GCOM Science Plan

March,March, 20102010Ver. 1.1
1.1

Contents
1. Introduction (Haruhisa Shimoda: Tokai University)................................................... 1
2.

Outline of GCOM Phase 1 Satellites (Haruhisa Shimoda: Tokai University) ......... 2
2.1

GCOM-W1 ............................................................................................................... 2

2.2

GCOM-C1 ................................................................................................................ 3

3. Objectives and Expected Outcomes of GCOM (Haruhisa Shimoda: Tokai University)........................................................................................................................................... 6
4.

GCOM Science Research Programme ......................................................................... 9
4.1

Atmospheric Science ............................................................................................... 9

4.1.1

Atmospheric Science by GCOM (Teruyuki Nakajima: University of Tokyo) 9

4.1.2

Clouds and Aerosols (Teruyuki Nakajima: University of Tokyo) .................11

4.1.3

Water Cycle (Taikan Oki: University of Tokyo) ........................................... 13

4.2

Marine Science ...................................................................................................... 18

4.2.1

Marine Meteorology and Physical Oceanography (Naoto Ebuchi: Hokkaido

University).................................................................................................................... 18
4.2.2
4.3

Land Area .............................................................................................................. 25

4.3.1

Land Cover and Its Changes (Haruhisa Shimoda: Tokai University) ....... 25

4.3.2

Vegetation (Yoshiaki Honda: Chiba University) .......................................... 29

4.3.3

Terrestrial Carbon Cycle (Yoshio Awaya: Gifu University) ........................ 32

4.3.4

Hydrology (Toshio Koike: University of Tokyo) ............................................ 34

4.4
5.

Marine Biology and Ocean Carbon Cycle (Toshiro Saino: JAMSTEC)..... 22

Snow and Ice (Fumihiko Nishio: Chiba University) .......................................... 36

Practical Use of GCOM .............................................................................................. 41
5.1

Improvement of Accuracy of Weather Forecasting (Kozo Okamoto: Japan

Meteorological Agency) ................................................................................................... 41
5.2

Monitoring and Prediction of Air Pollution by Yellow Sand and Other

Pollutants (Takashi Nakajima: Tokai University) ........................................................ 44
5.3

Improved Accuracy of Sea Route Data (Kenji Matsumoto: Japan Coast Guard).......................................................................................................................................... 49
5.4

Provision of Fishing Ground Data (Katsuya Saito: JAFIC) .............................. 52

5.5

Improvement of Coastal Water Management Efficiency (Joji Ishizaka: Nagoya

University) ....................................................................................................................... 55
5.6

Improved Prediction Accuracy of Crop Yields (Katsuo Okamoto: National

Institute for Agro-Environmental Sciences) .................................................................. 57
5.7

Observation of Volcanic Fumes and Prediction of Geographical Scope ofInfluence (Minoru Urai: National Institute of Advanced Industrial Science andiTechnology) ...................................................................................................................... 60
5.8

Forest Fire Detection (Haruo Sawada: University of Tokyo)........................... 62

5.9

Extreme Water Cycle Phenomena (Toshio Koike: University of Tokyo) ........... 64

5.10

Environment (Yoshifumi Yasuoka: National Institute for Environmental

Studies) ............................................................................................................................ 66
6.

Observation Objectives and Products of Onboard Sensors ..................................... 68
6.1

AMSR2 (Keiji Imaoka: Japan Aerospace Exploration Agency) ......................... 68

6.1.1

Objectives.......................................................................................................... 68

6.1.2

Products and Development of Algorithms...................................................... 69

6.1.3

Calibration and Validation .............................................................................. 76

6.2

SGLI (Hiroshi Murakami: Japan Aerospace Exploration Agency) ................... 80

6.2.1

Objective of the Development of GCOM-C/SGLI Algorithms ....................... 80

6.2.2

Calibration and Validation .............................................................................. 87

ii

1.

Introduction (Haruhisa Shimoda: Tokai University)

The Global Change Observation Mission (GCOM) is a mission designed to observe changes of
the global environment in a comprehensive and continual manner following the pathestablished by the ADEOS and ADEOS-II. The ADEOS was launched in August, 1996 andceased operation in June, 1997 when the solar cell paddle was damaged. Its successor, the
ADEOS-II, was launched in December, 2002 but its operation ended shortly afterwards in
October, 2003 as in the case of the ADEOS. The GCOM inherits the missions of these twoearlier satellites and is designed to conduct continual observation over a long period of time.
The accidents of the ADEOS and ADEOS-II have made the Japan Aerospace Exploration
Agency (JAXA) concentrate its satellite development efforts on small and medium satellites
rather than large satellites. The GCOM will, therefore, be supported by two series of medium
size satellites, i.e. GCOM-W and GCOM-C. The GCOM-W satellite principally carries the
successor to the AMSR and will primarily observe water and energy cycles. The GCOM-C
principally carries the successor to the GLI and will primarily observe climate change. Eachsatellite has a design life of five years and each series consists of three satellites. Follow-on
satellites will be launched four years after the launch of each preceding satellite to guarantee
mutual calibration and verification between satellites. In this way, each series will guarantee
continual observation for at least 13 years.
The GCOM satellites will not be sufficient to observe the complete range of global
environmental changes. For this reason, the GCOM should not only cooperate with otherearth observation satellites of the JAXA, including Aqua/AMSR-E (launched in 2002), ALOS
(launched in 2006), GOSAT (Greenhouse Gas(GHG) observation: launched in 2009), GPM(precipitation observation: scheduled to be launched in 2013) and EarthCare (cloud andaerosol observation: scheduled to be launched in 2013), but also have its sights set on possible
joint research with such foreign satellites as NPP (scheduled to be launched in 2010),
NPOESS and METOP, etc. of which the mission periods will overlap with those of the GCOM
satellites.
Earth observation work is only considered effective when it produces scientific and practicaloutcomes. However, such outcomes do not naturally result from the work. While the JAXA has
established the Earth Observation Research Center (EORC) for the active use of satellite data,
its limited human resources cannot be expected to produce all possible outcomes from theavailable satellite data. Collaboration with universities, independent research corporations
and other suitable organizations is essential.
Following the enactment of the Basic Space Law which mentions the review of the JAXA’sorganizational set-up, it is uncertain at present how the JAXA will be transformed in the
coming years. The present GCOM Science Plan is formulated to ensure that the JAXA will beable to produce scientific and practical outcomes through the GCOM regardless of change
which may greet the JAXA in the near future.

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2.

Outline of GCOM Phase 1 Satellites (Haruhisa Shimoda: Tokai University)

Here, the planned satellites for GCOM Phase 1 are outlined.
2.1

GCOM-W1

2.1.1

Orbit

Synchronous sub-recurrent orbit
Altitude: approx. 700 km
Local sun time at descending node: 13:30
Launch year: FY 2011
2.1.2

Main Specifications

Mass: approx. 1.9 tons
Power: approx. 4 kW
Data transmission: X band, 10 Mbps
2.1.3

Main Specifications of the Sensor (AMSR2)

Type
Antenna
Swath width
Quantisation Bit Rate
Incidence Angle
Polarisation
Cross Polarisation Characteristics
Main Beam Efficiency
Dynamic Range
Brightness Temperature Error

Geometric Error
Sampling Interval

Conical scanning microwave radiometer
Offset parabolic antenna diameter of 2.0 m≥ 1,450 km
12 bit (all channels)
55° (detection accuracy: 0.0833° or less)
Vertical and horizontal-20 dB or less (excluding 7.3GHz channel)≥ 90%
2.7 – 340 K
System error: ≤ ± 1.0 K (maximum amplitude: 150 K
equivalent)(target)
Fluctuation error: ≤ 0.3 K (3σ, equivalent to 150
K)(target)≤ 2.5 km (some 50% of the smallest IFOV)Approximately 5 – 10 km (no underwraps with the 3 dB
beam width excluding 89GHz)-2

CentralFrequency
(GHz)
6.925
7.3
10.65
18.7
23.8
36.5
89.0

2.2

GCOM-C1

2.2.1

Orbit

Ground IFOV
(km, cross-track)

Band Width(MHz)

Temperature Resolution(K, 1σ , 150 K)

35
35
24
13
15
7
3

350
350
100
200
400
1000
3000

0.34
0.43
0.70
0.70
0.60
0.70
1.2

Synchronous sub-recurrent orbit
Altitude: approx. 800 km
Local sun time at descending node: 10:30
Launch year: FY 2013
2.2.2

Main Specifications

Mass: approx. 2.0 tons
Power: approx. 4 kW
Data transmission: X band, 138 Mbps
2.2.3

Main Specifications of the Sensor (SGLI)The SGLI (Second Generation Global Imager) sensor consists of two types of radiometers:
visible and near infrared radiometer (VNR) and infrared scanner (IRS). The VNR is apush-broom type radiometer while the IRS is a whisk-broom type radiometer. The VNR isactually made up of two types of cameras: camera measuring non-polarisation and camera
measuring polarisation. The non-polarisation measuring camera is composed of three opticalsystems and has a spatial resolution of 250 m. The polarisation measuring camera iscomposed of two optical systems for each target wavelength band and measures polarisationin three directions in each wavelength band. The swath width is 1,150 km and the spatial
resolution is 1 km. Each optical system has the function of forward or backward tilting in thetravelling direction of the satellite to measure forward-scattered light.
The IRS is a radiometer with four short wavelength infrared channels and two thermal
infrared channels. The swath width is approximately 1,400 km on the ground. The spatial
resolution varies depending on the channel. One short wavelength infrared channel has aspatial resolution of 250 and offer 1 km. The thermal infrared channels have a spatial
resolution of 500 m. The observation wavelength and technical specifications of SGLI are
shown next.

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Table 2.2.1
Ch.

VNR Non-Polarisation Observation Unit

Central
Wavelengt

IFOV
[m]

⊿λ

Lstd

Lmax.

[nm]

[W/m2/st[W/m2/str/

r/µm]

µm]

h

S/N Main Targets

[nm]
VN1

380

250

10

60

210

250 Aerosol; CDOM

VN2

412

250

10

75

250

400 Aerosol; CDOM; PAR; EVI

VN3

443

250

10

64

400

300 Absorbent aerosol over the sea; CH
L; CDOM; PAR; EVI; snow/sea ice
covered area;
snow impurities

VN4

490

250

10

53

120

400 CHL (middle)

VN5

530

250

20

41

350

250 Cloud detection; CHL (high); PAR;
NPP;
snow/sea ice covered area

VN6

565

250

20

33

90

400 CHL (baseline); SS

VN7

673.5

250

20

23

62

400 Atmospheric correction; SS

VN8

673.5

250

20

25

210

250 NDVI; EVI; BSI; SI; snow/sea ice
covered area; NDSI

VN9

763

250/

12

40

350

1200 Geometric thickness of clouds

1000
VN10

868.5

250

20

8

30

400 Atmospheric correction

VN11

868.5

250

20

30

300

200 NDVI; EVI; BSI; SI; snow/sea ice
covered area;

snow grain size; ND

SI

Table 2.2.2
Ch.

VNR Polarisation Observation Unit

Central
Wavelengt

IFOV
[m]

⊿λ

Lstd

Lmax.

[nm]

[W/m2/st[W/m2/str/

r/µm]

µm]

25

250

h

S/N Main Targets

[nm]
P1

673.5

1000

20

250 Aerosols; BSI; SI; snow/ice BRDF; snow/
ice categories

P2

868.5

1000

20

30

300

250 Aerosols; BSI; SI; snow/ice BRDF; snow/
ice categories-4

Table 2.2.3 IRS Short Wavelength Infrared Unit
Ch.

Central
Wavelengt

IFOV
[m]

⊿λ

Lstd

Lmax

[nm]

[W/m2/st[W/m2/str/

r/µm]

µm]

57

248

h

S/N Main Targets

[nm]
SW1

1050

1000

20

500 Cloud optical thickness; NDII; typesof sea
ice

SW2

1380

1000

20

8

103

150 Cloud detection

SW3

1630

250

200

3

50

57

Cloud detection; snow grain size; cloudphase; vegetation moisture content;
soil
moisture content

SW4

2210

1000

50

1.9

20

211 Effective cloud grain size

Table 2.2.4 IRS Thermal Infrared Unit
Ch.

CentralIFOV⊿λWaveleng

[m]

[µm]

Tstd[K]

Tmin[K]

NEd

Main Targets/Tmax[K] T@T

th

std

[µm]
T1

10.8

250/500 0.7

300

340

0.2 Cloud detection; cloud top temperature;cirrus characteristics; LST; SST;
snow surface temperature

T2

12.0

250/500 0.7

300

340

0.2 Cloud detection; cirrus characteristics;
atmospheric correction; LST; snow s
urfacetemperature*IFOV 250m is the option-53. Objectives and Expected Outcomes of GCOM (Haruhisa Shimoda: Tokai
University)
The fundamental roles of the GCOM are to improve national life and to positively contributeto mankind. To be more precise, the GCOM will continue the work of the ADEOS andADEOS-II and will advance observations to build up the knowledge base required to clarify
global environmental issues. The main target is climate change and the GCOM will deal withthe requirements under the Kyoto Protocol and contribute to the Global Earth Observation
System of Systems (GEOSS). At the same time, the practical use of observation data will bepromoted to ensure the positive contribution of the GCOM to improving national life.
The long-term, continuous observation of physical quantities greatly affecting changes of the
global environment, particularly climate change, will achieve the following outcomes tocontribute to the understanding of these changes.
• Development and demonstration of a system capable of observing physical quantities (seasurface temperature, soil moisture and others), which are effective to clarify climate change
and the water circulation mechanism on a global scale, continually over a long period oftime• Improved accuracy of the existing climate prediction models by providing accurate
numerical values for parameters with a high level of uncertainty at present• Clarification of GHG sinks and sources• Verification of climate prediction models and improvement of the accuracy of these modelwith data on long-term change of physical quantities• Establishment of the trends of global environmental changes (global warming, vegetation
changes, desertification, changes of substances in the atmosphere, spread of wide area air
pollution, ozone layer depletion and others) based on long-term change of physicalquantities minus short-term (approximately 3 – 6 years) natural changes• Clarification of the unit processes of global environmental change using observed data
• Estimation of radiative forcing, energy/carbon flux, albedo and others by combining thephysical quantities observed by satellite, ground observation data and suitable models
through collaboration with user organizations to achieve the above goals• Advancement of the understanding of the earth system through the series of work listed
above• Contribution by Japan to international environmental strategies using the outcomes of the
GCOM activities
Further contribution will be made using the observed data to improve the following aspects ofnational life as a direct contribution.
-6• Improved accuracy of weather forecasting (especially in relation to the prediction of thelikely course of typhoons and the locations of downpours)• Improved prediction accuracy of abnormal weather and climate• Improved accuracy of ocean route information• Supply of fishing ground information• Improved efficiency of coastal management• Higher accuracy of predicted crop yields• Observation of volcanic fumes and prediction of the extent of impacts• Monitoring and prediction of air pollution caused by yellow sand and other causes• Detection of forest fires
The GEOSS 10 Year Implementation Plan announced by the GEO (Group on Earth
Observation) which functions as a working group for the Earth Observation Summit has set
the following nine key topics for the Earth Observation Plan for the next 10 years.
 disasters;  health;  energy;  climate;  water;  weather;  ecosystem;  agriculture;
biodiversity
The GCOM can contribute to the GEOSS especially in terms of climate, water, weather andecosystem although its contribution to the other five topic will be far from insignificant.
Any improvement of the accuracy of climate change prediction requires improved observation
accuracy for uncertain factors listed in the IPCC report. Those uncertain factors which can beobserved under the GCOM are listed next.
(1)

Uncertainty of radiative forcing• Aerosols• Clouds(2)

Uncertainty of flux
• Energy cycle- Energy flux between atmosphere and ocean- Ocean surface circulation- Energy flux between snow/ice and atmosphere- Earth radiation budget• Carbon cycle- GHG sinks and sources- Carbon flux
• Water cycle- Rainfall- Transportation of latent heat from ocean- Evapotranspiration- Water transportation on land
- Snow cover(3)

Uncertainty of process and feedback• Production and dissipating processes of clouds and aerosols-7• Impacts of climate change on rainfall and snowfall• Ecological process of land vegetation and impacts of climate change on land vegetation• Impacts of global warming on short-term climate change(4)

Signs of global warming• Changing snow and ice conditions in high latitude regions• Stratospheric temperature in high latitude regions

To solve the above uncertainties, the following concrete goals are set under the GCOM.
• Development of an algorithm to highly accurately calculate physical quantities• Preparation of weather values for input to models• Separation of trends from natural changes using long-term data sets and verification ofmodels• Understanding of regional characteristics• Clarification of the process and improved accuracy of models

-8

4.

GCOM Science Research Programme

4.1

Atmospheric Science

4.1.1

Atmospheric Science by GCOM (Teruyuki Nakajima: University of Tokyo)

The atmospheric observation function of the GCOM satellite series is designed to be optimalfor studying the state and changes of the earth's climate. To be more precise, global imaging
observation at medium spatial and spectral resolutions is provided by a set of the AMSR2, amicrowave scanning radiometer, and and the SGLI, an imager of the near ultraviolet, visible
and infrared regions, possessing important wavelength channels for the measurement ofaerosols, water vapor, clouds and rainfall. Sensor combination of NOAA/AVHRR and SSM/I,
ADEOS-II/GLI and AMRS, AQUA/MODIS and AMRS-E, and TRMM/VIRS and TMI are someexamples of sensors falling in this category. Using data provided by these sensors, global
maps of aerosols, water vapor, clouds and rainfall have so far been prepared and used forclimate studies, environmental monitoring and other purposes.
Remote sensing of the atmosphere using the AVHRR and SSM/I began around 1981 (1987 for
the SSM/I) despite problems with the sensor sensitivity and others. Taking this history into
consideration, data on the atmosphere has been collected for nearly 30 years, providing animportant data set for studies on climate change. As it is now becoming possible to separate
phenomena of a decadal oscillation, which is one of the critical scales for atmosphere-oceaninteraction, separation between natural climate changes caused by internal forcing andanthropogenic changes, such as global warming, is now thought to be possible for studies on
climate change. In the age of GCOM, climate changes of human origin, such as global
warming, are expected to become prominent and precious data sets on long-term changes ofthe monsoon cycle, regional rainfall and other matters will be acquired. Observation ofchanges of the continental scale aerosol distribution in line with changes of human activities
will also be made. In the case of clouds, while it is difficult to clarify long-term changes
because of the huge extent of spatiotemporal changes, there is a strong likelihood that thefield characteristics will become clearer through the combined analysis of multiple sensor
data and ground observation data. As a result, knowledge on the role of clouds in climatechange will be acquired. Existing general circulation models cannot determine whether clouds
accelerate or inhibit global warming. There are other phenomena of which clarification should
be sought through combined analysis. These include changes of the Hadley circulation due toglobal warming, changes of tropical cyclones and El Nino, changes of monsoon circulation andsubsequent changes of extreme phenomena, such as heavy rain events, etc., changes ofrainfall on the Asian continent, etc., changes of the distribution of atmospheric cooling andheating due to water vapor and clouds and changes of radiative forcing by aerosols and clouds.
These phenomena have not yet been clarified with sufficient accuracy by the existing meansof satellite observation and modelling.
As briefly described above, the GCOM promises major scientific achievements provided thattwo preconditions are met. The first is the development of long time-series data sets which areessential for climate study. The second is the supply of multi-sensors data from space as datafrom a single sensor is not sufficient to meet the need for the study of the relationshipbetween various parameters for the recent analysis of the atmospheric field. Particularly-9important is combined analysis involving active lider sensor, cloud radar and precipitation
radar data sets. To satisfy these two conditions, maximisation of the coordination potential
with other sensors must be attempted. In this case, it must be kept in mind that data fromdifferent local times are required for the long-term analysis of atmospheric phenomena withconsiderable diurnal changes. For example, analysis of the radiation balance at the top of theatmosphere requires the effective radiation temperature of water vapor. Meanwhile, a study
on the impact of global warming must eliminate apparent changes caused by shifts of satellite
orbit as much as possible. Such impact of a shifting orbit also occurs with long-term timeseries data on the effective cloud particle radius which is often used as an indicator for cloudfield changes caused by man-made aerosols. From this viewpoint, coordination with satellites
launched by other countries is extremely important. An initiative of achieving AM and PM
trains through the extension of the NASA’s A-Train is worthy of consideration. To be more
precise, the strategic combination of satellites on the 10:30 orbit and 13:30 orbit could
revolutionise the quality of the information obtained from satellite systems. Including suchpotential, the combined analysis of knowledge from the GCOM series with knowledge
obtained from other satellites should significantly contribute to weather and climate studies
as well as the science of the earth’s surface system. Satellite observation much progresses
hand in hand with the study of a model to interpret satellite data, necessitating thedevelopment of assimilation and other technologies. Moreover, as the calibration of onboard
satellite sensors is essential to produce data over a long period of time, the maintenance of theground observation network will prove to be a major contribution to the GCOM and theinternational satellite community.

