1) Snowfall distribution, snowfall area, permafrost
Investigating the changes in snow cover distribution and area is very importantfor monitoring greenhouse effects. There are many research themes since the rateof radiation is not well known for land. First, seasonal changes in vertically andhorizontally polarized microwave radiation from the land surface in each category
will be investigated from the SSM/I data. Second, we will investigate AMSR dataand examine whether snow cover and soil moisture can be derived fromtemperature data. Since microwave radiation rapidly changes its phase as it passes
from water to ice or from ice to water, it will be relatively easy to detect thesurface condition.
(2) Sea-ice distribution and sea ice density
Seasonal changes of sea ice regions surrounding Antarctica are significant. A
vast area of 3,500,000 square km is covered by sea ice during the summer, and 20
million square km, during winter. Furthermore, the Eurasian Continent and theNorth American Continent surround the Arctic Ocean. Since most of the Arctic
Ocean is covered by sea ice, the increase and decrease of sea ice play important20

roles in controlling the heat balance and the ocean circulation. Yearly changes in
sea-ice distribution are important early indicators of global warming. It will bepossible for AMSR to roughly determine the distribution in the southern andnorthern hemispheres.
Although many sea ice observations have been performed since the early 1970s
using microwave radiometers, the relationship between emissive of sea ice andphysical characteristics has not been well understood. This is because the spatial
resolutions of the radiometers aboard the satellites were several kilometers,
producing a large mesh and the kind of sea ice was not uniform in that range.
When snow accumulates on sea ice, emissivity changes, and the temperature ofsea ice or snow sometimes changes by 10 degrees depending on the atmospherictemperature. To obtain basic data, it is necessary to match AMR the observations
using to truth data.
(3) Fluctuation in ice sheet distribution
Continuous microwave observations of changes in the ice-sheet distribution inGreenland and Antarctica are important for determining the influences of global
warming. The observations of snow accumulation on ice sheets, peripheral
melting regions, outflow of glaciers, and seasonal and yearly changes ofbrightness temperature of the entire area of the ice sheet are also important.
In observing the ice sheet mass balance in the Antarctic, changes due toaccumulation of ice contained in ice sheets in inland regions will contributes to thebalance of mass as snow ice of ice sheet, and the accumulation of ice contribute tochanges of the ocean surface. For that reason, it is important to develop basic
research algorithms development for ice sheet mass balance in Antarctic and its
elementary process by using AMSR and GLI. To understand the present state ofglobal warming and ice sheet mass balance in the Antarctic, algorithm
development for estimating the quantity of surface snow cover using AMSR has
been accelerated. It is important to compare data related to mass balance withother Earth scientific data and investigate elementary processes of mass balance ofglaciers and ice sheets.
5.3

Science with Optical Observations(1) Snowfall distribution and snowfall area
A problem that usually occurs in snow-cover distribution is how to distinguishbetween cloud and snow cover. The research on AVHRR has shown theeffectiveness of combining infrared and thermal infrared, and we expect that the
1.6µ m channels of GLI will further improve the accuracy. In addition, analgorithm for estimating snowfall distribution in continental forests will be
developed, and improved accuracy is also expected in this area.

21

(2) Sea-ice distribution and sea-ice concentration
It will be possible to roughly estimate the overall sea-ice distribution by using
AMSR. However, GLI will be needed to investigate polynyas that occur locallyand the difference in sea-ice concentrations because GLI performs real timeobservations with higher resolutions than AMSR. Also, AMSR and GLI together
should provide highly accurate estimates of sea-ice distribution. Knowing thesurface state of sea ice (snow cover distribution and emissivity above the sea ice)and surface temperature will be essential for determining the heat balance of thesea-ice sphere. Furthermore, the combination of AMSR and GLI will be able tocategorize the thickness of sea ice and types of ice bars.
(3) Ice-sheet fluctuation in the Polar Regions
Observations of changes in ice sheets in Antarctica and Greenland, andespecially glacial outflow, are important for monitoring greenhouse effects.
Recently, a large-scale (exceeding several tens of kilometers) shelf ice collapse
has been reported in the Arctic Peninsula. GLI might observe this phenomenon.
Although high-resolution sensors are required for examining changes of glaciersin detail, the observations are important from the aspect of observing change inlarge areas.
(4) Monitoring gigantic icebergs
In recent years, numerous outflows of gigantic icebergs have been reported andtheir relationship to global warming has been discussed. Moreover, monitoring
icebergs is important because gigantic icebergs are very dangerous to vessels
under way. GLI will effectively monitor gigantic icebergs because it will be able
to observe the Polar Regions in 250m resolutions almost every day.
(5) Snow-ice surface Albedo
The albedo of snow ice regions is an important parameter for examining the
Earth heat balance and global warming. In particular, ice-albedo and feedback is
the greatest factor involving climate in the snow ice sphere on the Earth. The
albedo of snow ice changes depending on the wavelength, snow cover grain sizeand shape, impurities in snow cover, snow cover density, water content, state of
the surface, and other factors. Research on developing models of wavelength,
snow grain size, snow shapes, and snow cover depth has been advanced withalbedo. It should become possible to estimate snow cover distribution and snow
depth by observing albedo. Since the GLI has 36 wavelengths for observing snowice regions, it will bring important data that may reveal relationships between thedetailed snow ice state of the surface and albedo.
(6) Snow-ice surface temperature
The surface temperature of snow ice regions is important for estimating thesnow ice surface. It is also important information for estimating heat balance andatmosphere-snow ice interaction in snow-ice regions, provided that better
information cannot be expected for only surface temperatures. It is additional
information for analysis on combination with other wavelength ranges.
(7) Physical characteristics of snow cover
The albedo of snowfall changes depending on wavelength, snowfall grain
shapes, impurities in snowfall, snowfall concentration, water content, state of22

surface, and other factors, but the relation between them has not been understoodyet. These characteristics of snow cover change depending on weather conditionsin the regions, atmospheric precipitation or elapsed time after it stops snowing.
Consequently, the physical properties of snow cover tell the history of that snow
cover. For instance, snow grain size can be estimated by using wavelengths from
1.1 to 1.3µ m. Because fresh snow grains are generally small, it might be possible
to estimate whether or not there is fresh snow in snow covered regions. In addition,
the albedo in the near infrared range increases when a blizzard occurs. It thus
might be possible to estimate the velocity of land near surface wind by using this.
In this way, since the GLI will observe snow ice regions at 36 wavelengths, GLI is
expected to reveal the detailed relation between the snow-ice state of the surface
has with albedo.

23

6.

Vegetation

6.1

Importance of observing vegetation with ADEOS-II

ADEOS-II is a general Earth-observing satellite. It seeks to investigate the energywater cycle process and carbon circulation, estimate primary production, and monitor
greenhouse gasses to advance Earth science and understanding of the Earth’s
environment. AMSR, GLI, ILAS-II, SeaWinds, and POLDER will be carried by the
ADEOS-II and perform comprehensive observations for this purpose.
Vegetation plays an important role in the energy and water budgets in land areas
through energy-water cycle processes. To investigate carbon circulation and estimate
plant primary production, it is important to estimate carbon fixed quantity in forests,
grassy fields, and farms. From the viewpoint of monitoring greenhouse gasses, it isalso essential to estimate vegetation change due to global warming. Global
observations of vegetation by ADEOS-II will enable research to clarify the energy
and material cycle for the first time. It will thus make it possible for us tocomprehensively understand the Earth’s environment.

6.2

Investigation into water-energy cycle process

The atmosphere-ocean interaction is important in the global energy balance andcycle and is the main theme for the ADEOS-II mission. Effects of vegetation in land
areas on the global energy cycle might be less than over ocean areas. However,human beings have developed their civilization by developing vegetation on land
areas, and vegetation has been influenced greatly by human beings. This results in a
global energy balance. The albedo of a forest area is 0.11 or less. In comparison, thealbedo of soil in bare land areas is 0.5 or more. Because changes in vegetationinfluence the Earth’s energy balance and cycle, it is very important to estimate
vegetation change.
Vegetation significantly influences land-water interaction in the hydrologic cycle
process. All vegetation has a high water content. Furthermore, plants reduce the speedof outflow and increase underground percolation. In this way, vegetation retains
rainwater in and under the ground. There is concern that the reduction in vegetation isincreasing the area of dry land.
AMSR microwave observations will be used to estimate soil moisture andvegetation; GLI will observe vegetation and the ground temperature to produce highresolution global optical data.

6.3

Assumption of carbon cycle and plant primary productivity

Research on the carbon budget and gas budget indicates large differences between
the models and actual measurements of carbon dioxide gas sinks. One cause is theuncertainty in estimates of absorbed carbon dioxide gas in land vegetation. For thisreason, it is necessary to create precise land cover and vegetation maps on a global
scale and to obtain biomass carbon precisely by using these maps.
The global vegetation maps used the most are based on the normalized differencesin vegetation index maps (NDVI) calculated by NOAA/AVHRR. AVHRR is sensitive
to soil colors but greatly disturbed by snow cover. It is thus necessary to develop a24

new method to estimate biomass using the GLI data and to conduct observations inthe short wave infrared range.
To precisely estimate carbon circulation in land regions, we must acquire timeseries data of absorbed photosythetically active radiation data. If we know the
distribution of plant primary production, we can determine the carbon-fixing ability
from plants and contribute to estimating carbon circulation. At the same time, we canobtain important data that contributes to estimating the maximum population that will
be able to live on the Earth.
GLI aboard ADEOS-II will play a central role in estimating the carbon andprimary production mentioned above.
6.4

Monitoring greenhouse effects

It is important to monitor permafrost and investigate the melting to understandgreenhouse effects. When permafrost melts, it releases the greenhouse gas methane
into the atmosphere. GLI is expected to directly measure permafrost melting.
It is important to estimate conditions in marshy areas of Siberia and to calculate
the greenhouse gas methane discharged from marshy areas to understand current
global warming and to predict it in the future. The global sensors AMSR and GLI,
Monitor such marshy areas.
The GLI and the AMSR will play central roles in monitoring greenhouse gasses.
6.5

Contribution to vegetation research and practical use

It is important to properly estimate the agricultural area to determine food reserves
to quickly understand food shortages and problems, and to predict the maximum
population that can live on Earth. The Food and Agriculture Organization of theUnited Nations (FAO) has been collecting information from agricultural agencies andorganizations in each country. However, it is now necessary to perform things more
quickly and precisely. Use of Earth-observing satellite has been considered for thispurpose.
It is important to estimate the planting and growing crops and to create
agricultural production maps and estimated yields maps. The growing period of major
agricultural crops is four to nine months, and several observations are required in thisperiod. Water is necessary for growing crops, and the agricultural products of mostareas are grown in the rainy season. In many cases, high-resolution optical sensors canobserve crops only once in this period. Frequent observations and moderate highresolution of GLI will enable creating those maps.
By overlapping data from many observations, we can detect deforestation anddesertification regions and create maps. By using the GLI data from manyobservations, we will detect fires in forests and grasslands and regions made arable byslash-and-burn methods. We will also be able too create biomass maps. Thesefrequent observations and the moderate high resolution of GLI will enable us toestimate emergency situations, such as forest fires.
GLI will play a central role in vegetation research.

25

7.

For Comprehensive understanding

ADEOS-II is a complex Earth-observing satellite carrying various sensors on alarge platform. The reason for carrying various sensors on a large platform like
ADEOS-II is not only to reduce the number of launches by sending many sensors up
at once, but also to provide various data about the Earth environment. Therefore, wemust consider how to use such sensor data to realize specific scientific purposes.
Furthermore, various sensors being carried means the volume of data acquired will
become huge and there will be various data processing systems. By combing suchdata, we will be able to gain new knowledge.
ADEOS-II scientific purposes(1) Estimate the fixed quantity of the water-energy cycle in the climate system(2) Estimate the fixed quantity of biomass and primary production involved ingreenhouse effects.
The on-board sensors have been selected to accomplish these scientific purposes.
First, sensors such as GLI, AMSR, POLDER and SeaWinds will comprehensively
estimate the water-energy cycle. Radiation characteristics of clouds and aerosols,
global precipitation distribution, cloud water, water-vapor distribution, sea-ice
distribution, and soil moisture distribution are important subjects in the global waterand energy cycle. Estimates of biomass in land and ocean regions area important forcalculating the chlorophyll a distribution and vegetation distribution. Considering
these points, the following are specific tasks for multiuse of the sensors aboard
ADEOS-II.
#1 Estimate cloud water and cloud grain size distribution by complex analysis of
AMSR and GLI data.
#2 Analyze aerosols over oceans and land by complex analysis of GLI and POLDER
data.
#3 Estimate snow cover and sea ice distribution by complex analysis of the AMSR
and the GLI.
#4 Improve the estimate accuracy of water using AMSR and GLI.
#5 Improve the estimate accuracy of sea surface conditions, such as sea-surface
temperature, sea near-surface wind, and surges by combining AMSR and GLI.
#6 Improve the accuracy of estimating vegetation biomass by combining GLI and
AMSR.
#7 Investigate chemical processes in the Arctic and Antarctic atmosphere by combing
ILAS2 and AMSR.
Practical algorithms will be developed in the next task. One example of theanalysis methods expected from the research currently being conducted will be to
distinguish cloudy regions and clear sky regions in the visible spectrum using GLI.
Another algorithm for water vapor and cloud water is applied to the AMSR data.
Water vapor and cloud water data will thus be acquired with high accuracy.
Finally, we major points for understanding. The important thing is that simply
increasing data does not simply meaning improve understanding.

26

(1) Data are related, not independent. For instance, the cloud data provided from GLI
and the water vapor data provided by AMSR will be related, and there is no
guarantee that information about clouds or water vapor will get better just because
these two sources are used. Algorithm must be developed with careful attention tothese points.
(2) The relation may not be known at all. For instance, suppose that ocean color data
is provided correctly from GLI, and we try to combine this with data of the ocean
near-surface wind provided by the SeaWinds. It seems there is a relation between
living creature’s activity and the physical condition of ocean. However, if
someone asks what specific relations there were, and we would find that we have
only a general common sense. In such cases, we would have to start fromunderstanding the relation background.
(3) New errors are introduced by introducing other data. These errors involve
calculations as well as physical aspects. It is therefore necessary to validate thedata while developing an algorithm.
Considering the above, a practical approach to comprehensive understanding byusing ADEOS-II data in each category is presented below.
(1) Since the problems are difficult, we should not use all data comprehensively but
should pursue subjects that we can understand now recognizing like #1 to #7.
(2) We should consider algorithm development for Four-dimension analysis. We cansay that combined use of satellite data and the climatic models is a key tocomprehensive understanding. For example, information about temperatureprovided by the infrared radiometer aboard the polar-orbiting satellite is used by
inputting it into a dimension data assimilation system, and we have thought ofvarious approaches like static methods or using models. We must think about how
to use cloud information acquired by ADEOS-II because we have hardly everconsidered this before.
This is a fundamental problem in achieving scientific purposes with ADEOS-II.
Since this is not an easy or simple task, long-term measures will be needed.

27

8.

Combination of remote sensing data with numerical value models

In the previous chapter, we stated that combining satellite data and climate models
is a key to using satellite data. In this chapter, we describe this more specifically.
Satellite observation from space has the advantage of observing the Earthuniformly with the same instrument. However, it also has the disadvantage of limited
observational accuracy due to indirect measurement. The former uniform observation
is best used for mapping invisible spectrum channels, but this advantage becomes a
disadvantage of limited observational accuracy if parameters for monitoring climatechange are conducted.
To improve the accuracy of estimating parameters provided by satellite
observations, it is necessary to develop a highly accurate algorithm. Such algorithm
development generally requires understanding physical phenomenon and groundvalidation data. However, ground validation data cannot always be obtained wheneveror wherever we want. Furthermore, not all parameters can be observed.
In other words, estimating parameters is the inverse problem, meaning that wewill seek the cause after we know the effects. To solve this inverse problem, we mustsolve the order problems to find causes and effects. The knowledge to solve this orderproblem is the same knowledge that supports the numerical models.
The history of combing satellite data and numerical models goes back tothe1970’s when researchers groped for how to use data to build the FGGE observation
system. The major parameters at that time were the temperature provided by the
infrared radiometers aboard the polar orbiting satellite and the cloud migratory vector
provided by geostationary satellites.
To obtain the temperature distribution from the infrared radiometer data, it isnecessary to solve the radiation transfer problem the other way around. The
expression for radiation transfer is a nonlinear equation. To solve it, we have to makeit linear by using the gaps from the first estimated value or by using successive
approximations. Furthermore, this is an inverse problem, and uniqueness of thesolution is not guaranteed. In the 1970’s, the first estimated values were inaccurate, so
there were few vertical levels and few channels, and the accuracy was not highenough. For this reason, the temperature distribution used to be obtained by using
statistically relational formula with the observed values of the temperature andinfrared radiation. The rough accuracy data was previously obtained using thismethod. However, forecasting values improved with the advance of numerical
forecasting models. As a result, the polar-orbiting satellite data and the cloud transfer
vector at middle to high latitudes are no longer used because their accuracy isinadequate. The accuracy is poor because the algorithm is bad. Numerical-forecast
models now have significant effects because of the physical method for satellite data
used by the European Centre for Medium Range Weather Forecast (ECMWF). In the
future, satellite data will be used by introducing the four-dimension variation method.
However, this method requires enormous computer resources, so cooperation withother projects like the Earth simulator is necessary.

28

Furthermore, basic parameters, like wind and temperature, and various otherparameters, like clouds and cloud water, and soil moisture needed for describing
climate systems, tend to be observed using the same satellite. However, numericalmodels are still necessary to estimate these new parameters over a large area fromsatellite data. One reason is that parameters needed directly do not come from satellite
observations. For example, consider cloud water. Cloud water is obtained fromcorresponding microwave radiation levels. In this case, values of temperature andwater vapor are required. It is generally impossible to determine all of these data
simultaneously from satellite observations. Moreover, the temperature field and water
vapor field obtained from four-dimensional analysis based on numerical models are
not observational data. Instead, they are proper combinations of sonde data, forecast
models, and forecast values, with high accuracy. The accuracy of cloud water
estimates can be improved by using such cloud water values.
Another approach is to estimate quantities like soil moisture, which is notobserved directly (some argue that it is possible to determine soil moisture by using
microwave signals, but the accuracy is bad). In this case, we can consider soil
moisture to be determined by precipitation, evaporation, and water streaming intorivers. The soil moisture will be obtained by applying precipitation and evaporation,
which are estimated from observations of atmosphere models, to soil moisture models
and accumulating time. Evaporation from the ground requires ground heating by
sunlight or other sources. These quantities can be estimated from satellite
observations. Consequently, combining satellite data and numerical models generates
parameters that cannot be derived from satellite data or numerical models alone. In
this sense, the combination of the numerical models and satellite data is a beginningof a new age.
The ADEOS-II satellite is suitable for such a new age. It will be necessary toestablish various methods combining analysis, numerical models, and satellite data
suitable for this new age. A specific example is shown below.
(1) Estimating and Checking Cloud and Cloud Water
Cloud distribution and cloud water are the least accurate parameters among
numerical models. So far, each model has used a scheme considered most likely to
be right, though not fully satisfactory, and it has been impossible to validate thesemodels until today. ADEOS-II carries both AMSR and GLI and will make itpossible to estimate cloud water and cloud quantity. In so doing, it will also enable
checking the schemes of models by using cloud water and cloud fraction obtained
from ADEOS-II. Consequently, forecasting accuracy of numerical models will
improve, as will the accuracy of primary estimated values for cloud and cloudwater obtained from ADEOS-II data. This will improve the accuracy of cloud andcloud water estimation. Thus, combing of models and satellite data enables
improving the accuracy of both.
(2) Retrieving Temperature Field below the Ocean by Using Sea-Surface Temperature
and Ocean Near-Surface Wind
Satellites cannot observe subsurface ocean water directly. However, thecondition of such subsurface water is basically determined by boundary conditionsof the ocean surface. By inputting ocean surface fluxes, we can retrieve thecondition of the subsurface ocean with ocean circulation models.

29

ADEOS-II will carry a microwave scatterometer called SeaWinds, which is thefollow-on to the NSCAT satellite that flew aboard ADEOS. Oceanographers
worldwide have been anticipating its use. Ocean stress obtained from this sensor
can drive ocean circulation models. Of course, there are other data such as ocean
temperature. It should be possible to describe the temperature field of subsurface
ocean water using these data and the stress on the ocean surface.

30

9.

Field program

The field program that accompanies the satellite launch is not only a groundobservation program to acquire validation data for the sensor aboard the satellite. It isalso a scientific project having definite scientific objectives and will conduct researchby combining various data such as satellite observations, ground-observation data,and numerical modeling. This scientific project must be open to researchers
worldwide and must incorporate an implementation plan, data management, andproject centers. Previous field programs for satellite observations have lacked this
aspect. The previous satellite validation experiments were conducted separately only
for satellite observations. Data for validation was limited since it used to be difficult
to implement a global scale field campaign encompassing land, atmosphere, andocean data. Also, data obtained for validation or data products was rarely used for
Earth-science research. The Earth-science research groups, taking the satellite
observations into account, have proposed a satellite launch project with research
objectives. Nonetheless, it is rare to implement field observations simultaneously witha new satellite from practical viewpoints and usually goes no further than using
products of the satellite. In 1998, the cooperative GAME validation experiment andthe TRMM project, which observed the Asia monsoon under WCRP/GEWEX, wereconducted in the Tibet plateau and Thailand, and comprehensive, valuable data forenergy-water cycle research was acquired. Based on this cooperative and successful
research, it is even more desirable to establish an effective and cooperative
observation system of field observations of satellite validation experiments and Earthscience research.
A subprogram of the World Research Climate Program (WCRP), the Global
Energy and Water Cycle Experiment (GEWEX), has proposed an international
observation system synchronized with new Earth observing satellites (ADEOS-II,
EOS-AM/PM, and ENVISAT) launched by 2000, with the cooperation of fivecontinental-scale observation experiments in progress, cloud and radiation researchpanels, and model panels. Based on this, WCRP has planned the CoordinatedEnhanced Observing Period (CEOP) for the atmosphere, land, and ocean to address
problems such as yearly climate change and teleconnection, with the cooperation of
CLIVAR, which is also one of the subprograms. GEWEX investigates this global
energy-water cycle, modeling and evaluating influences on water resources. In
contrast, CLIVAR seeks long-term prediction of climate change including global
warming, focusing on investigation into the role the ocean plays in long-term climaticchange and on development of models. CEOP was proposed as the first international
project for WCRP, which connects these two subprograms. The objective of CEOP issummarized as follows.
To understand and model the influence of continental hydro climate process on
the predictability of global atmospheric circulation and changes in water resources,
with a particular focus on the heat source and sink regions that drive and modify theclimate system and anomalies.
As mentioned above, the main research objective of CEOP is to understand thediversity of interaction between the atmosphere and land surface; to develop, validate,and improve the scheme to confirm its transferability; and to improve the accuracy ofPredicting influence that teleconnection and seasonal and yearly changes in the31climate system have on water resources. For that purpose, it is necessary tocontinuously observe a fixed quantity of kinetic momentum of energy and water in theatmosphere, land surface, and ocean on various scales. Ideally observation
instruments should be distributed equally over the Earth, but ocean and land
distribution and large mountains make this impossible. As a result, the concept ofobserving the entire Earth with instruments carried by satellite has been adopted. In
this regard, researchers have been eagerly awaiting ADEOS-II. CEOP will provide thefirst opportunity for building an Earth-observing system by a practical group work of
the WCRP and satellite organizations and to promote the research of climate andwater resources.
For several years, beginning in 2001, it will be possible for the first time in humanhistory to observe the energy-water cycle multilaterally and comprehensively on local
to global scales and on large temporal scales from daily changes to seasonal andyearly changes. Performing observational experiments under various weather
conditions on a global scale synchronized with ADEOS-II and other large satellites isnecessary to acquire data for algorithms and data set validation and to establish global
satellite technology. These experiments will contribute to research on seasonal andyearly climatic change due to interaction between the atmosphere, land surface, andocean and on the influence such change has on water resources, and to build
fundamental to develop the results on a global scale. This is the CEOP strategy, and it
seeks to be effective by using the GEWEX heritage, adjusting the observation plan of
the on-going LIVAR, revising the plan concept if necessary and performing intensive
observations in the same timeframeing cooperation with satellite ground validation
experiments and the weather forecast organizations that are supported by space
agencies.

32

Chapter 2 Mission Instruments and Products

1.

GLI

1.1

Scientific objectives

One of NASDA’s two sensors aboard ADEOS-II, the Global Imager (GLI), isJapan’s first middle-scale optical sensor that produces spectral resolutions and spatial
resolutions with 36 channels, arranged in a mechanically scanning model. The GLI
characteristics are summarized below.
(1) GLI has more visible spectrum radiation channels than other ocean-color sensors
and atmospheric sensors. It also has atmospheric correction channels necessary for
ocean-color observations and large dynamic range channels necessary for land
observations.
(2) It has a tiltable scanning mirror to observe ocean color in the middle latitude
regions.
(3) It has channels with 250-meter resolution that are based on LANDSAT/TM; thesewill perform global surface observations with intermediate resolution.
(4) It has important spectrum channels, such as 0.38 µm in the near-ultraviolet region,
0.76µ m in the oxygen-absorption band, and 1.4µ m in the water-vapor absorption
band. For comparison, the EOS-AM/MODIS doesn’t have the 0.38 µm or 0.76µm
channels.
(5) It has 1.6, 2.2, 3.7, 8.3, 10.9, and 12.0µm channels as atmospheric windows. Only
MODIS and a few other middle-scale sensors are equipped with these channels.
The GLI also has 6.0, 7.6, 7.0, 7.3µ m channels that will provide indices of water
vapor vertical distribution.
With these characteristics, GLI and the Advanced Microwave ScanningRadiometer (AMSR) aboard ADEOS-II will be a good combination for monitoring
the lower layer of the atmosphere and the surface of the Earth. Specifically, the GLI
has absorption channels for atmospheric molecules extending from the visible
spectrum to thermal infrared and many channels in atmospheric windows. AMSR
contributes a 6.9GHz channel and other channels that are sensitive to both the surfaceof the Earth and clouds. These channels have better S/Ns than OCTS and the AVHRR,
and improvement of each sampling parameter is expected.
Employing the full abilities of GLI, the mission has the important purpose ofdeveloping and providing the following higher order processed products.
(1) Biomass and Primary Productivity Related to Ocean and Its Annual Change on a
Global Scale
The activities of marine creatures will be observed by the great ocean color
sensor, GLI, to enable more precise and accurate prediction of the matter andenergy cycle in the ocean. To investigate the oxygen cycle on a global scale, it isnecessary to make a time series of long-term biomass and primary productivity
measurements and to analyze average values and patterns of yearly changes. Useof various GLI wavelengths of the ocean channels will enable obtaining not only

33

chlorophyll a under the ocean but also the concentration of dissolved organic
materials and suspended minerals in coastal regions and other materials
originating from land. It will also improve the precision and accuracy of
estimating chlorophyll a in the coastal regions.
(2) Biomass and Primary Productivity Related to Land and Its Seasonal and Annual
Changes on a Global Scale.
Research on land vegetation, considered one of the missing carbon dioxide
sinks, will be carried out. Using six channels with 250-meter resolution, we will
generate global maps of detailed land biomass. Such maps will reveal seasonal
and annual changes in quantitative land carbon accumulation and the precise
details of the carbon cycle process. Interaction between vegetation and the climate
is remarkable and will be analyzed as well.
(3) Clouds, Water Vapor, and Aerosols
Research will be conducted on quantitative dynamics to understand various
feedback mechanisms in simulations of the global warming caused by clouds,
water vapor, and aerosols and other indefinite factors. It seems important
especially to investigate the quantity of lower layer and upper layer clouds, verysmall physical characteristics of clouds, water vapor distribution, and theinteraction between clouds and tropospheric aerosols.
(4) Full Monitoring of Processes Near the Surface of the Earth
Monitoring the lower layer of the atmosphere and the surface of the Earth is an
important GLI task to facilitate understanding of Earth-surface processes. It isespecially important to monitor the Polar Regions since these areas are very
sensitive to greenhouse effects. GLI will have a four-day sub-current orbit and has
250m-resolution channels. It is thus suitable for investigating Earth surface
conditions. For that reason, the data sets of geophysical parameters suitable fordetailed monitoring of the surface of the Earth will be generated.

34

1.2

Overview of Mission Instruments

GLI is an optical sensor that carries out frequent global observation of the solar
and infrared radiative light reflected from the atmosphere as well as from the Earth’s
surface, including land and sea areas. The objective of this system is to measuregeophysical parameters such as the chlorophyll concentration, dissolved organic
matter, surface temperature, vegetation distribution, vegetation biomass, aerosols,
cloud water distribution, and snow-ice albedo. These data are to replace dada that
was acquired by OCTS which was installed on ADEOS. The data should also further
expand the observation accuracy and objects. These missions include understanding
the global circulation of carbon; monitoring clouds, cloud-ice, and sea-surface
temperatures (which are indices of climate change); and understanding the basic
marine productivity.
GLI has 23 channels in the visible near infrared range (VNIR), six channels in the
short-wave infrared range (SWIR), and seven channels in the mid-thermal infrared
range (MTIR) for carrying out multispectral observation. The land-surface resolution
is 1km for a point directly below it. Some of the VNIR and SWIR channels have aresolution of 250m directly below the satellite and are capable of observing vegetationand clouds. The observation range with one scanning is 12 pixels (12km) in thealong-track direction and 1600km wide. The GLI carries out observation bymechanically rotating its two-sided mirror and scanning in the direction perpendicular
to the along-track direction. In order to request solar rays reflected from the sea
surface from directly entering the sensor and saturating it (sun glitter), the system isequipped with a tilt function, whereby the views point of observation can be moved
by approximately 20 degrees in either way with respect to the along-track direction.
Furthermore, out of the 36 observation wavelengths of the GLI, three VNIR bands(443, 565, and 666 nm) and one MTIR band (12.0 µm) transmit data, spread to aground resolution of 6 x 6 km, to local users via the 467.7 MHz (UHF) band (DTL).
The main specifications of the GLI are shown in Table 1.2-1. Figure 1.2-1 shows theobservation concept of the GLI.
Table 1.2-1
Item
Observation
Wavelength Band
Space Resolution
Measured Width
Data Rate

VNIR (nm)
SWIR (nm)
MTIR (µm)

1 km
250 m
1 km
250 m

GLI Main Specifications
Specification
380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 678,
680, 710, 710, 749, 763, 865, 865
460, 545, 690, 825
1050, 1135, 1240, 1380
1640, 2210
3.715, 6.7, 7.3, 7.5, 8.6, 10.8, 12.0
1 km or 250 m
1600 km
1 km Resolution: 3.9 Mbps
250 m Resolution: 16 Mbps*1
6 km Resolution: 23,529 Mbps

*1. During downlink, dummy data are added, and the rate will be 60 Mbps.

35

GLI Observation Concept

Along-Track Direction

1600kmScanning Direction by Mirror Rotation (Cross-Track Direction)

12 pixels or 48 pixcels
12km

Figure 1.2-1 GLI Observation Concept
Table 1.2-2 shows the GLI operation mode during a regular operation period. The
GLI mode-transition pattern for one complete rotation is shown in Table 1.2-2.

36

Table 1.2-2
Mode
Daytime
Observation ModeNight Observation
Mode
Solar Ray
Calibration Mode

Internal Lamp
Calibration Mode

Electric Calibration
Mode
Maintenance/ SafeMode

GLI Operation Mode during a Regular Operation Period
Operation Overview
Observation is made on all channels in grounddaytime zones; depending on the tilt angle, there are
0 degree mode, 20 degree mode, and – 20 degree
mode.
Observation is made using MTIR channels inground nighttime zones.
Mode to obtain calibration output for VNIR for
solar ray entering the GLI solar calibration window
when the solar ray begins to enter the sensors.
Optical calibration of VNIR and SWIR is
performed using the halogen lamp inside the GLI.
The “A” mode has 0 tilt angle; the “B” mode has atilt angle of +20 degree.
The electrical system is calibrated by the input of
six-step pseudo signals to pre-amp (VNIR, SWIR)
and post-amp (MTIR). The electric calibration
mode has 0 tilt angle; the electric +20 degreecalibration mode has a tilt angle of +20 degree.
The temperature of instruments is maintained
within the operational temperature range.

Application Conditions
Normal observation mode forground daytime zones
Normal observation mode forground nighttime zones.
Mode to which the satellite shifts
from the night observation mode orother calibration modes during eachrotation when the solar ray begins
to enter the sensors.
Mode to which the satellite shifts
from the night observation mode
once a week during satellite’s night
time.
Mode to which the satellite shifts
from the internal-lamp calibration
mode once a week during satellite’snight time.
Mode when a light-load-mode(LLM) command*1 is issued or
during orbit-attitude angle control*1. When there is trouble with the satellite, this command is issued by the on-board computer (OBC) installed on
ADEOS-II to each installed instrument as an autonomous operation of the satellite.

Figure 1.2-2 GLI Mode-Transition Pattern

37

1.3

Specific

1.3.1 Types of Higher-order Products(1) Products created in EOC (including geophysical parameter):Standard product, Near-real-time products, Browse data
(2) Products created in EORC (including geophysical parameter):
Near-real-time products, Research products
1.3.2 Definition of Higher-order Products
1.3.2.1 Standard ProductsStandard products are created by EOC and divided into planning production andorder production. After products are produced, they are stored in EOC and provided
for general users.
(1) A transition diagram for the standard product until Level 3 STA Map is shown in
Figure 1.3.2-1.
(2) The standard products described in Figure 1.3.2-1 and the geophysical parametercomprising each product are shown in Table 2.2.1-1.
(3) The standard algorithm code described in Figure 1.3.2-1 is listed in Table 1.3.2-2.
(4) The standard products produced in Level 2 processing from Table 1.3.2-1 and thelist of the geophysical parameters comprising each product are shown in Table
1.3.2-3.
1.3.2.2 Near-Real-Time Products
Near-real-time products will be created and distributed by EORC until EOC’s dataprocessing system enters the regular operation phase. (See NEB-98020 for more
specific information.) Its purpose is to distribute products in real time to specialorganizations and agencies. When EOC’s data processing system enters the regular
operation phase, EOC will create and distribute near-real-time products.
(1) Near-real-time products are created by applying standard algorithms to GLI-1km
data received at EOC.
(2) Level-2 sea-surface temperature (ST), level-2 map sea-surface temperature (ST),chlorophyll a (CHLA), and normalized water-leaving radiance (NW) are producedin near real time.
1.3.2.3 Browse Data
Browser data is the source data used to create the image catalog in EOC.
Chapter 1.3.5. describe services to display the image catalog (browse image)through EUS/GUI or EUS/WWW.