- 10

4.1.2Clouds and Aerosols (Teruyuki Nakajima: University of Tokyo)

Imaging of the microwave spectral region by the AMSR2 can produce global-scale data on
water vapor, cloud water content and rainfall intensity. Meanwhile, imaging of the near
ultraviolet, visible and infrared regions by the SGLI can produce data on clouds and aerosols
at a spatial resolution up to the best resolution of 250 m. The knowledge acquired throughsuch observation greatly contributes to clarification of the roles played by clouds and aerosols
in global warming and other climate change issues which we face today. For example, both the
3rd and 4th IPCC reports state that modelling of the impacts of clouds and aerosols on the
climate constitutes a major uncertain factor in the prediction of global warming. This
uncertainty surrounding cloud modelling originates from the facts that it is difficult tosimulate clouds with a general atmospheric circulation model of which the grid size is some
100 km and that it is difficult to evaluate changes of the cloud field caused by air pollution
due to man-made aerosols. In dealing with the question of cloud formation, it is crucial to
improve the accuracy of modelling of the stratification conditions of the troposphere,transportation of water vapor, formation of water clouds and ice-crystal clouds andmechanism of precipitation. For this purpose, the use of a regional model based on a system ofseveral km grids and a global non-hydrostatic model, such as NICAM, is necessary along withresearch based on the conventional general circulation model. These high resolution models
can realistically express convection cells and their organization. Their ability to express thetransportation of aerosols to a cloud system with the cells means that the process of cloud
droplet formation by cloud nuclei can be more realistically represented. Because the model
enables the formation of clouds without strong reliance on the cumulus parameterization of
cirrus used in a general circulation model, it becomes possible to accurately estimate the rolesof clouds in the climate system and the climate sensitivity strongly affected by clouds. Thismeans the extreme importance of acquiring accurate atmospheric data to verify various
simulation models. AMSR2 and SGLI, both of which can cover the entire earth with highresolution, should be able to provide effective data. The range of important information on
clouds includes water vapor content, cloud optical thickness, total cloud water path, amountof ice and crystal particles and rainfall intensity.
Meanwhile, the range of important information on aerosols and aerosol-cloud interaction
includes the aerosol optical thickness, aerosol Ångström exponent, aerosol type, cloud particle
radius and drizzle content. These can be estimated based on SGLI data as well as combined
data of SGLI and other microwave radiometers. In the case of the global distribution of theaerosol optical thickness, there is a systematic difference between satellite observation andthe model, making improvement necessary. This difference is believed to occur because of the
difficulty of observing optically thin aerosols and the difficulty of modelling due to thecomplexity of the aerosol generation and removal processes. For the improvement of remotesensing data, substantial improvement of the observation accuracy is expected with the SGLI
which is equipped with near ultraviolet, multidirectional and polarization observation
functions. The accuracy of land aerosol analysis will especially improve. The availability of a
250 m channel should prove effective to eliminate clouds of less than the pixel size. With such
improvement, the observation accuracy at the top of the global atmosphere should improve byapproximately 0.5 W/m2 in terms of radioactive forcing.
Regarding the impacts of aerosols on the cloud field, correlation between the cloud optical- 11thickness (and effective particle radius) and density of aerosols is being studied using satellite
data and a model but the study results are still too scattered to determine a meaningfulcorrelation. Especially vague is the impact of aerosols on clouds above land and deep
convective clouds. A high resolution model should be fully functional in the age of the GCOM
as mentioned earlier and the high spatial resolution data on clouds and aerosols supplied by
the AMSR2 and SGLI will constitute extremely timely data to deal with these problems.
Recent studies have established that radiative forcing at the surface created by man-madeaerosols cause changes of the cloud fraction and rainfall on a global scale. It is, therefore,
important to determine the global distribution of the aerosol optical thickness and lightabsorptive capacity from this viewpoint. For this purpose, an algorithm to determine thesingle scattering albedo of aerosols through the simultaneous analysis of data provided by theground observation network is required. The highly accurate estimation of the radiationbudget at the surface using such data is now possible.
The helitage and ground verification system developed through the ADEOS-II mission arevery useful for the analysis of cloud and aerosol data although further development isnecessary. Given the fact that a simultaneous analysis method for satellite and ground data
and analysis based on an assimilation method using a model are now available, thesemethods should be used to produce highly accurate products in the age of the GCOM. In
response to the arrival of a new age, new systems must be developed to compare models andto conduct assimilation analysis along with analysis using the inverse problems of
conventional satellite remote sensing.

- 124.1.3

Water Cycle (Taikan Oki: University of Tokyo)

The total volume of water on the earth’s surface is estimated to be approximately 1.4 x 1021 kg
which accounts for a mere 0.02% of the total mass of the earth. When the earth is looked atfrom space, however, nearly half of the surface is covered by clouds. Even if there are no
clouds, two-thirds of the earth’s surface is covered by oceans. Vegetation is generally rife onland, creating the impression that the surface of the earth is full of water. Despite the verysmall absolute share of the planet’s mass, it is no exaggeration to say that the earth isperceived as a planet of water from space. (Oki, 2005)
Radiation energy from the sun is unevenly absorbed by the latitude belts on the earth’s
surface. Consequently, the atmosphere and oceans transport energy to eliminate thisunevenness from the warm tropical zone to the cold high latitude regions. Water not only
circulates along the earth’s surface in line with the general circulation but also undergoes
phasal changes among the solid state (ice), liquid state (water) and gaseous state (vapour).
Through these phasal changes, energy is released or absorbed, affecting the atmospheric aswell as ocean circulations. The vapour content in the atmosphere has a dominant effect on theradiation balance on the earth’s surface. As such, the existence of water and its circulation is
one of the principal factors determining the environment of the earth’s surface.
Fig. 4.1.1 shows the location, amount and speed of movement of water on the earth’s surface
using data provided by Korzu (1978), Shiklomanov (1997), Oki (1999) and Dirmeyer et al.
(2006). The figures in parenthesis show the stored water volumes (103km3) and each figure at
the side of an arrow is the water displacement volume (103km3/year) which is called the flux.
The precipitation and evapotranspiration amounts are also shown for the main categories ofland use. The figures in parenthesis for these land use categories are their respective areas(106km2) on the earth’s surface. Macroscopic observation shows that the evapotranspiration
amount is higher than the precipitation amount on the ocean surface. This means a net
surplus transportation of vapour from the oceans to land where water falling in the form ofrain or snow may return to the atmosphere through evapotranspiration or to the oceans via
rivers. While this figure indicates the intake amounts for agriculture, industry and domesticuse, it is uncertain how much water is evaporated or returned to rivers or joins thegroundwater. However, one thing is certain. The impacts of human activities on the global
scale water cycle can no longer be ignored, making it essential for the scientific study on the
global environment to include the “real” water cycle where the impacts of human activities
are added to the “natural” water cycle in the study scope. (Oki, et al., 2006)- 13

Fig. 4.1.1

Schematic Chart of Global Water Cycle: Based on Data Provided by Korzun (1978),Shiklomanov (1997), Oki (1999) and Dirmeyer, et al. (2006)(Errors relating to farmland and other areas in Oki and Kanae, 2006 are corrected.)

Vapouramountabove
oceans< 10 >

Total
amount ofprecipitationon land< 111 >

Total amount of
evapotranspirationfrom oceans< 436.5 >Vapouramount above
land
<3>

Net amount ofvapourtransportation< 45.5 >Snowfall< 12.5 >Rainfall< 98.5 >Glaciers and snowcover
< 24,064 >Amount ofevapotranspirationfrom land< 65.5 >Forests< 40.1 >Water inorganisms<1>

Permafrost<300 >

Grassland< 48.9 >Farmland< 12.6 >Marshes< 0.2 >
Wetland< 11 >

Surfaceoutflow< 15.3 >Base outflow(groundwater)< 30.2 >Totalamount ofprecipitation
on oceans< 391 >

Others< 29.3 >Non-irrigatedfarmland(rainwater-fed
farmland)IrrigatedfarmlandLakes< 2.7 >

Lakes
< 176 >

Oceans< 1,338,000>

Domestic wateruseRivers<2>

Industrial
water use

Soil
moisture< 17 >

Groundwater< 23,400 >Only glaciers are considered for Antarctica.

-

Circulation amount: 103km3/year-

Stored amount: 103km3
Area: 106km2

The water cycle is not only related to such disasters as floods and drought but is also essential
to preserve life. A healthy water cycle is essential for the sound functioning of the ecosystem.
The quality of drinking water has short-term as well as long-term health implications whilewater is the main determining factor for the productivity improvement of food production insome regions. Water is also essential for industrial production as a medium for washing or
cooling. Hydroelectric power generation which only accounts for 4% of the total electric energy
generated in Japan is responsible for half or most of the power generation in Canada, Brazil
and Laos. Inland water surfaces, such as large rivers and lakes, play the important role ofproviding transportation routes for raw materials and products. In short, adequate
management of the water cycle is essential for the preservation of human health andsocioeconomic development.
- 14

The types of water used by human society are called water resources. When discussing theuse of water resources, the figures showing the water flow, i.e. figures accompanying arrows,
in Fig. 4.1.1 are more important than those shown in boxes. Unlike fossil resources, water
resources are used in terms of flow but not stock. To be more precise, when the use of riverwater is contemplated, the relevant amount of resources is an annual runoff of 45,500
km3/year not 2,000 km3 which is believed to exist in river channels throughout the world at
any given time. Any discussion about the availability of water for agricultural purposes, forexample, should be based on this runoff compared to the total water demand for agricultural,industrial and domestic use of some 3,800 km3/year. When the scarcity of the earth’s water
resources is discussed, it is sometimes explained that 97.5% of the earth’s water is seawaterand that a large proportion of the remaining freshwater is represented by deep groundwaterand ice beds, making the use of water by human really difficult. From the viewpoint ofunderstanding water resources as flows, it can be seen that this explanation is somewhat
misleading.
As a general rule for the remote sensing as well as observation of the global scale water cycle,
the observation of flow or flux is more difficult than the observation of stock or stated quantity.
The accurate measurement of precipitation over a wide area is especially difficult because of
its huge spatiotemporal fluctuations. Nevertheless, it is the main driving force of the water
cycle on land along with solar radiation and, therefore, precipitation must be
observed/estimated with a high level of spatiotemporal resolution to produce the necessary
accuracy for specific application purposes. From the viewpoint of the global water cycle, it isdesirable for such water flow figures as the river flow rate and amount of evapotranspiration
to be monitored by earth observation satellites along with precipitation. In regard to the river
flow rate, the observation of water level fluctuations of major rivers using an altitude meterhas at last recently commenced. In the case of the amount of evapotranspiration, the only
available option at the moment is either indirect estimation or numerical model simulation.
The estimated global scale water cycle and water balance figures include estimates based on
numerical model simulation. It is known that even if calculation is conducted using the same
weather data, discrepancies between different numerical models (here, numerical simulation
models called land surface models which calculate the water and energy balances on the land
surface) are larger than discrepancies of the estimated mean values of different models. Fig.
4.1.2 shows the global terrestrial water budget based on the findings of the Global Soil
Moisture Project Phase II (Dirmeyer, et al., 2006). Every model has its own inference of
whether precipitation takes place in the form of rain or snow. Observation of the amount ofprecipitation includes melted solid precipitation, such as snow, and there are no reliable
estimates on the proportions of rain and snow in the overall amount of precipitation. It is
hoped that observation efforts to acquire such basic data will be made for a better
understanding of the water cycle.

- 15

Global Terrestrial Water Budget
Unit: mm/year
103
71

771

97 108727 739

89
Snow

706

253

232

209
Subsurface
runoff

771

513

765
388

489
ET
111

21
471

105
98

242
Surfacerunoff 117

161
160

362

337

633

307

163
51

Interannualrange

600 727 739 765
706
Average ofInter12 models
model (1986-1995)
range

498

Rainfall96

Legend343

Total
runoffSoil water
storageFig. 4.1.2 Annual Changes in 10 Year Average Global Water Balance Estimation Using
Land Surface Models and Discrepancies Between Models (Dirmeyer, et al., 2006)

Fig. 4.1.3 schematically shows the long-term changes of the global scale water cycle and its
impacts on society. Not only the impacts of climate change but also increased food production
and the increased use of water due to demographic and economic growth and changes of theland cover lead to changes of the hydrological cycles, affecting society in the form of
increased/decreased water stress which represents vulnerability to drought or an
increased/decreased flood risk. When the global scale water cycle is viewed in terms of such aframework, it is clear that observation of GHGs causing climate change and observation of
the land cover, land use and vegetation distribution affecting food production are asimportant as observation of the “water stock” directly related to the water cycle, such as ice
bed, glacier and snow cover areas, distribution of lakes, soil moisture and groundwaterfluctuations, for observation of the earth’s environment from space in relation to the water
cycle system on the earth’s surface. It is thought that a collaborative research programme
contributing to clarification of the entire water cycle system on the earth’s surface and fully
utilising the observation and estimation results provided by other satellites and sensors inconnection with the water cycle should be developed under the GCOM.

- 16

Fig. 4.1.3

Impacts of Increased Human Activities Due to Population Growth and Economic
Development on the Global Scale Water Cycle Through Climate Change andFood Production (Oki, 2005)<References>
Dirmeyer, P.A., X. Gao, M. Zhao, Z. Guo, T. Oki and N. Hanasaki, 2006: The Second Global
Soil Wetness Project (GSWP-2): Multi-model analysis and implications for our perceptionof the land surface. Bull. Amer. Meteo. Soc., in print
Korzun, V. I., 1978: World water balance and water resources of the earth. Studies andReports in Hydrology, Vol. 25, UNESCO
Oki, T., 1999: The Global Water Cycle, Global Energy and Water Cycles, Browning, K. andGurney, R. (Eds), Cambridge University Press, pp. 10 – 27
Oki, T., 2005: The Hydrologic Cycles and Global Circulation, Encyclopedia of Hydrological
Sciences, Vol. 1, 656 pages, M. G Anderson and Jeffrey J. McDonnell (Eds), John Wiley &Sons, Ltd., 13 – 22
Oki, T., C. Valeo and K. Heal (Eds.), 2006, Hydrology 2020: An Integrating Science to MeetWorld Water Challenges, IAHS Publication, 300, 190+ xxxii pp.
Oki, T. and S. Kanae, 2006: Global Hydrological Cycles and World Water Resources submitted
for Science
Shiklomanov, I. A. (Ed.), 1997: Assessment of Water Resources and Water Availability in theWorld, Background Report for the Comprehensive Assessment of Freshwater Resourcesof the World, Paris, WMO/SEI

- 17

4.2

Marine Science

4.2.1
Marine Meteorology and Physical Oceanography (Naoto Ebuchi: Hokkaido
University)
4.2.1.1

Composition of Sea Surface Temperature Data Set

The sea surface temperature provides very important basic data for researches on theinteractions between the atmosphere and ocean. This data is also used to determine theboundary conditions for the various kinds of atmospheric general circulation models. In
addition to continual observations of the sea surface temperature using spaceborne infrared
radiometers since the late 1970’s, the use of microwave radiometers, such as the TRMM/TMI,ADEOS-II/AMSR and Aqua/AMSR-E, for sea surface temperature observation has been
becoming popular in recent years. Although microwave radiometers are inferior to infrared
radiometers in terms of the spatial resolution and observation accuracy, they have theadvantage of observation without cloud interferences. New efforts are in progress to compose
sea surface temperature data sets with high temporal and spatial resolutions by means ofblending the observation data of these two types of radiometers to utilise the advantages ofeach type.
The GCOM plans the global observation of the sea surface temperature using both infraredand microwave radiometers. It can be expected that sea surface temperature data sets withhigh temporal and spatial resolutions will be produced through the integration of the GCOM
data with the data observed by other active satellites orbiting the earth during the lifetime of
the GCOM satellites. The resulting global sea surface temperature data set based on
observations by infrared and microwave radiometers starting with the AMSR and AMSR-E
will cover a period of more than 20 years with the GCOM. The availability of very accurate
sea surface temperature data with a long observation period will help the detection of theemerging trend of climate change regarding the sea surface temperature and will make great
progress in studies on atmospheric and oceanic variations of decadal scales.
4.2.1.2

Development of Sea Surface Flux Data Set

Accurate estimation of the momentum and heat fluxes across the sea surface is extremely
important to clarify the mechanism of atmosphere-ocean interactions and the role of theoceans in climate change. In the ADEOS-II mission, the composition of a sea surface flux data
set was planned by combining the observation data of the GLI, AMSR and SeaWinds withdata from other satellites. Techniques have been developed to estimate four flux components(shortwave and longwave radiations, and latent and sensible heats), freshwater (evaporation– precipitation) and momentum flux using data provided by existing microwave radiometers,
microwave scatterometers and visible/infrared radiometers. At present, a technique toestimate the sea surface heat, freshwater and momentum fluxes using the bulk method isnear completion. This technique uses satellite observation data including the sea surface
temperature, microwave brightness temperatures, sea surface wind speed and direction andcloud cover.
Under the GCOM, this new technique will be used to prepare a sea surface flux data set. Its- 18

estimation accuracy will be examined through comparisons with in situ observations. The
development of such a data set for a period of more than 10 years through this mission will
help to disclose the ocean heat transports as well as the formation and advection processes ofwater masses, hopefully contributing to clarification of the atmosphere-ocean feedback
mechanism of decadal scales. The fusion or assimilation of satellite observation data withnumerical models, such as the atmospheric general circulation model, will make thedevelopment of a technology to establish more accurate sea surface flux data an importantresearch and development theme. It must be mentioned that sea surface wind vectors
observed by a microwave scatterometer is essential for the estimation of momentum flux. As
no microwave scatterometer is aboard the GCOM-W1, the use of observation data by the
QuikSCAT/SeaWinds and Metop-A/ASCAT operating in the GCOM period is planned for thispurpose. From the GCOM-W2 onwards, however, it is desirable for the GCOM satellites tocarry a microwave scatterometer. Moreover, the addition of the function to measure the sea
surface salinity to the microwave radiometer in the future should prove to be highly effective
for the estimation and verification of freshwater flux.
4.2.1.3

Continuation of Typhoon Database

Tropical cyclones, of which typhoons are a typical example, constitute a very importantresearch subject for not only the purpose of disaster prevention but also for the transfer ofwater and energy from the tropics to the mid-latitude region. The JAXA/EORC has beendeveloping a typhoon database on the basis of observation data provided by the TRMM,
ADEOS-II and Aqua/AMSR-E. The structure as well as the development, maintenance andweakening of a typhoon are becoming much clearer with the combined use of sea surface wind,integrated water vapour, integrated cloud liquid water and sea surface temperature.
Under the GCOM, the develpoment of such typhoon database can be continued and it is hopedthat understanding of such matters as the development process of a tropical cyclones and its
contribution to water and energy transfer will be advanced. Sea surface wind vectors observed
by a microwave scatterometer are essential for further clarification of the structure of atyphoon. As the GCOM-W1 satellite does not carry a microwave scatterometer, the use of theobservation data of the QuikSCAT/SeaWinds and Metop-A/ASCAT, both of which are orbitingat the same time as the GCOM-W1 satellite, is planned. It is highly desirable that the
GCOM-W2 satellite and subsequent satellites carry a microwave scatterometer. The use of
the observation data under the GCOM for weather forecasting, disaster prevention and otherpractical purposes will be discussed later.
4.2.1.4

Researches on Water Cycle over the Ocean

A new method to investigate the atmospheric water cycle over the ocean has recently beendeveloped using the sea surface wind vectors observed by microwave scatterometers, such as
the ADEOS-II/SeaWinds and QuikSCAT/SeaWinds, and the integrated water vapour
observed by microwave radiometers, such as the TRMM/TMI, ADEOS-II/AMSR and
Aqua/AMSR-E. This method is now used to investigate basin-scale water cycle over theoceans. Estimation of the moisture transport through a vertical cross-section requires, inprinciple, the vertical integration of the product of the water vapour and the wind velocity
along an orthogonal direction to the cross-section in each layer. Traditionaly data frommeteorological analyses are used for this purpose. The newly developed method expresses the- 19moisture transport as a product of the integrated water vapour and typical wind velocity. The
typical wind velocity in this method is empirically determined using the sea surface windvector observed by a microwave scatterometer. A similar method can be used under the
GCOM to clarify the water cycle over the ocean and its variations. As the GCOM-W1 satellite
does not carry a microwave scatterometer, the use of observation data of the
QuikSCAT/SeaWinds and Metop-A/ASCAT is planned as in the case of the development of asea surface flux data set. It is highly desirable for the GCOM-W2 satellite and subsequent
satellites to carry a microwave scatterometer. Moreover, the addition of the function tomeasure the sea surface salinity to the microwave radiometer in the future will greatly
contribute to the estimation of freshwater flux and clarification of the water cycle. The water
vapour transport estimated by this method can be used along with the results of atmosphericgeneral circulation and other numerical models in a mutually complementary manner. This
will lead to new researche directions, such as validation of the new method and models
through comparative studies and the development of data fusion or assimilation techniques.
4.2.1.5

Researches on Atmosphere-Ocean Interactions

The data products with a high accuracy and high temporal and spatial resolutions over a long
period of time produced under the GCOM will make it possible to investigate theatmosphere-ocean interactions of various scales. Possible research themes in the equatorial
and tropical regions include the El Nino/Southern Oscillation and variations of inter-tropicalconvergence zone, the Indian Ocean Dipole, intra-seasonal oscillations, and monsoon
variations and their influences to the water cycle.
In the mid to high latitude regions, recent studies using the all-weather sea surface
temperature data observed by microwave radiometers, such as the TRMM/TMI,ADEOS-II/AMSR and Aqua/AMSR-E, and the sea surface wind vector observed by microwave
scatterometers, such as the QuikSCAT/SeaWinds and ADEOS-II/SeaWinds, have establishedthat a new type of atmosphere-ocean interactions where sea surface temperature variations
lead to atmospheric changes is functioning. The traditional thinking is that the mid to highlatitude region has a mechanism whereby variations in the surface cooling by wind dominate
variations in the sea surface temperature through the latent and sensible fluxes. In this case,there is a negative correlation between the anomalies of the sea surface temperature and thesea surface wind. In contrast, the existence of a mechanism whereby oceanic changes cause
anomalies in the sea surface temperature and the stability of the marine atmosphericboundary layer, resulting in changes of the sea surface wind, has been discovered in areas ofwestern boundary currents, such as the Kuroshio, Gulf Stream, Agulhas Current andMalvinas Current, the Antarctic Circumpolar Current region and the Eastern Equator Pacificregion. In this case, there is a positive correlation between the anomalies in the sea surface
temperature and the sea surface wind. Many studies have been conducted on the impacts ofatmospheric and ocean fluctuations in the tropical region, such as El Nino/Southern
Oscillation on the global climate through various teleconnection patterns. However, theimpacts of oceans in the mid to high latitude region on the global climate have not yet beenclarified. It is hoped that the possible mechanism whereby oceans in the mid to high latitude
region drive atmospheric variations can be studied in detail using the various data sets to beobtained under the GCOM.
As final outcomes of the GCOM, data sets over a period of 10 years or nearly 20 years if the- 20

ADEOS-II mission period is added will be made available. Understanding of the variations in
the atmosphere-ocean system of decadal scales will be greatly improved with such data sets.

- 21

4.2.2Marine Biology and Ocean Carbon Cycle (Toshiro Saino: JAMSTEC)

4.2.2.1

Carbon Cycle and Primary Production of the Ocean

The ocean is the largest pool of water and CO2 on the Earth surface, hence it plays adetermining role in the cycles of water, heat, energy and carbon on the Earth surface. Both in
the terrestrial and oceanic environments, organisms are playing central roles not only in thewater cycle but also in carbon cycle, and linking the two cycles. Phytoplantkton living in theshallow waters of the ocean form the starting point of the material cycle as the primary
producer. Photoplankton by themselves as autotrophs, and heterotrophs such as zooplankton,
bacteria and almost all of the organisms depending their energy on supply from the primary
production by phytoplankton, form particles in the shallow layer of the ocean. The process
whereby some of these particles are transported to deep water by gravity can be taken, fromthe view point of the carbon cycle, as the transformation processes in which the CO2 dissolvedin the ocean from the atmosphere is converted into organic form in shallow ocean andexported into deep ocean. This mechanism is called the “biological pump” in the material cyclein the ocean.
For the prediction of global warming and its effects, such as climate change and changes of
the Earth’s environment, and for the formulation of preventive or mitigation measures,
understanding of the mechanisms of the water and energy cycles as well as the carbon cycle in
the Earth surface layer is essential to refine the climate change prediction model. However, it
is not an easy task to establish a proper understanding of these mechanisms because of thelikelihood that the responses of communities of living organisms and the ecosystem to thechanging climate and environment, particularly the “biological pump” in the carbon cycle, are
not linear. Any research on the response of the ecosystem in the ocean carbon cycle mustfirstly involve rigorous temporal-spatial observation of how the primary production in theocean responds to environmental changes in the ocean’s shallow layer for the purpose ofclearly understanding this phenomenon. The primary expectation of the GCOM/SGLI fromthe viewpoint of research on marine biology and the ocean carbon cycle is the inheritance ofthe series of observations made by practical ocean-colour satellite sensors up to the present
for the purpose of conducting long-term monitoring activities on communities of organisms in
the ocean’s shallow layer and quantitatively describing the changes.
4.2.2.2

GCOM/SGLI Data Products for Marine Biology and Ocean Carbon Cycle

The best characteristic of the GCOM/SGLI is the sensor’s ability to measure coastal sea areas
with a spatial resolution of 250 m using the visible band. In coastal sea areas, there are
significant temporal-spatial fluctuations of oceanic phenomena compared to the oceanic
region because of complicated coastal topography and tidal effects. The time-dependentchanges are also large in coastal sea areas which are prone to the effects of human activities.
Monitoring of the biological process in coastal waters by the GCOM/SGLI is perfectly suited tothe observation of these changes. While in this Science Plan the use of the GCOM/SGLI ocean
colour data products for coastal sea areas for coastal management is described separately,
there are great expectations of the GCOM/SGLI ocean colour data products from theviewpoint of studying marine biology and the carbon cycle in the ocean.

- 22Various efforts have been made to quantitatively estimate the ocean carbon cycle, especially
the CO2 flux on the ocean surface. The biggest uncertainty in these efforts originates fromunreliable estimates for coastal sea areas. Improvement of this unreliability is the mosturgent task and the launch of the GCOM with the SGLI sensor is quite timely.
4.2.2.3

Process Study on Ocean’s Shallow Layer Using Compound Satellite Data

The primary objective of the satellite observation of ocean colour is to monitor the activities ofcommunities of organisms and the carbon cycle in the ocean’s shallow layer and to quantify
their changes. In addition to seven standard products, the GCOM/SGLI will also produce
ocean colour-based applied products such as the depth of the euphotic zone, inherent opticalproperties of seawater, primary productivity, classification of functional photoplankton groups
and detection of red tides as well as compound products utilizing data from other satellites on
ocean colour and sea surface temperature. The applied products from GCOM/SGLI will
provide optical data to characterize photoplankton, other particles and dissolved organic
materials, and hence are promising the improved accuracy of satellite data products.
Refinement of the climate change prediction model requires clarification of the structure ofphotoplankton communities, changes of the primary production and the causal relationshipbetween a change of the primary production and a change of the ocean carbon cycle. For thispurpose, it is essential to conduct process study using a set of time-series data of standard
products and applied products relevant to carbon cycle research. The process study isdesigned to identify the causal relationship between the above data and another set oftime-series data for external forcing functions onto the ocean’s shallow layer. The latterincludes data on air-sea flux such as wind, solar radiation and precipitation, data on changesof the physical structure, such as the convergence, divergence and eddies in surface currents,
and data on the chemical material supply process, such as the deposition of aeolian dust andadvection of coastal seawater.
From the viewpoint of research on marine biology and the ocean carbon cycle, it is desirable todevelop algorithms for new data products. These include parameters for materials flux suchas primary production, new production and export production and parameters for materials’
abundance such as the CO2 partial pressure, pH and nitrate ion concentration in the ocean’s
shallow layer. The development of a new algorithm for the air-sea gas exchange coefficient isalso needed. These time-series satellite data will be highly valuable for verification of theclimate change prediction model.
4.2.2.4

Field Observation of the Ocean for Process Study Using Satellite Data

For the proper implementation of process study using satellite data, it is essential for satellite
data to accurately represent ocean phenomena. As the time-scale of the biological process inthe ocean’s shallow layer is approximately one day, field observation of the ocean to verify theprocess must be compatible with this time-scale. In the coastal sea areas for which
GCOM/SGLI data are expected to be available, a time resolution of a scale of several hours is
required in consideration of the tidal effects. As field observation of the ocean with this kind oftime-scale is only possible by automatic monitoring using various sensors, the development ofsuch sensors and measuring technologies, including sensors’ platform technology, is essential.

- 234.2.2.5

Development of a New Satellite Data Processing Method

What is the most problematic for the use of satellite ocean colour data is the observation bias
due to the lack of data for cloud-covered areas. Primary production, which forms the basis ofcarbon cycle in the ocean, responds to the intensity of light in a non-linear manner. The use of
only data acquired for cloudless areas leads to serious error. This kind of error may fall within
the allowable range in the case of GAC data when the number of the data is sufficient for
statistical processing, but is extremely serious for process study using individual LAC data.
The development of an objective cloud region interpolation method using ocean colour
satellite images is required to overcome this problem. Significant progress is currently being
made with the 4D data assimilation method. It is hoped that expansion of this method to an
integrated physical circulation and ecosystem model will make it possible to estimate theconcentration level of chlorophyll-a in the ocean’s shallow layer for the cloud covered regions.

- 24

4.3

Land Area

4.3.1
4.3.1.1

Land Cover and Its Changes (Haruhisa Shimoda)
Land Cover Maps

Land use maps and land cover maps provide basic information on the land area in the sensethat they indicate what is on the ground. For a country or an area smaller than a country,
these thematic maps can function as basic map for various development plans. A study onchanges of the land use and/or land cover can reveal historical changes of the land use of acountry, progress of urbanisation and changing situation of vegetation among others. In
contrast, a land cover map of an area bigger than a country or even a global land cover map
can provide basic information to estimate the global radiation balance and carbon cycle basedon the global ground reflectance and global vegetation coverage. By studying the changes ofthe said reflectance and vegetation coverage, it is possible to establish changes of radiative
forcing on the ground surface and to estimate the impacts of climate change on the groundsurface.
4.3.1.2

Land Use and Land Cover

Although land use and land cover sound the same, they are different concepts, requiring
careful attention. Land use literally means for what purpose specific land is used. In otherwords, it indicates the function of the land. In contrast, land cover means what is on the land.
Land cover is what can be directly observed by remote sensing while land use can be roughly
inferred.
For example, commercial and industrial sites in land use categories can vividly illustrate thedifference between the concept of land use and that of land cover. When there is a building
which is used for commercial purposes, the land in question is classified as a commercial site.
If the building is used by a manufacturer, the land is classified as an industrial site. As suchdifference cannot be clearly determined from above, the land is classified as something likethe site of a concrete building in terms of land cover. While land use categories may be
identical to land cover categories as in the case of paddy fields and cultivated land, theygenerally differ. As such, the following explanation primarily refers to land cover.
4.3.1.3

Flow of Land Cover Classification

The processing work for land cover classification is mostly identical to the general process forimage classification. Firstly, corrections by means of geometric correction and radiation
quantity correction are made to extract the characteristics. This may be followed bypost-processing and evaluation classification accuracy. The most important parts of thisprocess are the setting up of the classification categories and extraction of the land coverchanges.
The setting up of the classification categories has critical importance for land coverclassification as explained later. When the categories are incorrectly set up, the classification
accuracy suffers and/or the validity of the classification results is lost. The extraction ofchanges poses a major challenge for land cover classification. The extraction of secular- 25

changes in particular is an important issue to ensure the practical applicability of theclassification results. However, land cover changes on images can be caused by seasonalchanges and weather changes in addition to secular change and the extraction of changes
with a high level of accuracy is a daunting task.
4.3.1.4

Classification Categories

As mentioned earlier, the adoption of particular classification categories greatly affects theland cover classification. To determine the classification categories, it is essential to consider
both the characteristics of the images and the purpose of land use. When approached from theimage side, it is obvious that the characteristics of each classification category must differ
from those of other categories. When categories with similar characteristics are selected, thenumber of incorrect classification results increases.
In general, the classification categories are set up based on the purposes of land use. If there
are categories with similar characteristics, some arrangement is necessary to clearly
distinguish them or to unify them into a single category. Meanwhile, there are cases whereareas in the same classification category show different characteristics on images. For
example, trees of the same species can show different characteristics depending on thespecific features and type of soil of the tree sites. In such cases, it is necessary to differentiate
the classification categories used for finalised land cover maps and the classification
categories required for land classification on images. At the stage of classifying the land coveron images, subjects with different characteristics must be classified into different classes.
When different classes are identified, those classification categories belonging to the sameland cover category must be integrated at the end of the image classification process.
4.3.1.5

Mixels (Mixed Pixels)

Individual pixels of remote sensing data often contain several land cover categories. In fact, it
can be argued that the entire coverage of a pixel by the same land cover category is quite rare.
A pixel which contains more than one land cover category is called a mixel (mixed pixel). It is,therefore, necessary to infer the ratios of different land cover categories in a mixel. This
process is called un-mixing. These ratios can be obtained by solving simple simultaneous
equations if the spectral characteristics of individual land cover categories are different andknown and also if the number of spectral channels exceeds the number of land covercategories contained in a mixel.
In reality, such an instance is rare and the conditions stated above are not commonly met,
making the situation an ill-posed problem. To convert this ill-posed problem to a well-posed
problem, the constraint conditions must be established. Many methods have been proposed tosolve mixel-related problems. In the field of remote sensing, however, no methods capable ofperforming un-mixing in a stable manner have yet been discovered except in some very
special situations because of the general uncertainty about what exists on the ground.

4.3.1.6

Changes of Land Cover

The methods to extract changes of the land cover can be largely classified into those to extract- 26

changes between images and those to extract changes in the classification results after theclassification of individual images. The methods to extract changes are further classified into
methods to determine differences, methods to calculate an inner product of two images andmethods relying on analysis of the main constituents. In general, the accurate extraction ofchanges is difficult to achieve. This is because images can include such changes which are notrelated to land cover changes as seasonal changes and weather changes (for example, it
rained on the previous day) in addition to actual land cover changes.
In the case of the latter, i.e. extraction of changes of the classification results, the problems
described above for the extraction of changes between images are less prominent provided
that different classification classes are set up between two images.
Which ever method to detect land cover changes is used, accurate geometric correction isrequired. Misalignment by even a single pixel between images taken at different times meansa succession of changed pixels along the boundary of different land cover categories. This
misalignment is normally minimised by mean of the relative geometric correction of images
taken at different times.
4.3.1.7

Land Cover Classification by SGLIUp to the present, the AVHRR on board the NOAA, the MODIS on board the Terra and the
Aqua and MERIS on board the ENVISAT have been mainly used for the purpose of global andregional land cover classification. When the AVHRR was commonly used, the NDVI andseasonal changes of the NDVI were used as characteristic values for land cover classification
because of the constraints posed by the wave bands of the AVHRR. Another reason for the useof the NDVI as a characteristic value was the insufficient atmospheric correction by the
AVHRR. Use of the NDVI offers a simple atmospheric correction effect. Meanwhile, since thestandard products of the MODIS have become available, the ground surface reflectance data
provided by the MODIS has been increasingly used for land cover classification. The MODIS
uses seven channels from the visible band to the short-wave band to scan the land area andthe amount of information contained in these channels is far more than that of the NDVI. As
NBAR (Nadir BRDF Adjusted Reflectance) products which have recently become available
particularly offer far more accurate reflectance than conventional products, highly accurate
land cover classification can be expected.
Meanwhile, the MERIS is characterised by its use of advanced spectrometers and high spatial
resolution. 12 channels in the visible and near infrared regions can be used for land coverclassification and the bandwidth of each channel of 10 nm is quite narrow. The spatial
resolution of 300 m is higher than the 500 m of the land area observation channels of the
MODIS (250 m for two channels). Accordingly, the land use classification results of the
MERIS are, in theory, expected to provide a high level of classification accuracy based onthese characteristics. Up to the present, several experiments have been conducted to compare
the performance of the MODIS and MERIS and the MODIS offers a higher level ofclassification accuracy. The reasons for this probably lie with the facts that the MERIS lacks ashort-wave infrared channel and that the standard products of the MERIS do not include
ground surface reflectance.
Compared to these sensors, the SGLI is believed to be advantageous from several aspects.
- 27

Firstly, it has 10 usable channels from the visible to the short-wave infrared regions. The
number is greater than that of the MODIS and a short-wave infrared channel, which the
MERIS does not have, is included. The spatial resolution of these 10 channels is 250 m and ishigher than that of the MODIS and MERIS. In regard to atmospheric correction, it is believedthat better correction is achieved because of the superior aerosol amount extraction capacityof the SGLI than the MODIS and MERIS. The standard products of the SGLI include thevegetation roughness index and the shadow index. Polarisation products can also be usedeven though the spatial resolution is not as good as that of other products. The combined useof these products should be able to provide the best land cover classification results so far.