38

1.3.2.4 Research ProductResearch products are created from algorithms developed by EORC. The
algorithm to create this product is for research purposes and its quality is notguaranteed. Furthermore, it is not defined as a product to be created by EOC.
(1) Table 1.3.2-4. lists geophysical parameters comprising the research products.

39

GLI Standard Product Flow(1/3)

Ver.1.9b
Jun. 19, 2000

Level-0Legendprocess (program)planning production

Level-0

process (program)ordering production
L1B
L1B Generation
Generation

Level-1A

NASDA

Level-1

L1A
L1A Generation
Generation

product (file)

NASDA

Level-1B

ATSK1,2/CTSK1
ATSK1,2/CTSK1

Level-2A(CLFLG_p, PGCP:Level-2)

S.Ackerman/K.Stamnes
CLFLG_pLTSKGLTSKGOATSKDOATSKD

PGCP

T.HashimotoNASDA

LTSK10d
LTSK10d

PGCI(daily composite)(workfile)Level-2A_OA

L2ALC
_wk2

LTSK10f
LTSK10f(final composite)(workfile)LTSK10A.Huete/NASDA

ATSKD
ATSKD

pre_ATSK3_p
pre_ATSK3_p

NASDA(workfile)segment data

T.Nakajima
ice/water
flag(workfile)
ATSK3_p
ATSK3_p
T.Nakajima

Rmin
Rmingeneration(4days)
generation(4days)
NASDA
Rmin
Rmingeneration
generation
NASDA

Level-2A_LC

Rmin(4days)

OTSK1a_LR
OTSK1a_LR(workfile)

H.Fukushima

OTSK13_LR
OTSK13_LR

OTSK1a_FR
OTSK1a_FR

H.Kawamura

OTSK13_FR
OTSK13_FR

H.Fukushima

LTSK1
LTSK1

H.Kawamura

CTSK2b1_g
CTSK2b1_g

A.HueteCTSK2b1_s
CTSK2b1_s

K.Stamnes

K.Stamnes

Rmin

CLOP_p*1 (28days, workfile)

ATSK3_e
ATSK3_e

T.Nakajima
work_r_w

Detailed
Classification
CLFR

T.Nakajima

work_r_i

work_e(workfile)
RoughClassification

ATSK16
ATSK16
T.Takamura
CLTT_w_r

CLER_w_r

ATSK5
ATSK5

CLHT_w_r

ST_LR

NL_FR

ST_FR

ACLC

T.Nakajima
OTSK2/5/7_LR
OTSK2/5/7_LR

OTSK2/5/7_FR
OTSK2/5/7_FR

B.G.Mitchell

B.G.Mitchell

post_ATSK5
post_ATSK5

OTSK6_LROTSK6_LROTSK6_FROTSK6_FR

T.Nakajima

M.Kishino

M.Kishino

work_ar

CLTT_i_e
AROP

CLOP_w_r

NL_LR

Level-2

ATSK3_r
ATSK3_r(workfile)

L2ALC
_wk1

1 program(OTSK2567_LR)

LTSK9
LTSK9

*2

A.Huete1 program(OTSK2567_FR)
CS_LR

CS_FR

VGI

SNGI

SNGI_p

CLER_i_e
ARAE

CLOP_i_r

CLWP_w_r

Daytime

CLOP_i_e

Nighttime

AtmosphereOcean*1) ATSK3_r should be executed 2 times. One is to calculate *_w_r products and another is for *_i_r products.

Land

Cryosphere*2) The input data of LTSK9 is L2A_LC at the launch time. But ACLC will be used instead of L2A_LC after ACLC validated.

Figure 1.3.2-1 GLI Standard Product Flow (1/3)
40

GLI Standard Product Flow (2/3)

B

A

NL_FR

ST_FR

CS_FR

VGI

SNGI_pSNGI

Level-2

Legendprocess (program)planning production
process (program)ordering production
L2Map
L2MapGeneration
Generation

L2Map
L2MapGeneration
Generation

L2Map
L2MapGeneration
Generation

product (file)

L2Map
L2MapGeneration
Generation

SNGI_p

B

LA

CHLA

SS

QF_OC

CDOM

K490

NW

B: One of the following
products
CLFLG_p, CLOP_p*1
L3ASBin
L3ASBinST

QF_ST*L3ASBin = Level-3 Spatial
Binning Processing for
Atmosphere products

L3LSMap
L3LSMap(workfile)

L3ATBin
L3ATBin*L3ATBin = Level-3 Temporal
Binning Processing for
Atmosphere products

L3CSBin
L3CSBin

VGI(L3StaMap 16days)* Level-3 Ocean Products
are shown in next page.

SNWG
SNWI

L3CSMap
L3CSMap

SNWG

L3CSMap
L3CSMap

SNWI(L3Bin 16days)
L3ASMap
L3ASMap

A’(L3Bin 16days)(L3StaMap 16days)

A’(L3StaMap 16days)*L3ASMap = Level-3 StaMap
Product generating processing
for Atmosphere

L3CTBin
L3CTBin
All L2Map, Level-3 algorithms
are to be developed by NASDA.

L3ATBin
L3ATBin

L3CTBin
L3CTBinA’

L3ASMap
L3ASMap(L3Bin 1month)A’(L3StaMap 1month)
SNWG*1) L3ASBin in/out table
<in>
<out>
work_r_w : CLTT_w_r, CLOP_w_r, CLER_w_r, CLHT_w_r, CLWP_w_r
work_r_i : CLOP_i_r
work_e : CLTT_i_e, CLER_i_e, CLOP_i_e
work_ar : AROP, ARAE,
CLFR : CLFR

Atmosphere

SNWI

L3CSMap
L3CSMap

SNWG

L3CSMap
L3CSMap

SNWI(L3Bin 1month)

Ocean

Land

Figure 1.3.2-1 GLI Standard Product Flow (2/3)
41

Cryosphere(L3StaMap 1month)

Level-3

A’: One of the following products
CLTT_w_r, CLOP_w_r, CLER_w_r, CLHT_w_r,
CLWP_w_r, CLOP_i_r, CLTT_i_e, CLER_i_e,
CLOP_i_e, AROP, ARAE, CLFR

A’

Level-2Map

A: One of the following products
work_r_w, work_r_i, work_e,
work_ar, CLFR

L2Map
L2MapGeneration
Generation

GLI Standard Product Flow (3/3)

ST_LR

Level-2

CS_LR

NL_LR

Legendprocess (program)planning production
process (program)ordering production

L3OSBin
L3OSBin

NW

L3OSBin
L3OSBin

L3OSBin
L3OSBinCS

ST

product (file)LA(workfile, every path)(workfile, every path)

L3OTBin
L3OTBin

L3OTBin
L3OTBin

L3OTBin
L3OTBin

NW

L3OSMap
L3OSMap

NW

L3OSMap
L3OSMap

LA

CS
LA(workfile, every path)(L3Bin 1day)

L3OTBin
L3OTBin

L3OSMap
L3OSMap

SS

CDOM

K490

ST_allST

L3OSMap
L3OSMap

ST_DayNight(L3Bin 1day)(L3StaMap 1day)(L3Bin 1day)

CHLA(L3StaMap 1day)(L3StaMap 1day)

L3OTBin
L3OTBinLevel-3

L3OTBin
L3OTBin
L3OTBin
L3OTBin

L3OTBin
L3OTBin

NW

L3OSMap
L3OSMapCS
LA

L3OSMap
L3OSMap(L3Bin 8days)

CHLA

SS

CDOM

K490

ST_allNW
LA(L3Bin 8days)

L3OSMap
L3OSMap

ST(L3Bin 8days)(L3StaMap 8days)

L3OSMap
L3OSMap(L3StaMap 8days)

ST_DayNight(L3StaMap 8days)

L3OTBin
L3OTBin
L3OTBin
L3OTBin

L3OTBin
L3OTBin

NW
LA(L3Bin 1month)

L3OSMap
L3OSMap

NW

L3OSMap
L3OSMap

LA(L3StaMap 1month)CS(L3Bin 1month)L3OTBin
L3OTBin

L3OSMap
L3OSMap

CHLA

SS

CDOM

K490

ST_allST

L3OSMap
L3OSMap

ST_DayNight(L3Bin 1month)(L3StaMap 1month)

Ocean

Figure 1.3.2-1 GLI Standard Product Flow (3/3)
42(L3StaMap 1month)Table 1.3.2-1 Product Codes and Geophysical Parameters
discipline

product code

Atmosphere ARAE

coverage time resolution
globalonce/4days

Geophysical parameters

globalonce/4days

Aerosol optical thickness

CLER_w_r/i_e

globalonce/4days

Cloud effective particle radius

once/4days

Cloud fraction of 10 cloud types. (10 geophysical parameters,
CLFR0, CLFR1, ..., CLFR9)
Cloud top height

scene

CLFR

global

CLHT_w_r

global

CLOP_pscene

discipline

Aerosol angstrom exponent

AROP
CLFLG_p

Table 1.3.2-2

Commononce/4days

Cloud optical thickness

CLTT_w_r/i_e

globalonce/4days

Cloud top temperature

CLWP_w_r

sceneonce/4days

Cloud liquid/ice water path

NL_LR

path

14.25path/day

each path

Data processing for Level-2A_OA(sampling, scene connection, etc)

planeach scene

Algorithms for identifying clear sky and cloudy region

ATSK3_p

order

---

Retrieval algorithms of cloud parameters(pixel by pixel)

ATSK3_r

plan

4 daysRetrieval algorithms of cloud parameters(segment) (by reflection method)

ATSK3_eplan

4 daysRetrieval algorithms of cloud parameters(segment) (by emission method)

ATSK5

plan

4 daysRetrieval algorithms of aerosol parameters

ATSK16plan

4 daysAlgorithms for cloud type and fraction

ATSKD

plan

4 daysData segmentation algorithm for atmosphere

Rmin Gen.4

plan

4 daysMinimum reflectance generation program (4 atm. seg. → Rmin-4days)

Rmin Gen.

plan

4 daysMinimum reflectance generation program (7 Rmin-4days → Rmin )OTSK1a_LR

planeach path

Atmospheric correction algorithm

Normalized water-leaving radiance-NWLR(Normalized water-leaving radiance (13bands), Aerosol
radiance(4bands), α(749/865), τa at 865nm)-QF_OC(Quality flag for ocean color)

CS_LR

path

14.25path/day

Ocean(LR) OTSK2_LR

planeach path

Chlorophyll-a algorithm

Chlorophyll, SS and other parameters

OTSK5_LR

planeach path

K490 algorithm-CHLA(Chlorophyll-a),

OTSK6_LR

planeach path

Suspended solid algorithm-CDOM(Absorption of colored dissolved organic matter)-K490(Attenuation coefficient at 490nm)

OTSK7_LR

planeach path

Colored dissolved organic matter algorithm

OTSK13_LR

planeach path

SST(bulk) Algorithm---

Atmospheric correction algorithm-SS(Suspended solid weight)(FR) OTSK1a_FR order-QF_OC(Quality flag for ocean color)
ST_LR

path

14.25path/day

Sea surface temperature-SST_b(Bulk sea surface temperature)

NL_FR

CS_FR

scene

scene

globalscene

once/16days

VGI

globalonce/16days

Cryosphere SNGI_p

sceneonce/16days---

K490 algorithm
Suspended solid algorithm

---

order

---

Colored dissolved organic matter algorithm-NWLR(Normalized water-leaving radiance (13bands), Aerosol
radiance(4bands), α(749/865), τa at 865nm)-QF_OC(Quality flag for ocean color)

OTSK13_FR order---

SST(bulk) Algorithm

LTSKG

plan

each path

Precise geographical position

Chlorophyll, SS and other parameters

Land

LTSK1

plan

16 days

Algorithms for atmospheric correction and reflectance-CHLA(Chlorophyll-a),

LTSK9

plan

16 days

Vegetation Index Algorithm-CDOM(Absorption of colored dissolved organic matter)-K490(Attenuation coefficient at 490nm)

LTSK10d

plan

1 day

Data mosaicking (daily mosaicking)

LTSK10f

plan

16 days

Data mosaicking (final selection)

Cryosphere CTSK1

planeach scene

Cloud detection algorithm (1a:cloud/snow discriminator,1b:snow/ice discriminator)plan

16 days

Algorithm for snow grain size and impurities(global) CTSK2b1_g(scene) CTSK2b1_s

order

---

Algorithm for snow grain size and impurities

order

each scene

Map projection algorithm for scene type products-QF_ST(Quality flag for SST)

L2Map

L2Map Gen.

Atmospheric corrected data for land and cryosphere
Precise geometric corrected parameter

L3

L3ASBinplan

1day, 4 days

Atmosphere Spatial Binning algorithm

L3ATBinplan

16days, 1monthAtmosphere Temporal Binning algorithm

Vegetation index

L3ASMap

plan

16days, 1monthAtmosphere L3Sta Map product generation algorithm

Snow grain size and impurities-SCFG(Snow/Cloud flag)

L3OSBinplan

each path

Ocean Spatial Binning algorithm

L3OTBinplan

1, 8days, 1month Ocean Temporal Binning algorithm

L3OSMapplan

1, 8days, 1month Ocean L3Sta Map product generation algorithm

Snow grain size and impurities

L3LSMap

plan

16days-SCFG(Snow/Cloud flag)

L3CSBinplan

16daysCryosphere Spatial Binning algorithm-SNWI(Snow impurities)

L3CTBinplan

1monthCryosphere Temporal Binning algorithm-SNWG(Snow grain size)

L3CSMap

plan

16daysCryosphere L3Sta Map product generation algorithm-SNWG(Snow grain size)
global

order
order

-SNWI(Snow impurities)
SNGI

Chlorophyll-a algorithm

OTSK5_FROTSK7_FR

Sea surface temperature

ACLC
PGCP

---

OTSK6_FR-SST_b(Bulk sea surface temperature)
Land

order

-QF_ST(Quality flag for SST)-QF_OC(Quality flag for ocean color)
scene

OTSK2_FR

Normalized water-leaving radiance-SS(Suspended solid weight)
ST_FR

processplan_w:water cloud, _i:ice cloud, _r:by reflection method, _e:by emission method
Ocean

processing
intervalOATSKD

Cloud optical thickness(pixel by pixel)

CLOP_w_r/i_r/I_e global

product

Atmosphere ATSK1/2

Cloud flags

once/4days

algorithm
code

Algorithm Codes

43

Land L3Sta Map product generation algorithmTable 1.3.2-3

1. AtmosphereStandard Product
Classification
Aerosol property(14 products)

Cloud property

2. OceanAtmospheric Correction
Products

Discipline(6 products)
In-water Particles
Products

SST Products
3. Land(3 products)

Atmospheric correction
Precise Geolocation
Vegetation index

4. Cryosphere(2 products)

Snow grain size/
Impurities

List of GLI Level-2 Standard Products

Product Details (Product Code)Geophysical Parameter (Code)

Segment
analysis
Pixel-by-pixel
analysis
Segment
analysis

Aerosol Angstrom Exponent (ARAE)
Aerosol Optical Thickness (AROP)
Cloud flag (CLFLG_p)
Cloud Optical Thickness (CLOP_p)
Cloud Effective Particle Radius of water cloud by reflection method (CLER_w_r)
Cloud Effective Particle Radius of ice cloud by emission method (CLER_i_e)
Cloud Optical Thickness of water cloud by reflection method (CLOP_w_r)
Cloud Optical Thickness of ice cloud by reflection method (CLOP_i_r)
Cloud Optical Thickness of ice cloud by emission method (CLOP_i_e)
Cloud Top Height of water cloud by reflection method (CLHT_w_r)
Cloud Top Temperature of water cloud by reflection method (CLTT_w_r)
Cloud Top Temperature of ice cloud by emission method (CLTT_i_e)
Cloud Liquid / Ice Water Path of water cloud by reflection method (CLWP_w_r)
Cloud fraction of 10 cloud types (CLFR)- Normalized water-leaving radiance (NWLR)
Full resolution (1-km resolution, scene unit) (NL_FR)
that consists of Normalized water-leaving radiance (13 bands), Aerosol
Low resolution (4-km resolution, path unit) (NL_LR)
radiance (4 bands), Angstrom exponent, Aerosol thickness- 4-byte quality flag (QF_OC)
Full resolution (1-km resolution, scene unit) (CS_FR)- Chlorophyll-a (CHLA)- Absorption of colored dissolved organic matter (CDOM)- Attenuation coefficient at 490nm (K490)
Low resolution (4-km resolution, path unit) (CS_LR)- Suspended solid weight (SS)- 4-byte quality flag (QF_OC)- Bulk Sea surface temperature (SST_b)
Full resolution (1-km resolution, scene unit) (ST_FR)- 2-byte quality flag (QF_ST)
Low resolution (4-km resolution, path unit) (ST_LR)
Atmospheric correction (ACLC) 1)
Atmospheric corrected radiance data
Precise Geometric Corrected Parameter (PGCP)
Precise geometric corrected map projection parameter
Vegetation index (VGI) 2)
NDVI
EVI- Snow grain size (SNWG)
Scene data (1-km resolution) (SNGI_p)- Snow impurities (SNWI)
Global data (SNGI) 3)- Snow/Cloud flag (SCFG)

1）ACLC has 56 localized areas: The North and South Polar Regions (>50N, <50S) are divided into four areas each. Middle latitude region (60S-60N) is divided into 48 areas (30degree by 30degree)2）VGI has five localized areas: North Polar Region (>50N), North middle latitude region (20N-60N), Equator region (20S-20N), South middle latitude region (20S-60S), South Polar Region (>50S)
3）SNGI (global) has four localized areas: North Polar Region (>50N), North middle latitude region (20N-60N), South middle latitude region (20S-60S), South Polar Region (>50S)

44

Table 1.3.2-4 Research Products (Geophysical parameter)
SphereAtmosphere

Ocean

Land

Snow

CLTT i refl
CLWP i refl
CLWP i emi
CLER i refl
CLHT i refl
CLHT i emit
CLGTHK
CFSR
WVP
WVP p
ARER
ARTY
PRCP
SSRB
TSRB
PAR
CAROT
PHYCO
ONPP
COCCO
FLUO
TRICO
AP
APH
PAR
SST s
LCI
LBMSS
VGCI
LBBN
APAR
LNPP
LBMSS2
LST
SA
AOS
COS
ICEB
ICES
PAC
PAR
SAC
SIC
SIE
SNWA
SPA
SRB
SSA
STC
AMCD

Geophysical parameter
Cloud top temperature
Cloud liquid/Water path
Cloud liquid/Water path
Cloud equivalent grain size
Cloud top height
Cloud top height
Cloud geometrical thickness
Fine weather short wave radiation volume
Total water vapor
Total water vapor（pixels）
Aerosol etc. grain size
Aerosol type
RainfallSurface solar short wave radiation volume
Atmospheric peak short wave radiation volume
PAR
Carotenoid
Phycobilin
Basic production volume (Chlorophyll a method)
Cocorris density
Fluorescent strength
Tricodesmium
Suspended particle absorption coefficient
Phytoplankton absorption coefficientPAR
Sea surface temperature (Skin)
Earth insulation classification map
Biomass carbon volume
Vegetation change map
Biomass combustion map
Photosynthesis valid radiation absorption map
Plant basic production volume
Precision biomass map
Surface temperature
AlbedoAerosol properties over snow and ice
Cloud properties over snow and ice
Iceberg monitoring
Level of proximity to sea/ice
Spectrally-Integrated planetary albedo
PAR
Snow surface albedo
Sea/ice distribution
Polar region ice sheet／Ice edge pattern
Snow distribution
Spectral planetary albedo
Solar radiation budget at TOA and surface
Spectral surface albedo
Surface temperature
AMSR Combined data

Notes

FR（Note）, LR
FR（〃）, LR（〃）
FR（〃）, LR（〃）
FR（〃）, LR（〃）
FR（〃）, LR（〃）
FR（〃）, LR（〃）
FR（〃）, LR（〃）
FR（〃）, LR（〃）
FR（〃）, LR（〃）
FR（〃）, LR（〃）

SIE 1~4
SNWA 1~4

Note FR means Full resolution (1-km resolution, scene unit). LR means Low resolution (4-km resolution, scene unit).
See chapter 2.2.1, Table 2.2.1-1 2. Ocean.

45

1.3.3 Scene Definition
Scenes for Level 2 and Level 2 Map have a ground area of 1600km×1600km. Details
conform to “ADEOS-II GLI, AMSR Level-1 Product Specification (NEB-98016).”
1.3.4

Processing Level Definitions

1.3.4.1 Level 1
Level 1 product definitions and specifications are based on “ADEOS-II GLI, AMSR
Level-1 Product Specifications (NEB-98016). ”
1.3.4.2 Level 2A (GLI-1km)(1) Common processes for each science group are defined in Level 2A, and a prerequisite
is that all processes are performed.
(2) The level 2A types are as shown below.
(i) Atmospheric/oceanic Level 2A: Data from simple crop of level 1B every 4 pixels
4 lines.
(ii) Level 2A for land/snow and ice areas: 16-day cycle cloud-free composite data
(3) Table 1.3.4-1 lists level 2A data (geophysical parameter).
(4) Data size is the approximate size for each data unit、

Table 2.4.2-1 GLI Standard Higher-order Level 2A Geophysical Parameter List
Code

Geophysical parameter

Data
unit

L2A_OA

Common to atmospheric/oceanic

Path

L2A_LC*1

Common to land/snow and ice
Common to land/snow and ice Area

*1

L2A_LC

Process
frequency

Data size

Storage/project format

Each path

219 MB

4 pixel 4 line crop

Area

Once/16 days

1498 MB

Polar stereo

Area

Once/16 days

775 MB

Equi-rectangular*1. Areas are shown below. The South/North latitude Polar region of 50 degrees or more is expressed in PS as a map
projection in four parts. The central latitude band of 60 degrees or less is expressed in equi-rectangular (30-degree)intervals as a map projection in 48 parts. This makes 56 parts in total (South Pole 4 parts+ North Pole 4 parts +central latitude band of 48 parts). The figures surrounded by ◯ are fixed as numbers for each area.

46

90deg.

N
PS

60deg.

180deg.

50deg.
Equi ‑ rectangularequator90W
50deg.
60deg.

1

North PolePS

３50deg.
latitude

0deg.

４

２
90deg.

50deg.
latitude

90E
90W

54

55

South Pole
56

53

0deg.

S

90E

180deg.

60deg. of north
５

６

16

７

30deg. of north30deg.
the equator
30deg. of south
41

52

60deg. of south
0deg.

‑180deg.

30deg.

+180deg.

Figure 1.3.4-1 Area division
1.3.4.3 Level 2(1) Geophysical parameters are calculated from Level 1B data and processed asparameters.
(2) Table 1.3.4-2 lists Level 2 products.
(3) Projection method is equi-rectangular and polar stereo (PS).
(4) Data storage format is the method of laying out the pixels. The format is described for
those products not map projected.
(5) Data size is the approximate size for each data unit.
(6) For products created in 4-day cycles and 16-day cycles according to process
frequency; they are counted from the start of the year and reset from the start of thefollowing year. Fractional days arising from the end of the year are processed as thosedays only.

47

Table 1.3.4-2 GLI Standard Higher-Order Level 2 Product List
SphereAtmosphere

Code

Product
Aerosol Angstrom Index

GlobalOnce/4 days

2.2 MB

Equi-rectangular

Aerosol optical thickness

GlobalOnce/4 days

2.2 MB

Equi-rectangular

CLFLG_p *1

Cloud flag

Scene

Per scene

8.6 MB

CLFR

Cloud fraction

GlobalOnce/4 days

2.2 MB

CLOP_p *1

Cloud optical thickness

Scene

Order

4.3 MB

CLER_w_r *2

Cloud equivalent grain size

GlobalOnce/4 days

2.2 MB

Equi-rectangular

CLER_i_e *2

Cloud equivalent grain size

GlobalOnce/4 days

2.2 MB

Equi-rectangular

L1B

L1B

Cloud optical thickness

GlobalOnce/4 days

2.2 MB

Equi-rectangular

CLOP_i_r *2

Cloud optical thickness

GlobalOnce/4 days

2.2 MB

Equi-rectangular

CLOP_i_e *2

Cloud optical thickness

GlobalOnce/4 days

2.2 MB

Equi-rectangular

CLTT_w_r *2

Cloud top temperature

GlobalOnce/4 days

2.2 MB

Equi-rectangular

Cloud top temperature

GlobalOnce/4 days

2.2 MB

Equi-rectangular

Cloud top height

GlobalOnce/4 days

2.2 MB

Equi-rectangularEqui-rectangular*2

CLWP_w_r*2

Data Storage
FormatEqui-rectangular*2

CLTT_i_e

Cloud liquid/Water path

GlobalOnce/4 days

2.2 MB

NL_FR*3

Atmospheric adjustment

Scene

Order

90.2 MB

L1B

*4

NL_LR

Atmospheric adjustment

Path

Per path73.3 MB

L2A_OA*3

Ocean color

Scene

Order

19.3 MB

L1B

CS_LR *4

Ocean colorPath

Per path15.7 MB

L2A_OA

CS_FR*3

Sea-surface temperature

Scene

Order

8.6 MB

L1B

ST_LR *4

Sea-surface temperature

Path

Per path

14 MB

L2A_OA

ST_FR*5

Once/16 days

435 MB

Equi-rectangular

Once/16 days

210 MBPS

VGI

Vegetation index

Zone

VGI

Vegetation index

Zone*5PGCP *6

Precision geometric correction pattern

Scene

Per path

0.1 MBACLC

Atmosphere corrected global data

Area*7

Once/16 days

489 MB

Equi-rectangularAtmosphere corrected global data

*7

Once/16 days

946 MB

PS

*5

ACLC
Snow

Projection
methodData size

ARAE

CLHT_w_r *2

Land

Process
frequency

AROP

CLOP_w_r

Ocean

Data
Unit

Area

SNGI

Cloud grain and impurities

Zone

Once/16 days

1958 MBEqui-rectangular

SNGI

Cloud grain and impurities

Zone*5

Once/16 days

946 MBPS

SNGI_pCloud grain and impurities

Scene

Order

19.3 MB

L1B*1 Pixel unit parameters*2 w_r: water cloud reflectance, _i_r: ice cloud reflectance, _i_e: ice cloud emission*3 Product according to resolution 1km at Full Resolution*4 Product according to resolution 4km cropped Low Resolution.
*5 See “Figure 1.3.4-2 Zone division.”*6 This parameter can be used in combination with L1B to obtain the precision geometrical corrected image (PGCI)(work file).
*7 See chapter 1.3.4.2 “Figure 1.3.4-1 Area division.”
The zone division is as shown below. The South/North latitude polar region is expressed in PS as a map projectionof 50 degrees or more, and the central latitude band of 60 degrees or less （South/North latitude of 60 degrees orless is three parts of a 40 degree latitude band）is expressed as a map projection in equi-rectangular format of 1
degree intervals of 120 parts. The figures in circles are fixed as numbers for each area.
However, for snow and ice, there is no data for area from 20N to 20S.

48

90deg. N

180degPS

60deg.
50deg.

50deg.
latitude.
Equi – rectangular

90W

equator

North Pole1

50deg.
60deg.

90E
90W

0deg.
90deg.

South Pole

180deg.

60deg. north

1/120deg.
２

20deg. north３
1/120deg.

20deg. south
４60deg. south‑180deg.

+180deg.

Figure 1.3.4-2

Zone Division

1.3.4.4 Level 2MAP
(1)
(2)
(3)
(4)
(5)

(6)
(7)
(8)
(9)

90E

5

PS

S

50deg.
latitude

0deg.

Level 2 data is map projected, but this does not include the land VGI （vegetationindex）product already converted to the equi-rectangular map in level 2.
Table 1.3.4-3lists level 2MAP products.
Oceanic products are Full Resolution at a resolution of 1KM,
Choose bi-linear (BL), nearest neighbor (NN), or cubic convolution (CC) and makethe order.
Choose map projection from equi-rectangular, Mercator (MER) or Polar stereo (PS)and order（Figure 1.3.4-3 Map projection method, Table 1.3.4-4 Map projection
method）.
Earth shape（standard ellipsoid）is WGS84.
Choose basic latitude for map projection from scene-centered and specific latitude(five point intervals) and order.
Use half path 13 scenes and one path 26 scenes.
Data size is the approximate size for each data unit.

49

Table 1.3.4-4 GLI Standard Higher-Order Level 2 Map Product List
SphereCode

Atmosphere

Ocean

Snow

Product
*1

Data
Unit

Process
Frequency

Data size

Projection method

CLFLG_p

Cloud flag

Scene

Order

18.7 MBEqui-rectangular, MER, PS

CLOP_pCloud optical thickness

Scene

Order

9.4 MB

Equi-rectangular, MER, PS

NW

Normalized water-leaving radiance

Scene

Order

121.8 MBEqui-rectangular, MER, PS

LA

Aerosol

Scene

Order

56.2 MBEqui-rectangular, MER, PS

CHLA

Chlorophyll a

Scene

Order

9.4 MB

Equi-rectangular, MER, PS

SS

Weight of suspended soil

Scene

Order

9.4 MB

Equi-rectangular, MER, PS

CDOM

Dissolved organic matters

Scene

Order

9.4 MB

Equi-rectangular, MER, PS

K490

K490 extinction coefficient

Scene

Order

9.4 MB

Equi-rectangular, MER, PS

ST

Sea-surface temperature

Scene

Order

9.4 MB

Equi-rectangular, MER, PS

QF_OC*1

Ocean color quality flag

Scene

Order

18.7 MBEqui-rectangular, MER, PS

QF_ST*1

SST quality flag

Scene

Order

9.4 MB

Equi-rectangular, MER, PS

SNGI_pCloud grain and impurities

Scene

Order

23.4 MBEqui-rectangular, MER, PS*1 Orders for each product of CLFLG_p (cloud flag), QF_OC (ocean color quality flag), QF_ST (SST quality flag) arelimited to nearest neighbor (NN).

N

90deg.

60deg.

Polar stereo

50deg.
0deg.

Equi – rectangular/Mercator

50deg.
Polar stereo

60deg.
S

90deg.

Figure 1.3.4-3

Map projection method

Table 1.3.4-4

Map Projection Method

Scene-centered latitude is shown in the following table.
Latitude＼Projection method

Equi-rectangular

Mercator

Polar stereo

North/South latitude 0-50 degrees

○

○

×

North/South latitude 50-60 degrees

○

○

○

North/South latitude 60-90 degrees

×

○

○

50

1.3.4.5 Level 3 Binned(1)

Level 3 binned data refers to the sampled Binned data in the spaces between level 2
data.
(2) A Level 3 binned product list (no products for land) is shown in Table 1.3.4-5.
(3) The level 3 binned data includes the sum, root sum, number of samples and otherinformation required for each sphere.
(4) Excluding land area, the binned grid definition for each sphere is as follows (seeFigures 1.3.4-4, 1.3.4-5, and 1.3.4-6).
• Atmosphere: Equi-rectangular in the same way as for Level 2 global (0.25 degree
equi-rectangular mesh).
• Ocean: 9kmx9km equal area grid same as ADEOS OCTS.
• Snow: With the equi-rectangular diagram method, it is the global North/South
latitude of 0 to 90°. The space size is latitude direction 360 degrees/5
minutes=4320 points and latitude direction 180 degrees/5 minutes＝2160 points.
In the polar stereo (PS) diagram method, the equator (0 degrees) is taken as aborder and divided into Northern and Southern hemispheres. The space size at thistime is up to 10KM to the center of the projection.
(5) Products created with processing cycles of eight days and 16 days, are calculated
and counted respectively from the start of the year and reset at the beginning of thenext year. Fractional days emerging at the end of the year are processed as thosedays only.
(6)

Definitions of monthly processing according to the processing frequency are as
follows.
• Atmosphere: There are two types of data, created from one-day units and fourday units. At present, the monthly bin created from four-day data was four
integers amount. At this time, there is a rule that four-day data (defined as aperiod) is not double-counted over two months. Therefore, periods that straddle
two months go to the time bin for the month for which there are more days.
Example 1: If for period n, there are three days in n month and one day in monthn+1, month n rather than month n+1 is assigned to the time bin.

day ： 1

2

3

4

5

6

7

8

・・・・・

25 26 27 28 29 30 31

1

2

3

4

5

・・・・・

periodn period

n+1 period

month nmonth n+1

Example 2: If for period n, there are two days in month n and two days in monthn+1, the time bin is assigned to month n.
day ： 2

3

4

5

6

7

8

・・・・・

26 27 28 29 30 31

1

2

3

4

5

6

・・・・・

periodperiod nmonth n51

period n+1
month n+1••Ocean: Follows calendar (Western)
Snow/Ice: Follows calendar (Western)(7) Data size is the approximate size for each data unit.
Table 1.3.4-5 GLI Standard Higher-Order Level 3 Binned Product List (1/2)
SphereAtmosphere

Code
ARAE

Aerosol angstrom index

Processing
Frequency

Data size

Binned grid

Global16 days28.5 MBEqui-rectangular28.5 MB

〃

〃

〃

〃

Month

AROP

Aerosol optical thickness

Global16 days28.5 MBEqui-rectangular

〃

〃

〃

Month

28.5 MB〃

CLFR

Cloud fraction

Global16 days28.5 MBEqui-rectangular

〃

〃

〃

Month

28.5 MB〃

CLER_w_r *1

Cloud equivalent grain size

Global16 days28.5 MBEqui-rectangular

〃

〃

〃

Month

28.5 MB〃

CLER_i_e *1

Cloud optical thickness

Global16 days28.5 MBEqui-rectangular

〃

〃

〃

Month

28.5 MB〃

CLOP_w_r *1

Cloud optical thickness

Global16 days28.5 MBEqui-rectangular

〃

Month

28.5 MB〃〃〃

CLOP_i_r *1

Cloud optical thickness

Global16 days28.5 MBEqui-rectangular

〃

〃

〃

Month

28.5 MB〃

CLOP_i_e *1

Cloud optical thickness

Global16 days28.5 MBEqui-rectangular

〃

〃

〃

Month

28.5 MB〃

CLTT_w_r *1

Cloud top temperature

Global16 days28.5 MBEqui-rectangular

〃

〃

〃

Month

28.5 MB〃

CLTT_i_e *Cloud top temperature

Global16 days28.5 MBEqui-rectangular

〃

〃

〃

Month

28.5 MB〃

Cloud and water volume

Global16 days28.5 MBEqui-rectangular28.5 MB〃1

1

CLWP_w_r *

Ocean

Data
Unit

Product Name

〃

〃

〃

Month

CLHT_w_r *1

Cloud top height

Global16 days28.5 MBEqui-rectangular

〃

〃

〃

Month

28.5 MB〃NW *2

Normalized water-leaving radiance

GlobalDay

477.3 MB

Equivalent area grid

〃

〃

〃

8 days561.5 MB

〃

〃

〃

〃

Month

561.5 MB〃LA *2

AerosolGlobalDay

250.8 MB

Equivalent area grid

〃

〃

〃

8 days295 MB

〃

〃

〃

〃

Month

295 MB〃

CS *2

Ocean color

GlobalDay

194.2 MB

Equivalent area grid

〃

〃

〃

8 days

228.4 MB〃228.4 MB〃194.2 MB

Equivalent area grid

〃

〃

〃

Month

ST *2

Sea-surface temperature

Global

Day

〃

〃

〃

8 days

228.4 MB

〃

〃

〃

〃

Month

228.4 MB〃52

Table 1.3.4-5 GLI Standard Higher-Order Level 3 Binned Product List (2/2)
SphereSnow

Code

Product Name

Data Unit

Processing
Frequency

Data size

Binned grid

SNWG

Snow grain size

Global16 days76.9 MBEqui-rectangular

〃

〃

〃

Month

76.9 MB〃

SNWG

Snow grain size

Northern hemisphere

16 days62.3 MB

PS

〃

〃

〃

Month

62.3 MB〃

SNWG

Snow grain size

Southern hemisphere

16 days62.3 MB

PS

〃

〃

〃

Month

62.3 MB〃SNWI

Snow impurities

Global16 days76.9 MBEqui-rectangular

〃

〃

〃

Month

76.9 MB〃SNWI

Snow impurities

Northern hemisphere

16 days62.3 MB

PS

〃

〃

〃

Month

62.3 MB〃SNWI

Snow impurities

Southern hemisphere

16 days62.3 MB

PS

〃

〃

〃

Month

62.3 MB〃*1: _w_r: water cloud reflectance, _i_r: ice cloud reflectance, _i_e: ice cloud emission*2: Made from level2 ocean “_LR” product (see chapter 1.3.4.3, Table 1.3.4-2)

The diagram in Fig.1.3.4-4 shows each binned number as an atmosphere Level 3
binned grid 0.25 degree geocentric mesh（same latitude and longitude as Level 2
global）.This binned number is fixed.
1

2

1440

+90deg.