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4.3.2

Vegetation (Yoshiaki Honda: Chiba University)

Land vegetation greatly affects the global environment as it influences climate change
through involvement in the water and energy cycles. Mankind relies on land vegetation forthe supply of most of its food as mankind has largely obtained its food from vegetation bymeans of the harvesting of plants, hunting of animals which live on plant food, cultivation ofcrops and/or a nomadic life relying on grassland. Moreover, vegetation has been used toprovide fuel and the raw materials for various applications. The survival of mankind without
land vegetation is inconceivable. Food, energy and the environment (atmosphere and water,etc.) supporting the life of mankind are closely related to vegetation. Ancient civilisations
which emerged and prospered along great rivers were dependent on the rich vegetation
supported by fertile soil. Their decline is said to have been caused by the decline of vegetation
which was consumed to sustain human activities. It is, therefore, essential to consider the role
played by land vegetation when the global environment or the survival of mankind on theearth is questioned.
There is concern regarding the severe effect of the changing climate on the state of vegetation.
For example, global warming prompts those plants which favour a cold environment to moveto areas of a high latitude or high altitude. Coniferous forests in permafrost areas of Siberia,
etc. are expected to shrink in accordance with the shrinkage of the permafrost areas. Climatechange caused by global warming may cause a decline of forests and grassland throughchanges of the water cycle and other changes, reducing the plant diversity in the affectedareas. The richness of vegetation is generally said to be related to the types of local species. A
decline of vegetation is highly likely to reduce the overall biological diversity in areas inaddition to a reduction of the number of plant species.
The impacts of global warming vary from one area to another. In one area, an increase of thetotal annual precipitation may simply mean flooding during the rainy season due to theconcentration of precipitation in the rainy season and drought during the dry season as aresult of the changing pattern of precipitation. Or, global warming may simply mean
progressive aridification in other areas. Any prediction of the impacts of climate change on
vegetation has many uncertainties. It is essential to check the ongoing situation as frequently
as possible, making global observation in quasi-real time necessary.
The survival of vegetation depends on the interactive relationship of various elements,
particularly the carbon, nutrient, water and energy cycles. It is possible for human activities
to cause changes to all of these cycles.
Vegetation is in constant interchange with the atmosphere through photosynthesis andbreathing. Even after death, it releases carbon into the atmosphere through decomposition or
burning. Human activities have been changing the state of carbon stock through farming andother types of land use and changes of land use after the felling of trees and other activities.
The increased emission of GHGs because of the expansion of economic activities has greatlychanged the quantity of carbon stock in the atmosphere. Forest clearance from the highlatitude region to the tropical region has substantially reduced the carbon stock of vegetation.
It cannot be denied that human activities have changed the state of carbon stock on the earth.
An IPCC report in 2000 estimated the carbon stock by type of vegetation and soil for nine
- 29different vegetation zones (biomes) as shown in Table 4.3.1 but stated that the absolute values
were not very reliable.
Table 4.3.1

Global Carbon Stock of Vegetation and Top One Metre of Soil
Carbon Stock (Gt C)

Area(106km2)

Vegetation

Soil

Total

Tropical Forests

17.6

212

216

428

Temperate Forests

10.4

59

100

159

Boreal Forests

13.7

88

471

559

Tropical Savanna

22.5

66

264

330

Temperature Grassland

12.5

9

295

304

Deserts and Semi-Deserts

45.5

8

191

199

Tundra

9.5

6

121

127

Wetland

3.5

16

225

240

Cropland

16.0

3

128

131

Total

151.2

466

2,011

2,477

Biome

Note: Because of the ambiguous definition of biome, the values in the table have a high level of
uncertainty even though the table still roughly indicates the size of the carbon stock on
the ground.
Original Source: Table 1 of the IPCC Special Report on Land Use, Land Use Changes andForestry, 2000

The IPCC also reported the global annual budget of CO2 for 1980 to 1989 and for 1989 to 1998
expressed in Gt C yr-1 as shown in Table 4.3.2. The IPCC also reported the global averageannual budget of CO2 in the 1980’s and 1990’s as shown in Table 4.3.2-2. Even though theamount of carbon uptake by terrestrial ecosystems is quite uncertain, the table still show thatterrestrial ecosystems, including vegetation, may have served as a net sink for CO2. For
example, newly planted forests maintain the role of a CO2 sink for several decades. However,their role as a CO2 sink gradually declines to the extent that these forests may even become asource of CO2 emission. There is much room for future research on how vegetation adapts toclimate change and, therefore, the future of carbon absorption and release by vegetation as aresult of climate change is riddled with uncertainties.

Table 4.3.2

Average Annual Budget of CO2 for 1980 to 1989 and for 1989 to 1998 Expressed in
Ct C yr-1(The error limits correspond to an estimated 90% confidence interval.)
1980 to 1989

1989 to 1998

1) Emission from fossil fuel combustion and cement
production

5.5 ± 0.5

6.3 ± 0.6a2)Storage in the atmosphere3.3 ± 0.2

3.3 ± 0.2

3)

Ocean uptake

2.0 ± 0.8

2.3 ± 0.8

4)

Net terrestrial uptake = (1) – [(2) + (3)]0.2 ± 1.0

0.7 ± 1.0

5)

Emission from land use changes

1.7 ± 0.8

1.6 ± 0.8b6)

Residual terrestrial uptake = (4) + (5)1.9 ± 1.3

2.3 ± 1.3

- 30

aNote that there is a one year overlap (1989) between the two decadal time periods.
bThis figure is the average annual emission for 1989 – 1995 for which data is available.
Original Source: Table 2 of the IPCC Special Report on Land Cover, Land Cover Changes andForestry, 2000

Much of the knowledge on vegetation changes due to global warming is, in fact, uncertain.
The IPCC’s 4th Evaluation Report reported that there is a high likelihood that terrestrial
ecosystems, including forests, will function to increase the CO2 stock in the atmospherebecause of global warming, thereby possibly accelerating global warming, i.e. positive
feedback. This situation demands a comprehensive prediction model incorporating various
chemical and physical processes, including the carbon cycle process of ecosystems and others,in addition to the conventional climate model. R & D activities on a “global system model”
based on such a concept have already begun. In terrestrial ecosystems, animals and plants
migrate in an attempt to adapt to climate change. In the case of plants, there is a chance thatthe migration speed using seeds is insufficient and the development of a model which iscapable of expressing the migration and distribution of plants, incorporating the possible
consequences of such insufficient migration speed, is necessary. This type of model can be
described as a dynamic ecosystem model. Such a model is extremely important for theaccurate expression and prediction of the CO2 cycle of ecosystems.
An accurate understanding of terrestrial vegetation is essential for an accurate
understanding of the CO2 cycle in the natural world and also for an objective understandingof the present state of the global environment. Very precise observation on the ground as wellas time-series data at a global level is essential to accurately determine various parameters
for terrestrial vegetation. Data on precipitation and soil moisture is required to accurately
establish a picture of the water environment for vegetation. Meanwhile, the depth of laidsnow in a water basin constitutes important information to predict vegetation activities fromspring onwards. Such data can be provided by the AMSR-2. In arid areas, research on thehistorical changes of water stress on vegetation activities can establish the state ofdesertisation or devastation of land.
The SGLI is characterised by such functions as multi-angle observation and polarisation
observation which are beyond the capability of previous sensors and can provide
atmospherically-corrected land reflectance data in addition to geometrically correctedradiance. Atmospheric correction on the ground is expected to improve the accuracy of various
products and can generate products utilising the existing vegetation categories and researchoutcomes, products relating to the physical quantities of vegetation, such as biomass, andproducts which are useful for research on ecological models of vegetation. The types of data to
be made available by the SGLI include a vegetation index for use by various analyses andstudies on vegetation, vegetation roughness index indicating the roughness of the vegetation
surface which is related to the 3D structure of vegetation, shadow index indicating thequantity of shadows created by crowns, ground biomass related to vegetation biomass, groundsurface temperature related to vegetation activities, effective radiative absorption rate ofphotosynthesis and leaf area index. Moreover, it is hoped that research work will progress onsuch themes as the water stress trend related to the water stress on vegetation, land coverclassification which is useful for the monitoring of land use changes and fire detection which
is useful for the monitoring of forest fires.
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4.3.3

Terrestrial Carbon Cycle (Yoshio Awaya: Gifu University)

There is a common consensus that global warming is attributable to the rising concentrationsof CO2 and other GHGs. Clarification of the global carbon cycle based on detailed analysis isessential to realise effective global warming prevention measures. The land area covers some
30% of the earth’s total surface. Plants absorb and fix carbon through photosynthesis and arethought to constitute the largest carbon sink. As such, the terrestrial carbon cycle must be
clarified to establish a fair picture of the carbon budget.
The carbon budget of the ecosystem is shown by the following expression as the net ecosystemproduction (NEP).
NEP = NPP Rs = GPP R Rs
Where,
NPP :
Rs
:
GPP :
R
:

net primary production
breathing quantity of heterotrophic organisms
gross primary production
breathing quantity of plants

The carbon cycle in a land area can be clarified by means of quantitatively assessing eachelement of the above expression and mapping time-series changes.
The growth increment of plants is generally determined by the characteristics of the species
and site conditions. When the subject is a large community with large coverage, the site
conditions are likely to be the main determinant. The environmental factors affecting the
growth increment are the quantity of solar radiation, temperature, quantity of precipitation
and water conditions and the nutrients of the soil. Accordingly, it is possible to estimate thegrowth increment using a model in which all of the above environmental factors act as
variables. The breathing amount can mostly be expressed as a function to temperature. In thecase of trees, the surface area of the tree trunk and branches must be taken into considerationin addition to the amount of the leaves. The amount of the CO2 discharge by soil is, in fact, thebreathing amount of plant roots and heterotrophic organisms. At present, it is difficult tomeasure these separately on the ground.
There can be two approaches to estimating the NEP by remote sensing. One approach is theuse of remote sensing to estimate various parameters, which are components of a processmodel developed based on physiological and ecological measurements, and to use theseparameters as input data (process-based model). In this model, the leaf area index (LAI) andsurface temperature of plants are the principal input parameters. Another approach is to
firstly estimate the fraction of the absorbed photosynthetically active radiation (PAR) for aplant community using the normalised difference vegetation index (NDVI), etc. and to thenestimate the NEP using the quantity of solar radiation, light use efficiency and stressfunctions relating to the temperature and water content (community-based model). In thismodel, the NDVI and surface temperature are the principal input data. Data on the quantityof solar radiation estimated using meteorological satellite data is believed to offer a higher
accuracy than objective analysis data for areas with scarce observation points and constitute- 32important input data.
Meanwhile, estimation of the NEP poses the following problems.
(1)
(2)
(3)
(4)

Difficulty of estimating the soil moisture under dense vegetation by means of remotesensingDifficulty of estimating the CO2 release by soil over a wide area
Difficulty of accurately estimating the LAI as satellite data is saturated with high LAI
vegetation
Less realistic tuning for estimation of the NEP at the community level as the processmodel is based on parameters measured under limited conditions and for individual
leaves of specific species

Although the process-based model is the present-day mainstream NEP estimation method, it
is not exactly an appropriate method in view of problems (3) and (4) above. Today, many flux
observation sites are available in regard to various types of vegetation in the world and theCO2 flux is being measured by the eddy correlation method, clarifying the temporal-spatial
fluctuations of the NEP at the community level. There is a problem of a flux imbalance in thatthe observed flux value by the eddy correlation method does not match the value observed by
the bulk method, etc. However, as the eddy correlation method measures representativevalues at the community level and the scale of the measurement results is similar to that ofremote sensing data, the results can be easily compared and their combination is expected tooffer an effective tool for NEP estimation.
Based on the current situation described above, research on the terrestrial carbon cycle under
the GCOM must take the following points into special consideration.
(1)

The accuracy of the surface temperature product should be improved as a common
parameter for both the process-based model and the community-based model. As it isdesirable to measure the surface temperature between 13:00 and 14:00, the surface
temperature data from the GCOM on the morning path will require tuning.

(2)

The advantages and disadvantages of the process-based model and the community-basedmodel should be compared to develop or transplant a model which is suitable for the
GCOM.
In regard to the LAI, the estimation method using the reflectance factor which has
undergone atmospheric correction and topographical correction should be verified inaddition to those estimation methods using such vegetation indices as the NDVI and
EVI.

(3)

(4)

In regard to breathing by woody portions, the available research on this process should
be reviewed with a view to establishing a method to estimate the breathing quantity by
woody portions based on the woody biomass.

(5)

Flux observation data should be actively used for parameterisation methods.

(6)

A simple community-based model using the fAPAR and light use efficiency should beestablished as part of the efforts to develop a detailed model.
- 33

4.3.4

Hydrology (Toshio Koike: University of Tokyo)

The terrestrial water cycle has shown considerable temporal-spatial fluctuations, causing
significant damage to human society through floods and droughts which account for
two-thirds of both human casualties and material damage by all natural disasters. A proper
understanding of the fluctuation mechanism, improvement of the prediction accuracy and theestablishment of an international system to share information are essential to avoid
water-related crises. Quantitative and temporally as well as spatially continualcomprehension of the individual components of the water cycle from the viewpoint of thewater volume and heat flow is required to understand the relationship which is accompanied
by a time lag due to the water storage effects of deposited snow and soil moisture (weather
memory) and the water cycle system with a teleconnection system between separate places
due to advective flow so that the prediction accuracy can be improved.
In addition to the understanding and prediction of water cycle fluctuations under the current
climate, changes of the water cycle caused by climate change, typically global warming, havebeen indicated by the 4th Assessment Report of the IPCC. It is now imperative to develop thecapacity to monitor ongoing changes through clarification of the process of change in the past.
As a climate change model is essentially a question of boundary values, it is necessary todevelop a model which is capable of accurately describing changes in land areas as well aschanges in ocean areas.
The recent advancement of remote sensing by earth observation satellites has greatly
contributed to improvement of the monitoring situation. In particular, microwave radiometerscapable of conducting wide area and all weather observation day and night offer the prospectof the quantitative observation of water surfaces, soil moisture, water in vegetation, raindrops,
cloud water, deposited snow and ice particles through different combinations of wavelength
bands and polarised waves. The technique of quantitative earth observation using a
microwave radiometer on board a satellite was established by the mechanical scanning typespecial sensor microwave imager (SSM/1) of the Defense Meteorological Satellite Program(DMSP) of the US in 1987. The TRMM Microwave Imager (TMI) on board the TropicalRainfall Measuring Mission (TRMM) satellite in 1997 made it possible to observe thenon-synchronous 10 GHz band which is advantageous for land surface observation eventhough observation was limited from the equator to the mid-latitude region. In 2002, the
AMSR and AMSR-E, both of which were high performance microwave radiometers developed
by Japan, were launched in succession. They had a 6.925 GHz observation frequency with arelatively high spatial resolution useful for soil moisture observation. While observation by
the AMSR came to an end after a year because of the break down of the satellite proper, the
AMSR-E is still active today beyond its design life of five years.
Meanwhile, the Coordinated Enhanced Observation Period (CEOP) Project implemented as a
WCRP (World Climate Research Programme) project had made a huge contribution. The
CEOP is an international programme to conduct wide-ranging studies, including studies
designed to facilitate the understanding and prediction of the water and energy cycle
processes, studies on the monsoon system and studies to downscale global wide area
- 34

prediction data to regional data. For these purposes, terrestrial observation study groups,
satellite operating organizations and meteorological organizations joined together to prepare
data sets of the local, regional and global water cycles. Under this programme, observation
data from 35 reference sites and satellites and outputs from 11 numerical meteorologicalforecast centres under various weather conditions was gathered in a period of two years andthree months from 1st October, 2002 to 31st December, 2004.
What is required is the continuous observation of global water cycle fluctuations by means ofcombining quantitatively observed land area hydrological values with water cycle elements inthe atmosphere and oceans so that the combined information can be effectively used formeasures which are designed to alleviate damage by floods and droughts and also to adapt toclimate change. To successfully achieve this requirement, it is necessary to integrate theglobal water cycle data observed or predicted by various methods and of various scales as in
the case of the CEOP in addition to obtaining data from individual satellites. The following
activities should then be conducted to advance the terrestrial observation around the GCOM
using the database developed by the said integration of data.
(1)
(2)
(3)
(4)
(5)

Advancement of the microwave radiation transfer model targeting soil (including frozensoil), vegetation and deposited snow
Development of an algorithm for single sensor or multiple sensor operation
Advancement of the water cycle model using satellite data
Development of the optimum estimation method for water cycle model parameters
through data assimilation
Development of a land area hydrological data assimilation method

- 35

4.4
4.4.1

Snow and Ice (Fumihiko Nishio: Chiba University)
Development of the Science of Snow and Ice by GCOM

The impacts of global warming thought to be attributable to the trend of a temperature rise
since the Little Ice Age in the 18th century and the more recent increase of GHGs are expected
to show up most strongly in changes of the earth’s cryosphere, primarily the polar regions.
Long-term, continuous monitoring of the sea ice distribution and the ice sheet distribution in
the earth’s cryosphere is necessary as changes take place over a long period of time. Global
warming is thought to have direct impacts on the reduced distribution of sea ice, increasedsnow deposit on ice sheets, increased area of melting around ice sheets and increased
discharge of icebergs. Clarification of the elementary steps of various phenomena in thecryosphere and long-term, continual observation, particular in the polar regions, areextremely important to ascertain such impacts.
Satellite observation constitutes the most important method for continual observation andmonitoring in the polar cryosphere. While the combined use of multiple sensors for the visible
region to the microwave region is essential, the use of microwaves is particularly effective
because of the frequent cloud cover and long nights.
The GCOM, SGLI and AMSR2 will be used to observe in a much higher resolution than thosesensors in use today. For example, continual monitoring of the cryosphere can be conducted
for the mapping of sea ice, glacier and ice sheets and also to record changes of the margins due
to global warming.
It is known that there has been a general trend of receding glaciers on the earth because of
the gradual temperature rise since the Little Ice Age. Even though research on the recession
speed and mechanism is progressing through an increased number of actual observations andvarious models, not many details have been established because of the severe environment in
the polar regions. One of the satellite observation results which have been available since the1970’s is that the sea ice area in the Arctic Ocean has reached its smallest size in recorded
history of less than 4,300,000 km2 in September, 2007.
As the SGLI with many channels can conduct observation with high resolution and stereo
vision, it may be able to detect topographical changes as well as changes of the ice thickness.
Changes of the albedo on glacier, ice sheet and sea ice surfaces are closely related to the trendof a temperature rise and the SGLI can detect the optical characteristics of snow and ice
surfaces in a small area. Moreover, the spectral data provided by the SGLI is useful to detect
impurities caused by minerals, bacterial activities and others on snow and ice surfaces aschanges of the albedo or spectral changes.
Meanwhile, the multiple waveband and multiple polarised wave data provided by the AMSR2
is useful for the understanding of huge snow and ice areas on ice sheets and the interaction
between the atmosphere and snow and ice surfaces. Such data is vital for understanding of
the interaction for material transfer between ice sheets and the atmosphere and the massbalance process of ice sheets.