1441

2880

1036801

10382400deg.

‑90deg.
‑180deg.
Figure 1.3.4-40deg.

+180deg.

Atmosphere Binned Grid

The ocean Level 3 binned grid is a 9km×9km equal area grid as in the ADEOS
OCTS, and the binned number is the same as for OCTS. Each binned number isshown in the following diagram. This binned number is fixed.
The binned numbers for the ocean are applied from the South Pole to the North
Pole.

53

row 2160

North Polebin 5940422

1/12deg.

90deg.

bin

360.0 deg./Number of Binned Gridbinrow 1081
Equatorrow 1080

‑

bin 1

row 1

South Pole

90deg.

In the above diagram, if the central latitude for a row is Φ, the number of binned
grids for that row can be obtained using the following formula.
Number of Binned grids = [4320×cosΦ] (Note) The [ ] to the left of the numeric
formulas indicate rounding up and rounding down.
Example: The following shows an example of the number of binned grids for therow using this formula.
row1 : 3, row2 : 9, row3 : 15,……, row1080 : 4320, row1081 : 4320,……,
row2160 : 3
Figure 1.3.4-5

Ocean Binned Grid54

Two projection diagram methods, equi-rectangular (zone 1) and PS (zone 2,3), areused for snow.
The equi-rectangular diagram method is global at 0 to 90 degrees North and South
latitude. The size at this time is 360 degrees/5 minutes = 4320 points in the latitude
direction and 180 degrees/5 minutes in the longitude direction with each binned
number as shown in the diagram in Fig.1.3.4-6. These binned numbers are fixed.
1

4320

2

+90deg.

4321

8640
1/12 deg = 5 min

0deg.

‑90deg.
‑180deg.

0deg.

Figure 1.3.4-6

1/12 deg = 5 min

9331200

9326881+180deg.

Snow Binned Grid (1/2)

In the PS diagram method, there are two types with the equator (0 degrees) as aborder between the northern hemisphere and the southern hemisphere. The size at thistime is up to 10km from the projection center. Each Binned number is shown in the
diagram below and these binned numbers are fixed.

55

1

180deg.

2750

2751

5500

Area 2 Northern Hemisphere PS and
Projection Data
10km
90E

90W
North Pole

90deg.

N
7562500

7559751
0deg.
PS

0deg.

1

PS
90deg.

0deg.

2750

2751

5500

90W

90E

S
10km

South Pole
Area 3 Southern Hemisphere PS and
Projection Data
7562500

7559751
180deg.

Figure 1.3.4-6

Snow Binned Grid (2/2)

1.3.4.6 Level 3 STA Map(1) The atmosphere, ocean and snow level 3 STA (statistics) MAPs are maps projectedusing estimated representative values for each type of data from Level 3 Binned. Treetypes of statistical values are used in this estimate method, the simple arithmetic mean,
simple geometric mean, MLE （ maximum estimate ） . An appropriate value isselected for the calculation.
(2) The land Level 3 STA (statistics) MAP is projected using representative values
estimated from the corresponding data for Level 2. The statistical value used for theestimation this time is the simple arithmetic mean.
(3) Table 2.4.6-1. lists level 3 STA MAP products.
(4) The data size is approximate.

56

(5) The projection methods are equi-rectangular and polar stereo (PS).
• Atmosphere: Equi-rectangular at 0.25 degrees (25km×25km) intervals• Ocean: Equi-rectangular at 0.1 degrees (9km×9km) intervals• Land: This is global from 90 degrees South latitude to 90 degrees North latitude,and equi-rectangular with a grid of 12 parts in 1 degree.
• Snow: There are three types - equi-rectangular (global) PS, (Northern hemisphere)
and PS (Southern hemisphere). With the equi-rectangular diagram method, this isglobal from 90 degrees South latitude to 90 degrees North latitude. With the PS
diagram method, the Northern hemisphere is 90 degrees North latitude from theequator (0 degrees) to 90 degrees South latitude.
(6)

Products created in 8-day cycles and 16-day cycles are counted from the start of theyear and reset from the start of the following year. Fractional days arising from theend of the year are processed as those days only.

Table 1.3.4-6 GLI Standard Higher-Order Level 3 STA Map Product List (1/2)
SphereAtmosphere

Code

Product Name

Data Unit

Process
frequency

Data size

Projection
methodARAE

Aerosol angstrom index

Global16 days

2.4 MB

Equi-rectangular

〃

〃

〃

Month

〃

〃

AROP

Aerosol optical thickness

Global16 days

2.4 MB

Equi-rectangular〃〃

2.4 MB

Equi-rectangular

〃

〃

〃

Month

CLFR

Cloud fraction

Global16 days

〃

〃

〃

Month

〃

〃

CLER_w_r*1

Cloud equivalent grain size

Global16 days

2.4 MB

Equi-rectangular

〃

〃

〃

Month

〃

〃

CLER_i_e*1

Cloud equivalent grain size

Global16 days

2.4 MB

Equi-rectangular

〃

〃

〃

Month

〃

〃

CLOP_w_r*1

Cloud optical thickness

Global16 days

2.4 MB

Equi-rectangular

〃

〃

〃

Month

〃

〃

CLOP_i_r*1

Cloud optical thickness

Global16 days

2.4 MB

Equi-rectangular

〃

〃

〃

Month

〃

〃

CLOP_i_e*1

Cloud optical thickness

Global16 days

2.4 MB

Equi-rectangular

〃

〃

〃

Month

〃

〃

CLTT_w_r*1

Cloud-top temperature

Global16 days

2.4 MB

Equi-rectangular

〃

〃

〃

Month

〃

〃

CLTT_i_e*1

Cloud-top temperature

Global16 days

2.4 MB

Equi-rectangular

〃

〃

〃

Month

〃

〃

CLWP_w_r*1

Cloud liquid/Water path

Global16 days

2.4 MB

Equi-rectangular

〃

〃

〃

Month

〃

〃

CLHT_w_r*1

Cloud-top height

Global16 days

2.4 MB

Equi-rectangular

〃

Month

〃

〃

〃

〃

57

Table 1.3.4-6 GLI Standard Higher-Order Level 3 STA Map Product List (2/2)
Sphere
Ocean

Land area

Snow

Code

Product Name

Data Unit

Process
frequen

Projection
methodData size

NW

Normalized water-leaving radiance

GlobalDay

124.8 MBEqui-rectangular

〃

〃

〃

8 days

〃

〃

〃

〃

〃

Month

〃

〃

LA

AerosolGlobalDay

57.6 MBEqui-rectangular

〃

〃

〃

8 days

〃

〃

〃

〃

〃

Month

〃

〃

CHLA

Chlorophyll a

GlobalDay

8.5 MB

Equi-rectangular

〃

〃

〃

8 days

〃

〃

〃

〃

〃

Month

〃

〃

SS

Weight of suspended soil

GlobalDay

8.5 MB

Equi-rectangular

〃

〃

〃

8 days

〃

〃

〃

〃

〃

Month

〃

〃

CDOM

Ocean color

GlobalDay

8.5 MB

Equi-rectangular

〃

〃

〃

8 days

〃〃〃〃

9.6 MB

Equi-rectangular

〃

〃

〃

Month

K490

K490 extinction coefficient

Global

Day

〃

〃

〃

8 days

〃

〃

〃

〃

〃

Month

〃

〃

ST_DayNight*2

Sea-surface temperature(day and night)

GlobalDay

19.2 MBEqui-rectangular

〃

〃

〃

8 days

〃

〃

〃

〃

〃

Month

〃

〃

ST_all*3

Sea-surface temperature

GlobalDay

8.5 MB

Equi-rectangular

〃

〃

〃

〃

〃

〃

〃

8 days

〃

〃

〃

Month

VGI

Vegetation index

Global16 days

9.8 MB

Equi-rectangular

SNWG

Snow grain size

Global16 days

9.8 MB

Equi-rectangular

〃

〃

〃

Month

9.8 MB〃〃〃

Northern hemisphere

16 days

7.9 MB

PS

〃

〃

〃

Month

7.9 MB〃〃〃

Southern hemisphere

16 days

7.9 MB

〃

〃

〃

〃

Month

7.9 MB〃

SNWI

Snow impurities

Global16 days

9.8 MB

Equi-rectangular

〃

〃

〃

Month

9.8 MB〃〃〃

Northern hemisphere

16 days

7.9 MB

PS

〃

〃

〃

Month

7.9 MB〃〃〃

Southern hemisphere

16 days

7.9 MB

〃

〃

〃

〃

Month

7.9 MB〃*1. _w_r: water cloud reflectance, _i_r: ice cloud reflectance, _i_e: ice cloud emission*2. There are two types of geophysical parameter - ST_Dayn (Sea surface temperature in sunny areas) and ST_Night(Sea surface temperature in shaded areas).
*3. Average temperature for sunny areas and shaded areas.

58

1.3.5 Image Catalog (GLI 1km)
The image catalog allows image browsing of planning production products at thevarious levels shown below.
(1) Level 1 processing browse images follow the EOSD definition.
(2) Level 2A processing browse images are not created for the following reasons.
(i) L2A_OA are not map-projected, furthermore, their location is difficult todetermine from the browse image, so they are not practical and therefore are notrequired.
(ii) L2A_LC is taken from an average over 16 days. Judging of whether or not theclouds are map-projected is difficult, so they are not required.
(3)

Level 2 processing browse images(i) Atmosphere products: All products for planning production(ii) Ocean products: CS_LR CHLA and ST_LR SST(iii) Land products: ACLC by VGI and channel 19 (865nm).
(iv) Snow /ice products: SNGI SNWG

(4)

Level 2 Map and Level 3 Binned are not required.

(5)

Level 3 STA Map processing browse images(i) Atmosphere products: All products for planning production(ii) Ocean products: NW, LA, CHLA, SS, CDOM, K490, ST_DayNight, ST_all(iii) Land products: VGI(iv) Snow/ice products: SNWG, SNWI

59

1.3.6

Standard Product Processing Format

The standard product processing format（total number processed and process order）
is shown in Figure 1.3.6-1.
Primary process
L1A

Total number of pixels processed

L1B

Total number of pixels processed

L1B Map

Process order

Higher-order process

L2A

L2A for atmosphere/ocean

L2A for land/snow

Low Resolution

Below 60 degrees latitude South/North : Equi-rectangular(4km simple cropping)

Above 50 degrees latitude South/North : Polar stereo

Process total number

Process total number

Higher-order process
Atmosphere

Ocean

Land

Snow

Data unit

Global

Scene

Path

Scene

Area

Scene

Zone

Scene

L2

Process
total
number

Processorder

Process
total
number

Processorder

Process
total
number

Process
total
number

Process
total
number

Processorder

L2 Map------

Processorder------

Processorder------------------

Processorder

Higher-order process
Atmosphere

Ocean

Land

Snow

Data unit

GlobalGlobalGlobalGlobal

L3 Binned

Process total number

Process total number------

Process total number

L3 STA Map

Process total number

Process total number

Process total number

Process total number

1) L2A processes the total number. This total number includes all products planned for production.
2) All CLFLG_p from the atmosphere L2 and L2 Map scene unit products are processed.
3) The land area is map projected by L2, so L2 Map and L3 Binned are not processed.

Figure 1.3.6-1
Standard Product Processing Format(Process total number, process order)60

1.3.7GAIT

1.3.7.1 GAIT Organization
GAIT (GLI Algorithm Integration Team) is involved in the tasks of protecting theintellectual property rights of the researchers (PIs) while sharing their results and tools inan effective way and integrating one of these research results—the algorithm modules—
into operational systems. The individual members of this team are also actively involved
in research and development of the algorithms. The four GAIT modules are introduced
below. They are defined in such a way that the GLI observation data can be mosteffectively utilized.
1.3.7.2 GAIT Analysis and Evaluation Functions
GAIT, in cooperation with the PIs, designs and creates the following four modules:
GSS (GLI Signal Simulator), GRS (GLI Retrieval System), GSD (GLI Synthetic Data),and GMD (GLI Measured Data). The first two modules are based on hardware andsoftware. The latter two are data modules consisting of various datasets. These modules
are organically combined to form the GLI data-analysis environment.
The relationship among these four modules is explained via a simple example, basedon Figure 1.3.7-1. In this system, information on geophysical parameters is collectedfrom data (GMD) obtained by observation through sensors on existing satellites or byobservation on aircraft and vessels. This data then becomes input information for the GSS,
which simulates GLI observation radiance based on geophysical parameters. Based onthese geophysical parameters, which are independent of sensor-specific conditions suchas the response function and orbit, the GSS then prepares data (GSD) for all GLI
channels as if the actual location were actually observed.
The GRS reverses the data flow performed by the GSS. One can validate the GRS
entering the GSD (radiance data) obtained earlier into the GRS (GLI Retrieval System)
and by comparing the GMD output (geophysical parameters) with the output of the GRS,
which are the same geophysical parameters. As demonstrated here, the four modules of
the GAIT are organically combined. In such a data-analysis environment, theperformance of the GRS depends heavily on the performance of the GSS. Hence, thevalidation work of the GRS is directly connected with the performance evaluation of the
GSS. The performance of the GSS basically means modeling natural phenomena, an
index showing the level of our understanding of nature. Investing in the GSS thus
provides a tremendous advantage in that it will directly contribute to the improved
performance of radiation-calculation codes used in various modeling situations, e.g.,
atmospheric large-scale circulation models.
The followings explain various functions of these individual modules, promoting
further understanding of the reader.

61

GSS

System module

CalculateradianceGSD

GLI Signal Simulator

GLI Synthetic Data

Field Measurements

PP Distribution
Data module(GLI) Measured Data

nve
rt F

orw
ard
t

Parameteri-zationInput
radiance

Evaluation
PP Distribution
Evaluation
Flow 1

o In

ver
sio
n

GRS

Input
radiance

Co

Input P.P.

GMD

Data module

System module

GLI Retrieval System
OutputRetrieved PP
PP Physical Parameter
Flow 1Figure 1.3.7-1 The Four GAI Modules(1) GSS (GLI Signal Simulator)
The GSS is formed by taking a conventional radiative-transfer calculation programand adding various extension modules to it. It is installed on a dedicated computer asa system module that simulates the observed radiance of the GLI. Extension modules
include scattering of atmospheric molecule, scattering of small particles such as
clouds and aerosol, sea-surface reflection, snow-ice surface reflection, and vegetation
surface reflection. Some of these extension modules were originally integrated as
basic modules in the radiative-transfer calculation program while others were
submitted to GAIT as PI result and then integrated by GAIT (such as the snow-ice
surface reflection and vegetation surface reflection). However, if the GSS, whose
performance has been improved by the newly integrated extension modules, isreturned to all the PIs, with the program codes distributed to them, the intellectualproperty rights of the PIs that have developed these individual extension modules
cannot be protected. Hence, GAIT has constructed a system that keeps the mostrecently updated version of the GSS on a WWW site all the time and provides it as an
automatic calculation service to general users (Figure 1.3.7-2, 3). The GSS user canuse a general browser to access the Web site and use the GSS by entering appropriate
parameters in a dialogue format. The calculation results are automatically sent to theuser via email.

62

The performance of radiative-transfer calculations, extension modules, andinterfaces determine the overall performance of the GSS. For the reasons presented
in the next section, the performance of the GSS is also very closely related to theperformance of the GRS, the other system module produced by GAIT.

Figure 1.3.7-2 GSS Server and a Client

Figure 1.3.7-3 Web page of the GSS Automatic Calculation Service(http://bishamon.eorc.nasda.go.jp/ENTGSS/index.html)

63

(2) GRS (GLI Retrieval System)
The GRS system module estimates geophysical parameters based on radiance data.
In other words, it is a higher-order analysis system of the GLI. Note that the GRS andthe GSS perform inverse tasks each other. There are various methods for estimating
geophysical parameters from observed radiance. Recently, a powerful tool has
appeared to solve the inverse problem based on the “textbook” forward problem ofneural network. Therefore, efforts made to improve GSS performance as a priorinvestment for constructing of the GRS provide a head start in the remote-sensing
method as an aggressive strategy with a view to the future. When using theconventional remote-sensing method, i.e., applying an algorithm that faithfully solves
the inverse problem of a physical model, ultimately one uses datasets that are the setsof the forward problem and its solution as references. Hence, this strategy is highly
effective. For this reason, the performance of GRS is closely related to that of GSS.
Using GSS, each PI will create an algorithm module for the corresponding GRS
part. To these algorithm modules submitted by the PIs, GAIT adds data input/output
parts and login functions compatible with GLI higher-order analysis systems. The
team then completes the products by optimization procedures until final operational
forms are achieved. Because the program codes written by the PIs basically make up
the core of these higher-order analysis systems, it will be relatively easy to update theprogram codes to reflect the most recent research results.
(3) GSD (GLI Synthetic Data)
GSD is one of the GSS output. While the GSS is disclosed to general users and PIs
through Web browsers, the GAIT is also actively utilizing the GSS to prepare GSD.
If the geophysical parameters to be estimated can be presented on distribution maps,
the estimates can be entered into the GSS, enabling us to create data (GSD) thatemulate actual data as if all wavelengths available in the GLI were actually used toobserve the spot. Furthermore, if the distribution maps thus prepared are sufficiently
large in the area covered, it is also possible to cover an actual observation region of
the GLI. Figure 1.3.7-4 is an example of GSD prepared in this way. The geophysicalparameters estimated here include the sea-surface temperature estimated by remotesensing via the TRMM/TMI sensor, the snow-ice distribution estimated by the SSM/I
sensor, the cloud characteristics estimated by the NOAA/AVHRR sensor, and aerosol
characteristics calculated with a model. These geophysical parameters do not include
information specific to existing satellites, such as the orbit, response function, andwavelength. Therefore, it is possible to obtain observation images at sensor
wavelengths never possessed by mankind in history even prior to the launch of thesatellite.

64

Figure 1.3.7-4 Output Example of GSD: Observed radiance of GLI Channel 8(0.545 µm). The orbit is a simulation of the daylight region on January 29, 2001.
(4) GMD (GLI Measured Data)
Whereas GSD simulates radiance observed by a sensor, GMD refers to all data
observed and measured by existing sensors and measuring instruments. This includes
radiance data measured by NOAA/AVHRR, LANDSAT/TM, and ADEOS/OCTS, aswell as radiance and geophysical parameters observed and measured on vessels,
aircraft, and other means. As mentioned earlier, geophysical information is extracted
from GMD and entered into the GSS in order to create GSD. In addition, theradiance information observed by ADEOS/OCTS can be geometrically transformed
to the GLI observation region to create input data for the GRS. The radiance datathus obtained, however, contain information specific to the existing satellites, such as
the orbit, observation region, and response function. It thus requires understanding
and caution to use this information for research on future sensors.

65

1.3.8 GLI First-light images(1) Kyushu Island, southern Japan, and the East China Sea
This image was observed by the Global Imager (GLI) aboard Midori-II (ADEOSII) at 11:30 (JST) on January 25, 2003. The color composite image was derived fromthe GLI spectral channels 28 (1640nm), 23 (825nm), and 22 (660nm). The spatial
ground resolution is 250m. Thick cloud systems stretching from the Asian continent
to the East China Sea are visible in the image, with low altitude warm clouds
appearing white and higher altitude ice clouds appearing blue. Kyushu Island and thenorthern part of Taiwan can be seen through chinks in the cloud systems.

Figure 1.3.8-1 View of Kyushu Island, Southern Japan, and the East China Sea
The GLI instrument has 36 spectral channels ranging from ultraviolet to infrared. It
is designed for high-accuracy observations of the atmosphere, ocean, land, andcryospehre.
(2) Great winter cyclone
This image of a winter cyclone was captured at 9:45 (JST) on January 25, 2003.
This color composite image was derived from the GLI spectral channels 13 (678nm),
8 (545nm), and 5 (460nm). It has a spatial resolution of 1 km. A great cloud systemformed as a result of the cyclone over the eastern Hokkaido can be identified in theimage.
66

Figure 1.3.8-2

A great winter cyclone

The cyclone developed off the Pacific coast of Tohoku area between January 23
and 24, during which it brought severe weather conditions to eastern and northern
Japan. The GLI instrument observes and measures the types and structures of clouds
using a large number of spectral channels.

67

1.4

Calibration and Validation

1.4.1

Calibration and Validation Plan

1.4.1.1 Overview
GLI, a multi-purpose sensor used in observation research of the atmosphere, ocean,land, and cryosphere, is the core sensor in the ADEOS-II mission. The GLI has 19 visible
channels, 4 intermediate infrared channels, and 7 infrared channels with a resolution of 1
km as well as 4 visible channels and 2 intermediate infrared channels with a resolution of
250 m, thus having a total of 36 channels. Each channel observes the arriving spectral
radiance at the satellite level. Furthermore, by combining the spectral radiance values ofchannels established in various regions, it estimates various Earth geophysical parameters.
GLI is capable of global observation, providing observation data for helping to mitigate
the Earth’s environmental problems and predicting the changing patterns of the global
climate. In order to accomplish this goal, it is necessary to provide consistent observation
data on the geophysical parameters of the Earth, the amounts of which vary among thedifferent land and ocean regions. Other Earth-observation sensors, such as MODIS andMERIS will operate simultaneously. The Earth’s environmental problems cannot be
discussed without consistent observations of the Earth’s geophysical parameters. Hence,calibrating and validating GLI is expected to archive sufficiently accurate global
observations to tackle global problems.
Calibration refers to the procedure of determining the absolute value of spectral
radiance arriving at the satellite. The response characteristics of multiple detectors foreach of the GLI channels, spectral-reflection characteristics of both sides of the scanningmirror, polarization characteristics, and other performance properties need to beevaluated, and the accuracy of observation data must be maintained. Ground calibration
before launch, in-orbit calibration using the internal lamp and solar rays, vicarious
calibration using the spectral radiance observation data near the land surface, and othermethods of calibration are used to maintain the accuracy of spectral radiance values
during operation.
Validation refers to the procedure of validating the Earth’s geophysical parameters
estimated from the spectral radiance measured by each channel. Either standard products
or research products of geophysical parameters will be established for each region, andvalidated each region will be using validation standards. When upward spectral radianceis used as a geophysical parameter, Level-1B data will be validated.

681.4.1.2 Definitions(1) Calibration(i) Ground Calibration
Pre-launch calibration coefficients are given by measuring the GLI’s opticalcharacteristics on the ground.
(ii) On-Orbit Calibration
In the visible range, the internal lamp and diffused solar rays will be used as
calibration sources to calibrate each channel. The saturation radiance varies
between the ocean channels and other (atmosphere, land, and cryosphere)channels, so it is necessary to calibrate them by appropriately selecting thecalibration source, either the diffused solar rays or the internal lamp.
In the intermediate infrared and heat infrared wavelength ranges, the calibration
sources will be the internal black-body heat source and the radiant temperature ofdeep space. Because it is difficult to monitor the installed black body while there
is a tilt, some appropriate method of calibration must be discussed.
(iii)Vicarious Calibration
Vicarious calibration refers to the procedure of estimating the satellite-arrival
radiance on the upper surface of the atmosphere through an optical model by
combining the radiance measured upward on the ground with other in-situ
measured data such as the optical thickness of aerosols observed on the ground.
By comparing the values of this satellite-arrival radiance with the satellite-arrival
radiance obtained by the GLI, correction coefficients will be calculated for eachwavelength band of the GLI.
(2) Validation
This procedure is used to validate the geophysical parameters estimated based on
the radiance arriving on the satellite, measured by the GLI, using in-situ data and datacollected at other locations.
Validation is mainly carried out with match-up data sets. A match-up data set is aset of data consisting of in-situ observation data and GLI-generated data, collected at
the same time or almost at the same time at the same location. The method, period,and frequency of in-situ observation vary, depending on the geophysical parameters
under consideration. Eventually, however, parameters for higher-order processes will
be adjusted and algorithms will be revised based on the calculations of errors of GLI
higher-order products by comparing the geophysical parameters calculated from these
two sets of data. It is therefore necessary to have a system in which necessary data arecollected and match-up data sets are created efficiently and a system by which thesedata can be disclosed according to disclosure policy.

69

1.4.1.3 System
The GLI will be calibrated mainly by NASDA staff members, together with
contractors, manufacturers, and PIs. Groups 1 through 6 have been established to discussvarious problems regarding calibration, each group includes a cross section of the
NASDA departments. The actual calibration will be done by installation, ground, andanalysis groups. The NASDA-GLI Calibration Meeting supervises these groups, and the
GLI-CAL Working Group (Calibration Committee) handles calibration from the PI side.
The group assignment for each theme is as follows; the GLI calibration
implementation system is shown in Figure 1.4.1-1.
•
•
•
•
•
•

Calibration by solar rays or internal lamp, VNIR/SWIR: Calibration Group 1
Black-body calibration: Calibration Group 2
Geometric calibration: Calibration Group 3
Sensor characteristics, picture-quality evaluation: Calibration Group 4
Vicarious calibration, cross calibration: Calibration Group 5
L1-process software: Calibration Group 6

NASDA GLI Calibration system

GLI PI

External organizations(JMA, NOAA, JAFIC)
Other NASDA
departments

GLI Leader meeting(chaired by Prof. Nakajima)

Cal-Working Group(chaired by Dr. Senga)
Operation project
manager○Nakamura

NASDA-GLI
Calibration Meeting(Igarashi/Ishido/Tange)

Simultaneously holdAs the case may beGroups for
Common problems

Calibration 4th GroupCalibration 5th Group
General Sensor characteristics
Vicarious & Cross calibration
Asanuma
Murakami
Share information

SoftwarerevisionrequestCalibration 6th Group Level 1
softwareGroups forSpecific schemes

Calibration 1st Group
Solar & internal lamp calibrations
TanakaCalibration 2nd Group
Black body calibration
Kurihara

Hashimoto
NASDA calibration sub-groups

Softwareinformation
Technical
information

Calibration 3rd Group
Geometric correction

Figure 1.4.1-1

Inoue

Tsuzurabara/ 4th Group
EORC
Analysis & Research Project
Window for PI & Users

User’s Hand Book & WWW
Consistent window including validation

GLI Calibration Implementation System

70

GLI products will be validated in cooperation with PIs and NASDA staff members.
PI and NASDA staff members will organize four groups: the Atmosphere, Ocean, Land,
and Cryosphere Groups. The actual work will be done cooperatively within each group.
The GLI-Val Working Group (Validation Committee) coordinates these four groups.
Important decisions concerning calibration and validation will be made at the GLI
Leader Meeting. Figure 1.4.1-2 shows the GLI validation workflow.

OK?
GLI Leader
Meetinga.1.1.EOC

Algorithm improvement
Alg. PI, GAIT

Implement
GAIT, Alg PI

YesTiming: L+6, 18, 30M

L1B (DLT)
L2A(after 1
year)

No

feedback
Analysis &algorithm modification
PI-science groups and GAIT(EORC, Univ., Inst.)

Operational test
GAIT or operator（GLI operation system in EORC）

GLI L1,
L2
data

Validation working group,
PI-science groups and GAIT

1. Image check

GLI
MUD

2. Scatter diagram and statistics

Field
observations

3. Truth data check

Field observations
Coordinated by PI
leaders

Bug ?

OK?

Product evaluation
GAIT and Alg/Val PI(EORC, Univ., Inst.)

Information, results, and data exchange

CAL 1 and 2thGroup

EORC Val team, RESTECGAITRESTEC MUD
searchReportAccuracy
Problems

CAL 4 th Group

EORC/GLIWWWWork Flow of GLI Validation

71

CAL 3rd Group

PI’s door

put to web

Limited by permission

PI, Collaborators

CAL 4thGroup

NASDA CAL Groups
To Public

Physical parameterdatasets
Climatorogy

Murakami

Asanuma
CAL 5th Group

Reports ofalgorithm
revision

Reports ofproduct
validation

4. Comparison with other data sets

Figure 1.4.1-2

NoPI-science groups

EORC Val Team
Other
satellite data
MODIS
AMSR

No

Yes

Yes

Calibration?
Yes

Requests of Algorithm
Version Up

RESTEC

No

CAL
page

1.4.1.4 Schedule
There are four main phases in the GLI mission. The periods are denoted such that L +0M represents the first month after the launch. Calibration and validation results will bereflected in parameters and algorithms during the data-processing stage, so their timing
will constitute milestones for calibration and validation work. Table 1.4.1-1 shows themain milestones in GLI calibration and validation.
• Prior to Launch: L – 1M
• Initial Checkout Phase: L + 0M to 3M• Calibration and Validation Phase: L + 4M to 11M• Regular Operation Phase: L + 12M
Table 1.4.1-1 Main Milestones of GLI Calibration and Validation

CalibrationGroup

L-4M L-3M L-2M L-1M L+0M L+1M L+2M L+3M L+4M L+5M L+6M L+7M L+8M L+9M
Prior to Launch
Initial Checkout
Calibration and Validation△
L1 Process Ver. 1
fix
Launch of ADEOS-II

〜

EORC

▲
Start Providing
L1-Ver. 0(Algorithm Developing PI, Calibration and Validation PI)△
L1/2/3 Process
Start Implementation

EOC

Calibration
Group

L+10M L+11M L+12M L+13M L+14M L+15M L+16M L+17M L+18M L+19M L+20M L+1M L+22M L+23M L+24M
Calibration andRegular Operation
Validation(△*)(L1 Process Ver.2)(fix)△
L2/3 Process Ver.2
fix

EORC

EOC

Calibration
Group

EORC

EOC

△
L2/3 Process Ver.1
fix▲
Start Providing
L1/2/3-Ver.1△▲
Start Implementing
Start Providing
L(1)/2/3 Process Ver.2
L(1)/2/3-Ver.2
Start Re-Processing Past L(1)/2/3

L+25M L+26M L+27M L+28M L+29M L+30M L+31M L+32M L+33M L+34M L+35M L+36M L+37M L+38M
Regular Operation(△*)(L1 Process Ver.3)(fix)△
L2/3 Process Ver.3
fix△▲
Start Implementing
Start Providing
L(1)/2/3 Process Ver.3
L(1)/2/3-Ver.3
Start Re-Processing Past L(1)/2/3* : Dates and frequencies of L1-version upgrading after L + 12M are T.B.D.