- 36

Research on sea ice is facing the tough challenge of clarifying the interactions involving sea
ice, ocean and the biosphere in the polar regions. Monitoring of the sea level of sea ice and the
distribution of thin and thick sea ice is required and the relation between sea ice and primary
biological products must be examined. The types of information required for this research
include the mass balance of sea ice based on the thickness and moving speed of sea ice, heatexchange between the atmosphere and ocean, gas and water vapour flux, impacts of deposited
snow on the sea ice growth process, sea ice growth speed, discharge speed of brine, ocean
structure (ocean density and circulation), radiance balance and changes of the albedo on thesea ice surface, physical temperature on the sea ice surface and seasonal/spatial fluctuationsof primary biological products and production processes.
4.4.2

Changes of Sea Ice, Glacier and Ice Sheet Distributions: Science Using Microwaves

The AMSR2 can extract various physical quantities, mainly those related to water such as
vapour and cloud water contents and amount of precipitation in the atmosphere, deposited
snow volume and soil moisture content on land, ocean surface wind velocity, sea surface watertemperature, sea ice volume and deposited snow volume on the Antarctic ice sheet surface, bymeans of measuring weak microwaves radiated by the earth. As the radiation characteristics
of microwaves considerably differ for water (H2O) depending on its gaseous, liquid or solid
phase, such characteristics can be used to accurately extract physical quantities throughmulti-frequency observation.
The AMSR2 can achieve a high level of spatial resolution with its large antenna which is some2 m in diameter and can also use data in such low frequency bands of 7 and 10 GHz. The
analysis and monitoring of phenomena in the cryosphere will be conducted with emphasis on
the analysis of high spatial resolution data and low frequency data. For this analysis, datafrom the SGLI will also be used. Because of the availability of quasi-real time global data,
efforts will be made to demonstrate the possible use of microwave radiometer data forweather forecasting, etc.
An AMSR algorithm will be developed and improved using AMSR-E data and airborne
microwave radiometer (AMR) data to achieve the research objectives. The calibration andproduct preparation methods will have been finalised in advance to guarantee the data
quality.
4.4.2.1 Deposited Snow Distribution, Deposited Snow Area and Frozen Ground
Analysis of the changing distribution and area of deposited snow is important from theviewpoint of monitoring global warming. There are many research themes on land because of
the uncertainty regarding reflectance. Firstly, AMSR-E data will be used to check seasonalchanges of vertically and horizontally-polarised microwave radiation from various types ofground surfaces. Next, the feasibility of establishing the deposited snow volume and soil
moisture content based solely on brightness temperature data will be examined using AMR
data. As microwave radiation rapidly changes with the phase change from water to ice or
vice-versa, it is hoped that detection of the state of the surface will be relatively easy.

- 37

4.4.2.2

Sea Ice Distribution and Concentration

The sea ice area around Antarctica shows much seasonal change as a huge area of some 3.5
million km2 in summer and some 20 million km2 in winter is covered by sea ice. In the case of
the Arctic Ocean which is surrounded by the Eurasian Continent and the North American
Continent, most of the ocean is covered by sea ice throughout the year. As such, the size of sea
ice plays a dominant role in the heat balance and ocean circulation. Time-series changes ofthe sea ice distribution, therefore, are an important indicator for global warming. It is
possible for the AMSR2 to determine the general distribution of sea ice in both semispheres.
While sea ice observation by microwave radiometers has been in progress since the early
1970’s as basic research, the relationship between the emissivity and physical characteristics
of sea ice is not yet clearly understood. The reasons for this lie with the rough spatial
resolution of several tens of km of the radiometers on board satellites and the fact that thetypes of sea ice within this range are not uniform. Moreover, any snow deposit on sea icechanges the emissivity and the physical temperature of the sea ice or deposited snow may
change by as much as some 10°C due to the atmospheric temperature. It is, therefore,
necessary to compare the observation data of the airborne AMR and truth data to establish
more reliable basic data.
4.4.2.3 Changes of Ice Sheet Distribution
Continual observation of the changing distribution of the Greenland and Antarctic ice sheets
using microwaves is important to understand the process of how the impacts of global
warming emerge. The crucial observation targets in this context are the deposited snow
volume on ice sheets, melting zone around ice sheets, outflow of glaciers and seasonal as wellas yearly change of the brightness temperature in all areas of the ice sheets.
Calculation of the mass balance of the Antarctic ice sheet requires information on the massbalance of the ice sheet in the form of snow and ice and the contribution of ice deposition tochanges of the sea level, in turn affected by the ice deposition on the ice sheet located inlandfrom the grounding line. For this reason, it is important to proceed with basic research on themass balance of the Antarctic ice sheet and the development of an algorithm for theelementary steps using AMSR2/SGLI data. The development of an algorithm to estimate thedeposited snow volume data on the surface provided by the AMSR2 is urgently necessary tounderstand the actual state of global warming and the mass balance of the Antarctic ice sheet.
What is important here is the comparison of data related to the mass balance with othergeoscience data as part of the research on the elementary steps to determine the mass balanceof glaciers and ice sheets and its changes.
4.4.3

Science of Optical Observation

4.4.3.1 Snow Cover Distribution
What is normally problematic for observation of the snow cover distribution is how to
distinguish areas covered by snow from clouds. While research on the AVHRR has establishedthe effectiveness of combining intermediate-infrared and thermal-infrared observations, it isanticipated that the 1.6 µm channel of the SGLI will further improve the accuracy. Moreover,the development of an algorithm to estimate the snow cover distribution in continental forest- 38

areas should improve the accuracy even further.
4.4.3.2 Sea Ice Distribution and Concentration
While the general global distribution of sea ice can be observed by the AMSR2, use of the
SGLI which has a higher spatial resolution than the AMSR2 is essential for assessment of thestate of local polynyas and differences in the sea ice concentration. A highly accurate pictureof the sea ice distribution can be achieved with the use of both sensors. Information on thestate of the sea ice surface (snow distribution on sea ice and reflectance) and surface
temperature is essential to calculate the heat balance of sea ice areas. Data from thesesensors can also be used to classify the thickness of sea ice and the type of ice plate.

4.4.3.3 Changes of Polar Ice Sheets
Observation of the changes of the Antarctic and Greenland ice sheets, especially changes ofglacier movement, is very important from the viewpoint of monitoring global warming. The
large-scale collapse of the ice shelf at the Antarctic Peninsula has been reported in recent
years. The scale of this collapse is as large as more than several tens of kilometres and it islarge enough for observation by the SGLI. Although changes of glaciers require a highresolution sensor for detailed analysis, observation by the SGLI is important from theviewpoint of observing changes over a wide area.
4.4.3.4 Monitoring of Giant Icebergs
The outflow of giant icebergs has frequently been reported in recent years, raising questions
in connection with global warming. As giant icebergs pose a serious danger to ships, theircareful monitoring is necessary. As the SGLI can observe the polar regions with a resolution of
250 m on almost a daily basis, it should prove useful for the monitoring of giant icebergs.
4.4.3.5 Albedo of Snow and Ice Surfaces
The albedo in the cryosphere is an important parameter for analysis of the global heat budget
and global warming. Ice-albedo feedback is the biggest factor affecting the climate in the
global cryosphere. The albedo of snow and ice is liable to change by the wavelength, diameterand shape of the snow particles, impurities in the deposited snow, snow cover density,
moisture content and surface conditions. The research to develop a model on the relationshipbetween the albedo and wavelength, size and shape of the snow particles and snow cover
depth has made much progress. Spectral observation of the albedo can be expected to allow
estimation of the snow cover distribution and depth. As the SGLI observes the cryosphere in
19 wavelength bands, it is expected to produce valuable data to clarify the relationshipbetween precise snow and ice surface conditions and the albedo.
4.4.3.6 Surface Temperature of Snow and Ice
The surface temperature of snow and ice is important information for the accurate inferenceof the conditions of snow and ice surfaces. This information is also essential for assessment of
the heat balance in the cryosphere and the interaction between the atmosphere and snow/ice.
- 39

However, the GCOM cannot produce better surface temperature data than that provided by
the NOAA/AVHRR. Therefore, this data is considered to be additional data for analysis with
its combination with other wavelength bands.

4.4.3.7 Physical Properties of Snow Cover
Although the albedo of snow cover changes depending on the wavelength, size and shape ofthe snow particles, impurities in the deposited snow, snow cover density, moisture content andsurface conditions, the exact relationship is not clearly understood. Meanwhile, the physicalproperties of snow cover also change depending on the meteorological conditions of the snow
cover area, presence of any atmospheric fall-outs and time duration after snowfall.
Accordingly, the properties of snow cover contain information on the history of the said snow
cover. For example, the particle size of the deposited snow can be estimated using the 1.1 – 1.2µm wavelength band. As the particle size of fresh snow tends to be small, it may be possible todetermine whether or not there has been fresh snowfall in a snow covered area. It is known
that the albedo in the near-infrared region increases when drifting snow takes place. Using
this knowledge, it may be possible to estimate the wind velocity on the ground surface. As
mentioned earlier, the SGLI uses 19 wavelength bands to observe the cryosphere and it ishoped that it will be able to obtain valuable data to clarify the relationship between thealbedo and the precise conditions of snow and ice surfaces.

- 40

5.

Practical Use of GCOM

5.1
Improvement of Accuracy of Weather Forecasting (Kozo Okamoto: Japan
Meteorological Agency)
Accurate forecasting of the weather can improve the daily lives of people and contribute to thedevelopment of agriculture, forestry, fisheries and other industries. Accurate as well as timely
weather forecasting is extremely important to protect people’s lives and assets from natural
disasters caused by heavy rain and stormy winds. The core data for weather forecasting is
generated by numerical weather prediction (NWP) and its quality is largely determined by
the accuracy of the initial values. One of the most important tasks for the world’s major NWP
centres, including the Japan Meteorological Agency (JMA), is the preparation of highly
accurate initial values by means of assimilating highly accurate observation data over a wide
area using an advanced data assimilation system.
JMA has a nationwide observation network using weather radars, the AutomatedMeteorological Data Acquisition System (AMeDAS), wind profilers and radiosone balloons
and assimilates the data obtained by this network in an appropriate manner to forecast heavy
rain and other meteorological phenomena. However, this observation network extends only
just outside the Japanese archipelago and is insufficient to observe the inflow of wet air fromthe ocean which occasionally triggers localised downpours. Moreover, only limited data fromships, buoys, aircraft and island observation posts is available for ocean areas in the tropical
Pacific region despite the importance of such data for the prediction of the emergence andsubsequent path of a typhoon. For this reason, satellite observation is extremely important tofrequently obtain vital data covering a wide area of ocean and upper atmosphere. Typical dataobtained by geostationary satellites and polar orbit satellites and used in operational
assimilation systems are upper layer wind data from the continual images provided by highresolution visible and infrared imagers, vertical profile data for temperature and vapour andcumulative vapour and precipitation volumes observed by infrared and microwave
radiometers (sounders/imagers) and ocean wind data observed by microwave scatterometers.
Vapour data observed by a microwave imager in particular is known to improve the mesoscale
forecast of heavy rain, global distribution of precipitation and prediction of typhoon track. As
an example, Fig. 5.1.1 shows the 3, 6 and 9 hour forecasts at the time of a heavy downpour in
Fukui Prefecture in 2004. Analysis at the 18 UTC on 17th July, 2004 could not accurately
determine the moisture field because of the absence of vapour observation data for the Sea ofJapan and the South China Sea. The 6 and 9 hour forecasts using the observed values at thisparticular time as the initial values incorrectly predicted that the precipitation in Fukui
Prefecture would not persist and that precipitation would occur in Yamaguchi Prefecture. In
contrast, the assimilation of the potential precipitation calculated from Aqua/AMSR-E datawith the actual precipitation using the four-dimensional variational method produced amoisture field which actually reflected the actual situation. The 9 hour forecast based on thisinformation predicted weak precipitation in Fukui Prefecture and no precipitation in
Yamaguchi Prefecture. Meanwhile, Fig. 5.1.2 shows that the mean error of a typhoon track
forecast is reduced when brightness temperature data for the sea surface under a clear sky
provided by the SSM/I, TMI and AMSR-E is assimilated in the global data assimilation
system.
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As the observation accuracy and spatial resolution of the GCOM-W/AMSR2 are better thanthose of the current AMSR-E, further improvement of the accuracy of weather forecasting is
expected. Moreover, Very few of other microwave imagers is expected during the operation
period of the GCOM-W1. Therefore, the GCOM-W1 is quite significant for the preservation of
the global observation system as real-time coverage is crucial for operational data
assimilation systems.
Sea surface temperature (SST), snow cover depth and sea ice concentration products derived
by microwave imagers are now used for analysis of the SST, land surface and sea ice. The
analysis results are used as boundary or initial values for NWP. For example, analysis of SST
is extremely important for prediction of the emergence and track of a typhoon by NWP
models.

Fig. 5.1.1
3-hour precipitation computed by the JMA’s mesoscale numerical prediction
model using the analysis results at 18 UTC on 17th July, 2008 as the initial values. Those on
the left are 3.6 and 9 hour forecasts without the assimilation of AMSR-E data, those in the
middle are with assimilated AMSR-E data and those on the right are the actual precipitation
analysed by the ground radar and AMeDAS corresponding to each of the forecast times.
It is hoped that the GCOM-W/AMSR2 will further improve the accuracy of analysis. However,
the existing operational global data assimilation system can only use data above the ocean inclear regions. In addition, the observation data is not fully utilised as data in the 7 and 10
GHz bands is not used by either the global data assimilation system or the mesoscale data
assimilation system. To rectify the situation, efforts are being made to assimilate thebrightness temperature in precipitation areas and to utilise the surface information using low
frequency channels. As part of these efforts, an experiment is in progress to assimilate all
weather ocean wind data developed with the JAXA with a view to improving the accuracy of
the typhoon intensity. At present, the data assimilation system of the JMA only assimilates
statistically prepared pseudo-atmospheric pressure and wind data called typhoon bogus near
the centre of a typhoon. With the use of real observation data, it is hoped that pressure and- 42wind similar to the actual values can be analyzed and predicted.

Fig. 5.1.2

Prediction

Number of Samples

Path
(km)
Error
Predicted TimeThe mean prediction error of the central position of the typhoon by the global NWP
model is shown by the predicted time for Typhoon No. 11 through Typhoon No. 18 in
2004. The blue line shows the prediction without the assimilation of SSM/I, TMI andAMSR-E data while the red line shows the prediction with assimilation. The rounddots indicate the number of samples used to compile the statistics (right scale).
The data and products to be provided by the GCOM/SGLI are expected to be used for NWP
and other work by the JMA. The high resolution and high precision visible and infrared data
can be used to verify the cloud and radiative processes of the NWP model as albedo andradiance data as well as cloud and aerosol products. At present, the physical quantities
corresponding to the albedo and brightness temperature observed by geostationary satellites
are computed using the NWP results. These quantities are then compared with the actual
observation values to verify and improve the cloud process of the model in question. In regard
to aerosols, products of the MTSAT, AVHRR and MODIS are used to monitor the ongoingsituation of aerosols, including aeolian dust, and to prepare climatic values. The SGLI may beable to improve this work. There will be a growing need for the assimilation of cloud andaerosol data to prepare their initial values against the background of the increasing
resolution of the model, refinement of the cloud and precipitation processes and upgrading ofdisaster prevention and air pollution information. Many unsolved issues from the viewpointof technology and computational resources must be dealt with for the direct assimilation ofalbedo and brightness temperature data in the visible and near infrared bands. In the case ofozone, a highly accurate product, if such a product is made available, should be effective forthe preparation of ultraviolet information, application to climate analysis and verification ofchemical transport models. The idea of using the SST product of the SGLI for SST analysis, asin the case of the AMSR2 product, is currently being examined.

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5.2
Monitoring and Prediction of Air Pollution by Yellow Sand and Other Pollutants(Takashi Nakajima: Tokai University)
5.2.1

Necessity for Monitoring of Air Pollution Particles

Fine particles suspended in the atmosphere include those from natural sources, such as sea
salt particles generated from splashed seawater, dust blown up from the ground surface, sootfrom forest fires and volcanic fumes, and those primarily originating from human activities,
such as dust discharged through the use of fossil fuels in factories and by automobiles andsecondary particles converted from gaseous air pollutants. In this section, air pollution
particles refer to those with a high potential to cause damage to health and other damage indaily life among the various pollutants mentioned above. Air pollution particles released into
the atmosphere may travel several thousand of kilometres from their original source, riding
on the airstream. There is concern in recent years in regard to the increasing trend of forest
fires which produce a huge quantity of soot and the yellow sand phenomenon in East Asia,
making the monitoring of air pollution particles ever more important. The SGLI on board the
GCOM-C satellite has many observation channels, from the near ultraviolet band to theinfrared band, and can be used to observe air pollution particles.
5.2.2

Examples of Air Pollution Particles Observed by the GLI and MODIS

Figs. 5.2.1(a) and (b) show the state of a forest fire originating at the shore of Lake Baikal
captured by the Global Imager (GLI) on board the ADEOS-II satellite. The GLI is thepredecessor of the SGLI to be mounted on the GCOM-C satellite. It was possible to extractsoot in an effective manner using the near ultraviolet channel (380 nm) of the GLI. Fig. 5.2.2
shows the extracted air pollution particles, including soot from the forest fire, over a wide
area using the near ultraviolet and visible light channels of the GLI. From this image, it canbe detected that soot which was originally generated at the shore of Lake Baikal has travelled
a long distance to almost reach Alaska. The 380 nm near ultraviolet channel is a uniquechannel only used for imagers developed by Japan among moderate spatial resolution wideswath imagers. Because of its usefulness, its inclusion in the SGLI has been decided. In
addition to the 380 nm near ultraviolet channel, the SGLI also has two polarisation
observation channels at 670 nm and 865 nm in the visible to near-infrared band and are
expected to be a powerful tool for the monitoring of air pollution particles. Fig. 5.2.3 shows anexample of the observation of the yellow sand phenomenon captured by the MODIS sensor on
board the Terra satellite. It is clearly shown here that the continent’s yellow and has spreadwidely from the east coast of the continent to the Sea of Japan. As these examples suggest, theobservation of air pollution particles from space has been gradually entering the realms ofpractical use.

- 44

Fig. 5.2.1 State of soot generated by a forest fire near Lake Baikal (a) (19th May,
2003) and the spread of the soot to East Asia (b) (20th May, 2003). These images were
processed at the Tokai University Research and Information Center (TRIC) using
ADEOS-II/GLI data provided by the JAXA/EORC

Fig. 5.2.2 Distribution of air pollution particles on 19th May, 2003 as extracted through a
combination of the 380 nm and 400 nm channels of the GLI. It is clearly shown that the sootgenerated near Lake Baikal has travelled a long distance to reach Alaska. The image isprovided by the JAXA/EORC.

- 45

Fig. 5.2.3
Captured image of the yellow sandphenomenon spreading to the Sea of Japan as wellas part of the Japan islands (10:23 Japan local time
on 8th April, 2006). This image was processed at the
TRIC using data from the Terra/MODIS sensor(developed by the NASA) which was received by the
TRIC.

5.2.3

Satellite Observation Used for Prediction of the Distribution of Air Pollution Particles

The observation of air pollution particles from space is not only used to observe the state ofsuch particles at the time of observation but also for verification of the development process ofan aerosol transport model to be used for prediction in the future. To be more precise, thepractical applicability of an aerosol transport model can be checked by comparing thecomputation results of the aerosol transport model developed in accordance with various rulesof physics with the actual values observed by a satellite in both the short-term real field andin the long-term average field. Fig. 5.2.4 shows the density distribution of air pollution
particles (soot, organic carbon and sulphate aerosols) in East Asia as predicted by the
SPRINTARS, an aerosol transport model (developed by Takemura et al, 2000). Areas with ahigh density of aerosols are predicted for a wide area in Southeast Asia near the source andthe northern part of the Indian Sub-Continent. As the spatial resolution of aerosol transportmodels has been steadily improving, it will be necessary for the SGLI to observe with a lateral
spatial resolution of several kilometers.
5.2.4

Various Research Themes to Achieve Practical Application

The observation of air pollution particles by the GCOM-C/SGLI faces several problems.
Firstly, there is a problem of distinguishing these particles from clouds. Particularly in thecase of an optically thick particle layer made up of soot from forest fires or yellow sand, theconventional classification methods can mistakenly record a layer of air pollution particles as
cloud cover, making the development and verification of an algorithm to distinguish themessential. Next, it is necessary to improve the detection accuracy of air pollution particles
above land. The detection of particles in the atmosphere in a land area with generally a highlevel of surface reflectance is more difficult that detection in a sea area with a lower
reflectance. Therefore, the development of a highly accurate algorithm for this purpose is- 46

required. Identification of the types of air pollution particles is another important research
theme. A positive outcome of the GLI project is the proposal of a particle classification
algorithm using four wavelengths in the visible light through near infrared band (Higurashi
and Nakajima, 2002) which can be used with SGLI data. The practical application ofobservation data demands satellite and on board sensor operation over a long period of timeand brightness calibration.