72〜

1.4.1.5 Information Disclosure Policy(1) Calibration Information
The NASDA-GLI Calibration Group will establish the following five grades of
disclosure restrictions concerning the calibration information and other data. The
grades will be determined by mutual agreement of the provider of each type of
information and the NASDA Calibration Group members. Information falling under(i) or (ii) below, i.e., information that should be widely published, will be disclosed
after the completion of calibration work for each item, using the CAL pages of the
EORC/GLI Web sites as well as PI’s Door and GLI workshops.
(i) Publicly disclosed (to the extent possible, restrictions on information will beminimized).
(ii) Disclosed only to the NASDA Calibration Group, GAIT, and NASDA GLI PI(Disclosure restrictions are not necessary in the future for this information, butsome restrictions provisionally apply because the information may be in theprocess of being organized or may be necessary for developing higher-orderalgorithms even though its disclosure may cause problems due to issues related tointellectual property. Equivalent to PI’s Door.)(iii) Disclosed only to validation-related staff members in the NASDA Calibration
Group, GAIT, and EORC. (The information may not be organized and may be
misleading, or it may be necessary for creating initial images and images for PR
even though its disclosure may cause problems due to issues related to intellectual
property.)(iv) Disclosed only to the NASDA Calibration Group. (The information may not be
organized and may be misleading, or it does not fall under (iii) above even though
its disclosure may cause problems due to issues related to intellectual property.)(v) Disclosed only within each calibration group. (The information may not be
organized and may lead to gross misunderstanding, or there are serious problems
with disclosure due to issues related to intellectual property.)(2) Match-Up Data Sets
Policies are established as follows for match-up data sets used in validating GLI
higher-order products. These data will be classified according to their disclosurelevels and will be disclosed through the EORC/GLI Web sites and other means.
(i) NASDA will provide match-up data sets (in-situ observation data and GLIgenerated data) for calibration and validation (algorithm development) of the GLI
as well as Earth-scientific research.
(ii) Data sets will not be provided to a third organization.
(iii) Results of data collection will be disclosed publicly.
Tables 1.4.1-2 and 1.4.1-3 show the disclosure levels and level transition of in-situ
observation data; Table 1.4.1-4 shows the disclosure levels of GLI-generated data.

73

Table 1.4.1-2 Disclosure Levels of In-Situ Observation Data
GLI-related PI
Disclosure Level
Specified by In-Situ
Data Provider

EORC staff
members

Calibration
PI

Validationandalgorithm
PI

Earthscience PI

Registeredusers including
in-situ data
providers

3Internal use
within EORC

○

×

×

×

×

2

Calibration /validation PI (PIgroup)- Only during PI
contract- Terminates
after PI
contract
expires

○

○

○

×

×

1

Other PIs (PIgroup)- Other PIs in
and out ofJapan (e.g.,
SIMBIOS)

○

○

○

○

×

0

Others (generalregistered users
including in-situ
data provider)

○

○

○

○

○

74

Usages- GLI calibration and validation
results (scatter diagrams,
graphs, statistical data, andothers where raw data are
difficult to read or to estimate)
are to be disclosed, clearlystating that NASDA databases
are used and clearly stating
the data-collecting
organization (based on
NASDA’s product usage).
- Disclosure of raw dataprohibited.
- GLI calibration and validation
results are to be disclosed,
clearly stating that NASDA
databases are used and clearlystating the data-collecting
organization (based on
NASDA’s product usage).
- Usage for other purposesprohibited.
- Re-disclosure of dataprohibited.
- Results that are useful in Earthscience research are to bedisclosed with the data
provider’s consent (coauthorship or
acknowledgment), clearlystating that NASDA databases
are used (based on NASDA’s
product usage).
- Calibration and validation
results of other satellite
sensors are to be disclosed,
clearly stating that NASDA
databases are used and clearlystating the data-collecting
organization (based on
NASDA’s product usage).
- Re-disclosure of dataprohibited.
- For data use, the fact that
NASDA databases are used is
to be clearly stated. Research
results based on the data are to
be reported to NASDA (basedon NASDA’s product usage).
- Re-disclosure of dataprohibited.

Table 1.4.1-3 Disclosure Level Transition of In-Situ Observation Data(i)

According to the policy of the data provider. Levels are to be entered when the data areregistered.
Disclosure Level 1 from the beginning(ii)

Disclosure Level is set to 2; automatically changed to Level 1 three years later

Transition of Disclosure Levels(iii)

Disclosure Level 2 is maintained; to be used only by Calibration/Validation PI(iv)

Disclosure Level 3 is maintained; to be used only within EORC

Table 1.4.1-4 Disclosure Levels of GLI-Generated Data
GLI-related PI
Disclosure Level ofMatch-Up Data
1

Including all PIs(PI groups) andorganizations
with contractual
agreements

EORC staff○

Calibration
PI○

Validation/
Algorithm
PI

○

75

Earth
Science PI○

Registeredusers including
in-situ data
providers

Usages○- Products using match-up data
are to be disclosed, clearlystating that NASDA has
collected the data (based on
NASDA’s product usage).

1.4.2

Calibration Plan

1.4.2.1 Solar-Ray Calibration and Internal-Lamp Calibration(1) Overview
GLI Calibration Group 1 shall perform solar-ray calibration and internal-lamp
calibration. The solar-ray calibration shall be carried out for each path every 5
minutes while the angle between the satellite’s direction of motion and the directionof the Sun decreases from 20 degrees to 0 degrees upon passing the North Pole. The
solar-ray calibration data shall be obtained in the visible near-infrared band and shortwavelength infrared band (channels 1 to 29). The solar rays are attenuated through asolar-ray calibration window, diffused at a diffuser panel, and injected into thedetector through a scanning mirror. Therefore, the high-gain band (channels 1–12, 14,
16, 18, 20, and 21) plays a major role in this calibration.
The internal-lamp calibration shall be carried out for night paths and lasts
approximately 10 minutes each time (this calibration period will be adjusted after thelaunch); this is done every two regressions (eight days). The internal-lamp calibration
data shall be obtained in the visible near infrared band and short-wavelength infrared
band. Due to the intensity of the halogen lamp light source, the low-gain band(channels 6–10, 13, 15, 17, 19, 21–29) plays a major role in this calibration.
Other in-orbit calibration methods include electric calibration and deep-spherecalibration. For electric calibration, six-step signals generated at the analog signal
processor are input to the pre-amp for visible near infrared and short-wavelength
infrared bands or into the post-amp (analog signal processor) for the intermediate heatinfrared band in order to calibrate electric circuits). For deep-space calibration, offset
terms are calculated using more than 10 samples taken by observing deep space
during each scan.
Data from calibration, other than deep-space calibration, do not undergo normal
processing by the processing system; rather, they are a means for estimating thecalibration coefficients as part of the calibration work.
(2) Items(i) Solar-Ray Calibrationa. Prior to Launch• Evaluation of the GLI solar-ray calibration function• Proposal of GLI standard solar illumination (decided as Thuillier 2002)• Creation of an algorithm for deriving calibration coefficients• Organization of calibration products (file specifications, processing
facility)76

b. After Launch• Re-consideration of the GLI solar-ray calibration function• Evaluation of the solar-ray calibration results• Monitoring of secular change in the sensitivity of sensors(ii) Internal-Lamp Calibrationa. Prior to Launch• Evaluation of the GLI internal-lamp calibration function• Creation of an algorithm for deriving calibration coefficients• Organization of calibration products (file specifications, processing
facility)• Discussion of ideas for a calibration-mode operation pattern during regular
operation period (including the calibration/validation phase)b. After Launch• Re-consideration of the GLI internal-lamp calibration function• Evaluation of the internal-lamp calibration results• Monitoring of secular change in the sensitivity of sensors(3) Sharing of Responsibilities
The GLI-NASDA Calibration Group is jointly organized such that the specialty
field of each of its subgroups — Analysis subgroup, Ground subgroup, andInstallation subgroup — can be fully utilized. Therefore, the Group works together as
appropriate under each circumstance for almost every task. The primary work
responsibilities are below shown for each group.
(i) Installation Subgroup• Analysis of ground test data (drawing calibration curves, SNR, stray light,
cross talk, transient response, etc.)• Initial checkout work (evaluating the functionality of VNIR/SWIR sensors,
the installed calibration function, etc.)• Analysis of installed calibration source data after the launch (evaluating A/B
surface deviation and SNR)
• Evaluation of sensor characteristics after the launch (evaluating stray light,
cross talk, and transient response)(ii) Ground Subgroup• Implementation of L1 algorithm• Preparation of data for calibration (preparing products of solar-light, internallamp, and electric calibration)(iii)Analysis Subgroup• Analysis of Earth observation data (evaluating the deviation of scanningmirror surface, SNR, stray light, cross talk, and transient response)• Evaluation of effects on higher-order products• Evaluation of secular change in sensitivity

77

1.4.2.2 Black-Body Calibration(1) Overview
GLI Calibration Group 2 shall perform black-body calibration. Black-bodycalibration will use the installed standard black-body and black-body temperaturemonitor data and will be performed during each scan for the MTIR channels. Blackbody data cannot be obtained while there is a tilt, so gain information during Nadir
observations will be effectively utilized.
(2) Items(i) Before Launch• Drawing calibration curves and non-linear tables• Evaluating dynamic range• Evaluating sensitivity deviations among MTIR pixels• Evaluating the GLI black-body calibration function• Proposing a GLI in-orbit black-body calibration algorithm(ii) After Launch• Re-discussion of the GLI black-body calibration function• Re-discussion of in-orbit calibration methods• Monitoring of secular change in the sensitivity of sensors• Discussion of calibration methods using C1 tables while there is tilt
(3) Sharing of Responsibilities
The GLI-NASDA Calibration Group is jointly organized such that the specialty
field of each of its subgroups — Analysis Subgroup, Ground subgroup, andInstallation Subgroup — can be fully utilized. Therefore, the Group works together as
appropriate under each circumstance for almost every task. The primary work
responsibilities are shown below for each group.
(i) Installation Subgroup• Analysis of ground test data (drawing calibration curves, calculating NEΔT,
etc.)• Initial checkout work (evaluating the functionality of MTIR sensors, theblack-body calibration function, etc.)• Analysis of installed calibration source data (evaluating A/B surface deviation
and NEΔT)(ii) Ground Subgroup• Implementation of L1 algorithm (processing black-body calibration, preparing
C1 tables for periods when there is tilt)• Preparation of data for calibration (preparing archive data)(iii)Analysis Subgroup• Analysis of Earth-observation data (evaluating the deviation of scanningmirror surface and NEΔT)• Evaluation of effects on higher-order products• Evaluation of secular change in sensitivity

78

1.4.2.3 Geometric Calibration(1) Overview
Calibration Group3 is primarily responsible for Geometric calibration. Sensor
alignment will be calculated using L1A images and GCP. Evaluation will be divided
into Sides A and B of the mirror, with and without tilt, and by scene units. Secularchanges will also be considered, and the trend will be tracked. Registration between
bands will be evaluated for L1A images and L1B images. Although the focal-point
surface (whether the same or different) and difference in scanning angles are briefly
considered here, emphasis will be placed on the situation with no tilt.
(2) Items(i) Sensor Alignmenta. Prior to Launch• Develop GCP-obtaining tools.
• Organize GCP candidate-point library.
• Prepare error patterns on images for each error-causing factor.
b. Initial Checkout Phase• Obtaining GCP• Discussion of error patterns on imagesc. Calibration and Validation Phase• Obtaining GCP (mainly by the Analysis Subgroup and Ground Subgroup)• Calculations of sensor alignment (mainly by the Analysis Subgroup and
Ground Subgroup)d. Regular Operation Phase• Validation of sensor alignment trend (mainly by the Analysis Subgroup
and Ground Subgroup)(ii) Registration Between Bandsa. Prior to the Launch• Section of channels to be discussed through simulationb. Initial Checkout Phase• Evaluation of registration between bands
c. Calibration and Validation Phase• Evaluation of registration between bands (mainly by the Analysis
Subgroup and Ground Subgroup)d. Regular Operation Phase• None79

(3) Sharing of Responsibilities
The Analysis Subgroup and Ground Subgroup within Group 3 calculate error
patterns by GCP, calculate sensor alignment errors, and evaluate registration between
bands. Sensor alignment will be evaluated jointly by the Installation, Analysis, andGround. The Installation group will primarily evaluates star tracker and compare
results with other sensors installed on ADEOS-II.
1.4.2.4 Evaluation of Sensor Characteristics and Image Quality(1) Overview
Calibration Group 4 shall primarily correct the image quality and evaluate sensor
characteristics other than the GLI in-orbit calibration (black-body calibration,internal-lamp calibration, and solar-ray calibration). In addition, this subgroupprovides opportunities for discussion and information exchange in inter-groupsettings.
(2) Items(i) Correction of Image Quality
a. Deviation between pixels, mirror surface, and scanning angle sensitivity• Calculations of sensitivity deviation on the low-radiance side by the solarray calibration data
• Calculations of sensitivity deviation on the high-radiance side by theinternal-lamp calibration data
• Calculations of sensitivity deviation by black-body observation data
• Calculations of correction coefficients by Earth-observation data(ii) Evaluation of Sensor Characteristics
a. Noise• Calculation of SNR on the low-radiance side by solar-ray calibration data
• Calculation of SNR on the high-radiance side by internal-lamp calibrationdata• Comparison between SNR/NEΔT based on Earth-observation data and
SNR/NEΔT based on installed calibration source
b. Dynamic Range, and Non-Linear/Saturation Characteristics• Comparison between adjacent bands after the launchc. Piecewise Linear Continuity• Comparison between adjacent bands after the launch

80

d. Stray Light, Cross Talk, Transient Response• Actual sample analysis of Earth-observation data
• Evaluation of cross talk from MTIR band to VNIR/SWIR bands usingnight VNIR/SWIR observation data
• Discussion of flags(iii)Monitoring Sensor Characteristics• Monitoring temperature changes of various parts of the sensors, mainlycontained in archived data
• Monitoring levels in the dark(iv) Coordination with Higher-Order Product Development• For calibration changes that significantly influence higher-order products,
process testing shall be delegated to the higher-order processing group, andthe influence of revising the L1 algorithm on higher-order products will be
evaluated.
(3) Sharing of Responsibilities
The GLI-NASDA Calibration Group is jointly organized such that the specialty
field of each of its subgroups — Analysis subgroup, Ground subgroup, andInstallation subgroup — can be fully utilized. Therefore, the Group works together as
appropriate under each circumstance for almost every task. The primary work
responsibilities are shown below for each subgroup:(i) Installation Subgroup• Analysis of ground test data
• Initial checkout work
• Analysis of installed calibration source data(ii) Ground Subgroup• Implementation of L1 algorithm• Preparation of data for calibration(iii) Analysis Subgroup• Analysis of Earth-observation data
• Evaluation of effects on higher-order products

811.4.2.5 Vicarious Calibration, Cross Calibration(1) Overview
Calibration Group 5 shall perform vicarious calibration. In this procedure, in-situ
data and atmospheric radiative-transfer simulation are combined to estimate the GLI
observation radiance. The vicarious calibration coefficient Gcal is obtained bycomparing the result with the radiance of the L1B data. In calculating this coefficient,
the accuracy of Earth-observation data and the accuracy of the atmosphere simulation
conditions are crucial, so ample preparation and discussion are necessary. GLI
observation spans a large range from high radiance to low radiance, and a high levelof accuracy in observation is required at all radiance levels. Therefore, it is necessary
to cover both the high-radiance and low-radiance ranges in ground observation forvicarious calibration.
Before the launch, a vicarious calibration test shall be conducted for SeaWiFS or
MODIS. Upward spectral radiance, downward spectral luminance, aerosol optical
thickness, etc. are to be measured on the sea surface or land surface, synchronouslywith either SeaWiFS or MODIS. Ozone data are then collected from TOMS.
Applying these data to the radiative-transfer model, upward spectral radiance arriving
at the satellite will be calculated and compared with the corresponding radiance value
obtained by either SeaWiFS or MODIS. This test will yield the calibration coefficientfor SeaWiFS or MODIS.
In the initial checkout phase, vicarious calibration will, if possible, be carried out
synchronously with the GLI check period. As before the launch, vicarious calibration
combined with other usable satellites will also be carried out in a similar fashion.
During the calibration and validation phase and the regular operation phase,vicarious calibration will be carried out four times a year — at the spring equinox,
summer solstice, autumn equinox, and winter solstice — considering the change in
the Sun’s zenith angle.
After completion of the GLI checkout phase, vicarious calibration will be once
again carried out at an early time in order to determine the calibration coefficient. A
proposal for a permanent vicarious calibration coefficient will be considered every six
months.
(2) Items(i) Vicarious Calibration in Low-Radiance Regions• Observing upward spectral radiance, downward spectral luminance, andatmospheric parameters near the sea surface. (Buoy will be installed off thecoast of Ishigaki Island, or NASA’s Moby will be used for this purpose.)• Estimation of satellite-arrival radiances using radiative-transfer model(RSTAR, MODTRAN, etc.)• System organization for calculating observation and calibration coefficients(prior to the GLI operation)82

(ii) Vicarious Calibration in High-Radiance Regions• Measuring upward spectral radiance, downward spectral luminance, andatmospheric parameters in desert and snow fields• Estimation of satellite-arrival radiance values using a radiative-transfer model(RSTAR, MODTRAN, etc.)• System organization for calculating observation and calibration coefficients(prior to the GLI operation)(iii) Cross-Comparison with Other Satellites• Tentative vicarious calibration using values of ocean-color sensors that havealready been vicariously calibrated such as SeaStar/SeaWiFS andTerra/MODIS.
• System organization for data-handling and calculating calibration coefficients(prior to the GLI operation).
(iv) Simple Vicarious Calibration of Heat Infrared Based on Global Data
• Using one day’s GLI L1B data, Reynolds SST data, JMA’s objectively
analyzed data, and the radiative-transfer model (LOWTRAN 7), a correction
coefficient will be obtained such that the GLI radiance values will match thevalues obtained through simulation by LOWTRAN on a global scale.
(3) Sharing of Responsibilities
Calibration Group 5 mainly consists of members of the analysis group. However,in evaluating the absolute calibration coefficients found and their secular change, theGroup will work together with Calibration Groups 1 and 2, comparing and discussing
other calibration results (results of solar-ray, internal-lamp, and black-bodycalibration).

831.4.2.6 L1-Process Software(1) Overview
Initial checkout support work shall be carried out in accordance with the document“GLI Proper Parts Initial Operation Evaluation Plan (NEB-01026).” The evaluation
results will be incorporated in the L1-process software. This will be done by
Calibration Group 6.
(2) Items• Preparing and providing data requested by MEP (including preparing andproviding archived data for calibration)• Incorporating the evaluation results in the L1-process software(3) Sharing of Responsibilities(i) Installation Subgroup• Preparing MEP• Revising the algorithm standard document based on the evaluation results(ii) Ground Subgroup• Preparing and providing data requested by MEP• Incorporating the evaluation results in the L1-process software(iii) Analysis Subgroup• Preparing revision requests for the L1-process software based on the analysis
results84

1.4.3

Validation Plan

1.4.3.1 Atmosphere
GLI atmosphere products must be validated to accomplish the final objectives of the
ADEOS-II mission, as there is a considerable level of uncertainty in the conversion
process from radiance to geophysical parameters. Furthermore, it is crucial that thevalidation and vicarious calibration of satellite sensors be comprehensively integrated inorder to create an effective validation system.
Validation data of the GLI atmosphere products are gathered through continuous
observation at several validation sites as well as through intensive field experiments
conducted several times after the launch; existing data will also be used. At validationsites, several parameters are continuously measured, including the radiance for eachwavelength and for all wavelengths, radiative flux, and geophysical parametersconcerning clouds, aerosols, and the amount of water vapor. In intensive field tests, it isessential to carry out in-situ observation of the atmospheric conditions from aircraft,including physical movements of aerosols and clouds as well as the reflection from theland surface. Existing data sets used for meteorological purposes are indispensable,
particularly the sonde data and radiance data for various individual wavelengths and for
all wavelengths. In addition, it is useful to compare and study these products from othersatellite sensors with the GLI products. Because validation and vicarious calibration
require long-term, global observation as well as data collection, it is important toestablish a close relationship with domestic and international projects such as
WCRP/GEWEX and IGBP/IGAC/ACE-ASIA.
The fundamental validation strategy is to collect large-scale, long-term validation/
vicarious calibration data through regular observation at a network of sites. Of course,such a network cannot be maintained by one organization, so this will be done inpartnership with international joint research projects. Validation sites must be selectedwith the objective of spanning diverse atmospheric and ground conditions and a varietyof scientific interest. Sites in China and southeast Asia are crucial in order to collect data
concerning the effects of atmospheric pollution and aerosol accumulation caused byhuman activity in Asia. Ocean observation by vessels is necessary for collecting data inareas with clean air mass and for research on marine aerosols and clouds. Other sites withpresent plans will also be useful as sites for international joint work. Some of these sites
are already in operation and some are in the planning stage. Investment in instruments
must be carefully coordinated for each site.
The validation work of the GLI atmosphere products can be summarized as follows;details of the implementation plan are found in “GLI Atmosphere Validation
Implementation Plan” (NDX-000234).

85

(1) Asia Land Continuous Observation: Observation at Si Samrong, Hefei, Yinchuan,Dunhuang, Mandalgovi, Miyakojima Island, Fukuejima Island, Chiba, Amami
Oshima Island, Minami Torishima Island(2) Vessel Observation: Observation on Mirai, Yasaku Maru, Shinzan Maru
(3) Pre-Launch Validation Observation: Fukuejima Island intensive observation, aircraft
observation near Amami(4) Intensive Observation: Initial Validation Test, Test of the Effects of Aerosols Caused
by Human Activity, Follow-Up Intensive Test
Table 1.4.3-1 GLI Atmosphere Products and Target Accuracy
Product Code

Algorithm Code

ARAE

ATSK5
post_ATSK5

AROP
CLOP

Pre_ATSK3_p
ATSK3_p

CLER
CLFR
CLHT
CLTT

ATSK3_r
ATSK3_e
ATSK16

CLWP

GeophysicalParameter Code
ARAE

Aerosol Angstrom Exponent

AROP

Aerosol Optical Thickness*1

10% or 0.05

CLOP

Cloud Optical Thickness

*1

10%

CLER

Cloud Equivalent Grain Size

Geophysical Parameter

Target Accuracy
0.5

20%

CLFR

Amount and Type of Clouds

CLHT

Cloud -Top Altitude*2

1.0 km

CLTT

Cloud -op Temperature

0.5 K

CLWP

Cloud-Liquid Water Percentage

20%*1 The optical thicknesses of aerosols and of clouds are measured at a wavelength of 500 nm.
*2 The amount of clouds is the target error corresponding to the monthly average.

5
4

3
2
6

7
9

8
10

1
12

13

11

1. Si Samrong (Thailand)
2. Hefei (China)
3. Yinchuan (China)
4. Dunhuang (China)
5. Mandalgovi (Mongolia)
6. Miyakojima Island (Japan)
7. Fukuejima Island (Japan)
8. Chiba (Japan)
9. Amami Oshima Island (Japan)
10. Minami Torishima Island (Japan)
11. Mirai (vessel)
12. Yasaku Maru (vessel)
13. Shinzan Maru (vessel)

Figure 1.4.3-1

GLI Atmosphere Validation Sites
8610%

1.4.3.2 Ocean
Validation of the GLI Ocean products can be classified into validation of ocean color
and validation of sea-surface temperature.
In validating ocean color, vicarious calibration using data collected on the ocean is
indispensable. For this reason, validation ocean color is closely linked to vicarious
calibration. In the validation that follows this, satellite-measured radiance (which
incorporates the results of vicarious calibration) will be used to validate the normalized
water-leaving radiance (nLw), chlorophyll (CHL), suspended substances (SS), andcolored dissolved organic matters (CDOM), clarifying the accuracy of these values andpaving the way to discuss the algorithm for the next algorithm revision.
Data collection for validating ocean color can be mainly divided into global collection
and collection around Asia. Global validation is carried out through international
cooperation with PI, SIMBIOS, etc. and collects data corresponding to large-areastandard products. Regional data collection around Asia can be roughly classified into (1)field campaigns, (2) cooperative measurement with universities, (3) cooperative
measurement with the Fisheries Agency, and (4) cooperative measurement withresearchers in Asia.
Sea-surface temperature is validated as follows. In order to validate GLI bulk seasurface water temperature continuously around the globe, it is essential to have a wellplanned strategy and a system to implement it. The Ocean Group of the GLI Science
Team has a goal of maintaining an RMS accuracy of 0.6 K or less throughout the
duration, globally, for GLI bulk sea-water surface temperature. This target wasestablished based on the target accuracy of past global sensors. After the launch of
ADEOS-II, the accuracy will be further improved through close cooperation with thealgorithm developers.
The validation plan has been carefully made such that the bulk sea-surface watertemperature products will be validated soon after the launch of ADEOS-II, and theaccuracy-validated products will be introduced at the start of the regular operation phase.
The basic policy of this plan is as follows. In validating GLI sea-surface watertemperature, satellite-observed SST will be used in order to make sufficient comparisonswith GLI products prior to the beginning of the regular operation phase. After that, as thefrequency of GLI observation increases, more match-up data with in-situ observation will
be made, leading to the creation of more reliable algorithms.
The validation work of the GLI ocean products can be summarized as follows; detailsof the implementation plan are found in “GLI Ocean Validation Implementation Plan”(NDX-000235).
(1) Sea Water: Validation through data collection on vessels(2) Sea Surface Water Temperature: Validation through in-situ data and satellite data

87

Table 1.4.3-2 GLI Ocean Products and Target Accuracy
Product
Code

Algorithm
Code

NL_FR
NL_LR

OTSK1a_FR
OTSK1a_LR

CS_FR
CS_LR

ST_FR
ST_LR

OTSK2_FROTSK5_FROTSK7_FROTSK6_FROTSK2_LROTSK5_LROTSK7 LROTSK13_FR
OTSK13_LR

GeophysicalParameter CodeGeophysical Parameter
Normalized Water-Leaving Radiance (outer sea)

NWLR

Normalized Water-Leaving Radiance (coastal waters)

QF_OC

Ocean Color Product Quality Flag

TargetAccuracy
×1.5
×2.0
-

Chlorophyll a Concentration (outer sea)

×1.5

Chlorophyll a Concentration (coastal waters)

×2.0

CDOM

Dissolved Organic Matters Light-Absorption Coefficient

×2.0

K490

40nm Dissipation Coefficient

×1.5

SS

Suspended Substance Concentration

×2.0

SST_b

Bulk Sea-Surface Temperature

0.6 K

QF_ST

Sea-Surface Temperature Product Quality Flag

CHLA

1
2

3
5

4

6
7

1. Bering Sea, Northern Pacific Ocean
2. Eruption Gulf, off Kushiro
3. Sea of Japan
4. Sagami Gulf
5. Omura Gulf, Ariake Sea
6. Near Okinawa, East China Sea
7. Sea near Hawaii

Figure 1.4.3-2

GLI Ocean Validation Sites

88-

1.4.3.3 Land
The land group of the GLI Science Team has used visible and near infrared-range
channel data to create an algorithm that determines the vegetation index as a standard
product. Ground observation tests are planned to validate this algorithm and calibrate
accuracy and sensors, starting prior to the satellite launch and lasting for two years after
the launch. The standard product to be studied is the vegetation index. By obtaining
vertical spectral information, bidirectional reflection, and atmospheric characteristics on
the land surface synchronously with the satellite, the vegetation index can be calculated
through ground observation. It is then possible to determine how much of the influencecaused by the atmospheric and bidirectional reflection characteristics was eliminated byusing the standard algorithm. By analyzing the sensitivity of vegetation index, theaccuracy in terms of plant monitoring can be analyzed.
Before the launch of ADEOS-II, field-related ground tests will be carried out at
Mandalgovi (Mongolia), New Mexico (United States), and Yatsugatake (Japan). Spectral
information on land surface, bidirectional reflection characteristics, and atmosphericcharacteristics will be gathered, and a system will be organized to find the standard
product quantities through ground observation. After the launch, observation willcontinue at sites including Amburla (Australia), Miri (Malaysia), and Ho Chi Minh (VietNam). In order to complete the first round of validation activities within half a year of thelaunch, it is necessary to partially change the geographical regions for validation
activities for the year, depending on the season in which the launch will take place. For
example, if the launch is in the winter in the northern hemisphere, validation data on
fields will be collected in Mandalgovi during the summer in the northern hemisphere. If
the launch is in the summer in the northern hemisphere, validation data will be collectedin Amburla during the winter in the northern hemisphere; validation activities are to be
carried out as early as possible.
In actual validation activities, the vertical spectral information on the land surface will
be measured by a hand-held spectroradiometer and by a spectrometer on an RC helicopter.
The bidirectional reflection characteristics can also be obtained by the method in whichan RC helicopter observes one point on the land surface from the air. The atmosphericcharacteristics are to be synchronously measured by sun photometer. Biomass, ageophysical parameter for plants, needs to be obtained by cutting and collecting samples.
The leaf-area index can be obtained by measurement using devices such as the LAI2000.
The coverage rate can be determined by analyzing digital images taken from an RC
helicopter. It is impossible to measure everything at all the sites, so the items to bemeasured for each validation region will be coordinated.
Precise geometric correction parameters are used in creating higher-order products for
GLI land and cryosphere, for which precise geometric correction is already completed;these parameters are land standard products themselves. Hence, as a part of the GLI land
validation process, the precise geometric correction parameters will also be validated.

89

The validation work of the GLI land products can be summarized as follows; detailsof the implementation plan are found in “GLI Land Validation Implementation Plan”(NDX-000236).
(1) Observation at Mandalgovi(2) Observation at Amburla(3) Observation at New Mexico(4) Observation at Yatsugatake(5) Observation at Miri
(6) Observation at Ho Chi Minh
(7) Observation at Kii Peninsula(8) Observation at Tomakomai(9) Validation of Precise Geometric Correction Parameters
Table 1.4.3-3 GLI Land Products and Target Accuracy
Product Code

Algorithm
Code

GeophysicalParameter Code
NDVI

VGI

LTSK9

ACLC

LTSK1

PGCP

LTSKG

--

Precise Geometric Correction Parameter

Level-2A_LC

LTSK10--

Data Composite

Geophysical Parameter
Normalized Vegetation Index

TargetAccuracy
10%

EVI

Extended Vegetation Index

10%

ACLC

Reflectance with Atmospheric Correction Done

10%
-

1
7

4

8
3

6
5

2

1. Mandalgovi (Mongolia)
2. Amburla (Australia)
3. New Mexico (United States)
4. Yatsugatake (Japan)
5. Miri (Malaysia)
6. Ho Chi Minh (Viet Nam)
7. Kii Peninsula (Japan)
8. Tomakomai (Japan)

Figure 1.4.3-3

GLI Land Validation Sites

90<1 pixel

1.4.3.4 Cryosphere
In the cryosphere, the following standard and research products will be made.
Standard products are the snow grain size, snow impurity, and cloud search; research
products are snow and sea-ice distribution, snow-ice surface temperature, snow-ice
surface albedo, snow in forests, vertical distribution of snow grain size, and sea-iceclassification. These snow-ice geophysical parameters will be validated by direct
observation and optical observation of geophysical parameters on the ground as well as
remote observation from aircraft. Snow grain size, snow impurity, snow-ice surface
temperature, snow-ice surface albedo, snow in forests, and vertical distribution of snow
grain size are directly measurable geophysical parameters. By combining opticalobservations by spectrometer, FTIR, etc. with snow-ice cross-section observations, we
can study the effects that these snow-ice geophysical parameters may have on theradiance characteristics, thus enabling the algorithm and radiative transfer model to be
improved prior to the satellite launch. After the launch, the accuracy of the products thus
produced will be validated; if the accuracy is insufficient, the algorithm will be improved.
Cloud search, snow and sea-ice distribution, and sea-ice classification are geophysicalparameters that are difficult to measure directly; the accuracy of these parameters will bevalidated using remote observations from aircraft and/or other satellites as well as
statistical methods through fixed-point observations.
Locations where tests will be conducted for validation are snow-ice surfaces at the
Shinjou Branch of the Nagaoka Snow Ice Hazard Prevention Research Institute, around
Lake Saroma in Hokkaido, around Fairbanks and Barrow (both in Alaska), and the
Syowa Base and interior of Antarctica. Observations at the Shinjou Branch of the
Nagaoka Snow Ice Hazard Prevention Research Institute are intended to clarify basic
BRDF characteristics, so these will be done prior to the GLI launch. In eastern Hokkaido,
where the snowfall amount is relatively high, the rate of clear sky is high. Lake Saroma islocated in this area, where fresh water is mixed with sea water; the sea water is uniform
in this lake, which faces the Sea of Okhotsk. Therefore, snow in the inland and forests, aswell as various types of sea-ice, can be observed and measured in a short period of time.
In addition, this is a suitable site for aircraft observation, so most products can bemeasured here. The only exception is that the site is not suitable for validating of snowforest relationship products synchronous with the GLI because there is no uniform snowice surface over the GLI pixel size of 1 km x 1 km, except in the sea-ice of Lake Saroma.
Here, algorithm validation observations will be carried out every year before and after thelaunch. For observations synchronous with the satellite, Fairbanks and Barrow in Alaska
are suitable as these sites have large, uniform land surfaces. Products on snow-in-forest
will be mainly validated around Fairbanks, and products on snow and sea-ice will bemainly validated around Barrow. Barrow also has NOAA’s observation facilities for
CMDL and ARM, where aerosol and meteorological data can be collected. Validationand calibration work will be carried out in Fairbanks and Barrow immediately after thelaunch. Antarctica is a very geologically and scientifically interesting site. The snow
contains very few impurities, and the study of snow grain size there is expected to reveal
variations with respect to altitude, ranging from about 4000 m from the shoreline to theinterior, as well as season and time over years. One or two staff members will be sent to
Antarctica to conduct validation and calibration work.
91

The validation work of the GLI cryosphere products can be summarized as follows;details of the implementation plan are found in “GLI Cryosphere Validation
Implementation Plan” (NDX-000237).
(1) Indoor and ground tests at the Shinjou Branch of the Nagaoka Snow Ice Hazard
Prevention Research Institute(2) Ground observation in eastern Hokkaido(3) Ground observation at Fairbanks and Barrow(4) Ground observation at Syowa Base
Table 1.4.3-4 GLI Cryosphere Products and Target Accuracy
Algorithm
Code

Product CodeGeophysicalParameter CodeSCFG

Snow and Cloud Flags

SCFG

Snow and Sea-Ice Distributions

5%

Geophysical Parameter

TargetAccuracy5%

CLFLG_p

CTSK1

SNGI_pSNGICTSK2b1_s
CTSK2b1_g

SNWG

Snow Grain Size

20%

SNWI

Snow Impurity

30%

-

-

-

Snow-Ice Surface Temperature

2°C

-

-

-

Snow-Ice Land Surface Albedo

3%

-

-

-

Distribution of Snow in Forests

30%

-

-

-

Vertical Distribution of Snow Grain Size

25%

-

-

-

Sea-Ice Distribution

10%

3

2
1

4

Figure 1.4.3-4

1. Shinjou Branch of the Nagaoka Snow
Ice Hazard Prevention Research Institute(Japan)2. Eastern Hokkaido (Japan)
3. Fairbanks and Barrow (United States)
4. Syowa Base (Antarctica)

GLI Cryosphere Validation Sites

92

2. AMSR
2.1

Scientific Objectives

The Advanced Microwave Scanning Radiometer (AMSR) is a passive microwave
radiometer that measures weak microwave radiation emitted from the Earth. A
characteristic of microwave radiometers like AMSR is that they can measure fixedgeophysical parameters under almost any weather conditions, even during day and night.
Combining 14 channels of data, AMSR aims at observing the following geophysicalparameters related to water on the Earth.
(1) Accumulated atmospheric water vapor(2) Precipitation(3) Accumulated atmospheric cloud liquid water(4) Sea surface wind/speed(5) Sea surface temperature(6) Sea ice distribution(7) Snow runoff depth(8) Soil moisture amount
AMSR’s main mirror is 2 meters in diameter, and this is the biggest in all microwave
radiometers today. A big mirror enables observations with low frequency of 6.9 GHz. Asa result, it will acquire geophysical parameters like the sea surface temperature and soil
moisture that the conventional microwave radiometers cannot observe. AMSR also willobserve water vapor, cloud water, and precipitation like conventional microwave
radiometers, but with improved observation accuracy and precision due to high spatial
resolutions.
AMSR will be fly on both ADEOS-II and NASA’s EOS-PM1. The sensor aboard
EOS-PM-1 is called “AMSR-E” both sensors are called “AMSR.” Observational time for
ADEOS-II is 10:00 AM local time; that of EOS-PM1 is 1:30 PM. More observations will
be performed, and those two sensors will enable observing daily changes. AMSR-E’main mirror is 1.6 meters in diameter.
The AMSR data can be obtained as global data in near real-time through the data
relay satellites. It is expected to be used as initial values for of weather forecastingmodels and to enable proving the practical use of AMSR. In the future, satellite data andnumerical values will be assimilated, to provide analysis. Use of fixed AMSR data forthis is considered a future promising field.

93

2.2

Overview of the Mission Instruments

The advanced microwave signal radiometer (AMSR) performs high-accuracy
observation by receiving extremely weak microwave signals naturally emitted from theland surface and atmosphere in multiple bands, measuring a variety of geophysicalparameters relating to water (H2O), day and night and even under cloud cover. Its
primary purpose is to collect data for understanding water and energy circulation on a
global scale.
The AMSR microwave signal radiometer measures eight frequency bands from
6.9GHz to 89GHz using both vertically and horizontally polarized waves (except the two
frequency bands in the 50GHz band). Its antenna and other parts are mechanicallyrotated to do the scanning and to collect radiance data from land surface.
The AMSR has an antenna aperture, with a diameter of 2m; it is capable of collecting
data with spatial resolution of 5 km in the 89GHz band (the band with the shortest
wavelength) and 60 km in the 6.9 GHz band (the band with the longest wavelength). It
also reduces the influence of sea winds on the sea-surface temperature by performing
conical scanning so that the angle of incidence with respect to the land surface ismaintained constant at 55 degrees. With a scanning width of 1600 km, it is capable ofwide, large-scale observation. Furthermore, it obtains the radiant temperature of deep
space (2.7 K) as well as a high-temperature calibration source to calibrate observation
data. Table 2.2-1 lists the major AMSR specifications. Figure 2.2-1 shows the AMSR
observation concept.
Table 2.2-1

AMSR Main Specifications

Item
Central Frequency (GHz)

Specifications
6.9

Ground Resolution
Band Width (MHz)Polarization

10.65

18.7

50km
350

23.8

36.5

25km
100

200

400

15km

5km

1000

3000

Horizontal and Vertical

Observation Width

89.0

50.3

52.8
10km

200

400
Vertical

1600 km111.09 Kbps*1

Data Rate

1. During one scan (one rotation) of the AMSR, there is a period of time in which no data are collected; hence, theessential average data rate is 87.38 Kbps.

94AMSR Observation Concept

55°

61°
55°

Conical Scan by Antenna Rotation

55°

61°

1600 kmAlong Track Direction

Fig. 2.2-1

AMSR Observation Concept

Table 2.2-2 shows the operation mode of the AMSR during a regular operation period.

Table 2.2-2
Mode

AMSR Operation Mode during a Regular Operation Period
Operation Overview

Conditions for the Mode

Normal Mode

Mode in which observation data are collected or
stand-by mode for collecting observation data

During the regular operation, this is the default mode that ismaintained.

Sleep Mode

Rotating instruments maintain their normal constantrotation rates, maintaining the instrument
temperature within the operational temperature
range.

Mode when a light-load-mode (LLM) command*1 is issued
or during orbit-attitude angle control

1. When there is trouble with the satellite, this command is issued autonomous by on the ADEOS-II onboard computer(OBC) to each installed instrument.

95

2.3

Specific

2.3.1

Products(1)(2)

Products created in EOC
Standard products, near real time products、browse data
Products created in EORC
Research products

2.3.2 Definition of higher-order products
2.3.2.1 Standard productsStandard products are created by EOC as planning production and created from astandard algorithm. After data production, they are saved in EOC and distributed togeneral users.
(1) Table 2.3.2-1 lists Level 2 standard products.
Table 2.3.2-1 Standard Products
Processing Level
L2

Product
WV

Water Vapor

CLW

Cloud Liquid Water

AP

Precipitation

SSW

Sea Surface WindSST

Sea Surface TemperatureIC

Sea Ice Concentration

SWE

Snow Water Equivalence

2.3.2.2 Near real time products
Near real time products conform to（NEB-98020）.

ADEOS-II / EOS-PM1 Near-real-time Products

2.3.2.3 Browse data
Browse data is the source data that creates the image catalog in EOC.
Image catalog provision Services to display the image catalog (browse image)through EUS/GUI or EUS/WWW are described in Chapter 2.3.5.
2.3.2.4 Research products
Research products are created from algorithms developed by EORC. The algorithms
used for this product are for research purposes and quality is not guaranteed. These are
not defined as products to be created in EOC.
(1) Table 2.3.2-2 lists research products.
96

Table 2.3.2-2 Research Products
Product
RWV

2.3.3

Research of Water Vapor

RCLW

Research of Cloud Liquid Water

RAP

Research of Precipitation

RSSW

Research of Sea Surface Wind

RSST

Research of Sea Surface Temperature

RIC

Research of Ice Concentration

RSWE

Research of Snow Water Equivalence

RSM

Research of Soil Moisture

Scene definition(1) Scenes for level 2 are basic observational units. Taking half of one cycle (half cycle),
this definition includes pole to pole for each orbit.
(2) Scenes for level 2 Maps use 1600km (observed width)×1600km (orbit direction) .
(3) Level 3 contains not only scene units but also data for the whole globe.
2.3.4

Processing level definitions
The flow of the whole process is shown in Fig.2.3.4-1.
Level-0

Level-1A

Level-1B
Brightness Temperature(TB)

L2

Level-1B Map(Resampling)

WV

CLW

AP

SSW

SST

IC

SWE

Takeuchi＊Wentz＊

Petty＊

Shibata＊

Shibata or Wentz

Comiso＊Chang＊

L2 Map
WV

L3

WV

CLW

CLW

AP

AP

SSW

SSW

SST

SST

IC

IC

＊Described as reference information by the algorithm developers.

Figure 2.3.4-1

AMSR Total Process Flow

97

SWE

SWE

TB

2.3.4.1 Level 1
The Level 1 product definitions and specifications conform to
AMSR Level-1 Product Specification（NEB-98016）.

ADEOS-II GLI,

2.3.4.2 Level 2(1) Each geophysical parameter is calculated based on the Level 1B data.
(2) Geometrical correction information is added in the same way as for level 1B. Quality
information (1 bit - 8 bits of data=1 byte data) and additional information (ininternational digital time, the time of each scan, orbit number (Fig.2.3.4-2)
conforming to TAI93, which is the number of seconds passed since 1993).
(3) Shown in AMSR Standard Higher-order Level 2 Product List in Table 2.3.4-1.
(4) Data storage format is L1B（pixel layout）.
(5) Data size is the approximate size of each data unit.
North 90°

…

…

3

2

1

57

56

55

…

EquatorSouth 90°

The definition of the Orbit number conforms to “ADEOS-II Orbit Number and Path
Number (NEB-97005).”
Figure 2.3.4-1
Orbit Numbers
Table 2.3.4-1 AMSR Standard Higher-Order Level 2 Product List

WV

Water vapor

CLW

Cloud liquid/water

Scene

Half-cycle

2.7 MB/scene

L1B

AP

Precipitation

Scene

Half-cycle

2.7 MB/scene

L1B

SSW

Sea surface wind

Scene

Half-cycle

2.7 MB/scene

L1B

SST

Sea surface temperature

Scene

Half-cycle

2.7 MB/scene

L1B

IC

Sea ice concentration

Scene

Half-cycle

4.9 MB/scene

L1B

SWE

Snow water equivalent

Scene

Half-cycle

4.9 MB/scene

L1B

Product

Process
frequency
Half-cycle

Data storage format(Note)
L1B

Data
Unit
Scene

Code

Note LIB in the data storage format is defined in Chapter 2.3.4.1.

98

Data size
2.7 MB/scene2.3.4.3 Level 2 Map(1) Level 2 data is map projected.
(2) AMSR Standard higher-order Level 2 Map product list is shown in Table 1.4.3-1.
(3) 1600km（observed width）×1600km（orbit direction）size is used as a unit.
(4) The pixel size is re-sampled at 10km intervals.
(5) Select nearest neighbor (NN) and bi-linear (BL) and make an order.
(6) Select the map projection method from equi-rectangular, Mercator (MER) and Polar
stereo (PS) and order (Table 2.3.4-3 Map projection methods, Figure 2.3.4-3 Map
projection methods).
(7) Global form（standard ellipsoid） is WGS84.
(8) For standard latitude when cutting a scene, match with level 1 (see Chapter 2.3.4.1)
and select from the three types shown below - standard latitude, scene-centered,specific latitude (5 degree interval).
• Definition of basic latitude: This is the latitude of the connecting point when the
globe is projected on a flat map.
• Standard latitude: Standard latitude for equi-rectangular or MER is taken as 0
degrees (equator). Standard latitude for PS is ±90 degrees (pole).
• Scene-centered: Scene centered is literally the same as centered latitude specified
when the user cuts the map in the middle of a scene.
• Specific latitude: User specifies separately. However, the cutting of the specified
latitude is in 5-degree intervals.
(9) Data size is the approximate size for each data unit.
Table 2.3.4-2 AMSR Standard Higher-Order Level 2 Map Product List (1/2)
WV

Water vapor

Scene

Process
frequency
Order683.6 KB

Projection
methodEqui-rectangular

〃

〃

〃

〃

〃

MER

Scene centered

〃

〃

〃

〃

1093.8 KB

Equi-rectangularStandard latitude
Specific latitude

Code

Product

Data Unit

Data size

Basic latitude
Scene centered

〃

〃

〃

〃

〃

〃

〃

〃

〃

〃

〃

MER

Standard latitude

〃

〃

〃

〃

〃

〃

Specific latitude
Standard latitude

〃

〃

〃

〃

〃

PS

〃

〃

〃

〃

〃

〃

Specific latitude

CLW

Cloud/water

Scene

Order

683.6 KB

Equi-rectangular

Scene centered

〃

〃

〃

〃

〃

MER

Scene centered

〃

〃

〃

〃

1093.8 KB

Equi-rectangularStandard latitude
Specific latitude

〃

〃

〃

〃

〃

〃

〃

〃

〃

〃

〃

MER

Standard latitude

〃

〃

〃

〃

〃

〃

Specific latitude

〃

〃

〃

〃

〃

PS

Standard latitude

〃

〃

〃

〃

〃

〃

Specific latitude

AP

Precipitation

Scene

Order

683.6 KB

Equi-rectangular

Scene centered

〃

〃

〃

〃

〃

MER

Scene centered

〃

〃

〃

〃

1093.8 KB

Equi-rectangularStandard latitude

〃

〃

〃

〃

〃

〃

Specific latitude

99

Table 2.3.4-2 AMSR Standard Higher-Order Level 2 Map Product List (1/2)

AP

Precipitation

Scene

Process
frequency
Order1093.8 KB

Projection
method
MER

〃

〃

〃

〃

〃

〃

Specific latitude

〃

〃

〃

〃

〃

PS

Standard latitude

〃

〃

〃

〃

〃

〃

Specific latitude

SSW

Sea surface wind

Scene

Order

683.6 KB

Equi-rectangular

Scene centered

Code

Product

Data Unit

Data size

Basic latitude
Standard latitude

〃

〃

〃

〃

〃

MER

Scene centered

〃

〃

〃

〃

1093.8 KB

Equi-rectangularStandard latitude
Specific latitude

〃

〃

〃

〃

〃

〃

〃

〃

〃

〃

〃

MER

Standard latitude

〃

〃

〃

〃

〃

〃

Specific latitude

〃

〃

〃

〃

〃

PS

Standard latitude
Specific latitude

〃

〃

〃

〃

〃

〃

SST

Sea surface temperature

Scene

Order

683.6 KB

Equi-rectangular

Scene centered

〃

〃

〃

〃

〃

MER

Scene centered

〃

〃

〃

〃

1093.8 KB

Equi-rectangularStandard latitude

〃

〃

〃

〃

〃

〃

Specific latitude

〃

〃

〃

〃

〃

MER

Standard latitude

〃

〃

〃

〃

〃

〃

Specific latitude

〃

〃

〃

〃

〃

PS

Standard latitude

〃

〃

〃

〃

〃

〃

Specific latitudeIC

Sea ice concentration

Scene

Order

703.1 KB

Equi-rectangular

Scene centered

〃

〃

〃

〃

〃

MER

Scene centered

〃

〃

〃

〃

1250 KB

Equi-rectangularStandard latitude
Specific latitude

〃

〃

〃

〃

〃

〃

〃

〃

〃

〃

〃

MER

Standard latitude

〃

〃

〃

〃

〃

〃

Specific latitude

〃

〃

〃

〃

1250 KB

PS

Standard latitude

〃

〃

〃

〃

〃

〃

Specific latitude

SWE

Snow water equivalent

Scene

Order

703.1 KB

Equi-rectangular

Scene centered

〃

〃

〃

〃

〃

MER

Scene centered

〃

〃

〃

〃

1250 KB

Equi-rectangularStandard latitude

〃

〃

〃

〃

〃

〃

Specific latitude

〃

〃

〃

〃

〃

MER

Standard latitude

〃

〃

〃

〃

〃

〃

Specific latitude

〃

〃

〃

〃

〃

PS

Standard latitude

〃

〃

〃

〃

〃

〃

Specific latitude

Table 2.3.4-3 Map Projection Method
Latitude＼Projection Method

Equi-rectangular

Mercator

North/South 0〜50 degrees

○

○

×

North/South 50〜60 degrees

○

○

○

North/South 60〜90 degrees

×

×

○

100

Polar stereo

90deg. N

60deg.

PS

50deg.
Equi‑rectangle
Equator50deg.
60deg.

Figure 2.3.4-3

90deg. SPS

Map Projection Method

2.3.4.4 Level 3(1)
Level 3 data is used for processing mapping using the simple arithmetic mean of
the level 1B brightness temperature (TB) and level 2 geophysical parameter data for theeach global size (grid point interval *1).
Grid point interval *1: An interval of 0.25 degrees for the equi-rectangulardiagram method an interval of 25km, for the polar stereo diagram method.
(2)

Table 2.3.4-4 lists AMSR standard higher-order level 3 products.

(3)
Data units are the global data for each product. There are two types of global data
ascending global data and descending global data（see ascending/descending below）.
Ascending: Data when cutting the North/South polar points of the globe and observing
from the Southernmost point to the Northernmost point.
Descending: Data when cutting the North/South polar points of the globe and observing
from the Northernmost point to the Southernmost point.
(4)
Two different projection methods are created in equi-rectangular and polar stereo(PS) (Figure 1.4.4.1 diagram method definition ((a) to (d)).
The PS diagram method is defined in Figure 2.3.4-4 diagram method definitions (b) to (d).
(5)

The data size is the approximate size for each data unit.

101

Table 2.3.4-4 AMSR Higher-Order Level 3 Product List
Code

Product

TB

Brightness temperature

TB

Brightness temperature

TB

Brightness temperature

TB

Brightness temperature

TB

Brightness temperature

TB

Brightness temperature

WV

Water vapor

WV

Water vapor

CLW

Cloud/water

CLW

Cloud/water

AP

Precipitation

AP

Precipitation

SSW

Sea-surface wind

SSW

Sea-surface wind

SST

Ocean temperature

SST

Ocean temperature

IC
IC
IC
IC

Ocean distribution／
ocean proximity
Ocean distribution／
ocean proximity
Ocean distribution／
ocean proximity
Ocean distribution／
ocean proximity

SWE

Snow

SWE

Snow

SWE

Snow

SWE

Snow

Data Unit
Global(ascending/descending
Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)Global(ascending/descending)

Process
frequency

Data size

Projection method

Day

1.98 MB / Global*1

Equi-rectangularMonth1.98 MB / Global*1

Equi-rectangularDay

0.26 MB / Northern Hemisphere*1

PS

Month

0.26 MB / Northern Hemisphere*1

PS

Day

0.20 MB / Southern Hemisphere*1

PS

Month

0.20 MB / Southern Hemisphere*1

PS

Day

1.98 MB / Global

Equi-rectangularMonth1.98 MB / Global

Equi-rectangularDay

1.98 MB / Global

Equi-rectangularMonth1.98 MB / Global

Equi-rectangularDay

1.98 MB / Global

Equi-rectangularMonth1.98 MB / Global

Equi-rectangularDay

1.98 MB / Global

Equi-rectangularMonth1.98 MB / Global

Equi-rectangularDay

1.98 MB / Global

Equi-rectangularMonth1.98 MB / Global

Equi-rectangularDay

0.26 MB / Northern Hemisphere

PS

Month

0.26 MB / Northern Hemisphere

PS

Day

0.20 MB / Southern Hemisphere

PS

Month

0.20 MB / Southern Hemisphere

PS

Day

1.98MB / Global

Equi-rectangular

Month

1.98MB / Global

Equi-rectangularDay0.47 MB / Northern Hemisphere*2

PS

Month

0.47 MB / Northern Hemisphere*2PS

1. Brightness temperature (TB) is 14ch in total. The data size is 1ch=1 file.
The types of TB data (14ch) are as below.
Horizontal polarization (6ch): 6.9 GHz, 10.65 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz
Vertical polarization (8ch): 6.9 GHz, 10.65 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 50.3GHz, 52.8 GHz, 89.0
GHz
2. A map projection for the Southern Hemisphere for snow (SWE) is not map-projected using PS.

102

(a) Map projection using equi-rectangular methods cover the Southern latitude 90
degrees to Northern latitude 90 degrees as shown below.
North
90°

0°

South
90°
East 180°

East 0°

East 360°

Note The latitudes given in the above diagram are not especially regulated.

(b) Map projections using PS (Northern hemisphere, TB/IC) cover the areas surroundedby the thick frame lines in the following diagram.

lat.
long.

30.98deg.
168.35deg.

180deg.
210 deg.

150deg.

20deg.
40deg. ○240deg.

120deg.

60deg.
80deg.

○

90deg.

270deg.
lat.
long.

lat.
long.

○

33.92deg.
279.26deg.
300deg.

60deg.

○
330deg.

30deg.
0deg.
lat.
long.

Fig. 2.3.4-4

34.35deg.
350.03deg.

Diagram Method Definition (1/2)
103

31.37deg.
102.34deg.

(c) Map projections using PS (Southern hemisphere, TB/IC) cover the areas surroundedby the thick frame lines in the following diagram.
0deg.
330deg.
lat.
long.

30deg.

-20deg.

-39.23deg.
317.76deg.

lat.
long.

-40deg.

-39.23deg.
42.24deg.
60deg.

300deg.

○-60deg.

○-80deg.
90deg.

270deg.

○○120deg.

240deg.
lat.
long.

lat.
long.

-1.45deg.
225.00deg.

-1.45deg.
135.00deg.

150deg.

210deg.
180deg.

(d) Map projections using PS (Northern hemisphere, SWE) cover the areas surroundedby the thick frame lines in the following diagram.
180deg.
210deg.

20deg.

lat.
long.

150deg.
43deg.
180deg.

40deg.
240deg.

○120deg.

80deg.

lat.
long.

60deg.

270deg.
lat.
long.

○

○

90deg.

35deg.
270deg.
300deg.

60deg.

○
lat.
long.
330deg.

43deg.
0deg.
30deg.

0deg.

Fig. 2.3.4-4

Diagram Method Definition (2/2)

10425deg.
90deg.

2.3.5 Image Catalog
The services provided by the image catalog are the browse images using dataprocessed in 1-day units in level 3 as shown below.
TB

Brightness temperature(3ch)

1（Projection method：equi-rectangular）*2（ascending/descending）*3（6.9GHz,
36.5GHz, 89GHz） = 6 types.

WV

Water vapor

1（Projection method：equi-rectangular）*2（ascending/descending）= 2 types

CLW

Cloud water

1（Projection method：equi-rectangular）*2（ascending/descending）= 2 types

AP

Precipitation

1（Projection method：equi-rectangular）*2（ascending/descending）= 2 types

SSW

Sea wind speed1（Projection method：equi-rectangular）*2（ascending/descending）= 2 types

SST

Sea surface temperature

1（Projection method：equi-rectangular）*2（ascending/descending）= 2 types

IC

Ocean distribution /Ocean proximity

1（projection method：PS）*2（ascending/descending）*2（South/North）= 4 types

SWE

Snow water equivalent1（Projection method：PS）*2（ascending/descending）= 2 types

Note 3ch (6.9GHz, 36.5GHz, 89GHz) used by TB（Brightness temperature）are all vertically polarized.

2.3.6

Standard Product Processing Format

The standard processing format is shown in Fig 2.3.6-1 (process total number、
process order).
Primary process
L1A

Process total number of all pixels

L1B

Process total number of all pixels

L1B MAP

Process order
Higher-order process
L2

Process total number

L2MAP

Process order

L3

Process total number

Figure 2.3.6-1
Standard Product Processing Format(Process Total Number, Process Order)105

2.3.7 AMSR First-light images
A false-color composite image is developed from the data obtained by the Advanced
Microwave Scanning Radiometer (AMSR) on board Midori-II (Advanced Earth
Observing Satellite-II (ADEOS-II)).

Fig. 2.3.7-1

False-color composite image

Brightness temperatures of 36.5-GHz (horizontal polarization) and 89.0-GHz (bothvertical and horizontal polarization) channels were used. The data was acquired around
11 a.m. on January 18, 2003 JST. Over the Pacific Ocean and Japan Sea, the areas oflight blue correspond to high-concentration liquid water clouds. In the Sea of Okhotsk,
colors varying from light blue to white correspond to sea ice distribution.
106

Figure 2.3.7-2 is a false-color composite using data acquired at night (around 2030)
on January 18, 2003 JST. Brightness temperatures of 36.5-GHz (horizontal polarization)and 89.0-GHz (both vertical and horizontal polarization) channels were used. In the Seaof Okhotsk, colors varying from light blue to white correspond to sea ice. Newly formed
sea ice is indicated by light blue. Areas of light blue over the Pacific Ocean correspond tohigh-concentration liquid water clouds. An advantage of microwave observation is theability to make measurements night and day. Thus, the sea ice extension can be easily
distinguished through non-precipitating clouds using nighttime images.

Figure 2.3.7-2
False-color composite image

Figure 2.3.7-3
Sea ice distribution and motion vector

107

2.4

Calibration and Validation

2.4.1 Basic Plans for AMSR Calibration
Initial checks must be conducted for the first six months after launch, and monitoring
must be conducted until the end of the sensor observation. In order to perform monitoring,
EORC will produce necessary items from level 1B every day.
2.4.1.1 Brightness Temperature Calibration(1) Initial check(i) Absolute calibration
a. Evaluation of CSM count and hot load
The data acquired from eight measured CSM counts and hot loads at eachfrequency must be evaluated. First, relative differences among the eight points
and time changes are carefully evaluated. The relations between hot load andcounts, as well as the relation between hot loads and heater on and off, areinvestigated. Second, the number of data points used, the weight function, thenumber of scans for averaging, etc. are determined. The data term is one week.
b. Scan bias error
The TB bias between scanning positions is determined for each frequency
and polarization. For each scanning position, the data consists of one to threemonths observation after launch.
c. Correction of 89 GHz difference
AMSR-E has two horn antennas for 89 GHz (A and B). Differences in theincident angles of horns A and B differ (55.1 degrees and 54.6 degrees) andreceiver characteristics may also cause the brightness temperature to differ.
This difference is investigated, and a correction coefficient is determined asnecessary. The data consists of two to three days observation.
d. Cosmic observation
Aqua performs deep space observation on the fortieth day after launch
simultaneously with MODIS moon calibration.
(ii) Multiple calibration
Evaluating the calibration of brightness temperature involves a method-based
ambiguity, so a fixed method cannot be achieved. Multiple methods for evaluating
calibration are prepared, and the agreements of results from each method areviewed.
a. Inter-satellite calibration
Mutual calibration of former AMSR-E and SSM/I or AMSR-E and TMI is
performed. Accurate mutual calibration of AMSR and AMSR-E might bedifficult since both AMSR and AMSR-E are Sun-synchronous satellites,
resulting in a time lag of five hours between observations. Accurate mutual108

calibration of later AMSR-E and TMI also might be difficult since there are 3K
errors with TMI calibration although TMI is the not a Sun-synchronous satellite
and the time lag is only tens of minutes. Inter-satellite calibration, thus, iscomprehensively evaluated after the results are obtained. The central frequency
and incident angle have already been calculated by using an ocean radiativetransfer model. The term of calibration is six months.
Using a 0.25 scale grid of the entire sphere compares AMSR-E and SSM/I.
The SSM/I data is acquired from Marshall Space Flight Center (MSFC). The
average values, the numbers, and the scattering of the data are input as grid data.
Using 0.1 scale grid match-up data, obtained within ten minutes enables
comparison of AMSR-E and TMI.
AMSR and AMSR-E data will also be compared with SSM/I data of the past
14 years (1987 to 2000) and with its climate values every month.
b. Evaluation by model calculation
Absolute values are evaluated by comparing the satellite-observed brightness
temperature computed with a radiative transfer model using truth data withobserved values. By using upper air data acquired over observation vessels andsmall islands, brightness temperature compared with a radiative transfer modeland AMSR observed value are compared. The comparison must be made on asunny day with bright wind. The evaluation term is three to six months;
however, match-up data will be produced all the time during the satellite
operation.
c. Comparison with AMR
For one to two months after satellite launch, the flight of AMR will be
performed in the same period with ADEOS-II satellite. AMR’s flight altitude issupposed to be 4 to 5 km, and its two flight courses are supposed to be from
north to south, from Hokkaido to Sendai, and then from Sendai to Hachijo-jimaIsland. POS data, which is simultaneously acquired aircraft position andattitude data, is used to correct the angle of incidence. The difference between
the brightness temperature and the antenna temperature resulting from theantenna patterns is supposed to be determined in advance using data from test
flights over and around Aoga-shima Island. This depends on the AMR’s flight
altitude. AMR cannot validate channels of the 50 GHz band.

109

(2) Monitoring
The following items will be constantly monitored and sensor performance observed.
(i) Radiometric noise
Radiometric noise is computed from CSM and hot load calibration source data.
Monitoring is done for each revolution, seasonal change, short- and long-term
trends, and other time scales.
(ii) Calibration counts
Counts of CSM and hot load are monitored.
(iii)Physical temperature
Physical temperatures of the main reflector, calibration sources, receivers, etc.
are monitored.
(iv) Receiver gain variations
Receiver gain variations and AGC telemetry are monitored.
(v) Scan bias error
Scan bias errors, as described in Para. (1), are monitored regularly.
2.4.1.2 Geometric Calibration(1) Initial calibration(i) Rough evaluation of beam patterns
Beam width is roughly confirmed using coastlines and other domains withmajor contrasts in brightness temperature and clearly defined boundaries.
(However, this is only possible for low- frequency bands with a large overlap.)(ii) Inter-channel co-registration
The accuracy of the positional agreement between frequencies is evaluatedusing data of coastlines and regions with clearly defined boundaries. The accuracyof the positional agreement between t 89GHz horn A and B horn is evaluated.
Based on this, sensor alignment might be corrected if necessary. The term is a few
days to one week.
(2) Monitoring(i) Antenna rotation speed
Antenna rotation speed is monitored.
(ii) Attitude fluctuations
Attitude data is monitored.

1102.4.2

Post-launch validation

2.4.2.1 Water Vapor(1) Outline
The main activity related to total water vapor quantity will be validation using theworldwide radiosonde network.
(2) Product accuracy
Total water vapor will be calculated for all of the world's oceans and sea areas(excluding areas of sea ice). The required accuracy is 3.5 kg/m2 for values in therange 0 to 70 kg/m2.
(3) Ground-based observation and vessel observation(i) Hegura-jima Island and Minamidaito-jima Island (May 2002 to Mar. 2006)
Ground-based microwave radiometry observation
Total water vapor will be estimated by using a ground-based microwave
radiometer that has an observation frequency band in the 20 to 30 GHz region;
results will be compared with AMSR/AMSR-E estimated values. Collecting data
while scanning through different azimuth and elevation angles will enablecalculating spatial averages to some extent. In addition, it will be possible tocollect data continuously over long periods of time. Since AMSR/AMSR-E
estimated values are only valid over sea areas, the microwave radiometers will be
set up on Hegura-jima Island and Minamidaito-jima Island, which are both small
island situated more than 50km from other larger islands. Total water vapor willthen be observed continuously (Oct. 2000 to Mar. 2006). The equipment willbasically operate automatically, although a supervisor will visit each observationsite as necessary. The supervisor will recover the collected data and inspect theequipment (from the outside only).
The WVP-1500 Water Vapor Profiling Radiometer, which incorporates anazimuth turntable, a fan (TBD) and a computer for data collection and is made by
the US company Radiometrics Corp., will be used.
(ii) Oceanographic Research Vessel MIRAI (Aug. 2002 to Mar. 2006)
Vessel-based microwave radiometer and radiosonde observations
Truth data for validating water vapor estimated from AMSR/AMSR-E data will
be acquired using the Oceanographic Research Vessel MIRAI provided by
JAMSTEC as the observation platform. MIRAI will observe the region from the
Arctic Ocean to equatorial ocean for 280 days per year. MIRAI will be the best
facility observing various geophysical parameters about water over the ocean
without influence of land. Since AMSR/AMSR-E will estimate water vapor only
over the seas, in-situ vessel observation will be useful for to validating
AMSR/AMSR-E data.
A microwave radiometer (Water Vapor Radiometer WVR-1100 made byRadiometrics Corp.) will be carried on MIRAI and will measure two frequencies(23GHz and 31GHz). Water vapor will be estimated using the microwave
radiometer data and compared with the AMSR/AMSR-E data. Moreover,111

radiosonde observations will be made from MIRAI. Water vapor estimated fromradiosonde data will also be used for comparison with AMSR/AMSR-E data.
Radiosonde observation will be performed simultaneously with satellite
overflights as much as possible.
(4) Creating match-up data(i) Match-up with global radio sonde data
Once a day (from Apr. 2002 to Mar. 2006), total water vapor calculated from
air temperature, air pressure and relative humidity profiles collected byradiosonde will be compared with the AMSR and AMSR-E estimated values. For
worldwide radiosonde data, standard isobaric surface data that is distributed over
GTS lines and data that include singular points will be used.
Since AMSR estimates values only for sea areas, radiosondes will be installedin places such as small islands. Radiosondes that are not influenced by radiation
coming from land areas will be selected. Datasets that match the radiosonde datawith the AMSR and AMSR-E footprints in both space and time will be createdregularly, and will be validated. The Meteorological Agency has agreed toprovide the radiosonde data. The data will include both standard isobaric surfaces
and singular points.
⋅ AMSR⋅ AMSR-E

vs.
vs.