21:00 on 26th June, 2008

Low ←→ Extremely High
Fig. 5.2.4
Density of air pollution particles (soot, organic carbon and sulphate aerosols) inEast Asia as predicted by an aerosol transport model (SPRINTARS) at 21:00 on 26th June,
2008. The image is provided by the Research Institute for Applied Mechanics (RIAM) of
Kyushu University. (http://sprintars.riam.kyushu-u.ac.jp/indexe.html)
Active use of the ground observation network is important for the verification of satellite
observation results. As part of the Global Earth Observation System of Systems (GEOSS), the
SKYNET ground observation network (Takamura et al., 2004) has been developed, primarilyin East Asia, and is used for the verification of data for practical application. Satellite
observation data can be verified through the combined use of the estimated cloud cover by theskycamera installed at key SKYNET observation sites, observation of particle characteristics
by skyradiometers, etc. and observation of the vertical distribution by lidars. At present,
atmospheric observation is being conducted at more than 10 sites in East Asia, including
super sites located at Cape Hedo in Okinawa Prefecture, Tsukuba City in Ibaraki Prefecture
and Cheju Island in Korea. The further development and operation of observation sites isrequired with support under the GCOM project.
< References >Higurashi, A. and Nakajima, T., 2002: Detection of Aerosol Types Over the East China Sea
Near Japan from Four Channel Satellite Data, Geophys. Res. Lett., 29, 2836,- 47doi:10.1029/2002GL015357
Takamura, T., Nakajima, T. and SKYNET Community Group, 2004: Overview of SKYNET
and its Activities, OPTICA PURAY APLICADA, 37(3), 3303-3308
Takemura, T., Okamoto, H., Maruyama, Y., Numaguti, A., Higurashi A. and Nakajima, T.,
2000: Global Three-Dimensional Simulation of Aerosol Optical Thickness Distribution of
Various
Origins,
JournalofGeophysicalResearch,105,
17853-17873(http://sprintars.riam.kyushu.u.ac.jp/indexe.html)

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5.3

Improved Accuracy of Sea Route Data (Kenji Matsumoto: Japan Coast Guard)

Part of the Sea of Ohkotsk near Hokkaido is covered by ice every winter and this ice hampers
normal navigation and coastal fishery. In March, 1970, a large-scale sea disaster took place in
Hitokappu Bay of Etorofu Island due to drifting ice. This disaster led to the establishment of
the Ice Information Center at the Regional Coast Guard Headquarters in the First Region.
Every year from late December to early May, the Ice Information Center daily gathers
information on ice from Coast Guard stations, meteorological observation stations, patrolboats, vessels in the area, aircraft belonging to the Japan Coast Guard and the Ministry of
Defence and satellites and provides the rearranged information at 17:00 hours via theInternet and fax transmission.
Sea Ice Quick Report
Ice
Concentration10
gradeclassification of
ice concentrationin ice covered
areas

Cloud

Sources: Japan Coast Guard
Japan Meteorological Agency
Tokai University/TRICJAXA/METIThe use of satellites is extremely useful for observation of the sea ice distribution cover over awide area. The satellite data recently used for this purpose is provided by such optical sensors
as the MODIS on board the NASA’s Terra satellite and the NOAA’s AVHRR, a microwave
radiometer. However, as the Sea of Ohkotsk in winter experiences rough weather almost
every day with extensive cloud cover, it is not easy to constantly know the distribution of ice.
In order to facilitate the use of image data obtained by a synthetic aperture radar andmicrowave sensors capable of establishing the sea ice distribution through cloud cover, theHydrographic and Oceanographic Department of the Japan Coast Guard is conducting joint- 49

research with the JAXA on the development of a sea ice observation method using ALOS data
and the use of such data. This joint research aims at the development of a method to produce
ice concentration images by the automated processing of ALOS observation data with a viewto preparing ice quick report maps. The research also aims at improving the accuracy of ice
interpretation for the ice concentration images produced. The types of images currently used
to prepare sea ice quick reports for practical use are wide area ScanSAR images (including
images for which the antenna pattern is corrected), ice concentration images obtained by asynthetic aperture radar (PALSAR) and optical sensor and microwave radiometer images. For
the preparation of a sea ice quick report, wide area ScanSAR images provide a detailed
distribution of ice and allow estimation of the degree of unevenness of the ice surface. Ice
concentration images are very convenient for the preparation of a quick report as they show
the general distribution of ice and the degree of ice concentration for quick recognition.

Sea of Ohkotsk: ScanSAR image

Sea of Ohkotsk: Ice distribution image

When routine ScanSAR images are compared with aerial observation results of MODIS
images, the following problems can be recognised.
● A similar level of backscattering brightness can be produced by either a sea ice surface oropen water surface. A brightness difference which deters a definitive interpretation canoccur on an ice area.
● High and low brightness sites which can be easily misunderstood as ice can occur on the
open sea.
● Information on the backscattering brightness alone cannot confirm a newly generated ice
area.
Various steps are adopted to solve these problems when a sea ice quick report is prepared. For
this, various types of information are required in addition to the knowledge and experience ofthe staff members responsible. The verification of such thin ice as glease ice and nilas is very
important as far as ice information is concerned because these types of ice are formed at theearly stage of the sea ice generation process and can hamper the navigation of vessels by- 50

blocking the intake port for engine cooling water.
Two types of radiometers, i.e. GCOM-C/SGLI and GCOM-W/AMSR2, will be launched under
the GCOM. It is hoped that these radiometers will make a vital contribution to the solving of
the above problems by means of verifying  the separation between a sea ice area and open
water area and  the adequacy of the separation of a sea ice area into a sea ice area for which
the ice concentration is displayed and a thin ice area.
The sea ice quick reports currently use multiple satellite data and the suspension of any suchdata could hamper the provision of ice information. The launch of two sensors under the
GCOM should considerably improve the reliability of the continually provided ice
information.

< Reference >Examples of Graded Ice Concentration in a Sea Ice Quick Report
Concentration: Indicates the degree of ice concentration on average in a certain ice area in the
10 grade classification method.

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5.4
5.4.1

Provision of Fishing Ground Data (Katsuya Saito: JAFIC)
Current Conditions for Fisheries

Japanese fisheries are moving from the conventional growth-oriented fisheries to resourcesmanagement-type fisheries aimed at efficiently and permanently utilising marine resources
following the ratification of the UN Convention on the Law of the Sea in 1996. While thistransition is taking place, the actual conditions surrounding fisheries are quite severe
because of rising fuel prices and sluggish fish prices. The recent hike of fuel prices inparticular has inflicted massive damage to the entire fisheries industry of which the firstpriority now is to minimise the fuel oil consumption, including the efficient use of fish lamps,
rather than the management of marine resources.
5.4.2

Urgent Necessities

Under such severe business conditions, the fishing operation efficiency must be further
improved. Advancement of the information required for fishing operation is necessary for
efficient operation along with improvement of the performance and efficiency of fishing gearand fishing boats. The availability of real-time information directly related to operation on thelikely locations of dense schools of fish, efficient routes to reduce fuel consumption and sea
areas for safe operation plays an important role in efficient and safe operation.
In addition to the real-time sea data required for daily operation, information and time-series
data on the sea conditions over a wide area is also crucial for resources management-type
fisheries. Understanding of the entire food chain in the sea with phytoplankton at the bottom
provides a clue to the changing resources situation of fish which are higher predators. It is
known that the meandering of the Black Stream (Kuroshio) significantly affects the changing
size of true sardine and other fish resources. Therefore, information on the marine conditions
is essential to establish the cause(s) of fluctuating marine resources.
Moreover, real-time data on damage to fisheries by red tide, etc. and marine pollution over awide area should contribute to reducing the said damage when such data is made available.
5.4.3

Present Problems and Tasks

The water temperature, salt content and quantity of phytoplankton and zooplankton
constitute basic information on the marine conditions to assist fisheries. In regard to the sea
surface water temperature and colour in particular, efforts have been made to apply remotesensing images to fisheries since the 1980’s. However, remote sensing using the visible light toinfrared bands to observe the water temperature and sea colour is strongly affected by clouds
and the sea area around Japan tends to have many areas for which data is missing because of
the presence of cloud cover.
The NOAA/AVHRR of which the data is most frequently used for fisheries has a spatial
resolution of approximately 1 km which is the standard resolution for satellite-based fisheries
data. However, a better resolution is required to accurately establish the marine conditions offishing grounds. A higher resolution is particularly required for coastal areas in which many
- 52

fishing boats operate.
As the marine conditions are constantly changing like the atmospheric conditions, frequent
observation is important. The availability of data at very short intervals is crucial for suchdata to be useful for fisheries. There is much expectation regarding the further use of new
remote sensing data other than water temperature data such as sea level and night-time
visible band images, the application of which to fisheries has been increasing since 2000.
5.4.4

Provision of Fisheries Data by GCOM

The effectiveness of sea surface water temperature observation by generations of microwave
radiometers (TMI, AMSR-E and AMSR) is widely recognised by those involved in fisheries.
Among the various sensors of the GCOM, the highest expectations are for microwave
radiometers as a new generation of sensors is expected to provide images of a higher
resolution. The sea surface water temperature observation by the microwave radiometer onboard the GCOM-W will enable the provision of surface water temperature images with few
areas of absent data due to cloud cover as a microwave sensor is capable of conducting
all-weather observation. From the viewpoint of providing information to assist safe operation,
the provision of reliable water temperature images for fishery operators in operation will bepossible although the resolution will not be as high as that of images provided by other typesof sensors. From the viewpoint of applied research, the assured availability of sea surface
water temperature images even in the early summer rainy season or winter for which
sufficient data has not been available so far by the existing sensors covering the visible lightthrough infrared bands will make it possible to increase the number of matching-ups between
fishing ground data and satellite images to improve the accuracy of the prediction of sea
conditions and promising fishing grounds. Uniform observation data over a wide area will
form an important data set for clarification of the relationship between changes of the marine
resources and ocean conditions for which a relatively macroscopic approach is required. Suchdata will, therefore, greatly contribute to research on marine resources.
The data obtained by multiple wavelength visible light to infrared sensors, such as the OCTS
and GLI, has been indispensable for fisheries. The high resolution, multiple wavelength
observation by the visible light to infrared sensor on board the GCOM-C will provide various
information on the marine conditions, including the sea colour and sea surface watertemperature. From the viewpoint of providing vital information, frequent observation with ahigh resolution of 250 m will provide detailed data for fishing boats operating along the coastas well as those operating in adjacent waters, likely contributing to improvement of theoperational efficiency. From the viewpoint of applied research, the new sensor will enable theobservation of minor whirls and fronts, clarification of the conditions of fishing grounds
through the simultaneous observation of the water temperature and sea colour and detailed
analysis of differences between a water temperature front and sea colour front, all of whichhave been beyond the capability of satellite sensors with a resolution of some 1 km commonlyin use at present. The findings of such research will greatly contribute to improvement of theprediction accuracy for fishing grounds and sea conditions. Conventional analysis primarily
relies on chlorophyll density images but the various products to be provided by the GCOM
will enable analysis of the optical characteristics of fishing ground conditions and therelationship of such conditions to CDOM and SS. From the viewpoint of research on marine
resources, the effective use of products relating to basic production and other matters is hoped- 53

for. High resolution sea colour images may well be used for the early detection and predictionof red tide.
The ocean wind vector obtained by a microwave scatterometer should assist the safe andefficient operation of fishing and other vessels. Wave data is among the most appreciated typeof weather data by fishery operators. If almost real-time ocean wind vector and time-series
data on ocean wind are made available, they will constitute highly effective data for fisheries.
There has been hardly any research comparing actual fishing ground data with microwave
radiometer data and the availability of new sets of data is likely to provide an insight into theimpacts of ocean wind on the formation of fishing grounds and movement of fishing grounds.
Such knowledge will assist the accurate prediction of promising fishing grounds.
Multi-sensor remote sensing combining a microwave radiometer, microwave scatterometerand visible light to infrared sensor is likely to provide a new approach for research as well as a
new data supply route in the age of the GCOM.

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5.5
Improvement of Coastal Water Management Efficiency (Joji Ishizaka: Naggoya
University)
While coastal waters account for only less than 10% of the total sea area of the world, morethan half of the total fihs catch comes from these waters. Meanwhile, as more than half of the
global population live in coastal land areas, the impacts of human activities on coastal waters
are quite large. For example, various substances of artificial origin flow into coastal watersfrom factories located near the coast. Such substances are also discharged into coastal watersfrom rivers, the atmosphere and navigating ships, polluting these waters. The construction ofdams on rivers affects the quality of river water and the sediment transport mechanism while
reclamation and other work affecting the coastline are frequently conducted. In recent years,
global warming is believed to be causing a rise of the sea temperature and sea level.
Regular observation is essential for the management of coastal waters which are believed tobe subject to significant impacts of artificial activities. However, the scale of coastal waters ismuch smaller than that of the open ocean and it is known that considerable short-termchange occur in coastal waters due to tides, etc. In the case of red tides (harmful algal
blooms) which often cause considerable damage to the coastal fish culture industry forexample, most of them occur on a small scale of less than 1 km in length/width and are likely
to last for several days even though there are some exceptionally large cases. For themonitoring of such a phenomenon, a high resolution sensor with a lower observation
frequency cannot follow the actual changes and the application scope of a global sensor with alow resolution is limited. The SGLI capable of providing daily data with a resolution of 250 m
is the most suitable sensor for the monitoring of coastal waters.
The SGLI can be used to observe a number of phenomena for the purpose of coastal water
management. Firstly, there are the red tides mentioned earlier. Not all red tides are harmful
to human activities and it is not easy for current remote sensing technologies to accurately
identify the types of red tides. However, it is possible to determine the chlorophyll-aconcentration, i.e. total amount of phytoplankton, and colour which is believed to represent a
red tide. When such information becomes available for a specific sea area or time, it will bepossible to judge whether or not the observed red tide is indeed a harmful algal bloom throughcollaboration with field observers so that the information can be quickly transfered to fisheryoperators. Such efforts have already been made by the Fisheries Agency and fisheries
experiment stations using MODIS data, etc. processed by the JAXA. It is hoped that theavailability of the high resolution data obtained by the SGLI will make it possible to deal with
red tides, etc. of a much smaller scale in inner bays and other relatively small water areas.
When the classification of phytoplankton, which is currently difficult, becomes a reality, thedata will be much useful. Discussions on these topics are in progress by theIntergovernmental Panel on Harmful Algal Blooms of the UNESCO’s Intergovernmental
Oceanographic Commission (IOC) and the GOOS (Global Ocean Observation System)
Scientific Steering Committee (GSSC).
Meanwhile, it is important to mitigate influence of red tides after they occurred. A red tide isoften caused by eutrophication in coastal waters. Regular monitoring of the amount ofchlorophyll-a, transparency and primary production in coastal waters will become possible
with the launch of the SGLI and the accumulated data over a long period of time will enable a- 55

proper judgement of the state of eutrophication in a specific area, providing valuable
information for long-term coastal water management. This type of effort is currently inprogress under the Marine Environmental Protection of Northwest Pacific Region which is
jointly being implemented by the Ministry of the Environment and the Northwest PacificRegion Environmental Cooperation Center to assist the UNEP’s North-West Pacific Action
Plan (NOWPAP) using MODIS data processed by the JAXA. The high resolution data to beprovided under the GCOM is expected to be a great asset for this work.
In addition to chlorophyll-a and primary production, suspended solids (SS) can be used as anindicator for the resuspension of sediment and deposits from land. This indicator is useful forthe monitoring of sediment input to coastal waters, the volume of which declines with theconstruction of dams or due to other reasons, or increased input as a result of mining
exploitation or coastal development. Coloured dissolved organic matters (CDOM) can be used
as an indicator for freshwater inflow (salinity) on a very small scale, the measurement of
which by other existing remote sensing technologies is difficult. Oil spills can also be detectedbased on changes of the sea colour.
As explained so far, it is believed that the high resolution ocean colour data to be provided
frequently by the SGLI can be used for coastal water management. The problems emerging incoastal waters today are not restricted to Japan but world-wide and are especially noticeable
and critical in coastal waters in Asia where development is currently taking place at a great
speed. In this context, high resolution data which is obtained globally at a high frequency willplay a very significant role in global level coastal water management.

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5.6
Improved Prediction Accuracy of Crop Yields (Katsuo Okamoto: National Institute forAgro-Environmental Sciences)
5.6.1

Importance of Prediction of Agricultural Production with GCOM-C1“Agricultural policies will become important issues for all governments. Agricultural research
has two aspects. One is to increase the unit yield. The other is to swiftly and accurately assess
crop production on a global scale to determine the best balanced food supply and demand.”wrote Idso et al. in the journal Science in 1977. In the subsequent 30 years, there has been a
succession of reports predicting an increase of the food demand due to the population growthof the world and the great impact of abnormal weather due to climate change on crop
production in various parts of the world as if reinforcing the arguments put forward by Idso et
al. In the process of climate change, extreme events of weather occur frequently. Because of its
heavy dependence on imported food, the second aspect of agricultural research pointed out by
Idso et al. carries significant weight for Japan. For Japan to ensure a stable food supply, it isessential to know the domestic and overseas production (short-term production) each year assoon as possible. As the short-term production can be calculated using the planted area for theyear and the yield per unit area (unit yield), it is hoped that accurate prediction can be swiftlyand objectively made using satellite remote sensing data. As the data provided by the SGLI to
be mounted on the GCOM-C1 satellite will cover the entire globe for four days, it will bepossible to monitor the situation of crop growth using this data.
5.6.2

Food Production and Biomass Energy Production

Increasing attention is being paid to biomass energy as a substitute to fossil fuels in order toreduce the emission volume of CO2, one of the main GHGs believed to significantly affectclimate change. Maize and sugarcane can be used raw materials for ethyl alcohol but are also
types of food. While maize is consumed by human, a far greater quantity is consumed asanimal feed. As such, maize is closely linked to the production of dairy as well as livestock
products. Whether or not maize and sugarcane are used as food or raw materials for ethyl
alcohol depends on socioeconomic factors. However, the modes of consumption greatly affectthe food supply and demand situation and monitoring of the scale of the planting area andgrowth situation by the GCOM-C1/SGLI will be extremely important to predict the eventual
production of these crops.
Meanwhile, palm oil has emerged as a strong candidate raw material for bio-diesel althoughpalm oil is used as a raw material for detergent and edible oil. In the case of oil palm, theproblem mainly lies with the cultivation locations rather than the right mix between its use
as a food and its use as a biomass energy. As oil palm grows in the tropics, the cultivation sites
must be created by cutting down tropical rain forests. Newly planted areas of oil palm are said
to be entirely cut for replanting after some 25 years. This results in the contradiction thatsites which can function as carbon sinks in the form of tropical rain forests are cleared tocreate farmland to restrain the use of fossil fuels. Observation of the tropics by an opticalsensor is not very easy because of the high level of cloud cover. However, the use of the
GCOM-C1/SGLI with a high observation frequency will make it possible to establish logging
sites in tropical rain forests and to monitor the growth situation of maize and sugarcane.