GTS/sonde (Dec. 2002 to Mar. 2006)
GTS/sonde (May 2002 to Mar. 2006)(ii) Match-up with microwave radiometer data
Total water vapor will be calculated from the microwave radiometer datacollected at Hegura-jima and Minamidaito-jima. The values will be comparedwith AMSR and AMSR-E estimated values.
⋅ AMSR⋅ AMSR-E

vs.
vs.

microwave radiometer (Dec. 2002 to Mar. 2006)
microwave radiometer (May 2002 to Mar. 2006)(iii)Match-up with vessel observation data
Match-up data between AMSR/AMSR-E and water vapor data calculated fromthe vessel-based (MIRAI) observations will be created.
⋅ AMSR⋅ AMSR-E⋅ AMSR⋅ AMSR-E

vs.
vs.
vs.
vs.

microwave radiometer (Dec. 2002 to Mar. 2006)
microwave radiometer (Aug. 2002 to Mar. 2006)
radiosonde (Dec. 2002 to Mar. 2006)
radiosonde (Aug. 2002 to Mar. 2006)112

(5) Relationship to organizational setup and other research projects
The match-up datasets will be created at EORC; assessment and validation using
these datasets will be conducted by EORC and the algorithm development PIs. The
two ground-based microwave radiometers will be the property of EORC, and EORC
will perform the observations at Hegura-jima Island and Minamidaito-jima Island.
For Minamidaito-jima Island, suitable cooperation will be sought, for example
cooperation involving joint research with the Meteorological Agency. In addition,other organizations within Japan, such as the Meteorological Research Institute,possess similar equipment. It will thus be desirable to seek the cooperation of suchorganizations in order to form a multipoint observation network. In particular, inorder to obtain data for a number of different latitudes, observations at high latitudes
are required.
The collected data will be analyzed by both the algorithm development andvalidation PI and by EORC. NASDA hopes to form a multipoint observation network
through collaboration with the AMSR-E Team, ARM, CLIVAR (EPIC), CRYSTAL
and the GLI Atmosphere Team.
2.4.2.2 Cloud Liquid Water(1) Outline
Cloud liquid water will be validated primarily through comparison withobservation values obtained using ground-based (vessel) radiometers.
Cloud liquid water is a physical element that is difficult to validate quantitatively.
There are a number of ways of measuring cloud liquid water. One involves use ofvideo sondes; another, radar measurement from aircraft. However, clouds arescattered widely through space and cloud formations change rapidly over time, but avideo sonde takes observations at one point only. Observations by aircraft are also
limited both in the area in which they cover and the frequency with which they can betaken. Moreover, such observations do not constitute perfectly quantitative
measurements. Estimating cloud liquid water quantity by ground-based microwave
radiometer observation is an indirect remote sensing technique. However, theobservation background is background radiation from space that is uniform andremains at a stable level. This means that the accuracy is higher than estimates fromsatellite orbits.
Cloud bottom height will be observed by ceilometers. The presumed accuracy ofcloud liquid water will be improved by estimating cloud temperature from cloudbottom height data.
(2) Product accuracy
Total cloud water will be calculated for all of the world's oceans and sea areas(excluding areas of sea-ice). The required accuracy is 0.05 kg/m2 for values from 0 to
1.0 kg/m2.
(3) Ground-based observation and vessel observation(i) Hegura-jima Island and Minamidaito-jima Island (May 2002 to Mar. 2006)
Ground-based microwave radiometer and ceilometer observation

113

As in 2.4.2.1(3)(i), a ceilometer is installed and the cloud bottom height isobserved at Hegura-jima Island. At Minamidaito-jima Island, the data of theceilometer installed at the airport can be used.
(ii) Oceanographic Research Vessel MIRAI (Aug. 2002 to Mar. 2006)
Vessel-based microwave radiometer and ceilometer observation
As in 2.4.2.1(3)(ii), the ceilometer installed on MIRAI is also used.
(4) Creating match-up data(i) Match-up with microwave radiometer data
Total cloud water will be calculated from the microwave radiometer datacollected at Hegura-jima Island and Minamidaito-jima Island. Match-up data sets
between these and AMSR / AMSR-E will then be created.
⋅ AMSR⋅ AMSR-E

vs.
vs.

microwave radiometer (Dec. 2002 to Mar. 2006)
microwave radiometer (May 2002 to Mar. 2006)(ii) Match-up with vessel observation data
Match-up data between AMSR/AMSR-E and cloud liquid water data calculated
from the vessel-based (MIRAI) observations will be created.
⋅ AMSR⋅ AMSR-E

vs.
vs.

microwave radiometer (Dec. 2002 to Mar. 2006)
microwave radiometer (Aug. 2002 to Mar. 2006)(5) Relationship to organizational setup and other research projects
As in 2.4.2.1(5).

2.4.2.3 Sea-surface Wind Speed(1) Outline
Sea-wind velocity will be validated primarily through comparison with observation
values obtained using buoys.
(2) Product accuracy
Sea-wind velocity will be calculated for all of the world s oceans and sea areas(excluding areas of sea-ice). The required accuracy is 1.5 m/s for values form 0 to 30m/s.
(3) Vessel observation (Aug. 2002 to Mar. 2006)
Vessel-based wind-speed observation
Oceanographic Research Vessel MIRAI(4) Creation of match-up data(i) Match-up with buoy observation data114

Sea-wind velocities obtained using ocean buoys and AMSR / AMSR-E
estimated values will be compared. At present, the buoy data that can be used arefrom sources such as the National Data Buoy Center (NDBC), the TAO array, and
JAMSTEC. Datasets that match the buoy measurements (which also include
information such as wind direction and sea-surface temperature) with the AMSR
footprints in both space and time will be created and validated regularly. The
height at which wind velocity observations are made will vary according to thetype of measuring instrument used on the buoy, so the comparison will be madeafter adjusting for this factor.
The latest TAO buoy data are always distributed over the Internet, so this will
be used. For NDBC buoys, it will be necessary to use the Internet.
⋅ AMSR⋅ AMSR-E⋅ AMSR⋅ AMSR-E

vs.
vs.
vs.
vs.

GTS/buoy (Dec. 2002 to Mar. 2006)
GTS/buoy (May 2002 to Mar. 2006)
Internet/buoy (Dec. 2002 to Mar. 2006)
Internet/buoy (May 2002 to Mar. 2006)(ii) Match-up with vessel observation data
Match-up data between AMSR/AMSR-E and wind-speed data calculated fromthe vessel-based (MIRAI) observations will be created.
⋅ AMSR⋅ AMSR-E

vs.
vs.

wind vane and anemometer (Dec. 2002 to Mar. 2006)
wind vane and anemometer (Aug. 2002 to Mar. 2006)(5) Relationship to organizational setup and other research projects
NASDA hopes to collaborate with the Japan Meteorological Agency, the FrontierObservational Research Program for Global Change, and CLIVAR.
2.4.2.4 Precipitation(1) Outline
Observations from ground-based radar and rain gauges will be compared. Since
AMSR-estimated values for precipitation volume exist only for sea areas, it will benecessary to select observation values for rain gauges that are installed on islands thatare as small as possible.
The AMSR precipitation intensity retrieval will have to satisfy the following
requirements.
(i) A high-accuracy data set that covers all of the world's oceans and sea areas.
In particular, the presumed accuracy for high latitudes can still be improved,
and there is a lack of reliable measurement data.
(ii) Capable of contributing to the meteorological and water budget research of
GEWEX, CLIVAR, etc.
WCRP has decided to draw up plans for a Cooperative Enhanced Observation
Period (CEOP) that will focus on 2002 to 2004. Collaboration with CEOP will benecessary for precipitation validation.

115

(iii)Data assimilation with the NWP model must be considered.
In order to fulfill these goals, it will be necessary to prepare cases that arecomplete with as wide a variety of data as possible.
Accuracy of land precipitation measurements by the microwave radiometers has
not been confirmed since information on atmosphere above land is relatively limited
because emission from the Earth’s surface is strong and very unstable in space andtime. AMSR/AMSR-E will estimate geophysical parameters of soil moisture andvegetation water content that affect emissivity enabling, accurate land precipitation
measurement. Comprehensive data about the three-dimensional distribution of
precipitation, rain grain size distribution, water vapor, cloud water, air temperature,surface emissivity, and ground temperature must therefore be acquired in order toestimate and validate precipitation with relatively less information.
For this reason, soil moisture validation measurements soil moisture will be madein the Tibet Plateau, and the three-dimensional distribution of precipitation, verticalprofile of rain grain size distribution, cloud water and water vapor, cloud bottom andtop height, vertical distribution of water vapor and temperature, and air temperature
and wind distribution will be acquired using three-dimensional Doppler radar, microrain radar, microwave radiometer, lidar, sonde, wind profiler and RASS instruments.
Combining these acquired data will enable describing the radiative transfer process
when a satellite detects microwaves emitted by the land surface. It will also enablevalidating algorithms dealing with radiative transfer processes. Combined data will bevery valuable as observation data of assimilated precipitation and will contribute toestimating precipitation by AMSR and AMSR-E. Two types of algorithms have beendeveloped: one to calculate soil moisture supposing no precipitation, and another tocalculate soil moisture supposing either precipitation or no precipitation.
Comprehensive observation data will enable validating the latter algorithm.
(2) Product accuracy
Precipitation volume will be calculated for all of the world's oceans and sea areas(excluding areas of sea ice). The required accuracy is 10% for precipitation volumesof up to 20 mm per hour. Accuracy on land will be defined one year after the satellite
launch.
(3) Ground-based, vessel and aircraft observation
Data will also be acquired in land regions or snowfall regions because precipitation
volume will also be calculated in land regions one year after satellite launch.
(i) Wakasa Bay (Jan. to Feb. 2003)
a. Ground-based radar observations
Collect Ministry of Land, Infrastructure and Transport radar and privatecompany multi-parameter radar data, and directly compare this data with that of
TMI and PR.
Solid precipitation can at Wakasa Bay observed by TRMM.
b. Rain-gauge and Disdrometer observations
Observe precipitation on the ground and precipitation grain size.

116

c. Aircraft observation
NASA will observe by aircraft.
(ii) Tibet (May 2002 to Mar. 2005)
a. Automatic weather instrument (AWS) observationb. Ground-based radar observations (Aug. 2003, Jun. to Aug. 2004)
c. Wind profiler and rain gauge observations (Jul. to Oct. 2002, Apr. to Aug. 2003,
Apr. to Aug. 2004)(iii)Miyako and Yaeyama Islands (May to Sep. 2003)
a. Ground-based precipitation intensity observationb. Japan Meteorological Agency radar observations
Precipitation Intensity Observation Systems will be installed, and truth data
will be acquired on the small islands of Miyako and Yaeyama to estimate
precipitation on the ocean.
(iv) Oceanographic Research Vessel MIRAI (Aug. 2002 to Mar. 2006)
a. Vessel-based radar observations
b. Rain-gauge and disdrometer observations(v) Siberia (Oct. 2005 to Mar. 2006)
a. Ground-based radar observations
b. Rain-gauge and snow particle observation system observations(4) Creating match-up data
Match-up data between AMSR and AMSR-E and data from the above-mentioned
ground-based observations will be created.
⋅ AMSR⋅ AMSR-E⋅ AMSR⋅ AMSR-E

vs.
vs.
vs.
vs.

Radar AMeDAS (Dec. 2002 to Mar. 2006)
Radar AMeDAS (May 2002 to Mar. 2006)
Truth Data (Dec. 2002 to Mar. 2006)
Truth Data (May 2002 to Mar. 2006)(5) Relationship to organizational setup and other research projects
NASDA hopes to collaborate with the Japan Meteorological Agency, theNASA/AMSR-E Team, the GAME-Siberia Team, CEOP, the TRMM Team, and the
AMSR Snowfall and Soil Moisture Team.
2.4.2.5 Sea-ice Concentration(1) Outline
Sea-ice concentration will be validated primarily through comparison withobservation values obtained using other satellites or airplanes.
(2) Product accuracy

117

Sea-ice concentration will be estimated for places like polar regions and the Sea of
Okhotsk. The required accuracy is within 10%.
(3) Ground-based, vessel and aircraft observation(i) Okhotsk (Jan. to Feb. 2003, Jan. to Feb. 2004, Jan. to Feb. 2005)
a. Aircraft observation (AMR and PSR) will be conducted.
b. Photographs will be taken and video observations conducted from ships.
(ii) Antarctic (Jul. to Sep. 2002, Dec. 2002 to Feb. 2003)
a. Aircraft observation (PSR and AVIRIS) will be carried out (Aug. 2002).
b. Ground-based observation(4) Creating match-up data
Match-up data between AMSR/AMSR-E and data from the above-mentioned
ground-based observations or other satellites will be created.
⋅ AMSR⋅ AMSR-E⋅ AMSR⋅ AMSR-E⋅ AMSR⋅ AMSR-E⋅ AMSR⋅ AMSR-E

vs.
vs.
vs.
vs.
vs.
vs.
vs.
vs.

GLI (Dec. 2002 to Mar. 2006)
GLI (Dec. 2002 to Mar. 2006)
AVHRR (Dec. 2002 to Mar. 2006)
AVHRR (May 2002 to Mar. 2006)
RADARSAT (Dec. 2002 to Mar. 2006)
RADARSAT (May 2002 to Mar. 2006)
Truth Data (TBD)
Truth Data (TBD)(5) Relationship to organizational setup and other research projects
NASDA hopes to carry out joint observations with the AMSR-E Team and tocollaborate with the GLI Cryosphere Group; CRL; and the Japanese, USA andEuropean Antarctic Survey Teams.
2.4.2.6 Sea-surface Temperature(1) Outline
Sea-surface temperature will be validated, predominantly through comparison withbuoy observation values.
(2) Product accuracy
Sea-surface temperature will be estimated for all of the world's oceans and sea
areas; the desired accuracy is 0.5 deg. C for temperatures from -2 to 35 deg. C.
(3) Vessel observation (Aug. 2002 to Mar. 2006)
Vessel-based sea-surface temperature observation
The Oceanographic Research Vessel MIRAI(4) Creating match-up data(i) Match-up with buoy observation data118

Sea-surface temperatures collected using ocean buoys and AMSR/AMSR-E
estimated values will be compared. The same buoys can be used for sea-windvelocity (see above). Datasets that match the buoy (fixed and drifting)measurements (which include information on sea winds as well as on sea-surface
temperature) within the AMSR footprints in space and time will be created andvalidated regularly.
⋅ AMSR⋅ AMSR-E

vs.
vs.

GTS/buoy (Dec. 2002 to Mar. 2006)
GTS/buoy (May 2002 to Mar. 2006)(ii) Match-up with vessel observation data
Match-up data between AMSR/AMSR-E and sea-surface temperature data
calculated from the vessel-based (MIRAI) observations will be created.
⋅ AMSR⋅ AMSR-E

vs.
vs.

thermometer (Dec. 2002 to Mar. 2006)
thermometer (Aug. 2002 to Mar. 2006)(5) Relationship to organizational setup and other research projects
NASDA hopes to collaborate with the Japan Meteorological Agency, the FrontierObservational Research Program for Global Change, and the GLI Ocean Team.
2.4.2.7 Snow-Water Equivalent(1) Outline
Profiles of the snow-water equivalent, snow depth and grain size of snow and snow
temperature will be targeted and will be validated by acquired truth data. It will also
be necessary to observe the changes in these items over time (in a time series). In
addition, it will be necessary to confirm the influence of the forest canopy on snow
parameter estimation.
Snowfall will be estimated by microwave radiometers using the naturally occurring
radiation emitted by the ground beneath a snow cover and scattered in a snow pack.
The microwave radiometer will measure changes in amount of emission. Although
the change in the amount of emission also depends on the diameter of snow particles,
it fundamentally depends on the total ice quantity (snow depth x snow cover density)of the snow cover. The quantity directly obtained by a microwave radiometer is thesnow-water equivalent. Snow depth measured by ground-based observations isusually used to validate snow-water equivalent because there are few measurement
points for snow-cover density. In addition to snow water equivalent, snow depth will
also be provided as a sub product. Snow-water equivalent products are generally
employed to investigate water resources. In contrast, snow-depth products are
generally employed to investigate disasters.
Two algorithms have been proposed for snow-water equivalent estimates by
AMSR/AMSR-E, but results from algorithm comparison test using SSM/I indicate
the following three problems.
(i) Footprint size data for validating sensors on satellites have not been acquired.

119

(ii) Effects of vegetation, especially taiga, have not been sufficiently evaluated.
(iii) Snow metamorphosis leads to deterioration of estimates
Consequently, a special observation plan considering these problems will benecessary to validate snow cover.
To deal with problem (i), (a) it will be necessary to perform simultaneous satellite
observations considering footprint-scale data in observational areas that are as
uniform as possible, and (b) a ground observation system configuration that produces
a variety of footprints will be necessary. The observations that will be performed in
Tibet Plateau and Yakutsk, Siberia, are planned to resolve the problems (a) and (b).
Validation data will be acquired to deal with the problem (ii). Five flat and extensive
areas of the Tibet Plateau will be chosen for the observations, and a snow depth
sensor and an automatic weather station will be set up in the middle of these fourareas. An area of Yakutsk, Siberia, will be chosen for footprint scale observation, andseven automatic snow depth sensors will be set up in this area. Six of these sevensensors will be set up in taiga. Snow density and snow grain size will be estimated in
all theses areas during the winter for problem (iii). To resolve (iii), it will benecessary to perform in-situ measurement and observe state variables of snow(change in grain size) and microwave brightness temperature during winter. However,it will be very difficult to perform in-situ measurement simultaneously with footprint
scale satellite observations. For this reason, we will work out our strategy forvalidation with long-term observation using the microwave radiometer on the groundand will acquire fundamental data. The results will be reflected in algorithms, andproducts will be validated by footprint size data. Long-term ground observations will
be performed in the Colorado Rocky Mountains with the cooperation of the AMSR-E
team from the University of Colorado.
WCRP has decided to draw up plans for a Cooperative Enhanced Observation
Period (CEOP) that will focus on 2002 to 2004. Collaboration with CEOP will benecessary for snowfall validation.
(2) Product accuracy⋅ SWE
20% or 10 mm⋅ SD
20% or 5 cm(3) Ground-based and aircraft observation
Ground-based observations using both ultrasonic snow depth gauges and manual
observation will be conducted in areas of GAME concentrated observation in Tibet
and Siberia. Ground data, on snow depth, snow density, snow temperature, grain size,and vegetation moisture content will be collected.
(i) Siberia
a. Ground-based snow observation (Nov. 2002 to Mar. 2003, Nov. 2003 to Mar.
2004, Nov. 2004 to Mar. 2005)
b. Snow-depth measurement using automatic gauges (May 2002, Nov. 2002 to
May 2003, Nov. 2003 to May 2004, Nov. 2004 to May 2005)

120

(ii) Tibet
a. Snow observation on the ground (Jan. 2003, Jan. 2004, Jan. 2005)
b. Snow depth measurement using automatic gauges (May to Jun. 2002, Nov.
2002 to Jun. 2003, Nov. 2003 to Jun. 2004, Nov. 2004 to Jun. 2005)(iii)Colorado
a. Aircraft observation (Feb. to Mar. 2003).
b. Ground-based microwave radiometer observation (Sep. 2002 to Mar. 2003)(4) Creating match-up data
Match-up data between AMSR/AMSR-E and the above-mentioned ground-based
observation data (GTS/snow and snow depth gauge) will be created.
⋅ AMSR⋅ AMSR-E⋅ AMSR⋅ AMSR-E

vs.
vs.
vs.
vs.

GTS/snow (Dec. 2002 to Mar. 2006)
GTS/snow (May 2002 to Mar. 2006)
truth data (Dec. 2002 to Mar. 2006)
truth data (May 2002 to Mar. 2006)(5) Relationship to organizational setup and other research projects
NASDA hopes to conduct the joint observation with GEWEX / CEOP and the
AMSR-E Team and to collaborate with the AMSR Precipitation Group, the GLI
Cryosphere Group, the Frontier Observational Research Program for Global Change,
and CREST-JST.
2.4.2.8 Soil Moisture(1) Outline
Algorithms that estimate soil moisture using the 6.9 GHz channel will be comparedinternally. Ground-based observations of soil moisture and related parameters will be
conducted under various conditions of vegetation, soil, and climate.
These data will be compared with AMSR, AMSR-E and aircraft AMR data. AMR
observation is necessary to detect influences of land surface heterogeneity on thesatellite observation.
Although four algorithms have been currently proposed for soil moisture estimatedby AMSR/AMSR-E, the algorithm comparison test shows large gaps between the
results produced by each algorithm. For this reason, soil moisture has not yet been
established as standard products. The two major causes are described below.
(i) Foot size truth data for validating sensors on satellites have not been acquired.
(ii) Data for validating satellite sensors has not been acquired for various land covercondition.
Consequently, a special observation plan considering these problems will benecessary to validate soil moisture.
To deal with problem (i), it is necessary to perform simultaneous satellite
observation providing footprint scale data in observational areas that are as uniform121

as possible. It is also necessary to perform simultaneous satellite, airborne, andground observations considering a variety of footprints. Observations planned in
Mongolia will deal with the first problem. An area of 60km×60km will be observed
and estimate dense soil moisture, vegetation water content, ground temperature andthe moving observation on 100km will be estimated. To deal with the second problem,
airborne observations measuring extensive soil moisture distributions will be
performed in Iowa.
In order to deal with the problem (ii), effects of vegetation must be consideredparticularly important. Therefore, the observations will cover bare land in the Tibet
Plateau, grassland in Mongolia, cassava fields (Manihot utilissima) as crops in Thai,and teak groves (Tectona grandis) as deciduous forests in Thai land. The observations
will also cover soybean fields and cornfields in Iowa and grasslands and wheat fields
in Oklahoma. Validation data for soil moisture considering the interaction withfreezing and melting of permafrost will be acquired in the Tibet Plateau. Acquisitionof validation data under various land cover and hydrological conditions will improvealgorithm certainty and produce choices of algorithms that can be matched against allconditions.
WCRP has decided to draw up plans for a Cooperative Enhanced Observation
Period (CEOP) that will focus on 2002 to 2004. Cooperation between CEOP and the
AMSR-E team is necessary for validation.
(2) Product accuracy
TBD(3) Ground-based and aircraft observation
Ground-based soil-moisture observations will be conducted in Thailand, Tibet, andMongolia, where ground data such as soil moisture, vegetation moisture content,
ground-surface temperature, ground-surface roughness, and soil texture will becollected. In addition, Aircraft observation will be conducted as well.
(i) Thailanda. Ground-based soil moisture observation (May, Sep., Nov. 2002, May, Sep.,
Nov. 2003, Feb., May, Sep. 2004)
b. Ground-based automated observation (Meteorological data, soil moisture) (May2002 to Mar. 2005)(ii) Tibet
a. Ground-based soil moisture observation (Jun. to Oct. 2002, Aug. 2003, Jun. toAug. 2004)
b. Ground-based automated observation (Meteorological data, soil moisture) (May2002 to Mar. 2005)(iii)Mongolia
a. Ground-based soil moisture observation (Jun., Aug. 2002, Jun., Aug. 2003, Jun.,
Aug. 2004)

122

b. Ground-based automated observation (Meteorological data, soil moisture) (May2002 to Mar. 2005)(iv) Iowa
a. Ground-based microwave radiometer observation (Jun. to Jul. 2002)
b. Aircraft observation (PSR/C & A, ACMR, Step-C, PALS, ESTAR) (Jun. to Jul.
2002)(v) Oklahoma
a. Ground-based soil moisture observation (Jun. to Jul. 2003)
b. Aircraft observation (PSR/C & A, ACMR, Step-C, PALS, ESTAR) (Jun. to Jul.
2003)(4) Creating match-up data
Match-up data between AMSR/AMSR-E and the above-mentioned ground-based
observation data will be created.
Includes matching up with China routine data, Mongolia routine data and USA(Oklahoma) soil moisture data.
⋅ AMSR⋅ AMSR-E

vs.
vs.

truth data (Dec. 2002 to Mar. 2006)
truth data (May 2002 to Mar. 2006)(5) Relationship to organizational setup and other research projects
NASDA hopes to conduct joint observation with the WCRP / CEOP and NASA /AMSR-E teams and to collaborate with the AMSR Precipitation Team, the GLI Land
Team, the Frontier observational research program for global change, and CRESTJST.

123

2.4.3 schedule
AMSR/AMSR-E CAL/VAL Plan
WBS<--AMSR-E/EOSPM1(AQUA)<-- AMSR/ADEOS-II Launch
18 Feb. 2003
Year
2003
2004
2005
Month 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

Calibration
Aircraft observation

AMR(Pacific Ocean)

Validation
1 Water Vapor
Microwave radiometer observation
Vessel obeservation(Hegura-jima Island & Minamidaitoh-jima Island)(Vessel MIRAI)

Match-up data sets
2 Cloud Liquid Water
Microwave radiometer observation
Vessel obeservation
Match-up data sets

AMSR & AMSR-E vs GTS/Sonde & Microwave Radiometer
AMSR & AMSR-E vs Vessel observation data
(Hegura-jima Island & Minamidaitoh-jima Island)(Vessel MIRAI)AMSR & AMSR-E vs Microwave Radiometer
AMSR & AMSR-E vs Vessel observation data

3 Sea Surface Wind Speed
Vessel obeservation
Match-up data sets

(Vessel MIRAI)AMSR & AMSR-E vs GTS/Buoy & Int./Buoy
AMSR & AMSR-E vs Vessel observation data

4 Precipitation
Aircraft observation

PSR-A etc.(Wakasa Bay)

Ground-based observation(Radar, Precipitation)
Ground-based automatic observation(Tibet)5 Sea Ice Concentration
Aircraft observation(Wakasa Bay) (Tibet)(Miyako and Yaeyama Islands)(Vessel MIRAI)

PSR-C etc.(AntarcticPSR-C etc.(Okhotsk)(Okhotsk)(Antarctic)(Antarctic)(Vessel MIRAI)AMSR & AMSR-E vs GTS/Buoy
AMSR & AMSR-E vs Vessel observation data

7 Snow Water Equivalent
Aircraft observation

PSR-C etc.(Colorado)

Microwave radiometer observation(Colorado)

Ground-based snow cover observation

Ground-based snow depth observation
Match-up data sets
8 Soil Moisture
Microwave radiometer observation(Siberia)(Tibet)(Siberia)(Tibet)(Siberia)(Tibet)AMSR & AMSR-E vs GTS/Snow Depth & Field Campaign Data
(Iowa)(Iowa)(Thailand) (Thailand)(Thailand)(Tibet)(Mongolia)

Ground-based automatic observation(Thailand)(Tibet)(Mongolia)

Match-up data sets

(Siberia)(Tibet)(Siberia)(Tibet)Ground-based soil moisture observation

Aircraft observation(Okhotsk)

AMSR & AMSR-E vs GLI, NOAA, RADARSAT & Field Campaign Data

6 Sea Surface Temperature
Vessel obeservation
Match-up data sets

(Siberi

AMSR & AMSR-E vs Radar AMeDAS & Field Campaign Data

Ground-based observation
Match-up data sets

(Tibet)(Tibet)Vessel obeservation
Match-up data sets

AMR(Pacific Ocean)(Oklahoma)(Thailand) (Thailand)(Thailand)(Thailand)(Thailand) (Thailand)(Tibet)(Tibet)(Mongolia)(Mongolia)

PSR-C etc.(Iowa)

PSR-C etc.(Oklahoma)

AMSR & AMSR-E vs Truth Data

124

(Okhotsk)

3. SeaWinds
3.1

Scientific objectives

The SeaWinds scatterometer is a microwave radar sensor that measures ocean nearsurface velocity by sending pluses to the ocean surface and receiving backscatteredsignals.
SeaWinds is a follow-on mission to the NSCAT scatterometer, which was aboard
ADEOS. Both SeaWinds and NSCAT were provided by NASA JPL. The SeaWinds
mission is to observe the global distribution of ocean near-surface velocity, with anaccuracy 2m/s of for speed, an accuracy of 20 degrees for direction, and a spatial
resolution of 25km. It will observe 90% of the Earth’s oceans at least once in two days.
Data on the ocean near-surface vector will be a rare and precious source for open ocean
areas that previously were observed by vessels and other means for research on theatmosphere-ocean interaction or eolian cycle of the oceanic outer layer, which areimportant factors of the Earth’s climate system. Particular, the ocean near-surface winddata will be collected for over a decade by the ADEOS-II SeaWinds mission. Such data is
expected to understand and solve global yearly changes in phenomena such as EL Niño.
Also use of near-real-time ocean weather and weather forecasting is in planning as afollow-on to the ADEOS NSCAT mission.
Within the framework of ADEOS-II as an Earth environment-observing satellite,
combining SeaWinds data with GLI and AMSR data on ocean near-surface temperature
and water vapor amount as well as ocean color, is expected to provide higher-orderinformation about the ocean near-surface heat and water vapor fluxes. Also combining
SeaWinds with AMSR will enable atmospheric correction and rain flagging using
microwave radiometric data, which was impossible with NSCAT. Such are expected to
improve the observational accuracy for ocean near-surface winds. Incorporating
SeaWinds data into algorithms for obtaining ocean near-surface temperature and ocean
color will improve observation accuracy.
In addition to ocean wind observations, we began to realize that information about
land vegetation, snow ice, and ocean ice distribution can also be extracted frominformation about observed backscatter cross sections. Data use is expected to bepromoted in this field in the future.

125

3.2

Overview of Mission Instruments

SeaWinds is a sensor developed by NASA JPL and is an advanced successor of theNASA scatterometer (NSCAT) on ADEOS. Its parabolic antenna rotates for conicalscanning of the Earth’s surface. It then receives microwave signals scattered by the sea
surface and analyzes then to measure the direction and speed of winds on the ocean.
SeaWinds observes at least 90% of the entire ocean surface once every two days. It has
an accuracy of 2 m/s for wind velocity and 20 degrees for wind direction; the spatial
resolution is 25 km. The observation data from SeaWinds can be used by alone, but canalso be combined with data from AMSR and GLI. Such joint analysis is expected tocontribute significantly to the understanding of water circulation and various ocean
phenomena. Table 3.2-1 presents the main specifications of SeaWinds. Figure 3.2-1
illustrates the SeaWinds observation concept.
Table 3.2-1

Main Specifications of SeaWinds

Item
Observation Frequency
Spatial Resolution
Observation Width
Data Rate

Specifications
13.402 GHz
25 km
1800 km
35.378 Kbps (Min. 31.840 Kbps; Max. 38.208 Kbps)

SeaWinds Observation Concept

Orbit Track
800 kmSeaWinds

40°

Nadir Track
250 to 700 km Swath

250 to 700 km Swath

Nadir

900km

Cross Track

Figure 3.2-1 SeaWinds Observation Concept

126

3.3

Specific

3.3.1

Standard products

NASA JPL plans to provide the following three standard products, similar to those
provided by NSCAT.
•

Level 2: Global Backscatter Cross Sections
Area data on backscatter cross sections of land, ocean, and ice regions
Incidence, direction and AMSR brightness temperature data for atmosphericcorrection•

Level 2B: Ocean Vector Winds
Ocean near-surface wind vector data (resolution 25km)
Four solutions ambiguity removed are included.

•

Level 3: Time-Space average Wind Vectors
Grid data on time- and space-averaged ocean near-surface vector
Approximately 1 degree latitude ×1 degree longitude ×1 day (average)

3.3.2 Research product
When grid data exceeding the scale of standard product level 3 data (ex. Monthly
average) are produced, it is necessary to consider the problem of satellite observation
samples relative to the time and space scales of changes in wind fields. It also depends on
the purpose of product use, so standard creation techniques have not yet been established.
These creation techniques are future research subjects and will be prepared as extensionsof the ERS-1, the ERS-2, and the NSCAT missions.
SeaWinds will succeed three missions, develop the techniques, and produce
continuous, long-term ocean sea-surface wind vector.
NSCAT seeks to acquire comprehensive ocean near-surface wind data of higher
precision and with higher frequency, by combining its data with wind measurement
sensors such as ERS-2/AMI, ERS-2/ALT, SSM/I, and TOPEX/POSEIDON ALT. The
SeaWinds mission will succeed the missions of three latter sensors and will provide thesame kinds of products and check changes of the data quality affected by the transition ofeach sensor.

127

4.

POLDER

4.1

Scientific objectives

The Polarization and Directionally of the Earth’s Reflectance (POLDER) instrument
is the same sensor as flown aboard the ADEOS satellite. Since it is the only sensor to fly
aboard both ADEOS and ADEOS-II, it will perform time series observations andcontinue the ADEOS satellite mission, thereby enabling changes in time of informationacquisition to be explored.
Similar to taking a photograph (frame image), it collects reflected light ranging fromthe visible spectrum to the near-infrared spectrum from the Earth in a matrix CCD byrotating a filter wheel. The view zenith angle is 43 degrees along the track and 51 degrees
cross track, corresponding to an image of 1580 X 2200 pixels. Since the frame images are
obtained continuously satellite operation, the same target of the surface of the Earth will
be observed from different points change by 11 degrees. These multi-directional
observations (multi-observational angles) with polarization will produce data features.
Its instantaneous view angle is 6 degrees, and its brightness temperature resolution is
12 bits. It has eight spectral bands in the range from visible to near infrared, three of
which (0.443, 0. 670, and 0.865µ) are used for polarization observations. Also, 0.765µ isused to analyze the oxygen absorption range; it sets up two types of observations regions,large region and narrow region. It is expected to estimate cloud height using the oxygen
absorption range.
Using information about polarization, angle, and spectrum of the POLDER sensor, it
will be possible to introduce atmospheric aerosol, cloud optic characteristic and the Earth
Surface reflection characteristics. POLDER doesn’t have its own calibration system, so it
was designed for ADEOS to have the same bands as OCTS. Also, because POLDER
doesn’t have bands in infrared, infrared data of the OCTS was very good for the effective
use of the POLDER. This concept of combined use of POLDER and OCTS is continued
for the ADEOS-II mission. Cooperation between GLI and POLDER to produce evenmore abundant spectrum information than the OCTS is indispensable. ADEOS-II
POLDER seeks to create, renew, and store global distribution maps of atmosphereaerosols, clouds, and the Earth’s surface-reflection properties, based on compound use of
not both GLI and other sensors.

128

4.2

Overview of Mission Instruments

POLDER is a push-broom type sensor that developed by CNES of France; itmeasures the polarized light, bi-directivity, and spectral characteristics of the solar raysreflected from the Earth’s surface, aerosols, clouds, and the ocean. It is characterized bya large field of view (FOV), multi-band capability, and capacity to measure polarized
light. Its quasi-square footprint produced by a large field-of-view angle of +/- 43 degrees
times +/- 51 degrees moves along with the motion of the satellite, obtaining observation
data from a variety of field-of-view angles. Further, it is capable of carrying out
observation in eight bands from visible to near infrared ranges by turning its filter wheel.
Data on spectra and polarized light observed by POLDER are expected to provide
information useful for analyzing data obtained by other sensors. Table 4.2-1 presents themain specifications of POLDER. Figure 4.2-1 the observation principle of POLDER.
Table 4.2-1
Observation
WavelengthBand (nm)Field of View
Spatial Resolution
Observation Width
Data Rate

Main Specifications of POLDER

Item
No Polarized Light
Polarized Light (0, 45, 90 degrees)

Specification
443, 490, 565, 670, 763, 765, 865, 910
443, 670, 865+/- 43 × +/- 51 (degrees)
6 km × 7 km
1800 km × 2400 km
882.352 Kbps

CCD Array

Filter Wheel

Cross Track

Figure 4.2-1 POLDER Observation Principle

129

4.3

Specific

4.3.1

Standard products

Creation of 1.1-degree-mesh Global Distribution Map(1) Ocean aerosol optical properties
Categories: Optical thickness, grain size distribution, and reflective index(2) Cloud over the ocean
Categories: Optical thickness, grain size distribution, and cloud top height(3) Earth surface reflection properties(4) Radiation Balance
Category: upper atmosphere4.3.2 Research products(1) Ocean near surface windCategories: Wind speed and direction(2) Snow-ice surface reflectance factor(3) Land aerosol optical properties
Categories: Optical thickness, grain size distribution, and reflective index(4) Cloud in land region
Categories: Grain size distribution, cloud top height(5) Cloud in ocean region
Categories: Extent, height(6) Interaction between aerosols and clouds(7) Distribution of ocean coloring matter concentration

130

5.

ILAS-II

5.1

Science objectives

In the early 1970s, scientists expressed concern about the destruction of the ozone
layer by chlorofluorocarbons (CFCs) and pointed out the necessity of protecting the zone
layer. In the mid 1980s, the Antarctic ozone hole was discovered, and scientists revealed
that CFC and other chemical components were rapidly destroying the ozone layer in the
Antarctic region.
In response international controls were planed on producing and using CFCs andother chemical components. By the mid 1990s, the trend of increasing concentrations ofsome CFCs in the atmospheric began to slow down and seemed to even be reversing.
Chlorine in the stratosphere, which destroys the ozone layer, will continue to increaseover the next decade and then start to decrease. If so, and there is no change in otherconditions, destruction of the ozone layer will reach its peak in the next several years andthen start to decrease.
In the light of importance of the ozone depletion problem, the Environment Agencyof Japan launched the improved Limb Atmospheric Spectrometer (ILAS) on ADEOS in
August 1996 to monitor the ozone layer and promote investigation into the mechanism ofozone depletion. ILAS observed the altitude distribution of various ozone chemistry
atmospheric trace components, temperature, air pressure, and aerosol/cloud regions in thestratosphere in the polar regions. To understand changes in the ozone layer correctly, tovalidate the effects of ozone layer protection to gain knowledge about physical chemistry
processes in the upper layer of the atmosphere (including the ozone layer), and tocontribute to predicting of atmospheric environment change on a global scale, it isnecessary to conduct highly accurate observations for a long term. Although ADEOS
unfortunately failed and ceased operation in July 1997, ILAS aboard ADEOS was able toacquire valuable data from the observations for about eight months. The Ministry ofEnvironment decided to fly ILAS-II on ADEOS-II in FY2000 in order to continuously
observe the ozone layer and, at the same time, to make an additional category ofmeasurements and improve accuracy.
The scientific objectives of ILAS-II are to monitor changes in the ozone layer, and toinvestigate the ozone layer chemistry and physical processes, like those of ILAS. The
wavelength of the spectroscope on ILAS-II and the items to be measured objects areshown in Table 5.1-1.

131

Table 5.1-1
Channel1
2
3
4

Measuring wavelengths of the ILAS-II sensor and categories

Wavelength (frequency)
6.21-11.76µm(1610-850cm-1)
3-5.7µm(3333-1754cm-1)
12.78-12.85µm(782-778cm-1)
753-784µm(13280-12755cm-1)

Item(s)O3, HNO3, NO2, N2O, CH4, H2O, CFC-11, CFC-12, aerosol
Aerosol, H2O, CH4, N2O, O3, CO2,
Provided that CO2 is for pressure measurement
ClONO2Temperature, atmospheric concentration, aerosol

Aerosol” includes polar stratospheric clouds (PSCs). Also, data are measured ataltitudes of 10 to 60 km (sequence of measurements from cloud-top height to 250km),
and the altitudinal resolution is 1km. When ADEOS-II is in orbit and passes thedescending node at 10:30 AM local time, data will be measured from 57 to 73 degrees
north latitude and 64 to 88 degrees south latitude. Channel 1 and 4 are basically the same
as for ILAS, and channels 2 and 3 were added for ILAS-II.
ILAS observes O3, HN3, NO2, N2O, CH4, and H2O gas components that are important
trace elements for understanding physics and chemistry involving ozone layer. CFC-11
and CFC-12 supply chlorine atoms that destroy the ozone layer, and international actions
for eliminating these components have been taken. Monitoring not only enables us toknow the tendency of the components destroying the ozone but also to gain knowledge
on the export process from the troposphere to the stratosphere and other physicalmechanisms related to material transfer in the upper atmosphere.
Temperature is an important parameter that involves the atmospheric cycle and otherphysical environments, the speed of chemical reaction of gaseous components, and thegeneration and extinction of PSCs. Moreover, since the absorption coefficient of gaseous
molecules in the infrared range depends on temperature, temperature must be measured.
Measuring ClONO2, which acts as a reservoir of chlorine atoms that destroy theozone layer, is important for understanding the ozone-depletion mechanism. This newmeasuring category has been added to ILAS-II. Particles like aerosols and PSC2 produce
heterogeneous chemical reactions like ClONO2, HCL, and H2O and are believed to speed
up ozone depletion. Since particle composition, formation, and surface area determine
heterogeneous chemical reactions of particles over the surface, it is very important to
extract this information. For that reason, the ILAS-II middle infrared channels include
6µm and 6µm and above to improve accuracy.

132

5.2

Overview of Mission Instruments

ILAS-II is an atmospheric sensor developed by the Environmental Agency to monitor
and study the ozone layer in the stratosphere in high-latitude regions in both the northernand southern hemispheres. This sensor is designed for scientific research concerning
physical and chemical phenomena related to the destruction of the ozone layer and forvalidating the effects of measures such as the specific Freon control regulation. To
support such research, it will perform long-term observations of various phenomena inthe stratosphere, such as ozone holes caused by Freon and other factors. ILAS-II is aspectrometer that measures (by solar obscuration) the altitude and distribution of theatmospheric content concentration, temperature, and atmospheric pressure from the top of
the troposphere to the stratosphere by absorbed spectra in the atmospheric-peripheral
direction with the Sun as its light source, both at sunrise and at sunset on each revolutionof the satellite. Measurements use absorbed spectra in a total of four bands: three
infrared bands (3.0 to 5.7 µm, 6.21 to 11.76 µm, and 12.78 to 12.85 µm) and one visible
band (753 to 784 nm). As with ILAS, observation by ILAS-II is limited to the highlatitude regions on both hemispheres (56 to 70 degrees north, 63 to 88 degrees south) due
to the positional relations of the satellite and the Sun with respect to the Earth in a Sunsynchronous orbit. The system is capable of measuring many parameters related to thedestruction of the ozone layer through this spectral measurement: these parameters
include ozone, NO2, aerosol, water vapor, Freon (CFC-11 and CFC-12), methane, N2O,
CIONO2, air temperature, atmospheric pressure, and the altitude-distribution of theseelements. Table 5.2-1 presents the main specifications of ILAS-II. Figure 5.2-1
illustrates the observation concept of ILAS-II.
Table 5.2-1 Main Specifications of ILAS-II
Item
Observation Wavelength Bands

Observation Altitude
Altitude Resolution
Data Rate

Specifications
6.2 to 11.8 µm
3.0 to 5.7 µm
12.78 to 12.85 µm
753 to 784 nm
10 to 60 km
1 km
453.62 Kbps

ILAS-II Observation Concept
Zero100％ Reference
ILAS-II

Tangent Height
Sunlight
Atmospheric Absorption

SunriseEarthSatellite Altitude
800 kmSunsetFigure 5.2-1 ILAS-II Observation Concept

133

5.3

Specific

5.3.1

Standard products

The principles of ILAS-II measurements are based on solar occultation, whichmeasures the sunlight passing through the atmosphere in the regions. Since ILAS-II will
be on board a sub-synchronous, sub recurrent orbit satellite, the measurements will be
conducted at sunrise and sunset as viewed from the satellite. Thus, ADEOS-II will orbit
the Earth 14 times a day and measurements will be conducted at 14 points in the northernand southern hemispheres each day.
The standard ILAS-II products are the altitude distribution of each observational point
listed below. (Measurement errors are also provided.) For reference, the estimates ofmeasurement accuracy we expect with ILAS-II are shown for several altitudes (Table
5.3-1).
Table 5.3-1

Expected ILAS-II measurement accuracy

Figures indicate expected ILAS-II measurement accuracy; improvement is expected.
Altitude (km)
10
20
30
40
O3
5%
5%
5%
5%
HNO3
50%
10%
50%
n.d.
NO2
n.d.
5%
5%
20%
N2O
5%
5%
20%
100%
CH4
5%
5%
5%
10%
H2O
5%
5%
5%
10%
CFC-11
10%
Under study
CFC-12
Under study
CLONO2
Under study
Aerosol
extinction
Under study
Coefficient(Multiwavelength)
Temperature
1K
Air Pressure1%
Note: n.d. indicates “impossible to indicate”

50
5%
n.d.
100%
n.d.
n.d.
n.d.
n.d.

5.3.2 Research products
Extinction coefficients of complex wavelength are standard aerosol products. As
research products, parameters related to components of aerosols, and grain size
distribution will be derived from the extinction coefficients. Also, using standard
products, level 3 products will be generated as research products.

134

6.

DCS

The Data Collection System (DCS) is a system that collects observation data (uplinkmessages) transmitted from buoys set up on the ocean and from observation systems onland and, at the same time, transmits control commands (downlink messages) to thesebuoys and observation systems (DCP: Data Collection Platform). Table 6-1 shows themain specifications of the DCS.
Table 6-1

DCS Main Specifications
Item

Data Rate
Downlink Messages
Uplink Messages

7.

Frequency
Data Rate
Frequency
Data Rate

Specifications
10 Kbps465.9875 MHz
200 bps401.65 MHz
400 bpsTEDATechnical Data Acquisition Equipment (TEDA) gathers engineering data on therelationship between the space environment on the satellite’s orbit and the deterioration,
malfunction, and failure of satellite parts and materials; it was also installed on othersatellites such as ETS-V and ETS-VI.
The TEDA hardware consists of a technical data-collecting unit (TDU) and apollution-monitor sensor (COM-S), with five additional sensors and monitors: a heavyion observation unit (HIT), a radiation dosage monitor (DOM), a pollution monitor(COM), a static electric potential monitor (POM), and a memory-malfunctioning monitor(SUM).
Using these sensors and monitors, this system will acquire data concerning therelationship between the deterioration characteristics of parts and materials and the harshspace environment. Some of the data collected will be reflected in the development of aspace-environment model useful for future designs of various satellites. The data rate for
TEDA data is 672 bps.

135

Chapter 3 System and Operation

1.

System1.1

Overview of the Satellite System

ADEOS-II is expected to be launched by H-IIA in December 2002, from
Tanegashima Space Center. The satellite will be injected into a solar-synchronous subrecurrent orbit with an altitude of 802.9 km and an orbit inclination angle of 98.62
degrees. It orbits the Earth in 101 minutes. The satellite design is 3 years, but it carries
five years’ worth of fuel in order to maintain this orbit.
Table 1.1-1 presents the main specifications of the ADEOS-II satellite.

Table 1.1-1

Main Specifications of the ADEOS-II Satellite

Item
Launching Rocket
Time of Launch
Orbit Altitude
Orbit Inclination Angle
Body dimensions (x, y, z)
Solar Battery Paddle dimensions
WeightPower Generated (at the Completion of theMission)Attitude-Control Method
Design Life
Fuel CarriedSpecifications
H-IIA
December, 2002
802.8 km98.62 degrees
5×4×4m3 × 24 m
3,730 kg
5000W or more
Zero-Momentum 3-Axis Control
3 years
Sufficient for 5 years

ADEOS-II consists of a mission module in the front, equipped with observation
instruments, and a bus module in the rear of the satellite, where basic instruments are
stored.
The mission module contains the mission instruments (observation instruments)
shown in Table 1.1-2, which were developed by the NASDA and related agencies.

Table 1.1-2

Mission Instruments aboard ADEOS-II

Instrument
High-Performance Microwave Radiometer (AMSR)
Global Imager (GLI)
Improved Atmospheric Peripheral Infrared Spectrometer II (ILAS-II)
Sea Wind Observation Instrument (SeaWinds)
Land Surface Reflective Light Observation Instrument (POLDER)
Date-Collection System (DCS)
Technology Data Obtaining System (TEDA)

136

Developing Agency
NASDA
NASDA
Environmental Agency
NASA JPL
CNES
CNES
NASDA

The bus module contains the instruments and devices (bus instruments) shown in
Table 1.1-3. There are necessary for various satellite operations, including maintaining
the satellite’s orbit and controlling mission instruments.

137

Table 1.1-3
Instrument

Communication
and DataHandling
System
(C&DH)
Inter-Orbit
Communication
System (IOCS)
Mission-DataProcessing
System (MDP)
DirectTransmission
System (DT)
Optical Magnetic
Disk (ODR)

Electric Power
System (EPS)

Paddle System(PDL)

AttitudeOrbit Control
System (AOCS)
Propulsion
System (RCS)

Local User
Transmission
System (DTL)Bus Instruments aboard ADEOS-II

Overview
The communication and data-handling system (C&DH) receives and decodes command signals
transmitted from the tracking control office using a frequency in the 2GHz-band andcommunicates them to all the ADEOS-II instruments. It is also capable of editing thetemperature, voltage, and status of the interior of each instrument and transmitting the information
to the ground station using telemetry signals.
The inter-orbit communication system (IOCS) is a subsystem for data-relaying and tracking
control through a data-relaying satellite using the S-band and Ka-band.
The mission-data-processing system (MDP) selects the type of mission data to be transmitted,
adds necessary data to mission data, edits the information into packet- or multi-format, and thentransmits the information to the direct transmission system (DT) and the inter-orbit
communication system (IOCS); it is also capable of transmitting the information to the mission
data recorder (MDR).
The direct transmission system (DT) sends data observed by ADEOS-II and is capable oftransmitting data directly to the ground station using the X band. The DT transmits mid- to highspeed mission data at two rates in the X band (60 Mbps and 6Mbps data).
The ODR is a high-speed, large-volume data recorder using an optical magnetic disk system,
introduced in ADEOS-II for the first time. A large-scale recording experiment will be conductedwith high-speed, high-volume data. The ODR is contained in the DT unit.
The electric power system (EPS) has three functions: to supply the bus power to each subsystemof the satellite; to manage charging and discharging of the battery; and to control ignition of theordnance controller. During the night, it supplies power to the satellite by discharging the battery(BAT). During the day, excess power generated by the solar-battery paddles is used to charge thebattery. During the critical phase, which is the initial stage after the launch, it provides power toignite the ordnance controller through the explosive-tube control unit (ODC) to extend the solarbattery paddles, DCS antennae, and IOCS compartment as well as to release locks on AMSR and
SeaWinds.
The paddle system (PDL) converts solar energy into electric energy and transfers it to thesatellite’s power system. The paddle system to be installed on ADEOS-II is sufficient to satisfy
demands. It has a large power-generating capacity of at least 5 kw (EOL), is highly storable, hasbeen made lighten. The system uses an extension method wherein 50 flexible blankets with a
total of 55,680 solar-battery cells are extended on a jointed mast in orbit.
The attitude-orbit control system has four functions: to establish the three-axis attitude after the
rocket is separated from the satellite, to maintain the satellite’s attitude, to control the orbit, and todrive the solar-battery paddles. Sensors to detect the attitude include a control-standard unit
(IRC), an Earth sensor (ESA), and a fine sun sensor assembly (FSSA), Actuators to control theattitude include a reaction wheel (RWA) and a magnetic torque (MTQ). This system alsotransmits control signals necessary for attitude control and orbit control to the RCS.
The propulsion system (RCS) generates propulsion power necessary for initial-stage attitude
correction and orbit control according to the control signals from the attitude-orbit control system(AOCS), using the 1N thruster and 20N thruster.
The local user transmission system (DTL) is capable of modulating data extracted from four
(three visible and one infrared) of the 36 observation bands of the Global Imager (GLI) (ground
resolution 6 km × 6 km, data rate 23Kbps) into BPSK; it is also capable of transmitting the data to
local users such as vessels on the UHF band (467.7 MHz). The water-color and watertemperature data are used to study the ocean conditions, distribution of water temperature, andbasic productivity of the ocean.

138

1.2

Overview of the Ground System

The main components of the ground system, which carries out the mission operationof ADEOS-II, are listed below.
Facilities and Organizations within NASDA• ADEOS-II Mission Operating System: Earth Observation Center• Earth Observation Information System (Data Comprehensive Management
and Provision System): Earth Observation Center• Other Systems: Earth Observation Center⋅
EOC Control System⋅
Observation Request Enquiry System⋅
Shared Information Storing System• Tracking and Control System (TACC)• Earth Observation Data Analysis Research Center (EORC)
Facilities and Organizations outside NASDA• Overseas Stations• NASA Stations (ASF, WFF)
⋅ Kiruna Station• Sensor-Providing Organizations• Redu Station• Users⋅
Principal Investigator (PI)⋅Semi-Real-Time Data Users⋅
General Users
Figure 1.2-1 illustrates the overall structure of the ground system for ADEOS-II.

139

ADEOS-II

ARTEMIS

Mission Data (KSA)

Mission Data (KSA)DRTS

TT&C (SSA)
TT&C (SSA)
Mission Data
& TT&C (Ka)

Mission
Data (X)

TT&C (S)

Downlink / Uplink
Message (UHF)

TT&C (Ka)Redu
StationUSB Transmission/Reception Station

FeederLink Station

To Sensor−Providing
Organizations

NASA
Station

KirunaStationRaw Data

L0 Data
X-BandReception
Facility

Request for operation,
Operation Planning, etc.

To EOC

Observation Request,
Operation Planning, etc.

Sensor-Providing
Organizations
Semi-Real-Time Data
User Organization

Order
Desk

Facilities and Organizations
within NASDA
Facilities and Organizations
outside NASDA

GeneralUsers

Semi-Real-Time Data
Order
AMSR, GLI Products

Catalogue
Order

EORC

Mission Data
Other Files

L0 Data(Online or Medium)PI

AMSR, GLI Products(Online or Medium)

Earth Observation Information System/
Data Comprehensive Management and Provision System

Raw Data(GLI 250m)

Mission Data
& TT&C (Ka)To EOCL0 Data
Tracking and Control System

DCPs

Mission
Data (X)

Raw Data(back-up)

Observation Request (GLI)
Observation Plans,Observation Results, etc.

FeederLink
StationADEOS-II
Mission Operation System

Other
Systems

Earth Observation Center (EOC)

Figure 1.2-1 Overall Structure of the ADEOS-II Ground System(1) ADEOS-II Mission Operation System
The ADEOS-II mission operation system is a central-core system for the mission
operation of ADEOS-II, and is provided by NASDA for the Earth Observation Center(EOC). The ADEOS-II mission operation system establishes plans for the operationof mission instruments, recording and playing of MDR, etc., based on sensoroperation requests by sensor-providing organizations. Furthermore, it receives
mission data sent via a relay satellite or directly through X band and prepares level-0
data for each mission instrument. It also prepares standard the AMSR and GLI
products (level-1 products and higher-order products with level 2 and higher) andhandles DCS data.1 The level-0 data obtained by mission instruments other than
AMSR and GLI that have been processed by the ADEOS-II mission operation system
will be distributed to sensor-providing organizations online or through a medium.
The mission operation system of ADEOS-II also processes AMSR and GLI 1km
products on a semi-real-time basis and makes them available online to semi-real-timedata users.
Finally, the feeder-link station of the ADEOS-II mission operation system
functions as a back-up station that transmits commands through a relay satellite andobtains telemetry data when there is trouble or some failure at the feeder-link stationof the track-control system.

140

1 The DCS data processed by the mission operation system of ADEOS-II are limited only to data collected by
the DCP managed by the NASDA.

(2) Earth Observation Information System and Data Comprehensive Management andProvision System
The Earth observation information system and data comprehensive management
and provision system (EOIS-DDMS) provides a network environment in which data
is exchanged online between EOC and various related organizations in and outside ofJapan. In addition, these systems store and manage all ADEOS-II mission data as
raw data before they are processed at level 0; the systems also store and manage the
AMSR and GLI standard products.
Furthermore, the systems manage the catalog information on the AMSR and GLI
products, provide it to users, and provide specific data when requested by users.
(3) Other Systems
In addition to the Earth observation information system, EOC also has thefollowing systems related to the operation of the mission operation system of
ADEOS-II.
(i) EOC Control System
This system calculates information on several competing satellites (including
ADEOS-II) that collect data which EOC receives and coordinates the antennacompetition of the X-band direct-receiving station within the EOC. Based onthese coordination results, this system provides the mission operation system withantenna information that can be used to receive the mission data of ADEOS-II. In
addition, the EOC control system manages the operates the X-band receiving
equipment based on the X-band reception plan provided by the mission operation
system.
(ii) Observation Request Enquiry System
This system receives GLI observation requests from EORC, prepares
observation request files, and provides them to the ADEOS-II mission operation
system. It also discloses results of observation planning as well as the observation
results to EORC through its WWW server.
(iii)Shared Information Storing System
This system stores and manages information shared and used jointly by anumber of facilities (such as the orbit information of ADEOS-II) in its data server
and provides this information as it is required by various facilities.

141

(4) Tracking and Control System
The tracking and control system checks mission-instrument operation requests
established by the ADEOS-II mission operation system with respect to the restrictions
on the number of satellite commands, power restriction, and other restrictions andthen prepares commands to be uplinked to the satellite. The commands prepared aretransmitted directly from the USB transmission and reception station or through arelay satellite to ADEOS-II.
The tracking and control system also obtains HK telemetry data and measuringdistance data of the satellite, either through a relay satellite or by direct reception at
the USB transmission and reception station. It then monitors the satellite status aswell as that of the instruments installed on board and decides the orbit of the satellite.
The feeder-link station of the tracking and control system is able to serve as a backup station that collects mission data through a relay satellite if the feeder-link stationof the ADEOS-II mission operation system suffers an outage. In such cases, theacquired mission data are recorded in a medium and transmitted to the ADEOS-II
mission operation system.
(5) Earth Observation Data Analysis Research Center (EORC)At EORC, the Earth observation information system (EOIS) and the data analysis
research system (DAS) are used to make prototypes of research products and data setsother than standard products. At this center, observation requests from PIs for the
GLI tilt changes and the GLI 250 m data collection region are organized and provided
to the observation request enquiry system at the EOC.
(6) Overseas Stations
Two classes of X-band direct reception stations overseas support the ADEOS-II
mission operation: NASA stations and the Kiruna station. These stations collect
ADEOS-II mission data for paths that cannot be acquired by the X-band reception
station of the EOC. The NASA stations include the Alaska SAR Facility (ASF)
located in Fairbanks, Alaska, and the Wallops Flight Facility (WFF) located in
Wallops, Virginia.
At overseas stations, mission data in the X band are collected according to theoperation plan provided by the ADEOS-II mission operation system; all level-0 dataon mission instruments, except the GLI250m and POLDER data, are prepared there.
The level-0 data prepared at NASA stations are provided online to sensor-providingorganizations, the EOC, and other organizations. The level-0 data prepared at the
Kiruna station are provided online to the EOC.
The MDR data and the GLI 250m data, included in mission data obtained atoverseas stations, are regularly recorded on a medium as raw data before they areprocessed at level 0 and transported to EOC.

142

(7) Sensor-Providing Organizations
Sensor-providing organizations are related organizations and agencies—both inand outside the Japan—that develop mission instruments besides the AMSR and GLI.
They include the Environmental Agency of Japan (ILAS-II), the NASDA Space
Environment Measuring Group (TEDA) of Japan, NASA JPL (SeaWinds) and CNES(POLDER and DCS).
A sensor-providing organization collects level-0 data obtained by its own sensor,
prepared by the mission operation system of ADEOS-II or by an overseas station,either online or through a medium; it then prepares higher-order products at level 1,
level 2, or higher.
A sensor-providing organization prepares observation requests for its own sensor
and provides them to the ADEOS-II mission operation system. However, the TEDA
and DCS instruments require constant operation, so no observation requests arenecessary except in emergencies such as sensor failures.
(8) Redu Station
Redu station is an ESA ground station located in Belgium and serving as a feeder
link station for ARTEMIS. ARTEMIS is expected to collect GLI 250m data notvisible by DRTS, transmit commands, and receive telemetry. The GLI 250m dataobtained at the Redu station are recorded on a medium, transferred to EOC, andprocessed at level 0 and as standard products by the ADEOS-II mission operation
system.
However, the regular GLI 250m data will not be acquired using until the
ARTEMIS line quality is confirmed to be satisfactory.
(9) Data Users(i) PI
The term “PI” represents a researcher or a representative of a researchorganization that has responded with a proposal to a NASDA-originated research
proposal advertisement and has been selected to carry out research. A PI canplace a product order online and has the right to obtain, free of charge, AMSR and
GLI products online or through a medium. Requests for GLI tilt angle change
and GLI 250m data-obtaining regions can be submitted by a PI to the EORC,
where these GLI observation requests are organized.
(ii) Semi-Real-Time Data Users
A semi-real-time data user is an organization that has signed an agreement with
NASDA on the use of semi-real-time AMSR and GLI data. Currently, the NOAA
of the United States, the Meteorological Agency of Japan, and the Fishery
Information Service Center (corporation) are these users.
(iii)General Users
A general user is anyone other than PIs and semi-real-time data users who uses
ADEOS-II data. Standard AMSR and GLI products can be data-searched onlineby these users; however, the must use off-line services through the order desk toorder and get products.

143

2. User Services
2.1

Data Policy
TBD.

144

2.2

Operation Phase

The operation phase for AMSR, and GLI, and AMSR-E is subdivided into threephases as shown in Figure 2.2-1. Note that the operation phases for the AMSR/GLI and
the AMSR-E occur at different times.
(1) Initial Checkout Phase
The initial checkout phase is approximately the first four months after the satellite
launch. Checkout is performed to confirm that the sensor hardware on the satellite
functions properly in orbit and to evaluate the ground system. There is no user
service during this period.
AMSR-EAMSR/GLI

May 4 to early September 2002
December 2002 to March 2003

(2) Calibration and Validation Phase
The calibration and validation phase is the period of approximately one year after
the initial checkout phase after the launch of the satellite. Sensors will be calibrated
and the products validated. Data necessary for calibration and validation will be
provided only to Cal/Val PIs and standard algorithm PIs.
AMSR-EAMSR/GLI

Early September 2002 to early May 2003
March 2003 to December 2003

(3) Regular-Use Phase
The regular-use phase begins approximately a year after the launch. Standard
products will be provided to all users including general users.
AMSR-EAMSR/GLI
4
Event
Operation Phase: AMSR-E
Operation Phase: AMSR/GLI

Early September 2002 to early May 2003
March 2003 to December 2003

2002
5 6 7 8 9 10 11 12 1 2 3 4 5△AQUA Launch△ADEOS-II Launch

2003
6 7

8

9 10 11 12 1

2

3

4

5

Initial Checkout Phase Calibration/Validation Phase

2004
6 7

8

9 10 11 12 1

2

3

4

5

2005
6 7

8

9 10 11 12

Regular Use Phase

Initial Checkout Phase Calibration/Validation Phase

Regular Use Phase

Product Provision Limited to
PIs and Organizations with a
Signed Contract
AMSR-E
AMSR
GLI

T.B.D.
Algorithm Ver. 0
Algorithm Ver. 0

Algorithm Ver. 2

Algorithm Ver. 1～
Algorithm Ver. 1～

All Users
AMSR-EAMSR
GLI

Algorithm Ver. 2

T.B.D.
Algorithm Ver. 1

Algorithm Ver. 2
Algorithm Ver. 1

Figure 2.2-1 Operation and Product-Provision Schedule

145

Algorithm Ver. 2

2.3

User Definition

User service policies differ depending on the purpose of their usage. The following
five definitions are used to classify all users.
•
•
•
•
•

Cal/Val PIs and Standard Algorithm PIs
Research Algorithm PIs
Earth Science PIs
Organizations with a Signed Contract
General Users

Details are TBD.

146

2.4

User Services

2.4.1 Overview of Services
The term “user services” refers to the provision of Earth-observation data according
to the users’ requests so that they can do their research or carry out their businessefficiently and smoothly. The services can be classified into two groups: observationrequest service and data-request service. Figure 2.4-1 present an overview of the flow ofuser services. Users can receive observation-request service and data-request service
through the GLI and data-request service of AMSR.
Filing an Observation Request

Confirming the Observation Plan

GLI-250m and Tilt
Change Request
Service

Searching for Data

Requesting Data

AMSR/AMSR-E,
GLI Data Request
Service

Receiving Data

Figure 2.4-1 An Overview of the Flow of User Services
These services differ in policies and procedures, depending on the operation phases
defined in Section 2.2 and on the types of users defined in Section 2.3.
In the following pages, user services are described for each operation phase and,within each operation, the service provided to each type of user will be explained.

147

2.4.2 Services According to Operation Phase
2.4.2.1 Calibration and Validation Phase(Details of services are currently being determined.)
User services during the calibration and validation phase consist of providing (1)through (4) below; Table 2.4-1 identifies the services provided to each type of user.
(1) GLI Observation Requests
A request is necessary when a user wishes to schedule GLI 250m observation or tochange the tilt from the default tilt pattern. GLI 1km observation does not require arequest. Service details for each observation request are discussed below.
(i) Reception of Requests for GLI 250m Observation and Tilt Change
A user may request GLI 250m observation and a tilt change for a particularobservation day during or after the calibration and validation phase. The user
goes to the EORC web site (the URL is TBD), fills out necessary information on
the observation request form online, and submits it by a deadline TBD, (9 + α)
weeks before the observation date. If, however, new emergency observation isrequired or an observation request is changed, the request can be accepted up to
five weeks before the observation date.
For those requests mentioned in the NASDA official document “ADEOSII/GLI Observation Request Plans,” an email will be sent from the ADEOS-II
Mission Operation Office (a tentative name) to verify the requests details. The
party making the request is to verify the request content and reply to the email by
the date specified in the email text.
(ii) Coordination of Requests for GLI 250m Observation and Tilt Change
Each request for GLI 250m observation or a tilt change that has been received
is given an observation priority ranking. If there is time before the deadline, anobservation request simulation is carried out beforehand. If requests are clearly
not possible, the ADEOS-II Mission Operation Office (a tentative name) will sendan email stating that. The requester can then coordinate the request with the
ADEOS-II Mission Operation Office (a tentative name) and, if necessary, submit
a new observation request through the Web site.

148

(2) Observation Plan Confirmation
The user can verify the observation planning results for GLI 250m observation and
GLI 1km observation after the tilt change about two weeks prior to the observation
date through the EORC’s web site (the URL is TBD). (Contents to be displayed are
TBD.) At the same time, the URL will be given to the requester through an email(TBD).
The entire observation plan for the AMSR-E, AMSR, GLI 250m, and GLI 1km canbe verified beginning two weeks prior to the observation date (this is yet to be
confirmed) on the EOC’s home page http://eus.eoc.nasda.go.jp. For details, see“Earth Observation Data Usage Handbook—ADEOS-II.”Users must understand that sometimes data may not be made available according tothe observation plan due to reasons such as data loss during observation data
reception.
(3) Data Requests for AMSR-E, AMSR, and GLI
EORC will provide invalidated level-1B products, invalidated standard products,
match-up data, and research products.
Services that can be used vary according to the data type. See Table 2.4-1 and“Earth Observation Data Usage Handbook—ADEOS-II” for details concerning datatypes, services used, and how to use various services.
(4) User Support
User support is provided through the ADEOS-II Mission Operation Office (atentative name).
Address: TBD (NASDA or RESTEC?)
Phone:
Fax:
Email: ad2help@restec.or.jp
URL: http://www.eorc.nasda.go.jp/

149

2.4.2.2 Regular Use Phase(Details of service are currently being coordinated.)
Here, user services (1) through (4) below during the calibration and validation phaseare discussed; Table 2.4-2 presents the types of services provided to each user type.
(1) GLI Observation Requests
A request is necessary when a user wishes to carry out GLI 250m observation or tochange the tilt from the default tilt pattern. GLI 1km observation does not require arequest. Service details for each observation request are discussed below.
(i) Reception of Requests for GLI 250m Observation and Tilt Change
A user may request GLI 250m observation and a tilt change for a particularobservation day during or after the calibration and validation phase. The user
goes to the EORC web site (the URL is TBD), fills out necessary information on
the observation request form online, and submits it by the TBD deadline, (9 + α)
weeks before the observation date. If, however, new emergency observation isrequired or an observation request is changed, the request can be accepted until
five weeks prior to the observation date.
For requests mentioned in the NASDA official document “ADEOS-II/GLI
Observation Request Plans,” an email will be sent from the ADEOS-II Mission
Operation Office (a tentative name) to verify the details requests. The party
making the request is to verify the request details and reply to the email by thedate specified in the email text.
(ii) Coordination of Requests for GLI 250m Observation and Tilt Change
Each request for GLI 250m observation or a tilt change that has been received
is given an observation priority ranking. If there is time before the deadline, anobservation request simulation is performed beforehand. For requests that areclearly not possible, the ADEOS-II Mission Operation Office (a tentative name)will send an email stating the fact. The requester can then coordinate the request
with the ADEOS-II Mission Operation Office (a tentative name) and, if necessary,
submits a new observation request through the Web site.