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5.6.3

Estimation of Crop Cultivation Area

At present, the planting area is estimated with an error margin of 10 – 20% using satellite
remote sensing data. However, when the unit area for estimation is as small as anadministrative area (for example, the area of a municipality in Japan), the error margin tends
to be larger. Among the different types of cultivation sites, paddy fields for growing rice, themain staple food of the Japanese people, have the characteristic of being submerged during
the planting season. Because of this characteristic, paddy fields can be easily distinguishedfrom dry farmland growing other crops using data in the short wavelength infrared region
provided by the GCOM-C1/SGLI. In general, the use of optical sensor data can moreaccurately estimate the rice-planted area than microwave sensor data. The problem is thatrice cultivation areas in the tropical and temperate zones tend to have a high level of cloud
cover during the cultivation period because of the monsoon climate. It has, therefore, beendifficult to obtain reliable optical sensor data during the planting season which is suitable todetect planted paddy rice sites. The frequent observation by the GCOM-C1 will solve thisfundamental shortcoming of the use of optical sensors. Moreover, this characteristic frequent
observation capability will mean the provision of observation data throughout the cultivation
period. Accordingly, it should be possible to prepare a classification map of various planted
crops by combining observation data of suitable times, capitalising on the different cultivation
period for each crop.
5.6.4

Estimation of Crop Growth Situation and Prediction of Crop Yield

An approach from the crop growth model is very effective to estimate the unit yield of crops.
However, the use of such a model is restricted to a small area or site of which the parameters
are known because of the large number of parameters to be input to the model and the lack ofa global scale data set. Accordingly, the use of the satellite remote sensing technology isessential for the estimation or prediction of the unit yield for a wide area. Meanwhile, remotesensing data has a risk that different crop sizes due to different stages of growth may be
mistaken for different sizes caused by different growth speeds as such data only shows an
aspect of crop life at the time of observation. The high frequency observation by the GCOM-C1
can obtain observation data throughout the cultivation period, making it possible todistinguish different crop sizes due to different stages of growth and different crop sizes
caused by different growth speeds. Such distinction can enhance the estimation accuracy of
the growth situation of crops, leading to improved prediction of the eventual yield.
5.6.5

Research on Prediction of Agricultural Production and Contribution to Practical
Application

The data provided by a high frequency, multiple wavelength observation satellite such as the
GCOM-C1 is essential for the quick estimation or prediction of a cultivation area and yield offood, particularly main crops. The quasi-real-time prediction of the production for a year
requires advance knowledge of (1) the location of farmland and grassland (land use and land
cover classification), (2) which crop can be cultivated where (estimation of cultivation areas)and (3) which crop can produce how much (estimation of productivity). The GCOM-C1/SGLI
will make a major contribution in these areas. When such information becomes available, itwill be possible to achieve a stable food supply by means of studying (4) which crops arecultivated where in the current year (real-time planting classification) and (5) the- 58

expected/achieved crop yields (real-time estimation of production) every year.

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5.7
Observation of Volcanic Fumes and Prediction of Geographical Scope of Influence(Minoru Urai: National Institute of Advanced Industrial Science and Technology)
Satellite images are a powerful weapon for the observation of volcanic fumes which spreadover a wide area. These fumes contain volcanic ash which poses a serious threat to aircraft.
Suspended volcanic ash in the air is a real problem in the North Pacific Region whereimportant air routes pass over many active volcanoes. In a period of 12 years, at least 60
aircraft were damaged by suspended volcanic ash, seven of which experienced a fall of theengine output while in flight (Casadevall, 1993). Volcanic Ash Advisory Centers (VAACs)
provide information, including the actual conditions and predictions, on volcanic ash along airroutes to ensure the safe flight of aircraft from suspended volcanic ash blown into the air byvolcanic eruptions. There are nine VAACs around the world which constantly monitor
suspended volcanic ash using data provided by meteorological satellite and aircraft. For thesatellite monitoring of volcanic ash, it is crucial to distinguish between volcanic ash andclouds. Prata (1989) proposed a method for this using the difference in temperature data
provided by Channel 4 (10.3 – 11.3 µm) and Channel 5 (11.5 – 12.5 µm) of the AVHRR sensor
on board the NOAA satellite. This method has subsequently been used for data provided bygeosynchronous satellites and the MODIS (Tokuno, 1997 and Novak et al., 2007). Wen andRose (1994) estimated the amount of suspended volcanic ash using the temperature difference
data mentioned above provided by the AVHRR sensor. The height of the fumes following alarge-scale eruption can be estimated using the minimum temperature obtained from
infrared images of the fumes (Sawada, 1998). As the T1 channel (10.8 µm) and T2 channel (12µm) of the SGLI sensor correspond to Channel 4 and Channel 5 of the AVHRR respectively,
the techniques employed for the AVHRR data can be used for SGLI data. As the spatial
resolution of 500 m of the T1 and T2 channels is higher than that of the AVHRR or MODIS,SGLI data should make it possible to detect volcanic ash with a higher spatial resolution.
Volcanic ash is said to damage crops even if only a few millimetres are deposited. When the
thickness of the volcanic ash deposit is several centimetres, transport is paralysed and there
is a risk to health. A volcanic ash deposit thickness of several tens of centimetres can causehouses to collapse. There have been many examples of establishing the distribution of fallen
ash using visible light to near infrared satellite images. For example, the area of fallen ash
due to the eruption of Mt. Usu in 2000 was observed using data provided by the ASTER, etc.
At this time, satellite images identified the area of fallen ash which could not be trace by aground survey (Urai et al., 2001). The amount of spewed volcanic ash is a useful value toassess the scale of an eruption. Satellite images are important to interpolate an iso-weightline diagramme for volcanic ash established by a ground survey. Meanwhile, aerial MSS
observation provides data on an increase/decrease of the fallen ash distribution (Jitsubuchi, T.
et al., 2002). Using the visible light to near infrared channels of the SGLI, it should bepossible to detect areas of fallen ash.
To protect aircraft from volcanic ash, prediction of the movement of detected volcanic ash isimportant in addition to the detection of volcanic ash. Such prediction is also important topredict the overall amount of volcanic ash to fall. Searcy et al.. (1998) developed a model
called “PUFF” to predict the movement of volcanic ash which is spewed into and suspended in
the air following a volcanic eruption. This “PUFF” model predicts the vector of movement ofparticles in mini-scale time caused by wind, the gravity of particles and disturbance. The- 60

accuracy of 3D wind velocity and wind direction data and the accuracy of the particle size
distribution of volcanic ash affect the prediction accuracy of volcanic ash movement.
< References >Casadevall, T. J. (1993), Volcanic Hazards and Aviation Safety Lessons of the Past Decade,Aviation Safety Journal, 2(3), pp. 3-11
Jitsubuchi T. et al. (2002), Multi-Temporal Observation of the 2000 Eruption of Mt. Usu Using
an Airborne Multiwavelength Scanner, Kazan Vol. 2, 47(4), pp. 297 – 323
Novak, M. A .M. et al. (2007). Volcanic Emissions from Popocatepetl Volcano, Mexico,
Quantified Using Moderate Resolution Imaging Spectroradiometer (MODIS) Infrared Data: A
Case Study of the December, 2000 – January, 2001 Emissions, Journal of Volcanology andGeothermal Research
Prata, A. J . (1989), Observation of Volcanic Ash Clouds in the 10 – 12 µm Window Using
AVHRR/2 Data, International Journal of Remote Sensing, 10 (4 and 5), pp. 751 – 761
Sawada, Y. (1998), Correction of Highest Altitude and Position of Volcanic Fumes Following
the Eruption at Izu Oshima Island on 21st November, 1986 Using GMS Images, Bulletin of
the Remote Sensing Society of Japan, 18(4), pp. 359 – 368
Searcy, C. et al. (1998), PUFF: A High Resolution Volcanic Ash Tracking Model, Journal ofVolcanology and Geothermal Research, 80, 1-16
Tokuno, M. (1997), Satellite Observation of Volcanic Ash Clouds, Metrological Satellite Center
Technical Note, 33, pp. 29 – 48
Urai, M. et al. (2001), Observation by the ASTER of Fallen Ash Areas Following the Eruptionof Mt. Usu in 2000, Bulletin of the Geological Survey of Japan, 52(4/5), pp. 189 – 197
Wen, S. and Rose, W. I. (1994), Retrieval of Sizes and Total Masses of Particles in Volcanic
Clouds Using AVHRR Bands 4 and 5, Journal of Geophysical Research, 99(D3), pp. 5,421 –
5,431

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5.8

Forest Fire Detection (Haruo Sawada: University of Tokyo)

In 2000, some 360 million ha of the world’s forest land and grassland were lost or damaged byforest fires. According to a report by the FAO, the number of forest fires is increasing
worldwide. The annual loss of vegetation due to fire is estimated to be 5.1 billion tons andsuch a huge loss of vegetation is believed to be affecting the increase of CO2 in the atmosphere.
This is the reason why there is a strong argument in favour of firstly reducing the number offorest fires before planning an increase of the planting sites.
Forest fires are predominantly caused by human activities in all regions, for example, 95% in
the Mediterranean area and 90% in South Asia. While the dry conditions of forest areas due
to El Nino was a major cause of the large-scale forest fires which frequently occurred inSoutheast Asia and other regions from 1997 to 1998, the lack of control of human activities in
the fact of the dry conditions facilitated the occurrence of fire. Moreover, the failure to convey
accurate information on the fires to the fire-fighting teams despite the importance of initial
fire-fighting to effectively deal with a fire apparently exacerbated the scale of the damage.
The types of information which can be used as forest fire-related information are early
warning information which can be used to issue a dry weather warning, early fire detection
information enabling initial fire-fighting and a damage assessment map which is required forthe rehabilitation of fire-damaged areas. Meanwhile, the risk of forest fires consists of theignition risk, fire spread risk and fire-fighting difficulty. The ignition risk is directly
attributable to human activities and the distance from residential areas and road network
data are important factors to assess this risk. The fire spread risk means the risk of an ignitedfire spreading and the degree of dryness of the vegetation and the weather are importantfactors to assess this risk. The fire-fighting difficulty is assessed in terms of the distance fromthe nearest road to the scene of a fire and the gradient of the land at the scene of a fire.
5.8.1

Early Warning of Forest Fires

The role of remote sensing in the early warning of forest fires is to improve the accuracy of
information on the fire risk. The ignition risk and fire-fighting difficulty can be establishedbased on GIS data. In the case of the fire spread risk which varies depending on the actual
weather, mapping of the moisture content of the vegetation is effective as the moisture
content can act as an indicator for the degree of dryness. Under the GCOM, the shortwave
infrared SW3 channel of the IRS can be used for this purpose. As the ground resolution of thissensor is 250 m, it will be possible to prepare a detailed fire spread risk map which can beeffectively used for the restriction of fire use and the prioritisation of patrol areas. However, aparticular technique which is capable of generating prediction data through processing by atime-series model (use of the LMF-KF, etc.) will be required as it will be necessary to generateprediction data on the dryness of the vegetation. In regard to the fire spread risk, it isimportant to improve the fire spread risk assessment through the accumulation and feedingback of data. In this way, it is hoped that assessment of the fire spread risk will be improvedat all levels, ranging from the global level to the national and regional levels.

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5.8.2

Early Detection of Forest Fires

Early detection is extremely important to deal with forest fires which require swift initial
fire-fighting. As early detection primarily depends on frequent observation, the GCOM alone
will be unable to provide sufficient information. The forest fire early detection system inJapan ensures a high level of observation frequency through the use of all usable NOAA,TERRA and AQUA satellites and provides early detection data through the high speed
processing of the original observation data. For the early detection of forest fires, the data
provided by all of the relevant satellites should be used along with the GCOM satellite data toincrease the observation frequency to make practical application of the early detection system
possible. The thermal infrared IRS sensor has an advantage over other sensors in that its
spatial resolution of 250 m is far higher than the spatial resolution of the NOAA/AVHRR (1km) and others. Meanwhile, the thermal sensitivity of a maximum of 340 K is similar to thatof the TERRA/MODIS.
5.8.3

Disaster Area Assessment

Areas which are subject to forest fires release CO2 into the atmosphere in addition to thedepletion of forest resources. Clarification of the degree of damage is important for theselection of the method to restore the forest environment. It is already known that the degreeof damage can be established to a certain extent using the near infrared channel and far
infrared channel. Estimation of the CO2 volume corresponding to the degree of damage is anurgent task and it is hoped that the GCOM will act as an information source for the REDD
(Reduction of Emission from Deforestation and Forest Degradation) which is to be
implemented in the second commitment period in accordance with the Kyoto Protocol.
The GOFC-GOLD (Global Observation of Forest and Land Cover Dynamics) acts as a global
information network for forest fires and is at the advanced stage of information accumulationand supply. The GCOM should consider intensive collaboration with the GOFC-GOLD.

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5.9

Extreme Water Cycle Phenomena (Toshio Koike: University of Tokyo)

Serious water-related problems, such as disasters caused by heavy rain and water shortages,
have been spreading throughout the world in recent years and the 21st Century is said to be a
century of a water crisis. At the same time, there is much strong evidence that climate change
is taking place due to GHGs which are being continuously emitted into the atmospherethrough human activities. Observation data in the late 20th Century confirmed the trends ofan increasing frequency of heavy rain, expanding geographical areas affected by drought andgrowing intensity of tropical depressions. Moreover, a prospect of the continuation of thesetrends in the coming years is suggested by integrated analysis of the outputs of many climatechange prediction models. Changes of the water cycle at present and in the future,
particularly such extreme phenomena as heavy rain and drought, can cause serious damage ifthey occur in areas which are vulnerable to disaster, including urban areas with a highpopulation concentration and intermediate and mountainous areas with weak disaster
resistance. As stated in the report entitled “Desirable Measures in the Water Disaster Field to
Respond to Climate Change Caused by Global Warming” of the Panel on Infrastructure
Development (June, 2008), strengthening of the monitoring and changes of thedisaster-resistant social structure are required in addition to the conventional disasterprevention and disaster mitigation measures. One of these new measures should be thedevelopment of a scientific base to properly understand the mechanism of changes of thewater cycle and to improve the accuracy of the prediction of likely changes to provide society
with useful information to avoid water damage and crises.
The water cycle is an important sub-system of the global climate system along with theenergy flow and the global and regional water cycles are closely related to hydrologicalphenomena at the basin level. In other words, precipitation and discharge to rivers at thebasin level are strongly affected by fluctuations of the global water cycle. Accordingly, thedevelopment of a method which is capable of effectively downscaling water cycle fluctuation
data at the global and regional levels to prediction data at the basin level will be extremely
useful to deal with the problems of basin-level flooding and water shortages.
Meanwhile, the development of not only a prediction system but also an observation system
has been very slow in developing countries. The 10 Year Implementation Plan for the Global
Earth Observation System of Systems (GEOSS) agreed at the Third Earth Observation
Summit (February, 2005) encourages the access of developing countries to data and models,calling for the strengthening of capacity development for disaster damage mitigation andenvironmental improvement through the provision of useful information and methods using
earth observation data and global scale prediction models. Furthermore, the G8 Hokkaido
Toyako Summit Leaders Declaration clearly states that “we will continue to enhance thecooperation with developing countries, particularly the most vulnerable countries, for theirefforts to adapt to climate change, including disaster risk reduction”. One way to achievethese objectives is to develop a method which is capable of providing data for country andbasin level predictions using earth observation data and for water cycle fluctuation
predictions at the global and regional levels.
For sound decision-making regarding the reduction of flood or drought damage, it is necessary
to downscale global and regional level data for prediction to data for local level prediction. To- 64

achieve this, the conventional numerical weather prediction models use a method which
combines the statistical characters of the rough grid level model outputs for nesting or wide
area prediction with the statistical characters of local observation data for the target area.
However, this method cannot obtain important initial values for short-term numericalprediction, such as vapour content and cloud water content in the atmosphere and the soil
moisture content and other hydrological data for land areas, with physical consistency
corresponding to a small target area. As a result, the effective use of wide area prediction data
for small area prediction with high accuracy has been difficult. The principal reasons for thisare the difficulty of estimating the soil moisture content on a global scale and the absence of areliable method to express the estimated soil moisture in a numerical model. Other reasons
include the absence of a quantitative observation method for the vapour and cloud watercontents above land areas even though such a method has been developed for its inclusion innumerical models for ocean areas.
In the case of long-term numerical prediction, modelling of the boundary conditions withphysical relevance and stability is essential. The boundary conditions for a global model are
the ocean surface and ground surface. In regard to the ocean surface, efforts have been madeto establish physically consistent boundary conditions by means of integrating ocean andatmospheric models. In regard to the ground surface, many models have been proposed todescribe the vertical one-dimensional energy and water flow. The systems developed, however,are a kind of one-way street in that a ground surface model is developed with a limited data
set selected for its application to numerical prediction because of its global applicability andthat the prediction results are verified by actual observation data despite the fact thatsufficient accuracy is not guaranteed regarding the diversity of the land surface, particularly
the physical characteristics of the soil and seasonal changes of the vegetation. Hardly any
efforts have been made to improve the prediction accuracy through two-way examination, i.e.
identification of model parameters under the condition where the observation data is
consistent with the model outputs.
Under these circumstances, it is essential to develop earth observation and downscaling
methods capable of effectively incorporating global and regional data on changes of the water
cycle observed by satellites and then to provide quantitative local water cycle data which canbe used for concrete measures. For improvement of the prediction accuracy of extreme
phenomena on a basin scale and the provision of adequate data for river and water resourcesmanagement based on the prediction data, it is also necessary to develop a data integration
and analysis system which is capable of providing the necessary data by means of integrating
global and regional data to data which can be used for decision-making on the concrete local
measures mentioned above.

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5.10

Environment (Yoshifumi Yasuoka: National Institute for Environmental Studies)

The concept of “the environment” is quite broad and satellite observation of the environment
includes many fields and phenomena. Here, the focus is placed on the gathering of useful
information to solve continental-scale environmental problems, especially those relating to
the land, ocean and atmosphere in Asia, in view of the wide area, high frequency and multiple
wavelength observation functions of the GCOM sensors. The acquisition of scientific and basic
information is essential to solve environmental problems in individual fields. As GCOM
satellite observation relating to individual subjects is described in 3., this section only deals
with observation contributing to solutions to environmental problems of a regional tocontinental scale which are expected to be present at the time of the launch of the GCOM
satellite. The GCOM sensors are considered to be the successors to such wide area, highfrequency sensors as the AVHRR and MODIS, and the inheritance and further progress of theobservation conducted by these sensors are emphasised. There are many parameters,
including those listed below, relating to “the practical use of the environment” of whichobservation is expected to be conducted under the GCOM Project.
Land

:

Ocean

:

Atmosphere :detailed land cover; vegetation classification; plant seasons (phenology; cropclassification and growth stages; forest fires; net primary production; snow
cover; water stress trend; ground surface temperature
chlorophyll-a concentration; amount of planktons; clarity; suspended solids
concentration; basic production; red tide and blue tide; freshwater HAB;
aquatic plants; sea surface temperature; water surface temperature
aerosols; clouds (including cloud types); rainfall; precipitable water

Below are parameters selected from the viewpoint of their possible practice use in the near
future.
5.10.1
5.10.1.1

Land
Evaluation of Wide Area Vegetation

Many continental-scale vegetation maps are already available based on data provided byvarious satellites. However, they are not necessarily suitable for practical use because of thesmall number of plant species listed or insufficient classification accuracy. Given the superior
specifications of the GCOM/SGLI compared to the sensors in use so far, it is possible to aim at
preparing a vegetation map for practical use. To be more precise, work will be conducted to
expand the Green Census Map (Vegetation Distribution Map of the Basic Study on Natural
Environmental Conservation) of the Ministry of the Environment, which is said to be themost detailed vegetation map in the world, to cover Asia (Asia vegetation map). A level ofvegetation which can be properly identified based on phonological data created fromtime-dependent satellite data will be set by selecting 20 – 50 plant species.
5.10.1.2

Evaluation of Urban Heat Environment

There is very strong concern that the adverse impacts of global warming on urban areas will
manifest in megacities in Asia along with the phenomenon of urban heat island (UHI). The- 66

SGLI is capable of measuring the ground surface temperature distribution in consideration of
the emissivity using thermal infrared multi-wavelength band data. The preparation of anurban heat environment map for the world’s megacities will allow the clear identification of
UHIs and evaluation of remedial measures.
5.10.25.10.2.1

Ocean
Red Tide Monitoring

Recent years have seen the increased occurrence of red tide due to eutrophication in coastal
sea areas in developing countries in Asia. Red tide not only damages fisheries but also causes
the considerable deterioration of the coastal environment. The development of the practicalcapability to monitor, evaluate and predict red tide is essential. As the SGLI has wavelengths
to observe chlorophyll-a and planktons, the preparation of a water temperature, chlorophyll-a
and plankton distribution map will greatly assist the semi-real-time monitoring of red tide.
5.10.35.10.3.1

Atmosphere
Atmospheric Pollution Map

The quantity of substances released into the atmosphere has been constantly increasing due
to the growing intensity of economic activities in developing countries. The increase ofaerosols into the atmosphere is said to be closely linked to global warming and there isconcern in regard to the adverse impacts of photochemical smog on human health at the levelof daily life.
Meanwhile, another problem of atmospheric pollution is attracting attention. This involves
the long-range transportation of dust lifted into the air as a result of the progress ofdesertification and other causes. The arrival of yellow dust in Japan from China is one such
example. As this kind of cross-national boundary pollution via the atmosphere covers a widearea, monitoring and evaluation by means of satellite observation are essential. The SGLI has
a waveband which is capable of observing air constituents and it is hoped that suchobservation will lead to the preparation of an air pollution map (meteorological chart).
5.10.3.2

Vapour Distribution

Global warming in recent years has increased the precipitable water in the atmosphere.
Although precipitable water does not proportionally determine the level of rainfall, theavailability of a precipitable water map can play an important role in meteorologicalforecasting. While precipitable water has so far been observed by radiosonde balloons andGPS satellites, the observation network does not fully cover developing countries. The
thermal infrared data of the SGLI will enable the estimation of precipitable water and it ishoped that the relevant wide area observation will materialise. More accurate weather
observation will also be possible through combination with the rainfall observation data
provided by the AMSR-E satellite sensor and the GPM satellite system.