150

(2) Observation Plan Confirmation
The user can verify the observation planning results for GLI 250m observation and
GLI 1km observation after the tilt change about two weeks before to the observation
date through EORC’s web site (the URL is TBD). (Displayed contents are TBD.) At
the same time, the URL will be given to the requester through an email (TBD).
The entire observation plan for the AMSR-E, AMSR, GLI 250m, and GLI 1km canbe verified beginning two weeks before to the observation date (to be confirmed) on
the EOC’s home page http://eus.eoc.nasda.go.jp. For details, see “Earth-Observation
Data Usage Handbook—ADEOS-II.”Users must understand that sometimes data may not be made available according tothe observation plan due to reasons such as data loss during observation data
reception.
(3) Data Requests for AMSR-E, AMSR, and GLIThe EORC will provide the following data: invalidated level-1B products
invalidated standard products, match-up data, and research products.
EOC will provide level-1 products and standard products.
Services that can be used vary according to the data type. See Table 2.4-1 and “Earth
Observation Data Usage Handbook—ADEOS-II” for details concerning data types,
services provided, and how to use the various services.
(4) User Support
User support is provided through the ADEOS-II Mission Operation Office (atentative name).
Address: TBD (NASDA or RESTEC?)
Phone:
Fax:
Email: ad2help@restec.or.jp
URL: http://www.eorc.nasda.go.jp/151

Table 2.4-1 Overview of User Service in the Calibration and Validation Phase
Initial Checkout Phase No Service
Calibration and Validation Phase
GLI 250m
Observation
Requests,
Coordination

Users

Cal/Val PIs
and Standard
Algorithm
PIs
Research
Algorithm
PIs

Observation Plan
Confirmation

Received
at:

I/F

Received
at:

I/F

EORC

WWW,
email

EORC

WWW,
email

EOC

EUS/OREQ(WWW,GUI)*3

Not Available
Earth
Science PIs
Users withSignedContract
GeneralUsers

AMSR-E, AMSR, GLI Data Requests *4

Scene
Orders*5

Data Provided

Invalidated Level 1Products,
Invalidated Standard
Products, Invalidated
Research Products*1*1

Not Available

Not Available

Not Available

Receivedat:

I/F

ODD (WWW),FTP*2
Available

Not Available

EORC
Disclosure System
for Data for
Validation(WWW)

Match-Up Data

*1

Standing
Orders*6

1. For organizations with a signed contract, the available data depend on the contract.
2. Making AMSR-E/ AMSR data available through FTP is being discussed currently (to be confirmed).
3. Details concerning which EUS/OREQ observation plans are to be confirmed are currently being discussed.
4. PI production requests necessary for calibration and validation will not be honored.
5. A scene order is an order that uniquely specifies a scene or a product among processed data.
6. A standing order is a pre-order of data that are to be obtained and/or prepared in the future.

152

Table 2.4-2 Overview of User Service in the Regular Use Phase
Regular Use Phase
GLI 250m Observation Requests, Coordination
Received at:
I/F

Users
Cal/Val PIs and Standard
Algorithm PIs
Research Algorithm PIs
Earth Science PIs
General Users
Users with Signed
Contract(EOC Window)
Users
Cal/Val PIs and Standard
Algorithm PIs
Research Algorithm PIs
Earth Science PIs
General Users
Users with Signed
Contract(EOC Window)
Cal/Val PIs and Standard
Algorithm PIs
Research Algorithm PIs
Earth Science PIs
Users with Signed
Contract
General Users

EORC

Observation Plan Confirmation
Received at:
I/F
EORC

WWW, email

EOC

EUS/OREQ (WWW,
GUI)*2

WWW, email

Not Available
1

1

5

Data Provided

Level-1 Products, Standard
Products

Scene Orders
Available

AMSR-E, AMSR, GLI Data Requests 4Standing Orders6
Available

EOC

Received at:

I/F
EUS (GUI, WWW)
EUS (WWW)

1

Invalidated Level-1
Products, Invalidated
Standard Products,
Research Products

ODD (WWW), FTP3
Available

Not Available

EOC
Disclosure System for Data for
Validation (WWW)1

Not Available*1

For an organization with a signed contract, the available data depend on the contract.
Details are being discussed currently concerning which EUS/OREQ observation plans are to be confirmed.
*3
The data referred to here are AMSR-E/ AMSR data (being confirmed currently).
*4
As a rule, production requests from PIs necessary for calibration and validation will not be honored.
*5
A scene order is an order that uniquely specifies a scene or a product among processed data.
*6
A standing order is a pre-order of data that are to be obtained and/or prepared in the future.
*2

153

2.5

Data Comprehensive Management and Provision Services

The Earth observation information system (EOIS) is a user front-end system thatprovides catalogue information service and on Earth-observation data products, forpromoting the use of Earth-observation data. Users can receive various services
provided by the EOIS online through the Internet.
2.5.1 Overview of Data Provision Service
An overview of services provided by the EOIS is presented here. The types ofservices available vary depending on the tools. Section 2.5.4 discusses an overview ofvarious tools and methods for accessing the EOIS.

Inventory

Catalogue
Information
Services

EOIS Provision Services
GUI
ADS
OREQ
DRS

Catalogue

Service

Service

Guide Information Service

PIs

WWW

GUI
WWW

WWW

GUI
WWW

ADS

ADS

Order Enquiry
ServiceStandard Product Online Order Enquiry
Service

Observation
Planning
Service

Observation Planning Service

OREQ

OREQ

Online Data Provision Service

DRS

DRS

Other
Information
Services

WWW

Image

Information

General
Users

GUI
WWW

Standing Order Progress Check Service

ADS

can be used through EUS/WWW service sites(* Order services are available only to PIs.)
can be used through EUS/GUI service sites
can be used through Guide Information service sites
can be used through observation plan service sites
can be used through online data-provision service sites

Figure 2.5.1-1 Overview of EOIS Provision Service
As shown in Figure 2.5.1-1, various services will be provided through various
systems and servers such as EUS/WWW, ADS, OREQ, and DRS, but users do not need
to be aware of this. Users can simply visit the respective URL addresses and accessthese services. The EUS/GUI is available only to PIs.

154

2.5.2 Catalog Information Service
2.5.2.1 Inventory Information
Catalog data on AMSR and GLI standard products processed by the ADEOS-II
mission operation system are prepared and made available to users. The inventory
information on AMSR and GLI data managed and provided by the EOIS is shown in
Table 2.5.2-1.
Catalog information is also called inventory information. It consists of characters
indicating the satellite name, name of the sensor used, observation date and time,observation region, dataset name, and other information concerning the standard
products of the Earth observation data.
Table 2.5.2-1 Inventory Information of the AMSR and
GLI Managed and Provided by EOIS
Level

Geophysical Parameters

AMSR
1A, 1B2

3

Days,months

GLI 1km
1A
1B
2A

2

3 binned

16th,months
1st, 8th,months16th,months

3 STA
MAP
st

th

1 ,8 ,
months

Projection Method*1
EQR
PS
PN
-

-

-

Amount of vapor, cloud liquid water, precipitation, ocean windvelocity, ocean surface temperature, ocean ice, snow
accumulation

-

-

-

Radiance temperature (all 14 ch.)

Y

Y

Y

Amount of vapor, cloud liquid water, precipitation, ocean windvelocity, ocean surface temperature

Y

-

-

Ocean ice-Y

Y

Snow accumulation

Y

-

Y

VNIR, SWIR, MTIR observation data, calibration data
VNIR, SWIR, MTIR observation data, SLPT
Common in atmosphere and ocean regions
Common in land and cryosphere
Aerosol Angstrom exponent, aerosol optical thickness, cloudamount, optical thickness of cloud (p/wr/ir/ie)*2, cloud
equivalent grain size (wr/ie)*2, cloud top temperature (wr/ie)*2,
cloud top altitude, cloud liquid water
Cloud flag
Atmospheric correction, ocean color, ocean surface temperature
Vegetation index, global data after atmospheric correction,snow grain size and impurity
Precise gasification correction parameter
Aerosol Angstrom exponent, aerosol optical thickness, cloudamount, cloud optical thickness (p/wr/ir/ie), cloud equivalentgrain size (wr/ie), cloud top temperature (wr/ie), cloud top
altitude, cloud liquid waterSnow grain size, snow impurity
Radiance leaving the ocean water, aerosol, water color, ocean
surface temperature
Aerosol Angstrom exponent, aerosol optical thickness, cloudamount, cloud optical thickness (p/wr/ir/ie), cloud equivalentgrain size (wr/ie), cloud top temperature (wr/ie), cloud top
altitude, cloud liquid waterSnow grain size, snow impurity
Radiance leaving the ocean water, aerosol, chlorophyll a,suspended substance, ocean color, K490 dissipation coefficient,ocean surface temperature (day and night), ocean surface
temperature
Vegetation index

Y

Y

Y

Y

-

-

-

-

-

Y

Y

Y

-

-

-

Y

-

-

Y

Y

Y

-

-

-

Y

-

-

Y

Y

Y

Y

-

-

Remarks

Ascending /Descending nodes
Ascending /Descending nodes
Ascending /Descending nodes
Ascending /Descending nodes

4 km resolution

Y
16th
GLI 250m
L1A
Observation data, calibration data
L1B
Observation data
1. EQR…Equi-rectangular, PS…polar stereographic (southern hemisphere), PN…polar stereographic (northern hemisphere)
2. p…analysis for each pixel, wr…water cloud (reflection method), ir…ice cloud (reflection method), ie…ice cloud (emission
method)155

2.5.2.2 Image Catalog
Image catalog data are first extracted and processed for browsing by the ADEOS-II
mission operation system and then made visible by the image catalog data transport
subsystem at the EOIS. This service can be used via EUS/WWW or EUS/GUI. The
image catalog data of AMSR and GLI standard processed products made available tousers are as follows.
Table 2.5.2-2 Overview of ADEOS-II Image Catalog Data
Level

Geophysical Parameters

Projection Method
EQR
PS
PN

Remarks

AMSR
Days
3

Days,
monthsRadiance temperature (6.9, 36.5, 89 GHz band, verticalpolarization)
Amount of vapor, cloud liquid water, precipitation, ocean windvelocity, ocean surface temperature2

16thmonths
3 STA
MAP
1st, 8thmonths
16th
GLI 250m
L1B

Y

Y

Y

Y

Y

Ocean ice
Snow accumulation

GLI 1km
1B

Y

Y

VNIR (ch. 13, 8, 5: RGB), SWIR (ch. 26), MTIR (ch. 35)
Aerosol Angstrom exponent, aerosol optical thickness, cloudamount, optical thickness of cloud (p/wr/ir/ie), cloud equivalentgrain size (wr/ie), cloud top temperature (wr/ie), cloud top
altitude, cloud liquid water
Atmospheric correction, ocean color, ocean surface temperature
Vegetation index, global data after atmospheric correction,snow grain size and impurity
Aerosol Angstrom exponent, aerosol optical thickness, cloudamount, cloud optical thickness (p/wr/ir/ie), cloud equivalentgrain size (wr/ie), cloud top temperature (wr/ie), cloud top
altitude, cloud liquid waterSnow grain size, snow impurity
Radiance leaving the ocean water, aerosol, chlorophyll a,suspended substance, ocean color, K490 diffusion coefficient,ocean surface temperature (day and night), ocean surface
temperature
Vegetation index

Y

Ascending /Descending nodes
Ascending /Descending nodes
Ascending /Descending nodes
Ascending /Descending nodesY
4 km resolution
Y

Y

Y

Y

Y

Y
Y
Y
Y

Observation data (ch. 22, 21, 20: RGB)

1. EQR…Equi-rectangular, PS…polar stereographic (southern hemisphere), PN…polar stereographic (northern hemisphere)
2. p…analysis for each pixel, wr…water cloud (reflection method), ir…ice cloud (reflection method), ie…ice cloud (emission
method)156

2.5.2.3 Guide Information
General users can access the AMSR and GLI guide information (visible images)
shown in Table 2.5.2-3 on the Internet via a WWW browser.
Guide information is a part of the image catalog of Earth-observation satellite data
belonging to EOC and processed so that the information can be viewed on the Internet.
It is available on EOC’s homepage.
Table 2.5.2-3 Guide Information
Level

Projection Method
EQR
PS
PN

Geophysical Parameters

Remarks

AMSR
Days
3

Days,
monthsRadiance temperature (6.9, 36.5, 89 GHz band, verticalpolarization)
Amount of vapor, cloud liquid water, precipitation, ocean windvelocity, ocean surface temperature

Y

Y

Y

Y

Y

Ocean ice
Snow accumulation

Y

Y

Y

Ascending /descending nodes
Ascending /descending nodes
Ascending /descending nodes
Ascending /descending nodes

GLI 1km

3 STA
MAP

16th
months8thmonths
16th

Aerosol Angstrom exponent, aerosol optical thickness, cloudoptical thickness (p/wr/ir/ie), cloud equivalent grain size
(wr/ie), cloud top temperature (wr/ie), cloud liquid waterSnow grain size, snow impurity
Chlorophyll a, suspended substance, ocean color, ocean surface
temperature (day and night)
Vegetation index

Y
Y

Y

Y

Y
Y

1. EQR…Equi-rectangular, PS…polar stereographic (southern hemisphere), PN…polar stereographic (northern hemisphere)
2. p…analysis for each pixel, wr…water cloud (reflection method), ir…ice cloud (reflection method), ie…ice cloud (emission
method)2.5.3

Data Provision

2.5.3.1 Ordering Data
(1) Scene Orders(2) Standing Orders(3) Order-Specific Items

2.5.3.2 Data provision flow
T.B.D.

2.5.3.3 Provision MediaTable 2.5.3-1 shows the media for providing ADEOS-II images
Order Type
Scene orders
Standing orders

Medium
8 mm, CD-ROM
8 mm, DLT

157

Remarks
For all users (provided on DLT to EORC)
Only to specified users such as PIs and EORC

2.5.3.4 Online Data Provision
Table 2.5.3-2 shows the services provided on the Internet whereby registered users
can obtain AMSR and GLI low-capacity data (downloadable through FTP).
The data provided here cover the most recent one-month period; data older than amonth are automatically deleted.
Specified users who can order data using either EUS/GUI or EUS/WWW may
obtain requested data online through this data-provision service.
Table 2.5.3-2 Low-Capacity Data Provided via the Internet
Level

Geophysical Parameters

Projection Method1
EQR
PS
PN

Remarks

AMSR

3

Days,
monthsRadiance temperature (all 14 ch.)Y

Amount of vapor, cloud liquid water, precipitation, ocean windvelocity, ocean surface temperature

Y

Ocean ice
Snow accumulation

Y

Y

Y

Y

Y

Y

Ascending /descending nodes
Ascending /descending nodes
Ascending /descending nodes
Ascending /descending nodes

GLI 1km

3 STA
MAP

16th
months
1st, 8thmonths
16th

Aerosol Angstrom exponent, aerosol optical thickness, cloudamount, cloud optical thickness (p/wr/ir/ie), cloud equivalentgrain size (wr/ie), cloud top temperature (wr/ie), cloud top
altitude, cloud liquid waterSnow grain size, snow impurity
Chlorophyll a, suspended substance, ocean color, ocean surface
temperature
Vegetation index

Y
Y

Y

Y

Y
Y

1. EQR…Equi-rectangular, PS…polar stereographic (southern hemisphere), PN…polar stereographic (northern hemisphere)
2. p…analysis for each pixel, wr…water cloud (reflection method), ir…ice cloud (reflection method), ie…ice cloud (emission
method)158

2.5.4 Uses of On-Line Services
The services provided by the EOIS, described in Section 2.5.1, can be used throughthe following tools.
2.5.4.1 WWW Browser
Below is an overview of the services that can be used via the Internet using a
WWW browser.
(1) EUS/WWW-Based Services
By accessing the following address on the Internet using a WWW browser, a user
can use services such as catalog search and image catalog search. Data-ordering
service on EUS/WWW is provided only to PIs.
[URL for EUS/WWW: http://eus.eoc.nasda.go.jp/]

Figure 2.5.4-1

EUS/WWW Screen Display(2) Guide Information Service
All users can look at the guide information (visible images) by accessing thefollowing address on the Internet using a WWW browser.

159

[URL for Guide Information Service:
http://www.eoc.nasda.go.jp/www/index_j.html]

Figure 2.5.4-2

Screen Display of the Guide Information Service Page

(3) Online Data Provision Service
Registered users can obtain low-capacity data (downloadable through FTP) byaccessing the following address on the Internet using a WWW browser.
Even unregistered users can register as users at this Web page.
[URL for Online Data Provision Service: http://drs.eoc.nasda.go.jp/]

Figure 2.5.4-3

Screen Display of the Online Data Provision Service Page

160

PIs who have placed orders using EUS/GUI can also obtain the data orderedthrough this Web page.
(4) Observation Plan Service
All users can view the observation plan at the site “WWW Service at theObservation Request Enquiry System (OREQ)” (observation plan) on the Internet
using a WWW browser. More precisely, after a user logs in at the EUS/WWW site
mentioned above (whose URL is stated under (1) above), the browser takes the user
to the observation plan site.

Figure 2.5.4-4 Screen Display of the Observation Plan Service Page
(5) Standing-Order Status Checking Service
All users can check the production status of a particular product from theirstanding orders by accessing the following address on the Internet using a WWW
browser.
[URL for the Standing-Order Status Checking Service:
http://www.eoc.nasda.go.jp/www/guide/status_j.html]

Figure 2.5.4-5 Screen Display of the Standing-Order Status Checking Service

1612.5.4.2 EUS/GUI
EUS/GUI is a comprehensive online information service program that enables
various services provided by the EOC server to be used in a GUI control environment.
Enquiries concerning how to obtain EUS/GUI should be addressed to the RESTEC
order desk.
The user selects a desired service on EUS/GUI, enters parameters such as search
conditions, and sends the search request; the search results will be returned from eachserver and displayed (see Figure 2.5.4-6).
Users with special permission such as PIs can also send order requests.
Currently, the functioning EUS/GUI software is EUS/PC Ver. 3.x (Windows 95,
Windows 98, Windows NT4.0 compatible). EUS/PC Ver. 2.x must first be upgraded.
EUS/GUI provides following functions.
(1) Catalog Information Search
The catalog information can be searched for standard products. Search key items
include the date (period) of observation, latitude and longitude, name of the dataset,name of the satellite, name of the sensors used, and other options. Search keys areprovided for these options.
(2) Image Catalog Data Search
The image catalog data can be searched for standard products, and the images canbe displayed. The following can be displayed.
•
•
•
•
•
•
•
•
•

Enlargement, reduction, and moving displays
Enhanced display
Pseudo-color display
Band-switching display
Longitude and latitude overlay
Multiple-data display
Level-slicing display
Image-position information display
Layer display (superimposed display)

By customizing options, to be discussed later, it is also possible to use userdefined viewers and to print the image catalog.
(3) Map Displays
The system can display maps of the world and maps of Japan and show search
results on the coverage of various scenes. Projection methods for these maps
include cylindrical projection and the polar stereographic projection. The following
information can be shown on the maps.
•••

Inventory Search Results
Observation Requested Locations
Specified Regions on Maps

162

(4) Order Requests (available only to users with permissions to request orders)
Authorized users can place orders for standard products based on the catalog
information search results. These orders can be submitted to the server online andprinted out as order request forms.
(5) Request Status Search
Users can search for status information that shows the reception status and/or
work progress for the orders they have placed.
(6) Customizing
Users can customize EUS/GUI settings such as the selection of gateway used foraccess, image viewer, and color printer used.
Search for inventory information

Catalogsubsystem

Observation data information
EUS/GUI
Search for image catalog information

Image catalog data

Image catalog
subsystem

Online information system,
catalog datatransmission system

User (PI)Information on the observation data
requested by the user is verified.

Images of the observation data
requested by the user are verified.

Information necessary to orderobservation data is displayed to theuser.

Order information request
Order
Requestform

Request-processing
subsystem

Order status

Ordered dataprocessing

Schedule management
system, data provisionsystem

・・・・・
・・・・・
・・・・・
・・・・・

Data are processed according to theuser’s order.

CD-ROM
Prototype prepared
Data prototypes are prepared according
to the user’s order.
8mm/DLT
NASDA EOSD IS
Bridge subsystem

Information requested by the user,
located at a data center, is verified.

EOSD IS IMS
serverLocation information on the data,
requested by the user and located at an
overseas data center, is verified.

Figure 2.5.4-6

Overview of EUS/GUI

163

2.5.4.3 Service Limitations
The following restrictions apply when various online services provided by the EOC
are used,••

EUS/GUI performance restrictions
Service restrictions due to the server(1) EUS/GUI performance restrictions
EUS/GUI performs a “masking” task, i.e., it displays only those services
available to the particular user by identifying the user through a verification process
when the program is started up. Hence, only those services available to the user canbe accessed.
(2) Service restrictions due to the server
Services provided by EOC may have disclosure restrictions for certain types of
information.
Such service restrictions differ depending on the service type, so the restrictions
are placed on the server system providing each service.
In order to restrict service disclosure for individual users, the request message
transmitted from EUS/GUI to the server contains the username.
The services that vary depending on the user are as follows.
•••

Inventory Information Service
Standard Product Order Enquiry Service
Image Catalog Service

164

2.6

Data Analysis Research System (EORC)

2.6.1 Overview of the System
Figure 2.6.1-1 presents an overview of the Data Analysis Research System at EORC.

Figure 2.6.1-1

Structure of the Data Analysis Research System

Details of each of these systems are presented beginning in Section 2.6.1.1.

165

2.6.1.1 System Details(1) ADEOS-II Research Higher-Order Processing System (AMSR/AMSR-E)
The ADEOS-II Research Higher-Order Processing System (AMSR/AMSR-E)
processes AMSR data and AMSR-E data obtained by ADEOS-II and AQUA.
(2) ADEOS-II Research Higher-Order Processing System (GLI)The ADEOS-II Research Higher-Order Processing System (GLI) processes GLI
data obtained by ADEOS-II.
(3) Observation Request System (ORS)The Observation Request System (ORS) requests ADEOS-II/GLI 250m mode
observation to the EOC. The main functions of the ORS are as follows.
••

In preparing a GLI 250m observation planning document, ORS prepares a file
requesting observation for simulation in order to perform the simulation at the
EOC/MMO.
ORS prepares a file requesting GLI 250m observation based on the GLI 250m
observation planning document.

(4) Online Data Distribution System (ODD)
The ODD provides data processed by the ADEOS-II research higher-orderprocessing system through the ADEOS-II/EOS-PM1 disclosure system online(FTP).
(5) Media Conversion System (MCS)The MCS converts the medium of data processed by the ADEOS-II researchhigher-order processing system as requested by users such as PIs and provides thedata offline.
(6) On-Demand Data Server System (ODS)The ODS transfers existing processed data that have been produced and storedoffline at EOC offline to EORC based on requests from the EORC.

166

Appendix

1. AbbreviationA
ACE-ASIA : Aerosol Characterization
Experiments-ASIA
A/D: Analog to Digital
ADEOS: Advanced Earth Observing
Satellite
ADEOS-II : Advanced Earth Observing
Satellite-II
ADS: Advertisement Subsystem
AGC: Auto Gain Control
AGSID: ADEOS-II to Ground Stations
Interface Document
ALT: Altimeter
AMI: Active Microwave Instrument
AMR: Airborne Microwave Radiometer
AMSR: Advanced Microwave ScanningRadiometer
AMSR-E: Advanced Microwave ScanningRadiometer-EOS
ANSI: American National Standard
InstituteAOD: ADEOS-II Operational Document
AOS: Acquisition Of Signal
APAR: Absorbed Photosythetically Active
Radiation
API: Application Programming
Interface
APID: Application Process Identification
AQUA
: N/A
ARCH
: Archive Subsystem
ARM: Atmospheric Radiation
Measurement program
ASCII: American Standard for Computerand Information Interchange
ASF: Alaska SAR Facility (Universityof Alaska)
AVHRR: Advanced Very High Resolution
Radiometer
B
BDS: Browse data Distribution
Subsystem
BRDF: Bidirectional Reflection
Distribution Function
BPSK: Biphase shift keying
C
C&DH: Command and Data Handling(Subsystem)
CADS: Catalogue data Distribution
SystemCATS: Catalogue Subsystem
CCD: Charge Coupled Device
CCITT: International Telegraph andTelephone Consultative
Committee
CCSDS: Consultative Committee for Space
Data Systems
CCT: Computer Compatible Tape
CD
: Compact Disc

CDR
CEOP

: Critical Design Review: Coordinated Enhanced Observing
PeriodCEOS: Committee on Earth Observation
Satellites
CEOS-IDN : Committee on Earth Observations
Satellites-International Directory
Network
CFC: Chloro Fluorocarbon
CIS: Catalogue Interoperability
Subsystem
CLIVAR: Climate Variability Research
Program
CLS: Collect Localization Satellites
CMDL: Climate Monitoring and
Diagnostics Laboratory
CNES: Centre National d’Etudes Spatiales
COM-S: COntamination Monitor Sensor
COMETS : Communications and Broadcast
Engineering Test Satellite
CRC: Cyclic Redundancy Code
CREST
: Core Research for Evolutional
Science and Technology
CRL: Communications ResearchLaboratory
CTLG: Catalogue data fileD
DCS
: Data Collection System
DDMS: Data Distribution and Management
SystemDDS: Data Distribution Subsystem
DES: Data Editing Subsystem
DGS: Data Generation System
DMMC: Downlink Messages Management
CenterDMSP: Defense Meteorological Satellite
Program
DOM: Dose Monitor
DRS: Data Retrieval Subsystem
DRTS: Data Relay and Tracking Satellite
DSS: Data Storage System
DT: Direct Transmission
DTL: Direct Transmission subsystem for
Local Users
E
EA: Environment Agency of JapanECI: Earth Center Inertial coordinates
ECMWF: European Center for MediumRange Weather Forecast
ED: Definitive orbital Element
ENVISAT : Environmental Satellite
EOC: Earth Observation Center
EOIS: Earth Observation Data andInformation System
EOL: End Of Life
EOM
: End Of Mission
EORC: Earth Observation Research Center
EOS: Earth Observing System
EOS-PM1 : Earth Observing
System-PM1 (Aqua)

167

EOSD

: Earth Observations System
Engineering Department(NASDA)
EOSDIS: EOS Data and Information System
EP: Predictive orbital Element
EPIC: Equatorial Pacific Information
Collection
ERS-1/AMI : European Remote Sensing
Satellite-1
ERS-2/ALT : European Remote Sensing
Satellite-2
ESA: Earth Sensor Assembly
ESA: European Space Agency
ESDIS: Earth Science Data andInformation System
ETS-V: Engineering Test Satellite-V
EUS: EOIS User interface SoftwareF
FAO: Food and Agriculture Organizationof the United Nations
FAX: Facsimile Message
FCMWF: Fuci no MOS Receiving Station
FD: Floppy Disk
FDDI
: Fiber-optic Data Distribution
Interface
FGGE: First GARP Global Experiment(GARP: Global AtmosphericResearch Program)
FGS: Foreign Ground Station
FIFE: First ISLSCP Field Experiment(ISLSC:Internationa Satellite Land
Surface Climatology Project)
FOV: Field of View
FRR
: Flight Readiness Review
FTAM: File Transfer Access andManagement
FTIR: Fourier Transform Infrared
Radiometer
FTP: File Transfer Protocol
G
GAIT: GLI Algorithm Integration Team
GCI
: Geocentric Celestial Inertial
GCM: General Circulation Model
GCOM: Global Change Observation
Mission
GCP: Ground Control Point
GDR: Ground segment Design Report
Meeting
GEO: Geostationary
GEWEX: Global Energy and Water Cycling
Research Experiment
GLI: Global Imager
GN: Ground Network
GOES: Geostationary Operational
Environment Satellite
GOOS: Global Ocean Observing System
GOSAT: Geodetic Satellite
GPCP: Global Precipitation Climatology
Project
GPM: Global Precipitation Mission

GPS
GRS
GSFC
GSWP
GTS
GUI
HDDT
HDF
HIT
HK
HK TLM
HKMU
IEOS
IF
IFOV
IGAC
IGBP
IGOS-P
IIP
ILAS
ILAS-II
IOCS
IP
IP
IPCC
IPCN
IPCP
IR
IRD
JAFIC
JAMSTEC
Jason-1
JFIF
JGOFS
JMA
JPEG
JPL
JPRD
JST

168

: Global Positioning Satellite
System
: Global Reference System: Goddard Space Flight Center: Global Soil Wetness Project: Global Telecommunications
System: Graphical User Interface
H: High Density Digital Tape
: Hierarchical Data Format: Heavy Ion Telescope: Housekeeping: Housekeeping Telemetry: House Keeping Memory Unit
I
: International Earth Observing
System: Intermediate Frequency: Instantaneous FOV: International Global Atmospheric
Chemistry: International Geosphere andBiosphere Research Program: Integrated Global Observation
Strategy Partnership: Instrument Implementation Plan
: Improved Limb AtmosphericSpectrometer: Improved Limb AtmosphericSpectrometer-II: Inter-Orbit Communication
Subsystem: Implementation Plan
: Internet Protocol: Intergovernmental Panel on
Climate Change: Implementation Plan Change
Notice: Implementation Plan Change
Proposal: Infrared: Interface Requirements Document
J: Japan Fisheries Information Center: Japan Marine Science andTechnology Center: N/A: JPEG File Interchange Format: Joint Global Ocean Flux Study: Japan Meteorological Agency: Joint Photographic Coding Experts
Group: Jet Propulsion Laboratory(California Institute ofTechnology): Joint Program Requirement
Document: Japan Science and Technology
Corporation

LAN
LANDSAT
/TM
LLM
LNA
LOICZ
LOS
LOWTRA
N
MCS
MDR
MERIS
MMO
MMOFE
MOA
MOBY
MOIP
MOIS
MOM
MOU
MRT
MTIR
N/A
NASA
NASDA
NDVI
NEΔT
NESDIS
NGN
NIES
NOAA
NRT
NSCAT
NTSK
NWP
OCTS
OCL
ODD
ODR

L
: Local Area Network: Land Satellite: Low Light Mode
: Low Noise Amplifier: Land-Ocean Interactions in the
Coastal Zone
: Loss of Signal: Low Resolution Transmission
M: Media Conversion Subsystem: Mission Data Recorder: Medium Resolution Imaging
Spectrometer Instrument: Mission operation Management
Organization: Mission operation Management
Organization Front-End(Directory): Memorandum of Agreement: Marine Optical Buoy
: Mission Operations
Implementation Plan
: Mission Operations Interface
Specification: Mission Operations Meeting: Memorandum of Understanding: Mission Real Time: Middle wavelength Thermal
Infrared RadiometerN: Not Applicable: National Aeronautics and Space
Administration: National Space Development
Agency of Japan: Normalized Difference Vegetation
Index Maps
: Noise Equivalent Differential
Temperature: National Environmental Satellite
Data and Information Service: NASA/NOAA Ground Network: National Institute forEnvironmental Studies: National Oceanic and Atmospheric
Administration: Near Real Time Data (Directory): NASA Scatterometer: NASDA Transportable StationKiruna: Numerical Weather Prediction
O: Ocean Color and Temperature
Scanner: Operations Coordination Letter: Online Data Distribution Service: Optical Data Recorder

ODS
Opr.
OPLN

: On-demand Data Server System: Operational: Operation Plan (between EOC andan agency)
OPL1: Operation Plan (between EOC and
CNES / POLDER)
OREQ: Observation Requests handling
SystemORR: Operational Readiness Review
ORS: Observation Requests Subsystem
ORST: Operation Result StatusOS: Operating System
OSDPD: NOAA/NESDIS Office of Satellite
Data Processing and Distribution
P
PC: Personal Computer
PCD: Payload Correction Data
PCM
: Pulse Coded Modulation
PDR: Preliminary Design Review
PFM: Proto-Flight ModelPI: Principal Investigator
PO.DAAC : Physical Oceanography
Distributed Active Archive Center
POLDER: Polarization and Directionality of
the Earth's Reflectances
POM: Potential Monitor
PR: Precipitation Radar
PROC: Processing Subsystem
PSC: Polar Stratospheric Clouds
Q
Q/L: Quick Look
QQC
: Quality, Quantity and Continuity
QuickScat : Quick Scatterometer Satellite
R
RADARSA : N/A
TRAM: Random Access Memory
RASS: Radio Acoustic Sounding System
RCV: Receiving Subsystem
RCD: Recording Subsystem
RDRD: Readability report of Raw Data
RDZD
: Readability report of level Zero
Data
REAC
: Result of Acquisition
REQ: Request for Operation (between
TACC and EOC)
REQA
: Reply on 4 week Request(particular)
REQQ: Request for 4 week period
REQR: Request for Raw data record
RESTEC: Remote Sensing Technology
Center of JapanRF: Radio Frequency
RGS: Receiving Ground Station
ROM: Read Only Memory
RORR: Routine Operation Readiness
Report meeting
RS: Reed Solomon
RSP: Reference System for Planning

169

RSTAR
RTIG
SAR
S/C
SCID
SeaPAC
SeaWiFS
SeaWinds
SIMBIOS
SITE
SMMR
SMS
SMSS
SN
S/N
SOOH
SOP
SRRD
SRZD
SSM/I
STA
STAD
STGS
SUM
SWIR
TACC
TACS
TAO array
TBD
TCP
TCP/IP
TD
TDU
TEDA
Terra
TKSC
TL
TLM
TMI
TOMS

: R- System for Transfer
Atmospheric Radiation: Real Time processing Information
for GLI dataS
: Synthetic Aperture Radar: Spacecraft: Spacecraft Identifier: SeaWinds Processing and Analysis
Center: Sea-viewing Wide Field-of-view
Sensor: NASA-JPL Scatterometer On
ADEOS-II: Sensor Intercomparison andMerger for Biological andInterdisciplinary Oceanic Studies: System Integration and Test
Building: Scanning Multichannel Microwave
Radiometer: Schedule Management System: Schedule Management Subsystem: Space Network: Signal to Noise: Spacecraft Orbital Operations
Handbook: Spacecraft Operation Procedure: Shipment Report of Raw Data
: Shipment Report of level Zero
Data
: Special Sensor Microwave/Imager
DMSP: Science and Technology Agency: Status information on ADEOS: Status of Ground Station: Single Event Upset Monitor: Short Wavelength Infrared
RadiometerT: Tracking and Control Center: Tracking And Control Station(NASDA): Tropical Atmosphere Ocean array: To Be Determined: Transmission Control Protocol: Transmission Control
Protocol/Internet Protocol: Time Difference file
: TEDA Unit
: Technical Data Acquisition
Equipment: Earth Observing System
AM1(EOS-AM1): Tsukuba Space Center (NASDA): Time of Launch: Telemetry: TRMM Microwave Imager: Total zone Mapping Spectrometer

TOPEX/POSEIDO
N
TRMM
TRR
TTY
TX
UHF
URL
URS
USB
UTC
UTCF
VCID
VNIR
WCRP
WFF
WGS
WRS
WS
WWW

: The Ocean Topography
Experiment: Tropical Rainfall Measuring
Mission: Technical Readiness Review: Teletype: TransmitterU: Ultra High Frequency: Universal Resource Locator: User Request Management
Subsystem: Unified S-Band: Universal Time Coordinate: Universal Time Correlation Factor
V: Virtual Channel Identification: Visible and Near Infrared
RadiometerW: World Climate Research Program: Wallops Flight Facility: World Geometric System: World Reference System: Workstation: World Wide Web
X
YZ

170