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6.

Observation Objectives and Products of Onboard Sensors

6.1

AMSR2 (Keiji Imaoka: Japan Aerospace Exploration Agency)

6.1.1
6.1.1.1

Objectives
Observation of the Earth Environment

The GCOM-W satellite will be responsible for the long-term continual observation of the
global water and energy cycles, including those in the polar regions, as part of the overall
objectives of the GCOM to monitor and to develop a further understanding of climate change.
To be more precise, it will observe (i) the cryosphere where sea ice, ice sheets and snow cover
are said to manifest the signs of global warming most significantly before anything else, (ii)changes of the sea surface temperature, precipitation and vapour content, typically associatedwith El Nino, as a result of the interaction between the atmosphere and oceans and (iii) sea
wind and soil moisture to quantitatively establish the water and energy exchanges involving
the atmosphere, oceans and wind because of the need for data on such exchanges to properly
understand various aspects of climate change.
Such wide-ranging observations will require a system which is capable of quantitatively
measuring the entire globe accurately and frequently. Observation using a microwave
radiometer which receives microwave radiation from the earth’s surface and the atmosphereis suited to quantitative observation as the data contains volumetric and surface informationon the atmosphere, clouds, snow cover and soil because of the high level of transmission of
microwaves. Because the measurement is not that of reflected sunlight, it has the advantagesof night-time and polar region observation. A microwave radiometer can, therefore, achievefrequent observation of the targets under cloud cover, capitalising on the high level oftransmission of microwaves. The AMSR2 onboard the GCOM-W will inherit the missions of
the AMSR and AMSR-E and will continue observation of the sea surface temperature, sea
surface wind velocity, vapour content, cloud liquid water, precipitation, sea ice concentration,snow water equivalent and soil moisture. The emphasis of the AMSR2 will be thecontinuation of the observation currently conducted by the AMSR-E so that uninterrupted
data sets can be produced based on long-term observation for some 20 years by three
generations of microwave radiometers, i.e. ADEOS-II/AMSR, Aqua/AMSR-E and
GCOM-W/AMSR2.
6.1.1.2

Contribution to SocietyHigh sensor accuracy and stability are imperative to achieve the effective monitoring ofclimate change while the steady provision of data to and collaboration involving scientists,climate model development organizations and other stakeholders are essential for a proper
understanding and prediction of climate change. To achieve these, a system which is capableof swiftly processing the observation data and distributing the processed data to allorganizations concerned is required. The processing of satellite data should be conductedalong with the processing of ground station data in an integral manner.
There is a growing demand for continually observed, reliable satellite data for such practical- 68

applications as numerical weather forecasting and fisheries. The AMSR-E data is alreadybeing used for the long-term observation of the sea surface temperature not affected by cloud
and also for weather forecasting, especially the prediction of the precipitation level inassociation with typhoons and heavy rain. For these purposes, the supply of uninterrupted
time-series data as well as quasi-real-time data is required.
6.1.1.3

Relationship with Other Missions

It is impossible for a single polar orbit satellite to gather a sufficient quantity of data on such
fast changing phenomena as precipitation and clouds for practical application. Even for along-term observation mission, such as the monitoring of climate change, there is a limit to
the achievable accuracy by a single polar orbit satellite because of sampling errors anddifferences in climate change signals caused by biased local times for observation. Because ofthis, the GCOM will be mutually complementary to such satellite system as the JPSS (Joint
Polar Satellite System).

6.1.2

Products and Development of Algorithms

6.1.2.1

Products

The products based on AMSR2 data are explained next.
(1)

Brightness Temperature
The brightness temperature is a basic quantity to be used as an input to estimate
physical quantities and can be directly used for the time-series analysis of a land area or
ice sheet. In the recent assimilation process of numerical models, there is a tendency forthe direct assimilation of the brightness temperature to prepare better initial values. For
this reason, it is essential that the brightness temperature is strictly calibrated so that it
can be prepared and supplied as a stable product.

(2)

Atmosphere
The standard products relating to the atmosphere will be the vertically integrated water
vapour content and cloud liquid water and the precipitation amount covering the groundsurface. In addition, the vertical profile of the precipitation amount using
multi-frequency data will be estimated for research purposes. Water vapour is a major
element in the large-scale water cycle and is also the gaseous body with the highest
greenhouse effect on the earth. As suggested by satellite data provided by the Microwave
Sounding Unit (MSU) and SSM/I, the water vapour content is understood to increase inaccordance with a temperature increase. Meanwhile, the phenomenon of precipitation
plays the role of returning water to the earth’s surface and precipitation data is
indispensable for computation of the fresh water flux. Precipitation is also important asa source of energy supply to the atmosphere through the release of latent heat. Changes
in the phenomenon of precipitation due to climate change directly affect humanactivities in terms of water resources and water disasters and, therefore, precipitation
data is very important from the viewpoint of daily life. Data on the precipitation amount- 69

and data on the water vapour content are already used by numerical weather forecastingmodels.
(3)

Ocean
The ocean-related standard products will be the sea surface temperature and sea surface
wind speed. The former is a physical quantity which can indicate natural fluctuations,such as El Nino, and is considered to constitute an index for global warming. The AMSR2
will enable frequent observation of the sea surface under non-precipitating clouds
utilising the characteristic of microwave observation. Its operation is not affected byclouds or volcanic aerosols and is mutually complementary to infrared observation interms of the observation error. The AMSR2 can, therefore, improve the monitoring
accuracy of climate change through combined observation with infrared sensors. Sea
surface wind data is essential for estimation of the energy flux and CO2 flux between theatmosphere and ocean. Both sea surface temperature data and sea surface wind speed
data are useful for the monitoring and prediction of the generation, central position andintensity of typhoons and, therefore, are vital for actual meteorological observation andforecasting.

(4)

Cryosphere
The standard products related to the cryosphere will be the sea ice concentration andsnow accumulation on the land area. The AMSR2 is also capable of estimating the sea ice
thickness for research purposes through combination with other satellite observation
data (polarisation ratio by the SAR and observation by a laser altimeter). The recedingsea ice area, snow cover and ice sheets are believed to be early signs of global warming.
Detection of their changes by satellite monitoring is relatively easy. Accurate
understanding of the distribution of sea ice, snow cover and ice sheets is essential as they
not only greatly change the amount of heat transport by providing thermal insulation
between the atmosphere and the earth’s surface but also affect the radiation balancethrough the albedo effect. In addition to the distribution, data on the state of freezing
and melting is required along with thickness data. For example, the amount of heattransport from the sea ice surface to the atmosphere considerably depends on the ice
thickness, making estimation of the ice thickness, especially in thin ice areas, very
important. For observation of the Antarctic ice sheet and the ice shelves at its edges, thebrightness temperature is directly used. For an ice sheet made of pure water, thepenetration depth greatly varies depending on the observation frequency. The depth isbelieved to be approximately 10 m in the case of the 7 GHz channel while it reflects thetemperature near the surface in the case of the 89 GHz channel. The use of a highfrequency can track the short-term phenomenon of a rising temperature while the use ofa low frequency can retrieve signals roughly indicating the mean annual temperaturereflecting the state of the temperature over a longer period.

(5)

Land
The standard products relating to land will be the soil moisture content and snow waterequivalent already mentioned in 2) Cryosphere. The water content of vegetation and theground surface temperature will also be measured for research purposes. The behaviour- 70

of soil moisture determines the level of evapotranspiration and recharging level ofgroundwater, affecting the water cycle on land. However, the wide area, quantitative
measurement of these phenomena is impossible without the use of a satellite. For thisreason, the observation data of the AMSR2 will play an important role in the
improvement of medium to long-term weather forecasting and the prediction of climatechange. Similarly, the ground surface temperature determines the water and heatbalance on land and is considered to be an indicator for global warming. As the soil
moisture and ground surface temperature are factors affecting the vegetation, this data
should prove useful for the observation of vegetation by the GCOM-C/SGLI.
Standard products are the results of the routine processing of entire data for general
distribution. In contrast, research products are experimental products for research
purposes and include prototype versions of standard products. The decision on whichtypes of research products are to be produced is made based on the latest observation
outcomes. The classification of standard and research products is based on the stage ofprogress of algorithm development and the validation results after satellite launch. Table
6.1.1 lists the standard products of the AMSR2. R & D on research products will be
conducted based on a new concept or necessity or through the combination with othersatellite/sensor data and the likely research products at present including the following.
• Integrated cloud liquid water over sea ice areas and land areas• Precipitation over cold latitudes• Sea ice thickness• All-weather sea surface wind speed• Hydrological assimilation data over land area

Table 6.1.1
Product*1

Areas

ResolutionStandard Products of the AMSR2
Accuracy*2
Data
Release*3

Standard*4

Goal*5Brightness

System error (maximum

Temperature (6Frequency

Global5-50 km± 1.5 K

± 1.5 K

Geophysical Quantities

Channels and 2± 1.0 K
± 0.3 K

amplitude;
2.7~340 K

Water Vapour

Global,
over

15 km

ocean
Global,

Cloud

over

Liquid

15 km

ocean
Global,PrecipitationK

equivalent)
K equivalent)

Integrated
Water

150

Fluctuation error (3σ; 150

Polarised)
Integrated

Remarks

Range

exceptcold
latitude

15 km

Verticallyintegrated

± 3.5

± 3.5

± 2.0

± 0~70

kg/m2

kg/m2

kg/m2

kg/m2

± 0.10± 0.05± 0.02

0~1.0

kg/m2

kg/m2

kg/m2

kg/m2

Ocean:

Ocean:

Ocean:

± 50%

± 50%

±

20%

0~20

ground

Land:

Land:

Land:

mm/hr

error for an average of 50± 120%± 120%

±

- 71

80%

amount; excluding sea ice
and precipitation areas
Verticallyintegrated

amount; excluding sea ice
and precipitation areas
Precipitation

amount on

surface;relative

km (ratio of RMSE to means
Sea

SurfaceTemperature

Sea

SurfaceWind Speed

precipitation intensity)
ExcludingGlobal,
over

50 km

± 0.5°C± 0.5°C± 0.2°C-2~35°C

ocean

ice

and

accuracy is mean monthly
bias value per 10° latitude

Global,oversea

precipitation areas; target

15 km± 1.5 m/s

± 1.0 m/s

± 1.0 m/s

0~30 m/s

15 km

±10%

±10%

±5%

0~100%

ocean

Excluding

sea

ice

and

precipitation areas

Polar
Sea

Ice

Concentration

regions,overAccuracy is indicated bysea ice concentration (%)

ocean
Excluding ice sheet anddense

forestareas;

accuracy is shown in terms
Snow Depth

Land

30 km

± 20 cm± 20 cm± 10 cm

0~100 cmof snow thickness which isexpressed as the absolute
mean

error

of

instantaneous values
Volumetriccontent

inmoisture
globalland

areas (including arid andcold

areas)

vegetation
Soil Moisture

Land

50 km

± 10%

± 10%

± 5%

0~40%

where

cover

is

equivalent to a moisture
content of 2 kg/m2 or less;excluding ice sheet anddense

forestareas;

accuracy is shown as theabsolute

mean

error

instantaneous values

*1

*2

*3
*4
*5

The brightness temperature is the conversion of the sensor’s engineering output to the mostbasic observation value while geophysical quantities are the results of the conversion of thebrightness temperature via the respective conversion algorithm.
Unless otherwise stated, the accuracy is shown as the root mean square error (RMSE) of theinstantaneous values. The accuracy of the brightness temperature depends on the onboard
and ground calibration accuracy. The accuracy of geophysical quantities depends on theaccuracy of the brightness temperature, performance of the relevant conversion algorithm
and the validation method.
The minimum level of accuracy for the release of data which can contribute to the analysis ofclimate change
Useful and standard level of accuracy, taking the past performance of the AMSR and
AMSR-E into consideration
Level of accuracy containing many issues for further research in relation to the performanceof the algorithm and calibration accuracy, etc.

- 72

of

6.1.2.2(1)

Algorithms

Target Algorithms
1)

Products
Algorithms will be developed for each of the standard products listed in Table
6.1.2.1-1 and research products to be described later.

2)

Processing Levels
The assumed processing levels are shown in Table 6.1.2
Table 6.1.2

Processing
Level

(2)

Processing Levels of AMSR2 Data

Definition

1A

Storage of sensor output engineering values; satellite altitude and position data,
sensor status data, brightness temperature conversion factor, latitude/longitude
information for each observation point and other; no map projection

1R

Level 1A data converted to the brightness temperature for storage; no map
projection

1R

Spatial resolution matching for Level 1B1 data

2

Computed geophysical quantities from Level 1B/1C data; no map projection

3

Temporal-spatial statistical processing of Level 1B/1C data and map projection

Level 1 Algorithms
Various codes and algorithms are required to produce Level 1 products, and the mainalgorithms related to data directly accessed by users are explained below.
1)

Brightness Temperature Computation Algorithm
This algorithm computes the brightness temperature from the engineering values
observed by the AMSR2 and the inputs are the observed and calibrated countvalues, monitored physical temperature values and others. The radiometric modeland related factors are decided prior to satellite launch based on the ground sensor
test and analysis results. Further adjustments are made during the on-orbit
calibration prior to the supply of data for processing.

2)

Geometric Data Computation Algorithm
This algorithm computes the observation position, angle of incidence and others of
the AMSR2 using the observation geometry of the sensor and satellite altitude as
well as position data. The geometric model for the sensor and related factors are
determined prior to launch based on the satellite and sensor design data andground test and analysis results. Further adjustments are made during the on-orbit- 73

calibration to supply data for processing.
3)

Quality Information Adding Algorithm
This algorithm computes the data quality for each observation point, scan and fileof the AMSR2 so that the computation results can be attached to products in theform of a quality flag, etc. The AMSR2 will newly add a quality flag to radio wave
interference using 6.925 GHz and 7.2 GHz.

4)

Spatial Resolution Matching Algorithm

The spatial resolution of a microwave radiometer considerably varies depending on thefrequency channel because of its principles of observation. When a geophysical quantity
is computed through a combination of several frequencies, the difference in the spatial
resolution between the different frequencies must be taken into consideration. Thisalgorithm ensures that the Level 1R products have a matched spatial resolution forvarious frequencies for their convenient use.
(3)

Level 2 Algorithms
The priority issues for future research are described below for algorithms to compute
Level 2 geophysical quantities for products which are considered to be standard products
at present.

1)

Integrated Water Vapour
The development of an improved version of the algorithm used by the AMSR-E isnecessary in view of the likely collaboration for quality evaluation and a data
assimilation technique utilising the outputs of the current numerical weather
forecasting systems. The reliable algorithms to be developed will be used for (i)estimation of the integrated water vapour based on accurate temperature, vapour
and sea surface wind data using the analytical or forecast values produced by a highresolution numerical weather forecasting model assimilating GPS occulation
observation and a microwave scatterometer, (ii) improvement of the estimation
accuracy in areas with thick cloud cover or precipitation and (iii) analysis of
long-term changes.

2)

Integrated Cloud Liquid Water
As recently launched satellites carry a cloud radar and/or lider, it is becoming
possible to obtain global data on the vertical distribution of clouds. A new algorithm
to compute vertical integrated cloud liquid water using such data will be developed
as an upgraded version of the algorithm used by the AMSR-E.

3)

Precipitation
Further improvement of the algorithm for scattered precipitation, which restricts- 74the accuracy of estimation in the torrid zone through temperate land and frigid zone,
is essential. To be more precise, improvement is required in regard to the radiativetransfer models which consider non-spherical ice crystals, land-precipitation
retrieval over various types of surfaces, such as snow areas, retrieval of solid
precipitation in high latitude areas and effective use of ancillary data, including
objective analysis data. It is hoped that the precipitation algorithm of the AMSR2 isa general algorithm which can be used for similar sensors in consideration of its use
in the GPM Plan.
4)

Sea Surface Temperature
Estimation of the sea surface temperature by the AMSR2 will be an effective
method of all-weather observation. The development of a more accurate algorithm
is essential through precise surface emissivity modelling which will consider suchenvironmental conditions as the temperature difference between the sea surface
and atmosphere, sea surface wind direction and others based on the algorithm usedby the AMSR-E. The possibility of improved accuracy will be examined using the 7.3
GHz channel to be newly added to the AMSR2 for reduction of the impact of radio
wave interference and other purposes.

5)

Sea Surface Wind Speed
As in the case of the sea surface temperature, the development of a model toestimate the precise sea surface-emissivity which is capable of considering various
sea environment conditions, including the precise correction of the sea surface winddirection, is required. The progress of research to estimate the sea surface windspeed using such low frequencies as 7 GHz and 10 GHz in any weather conditions
may lead to the development of a research product.

6)

Sea Ice Concentration
Improvement will be sought primarily for reduction of the land effects, reduction of
the atmospheric effects and a better estimation accuracy of the sea ice concentrationin thin ice areas using the Bootstrap algorithm and NASA Team algorithm, both ofwhich are typical sea ice concentration computation methods at present, as the base.
The processing of data over thin ice areas is linked to not only the improved
estimation of the sea ice concentration but also the accuracy of the ice thickness
estimation affecting the amount of heat transport between sea ice and theatmosphere. Therefore, much care is required in regard to these areas.

7)

Snow Depth
There is much room for improvement of the quantitative estimation of the snow
depth. To be more precise, such estimation must take into consideration the state of
the soil under snow cover, distinction between snow cover and dry cold areas
without snow cover and continual estimation from the dry snow season to the snow
melting season, particular the impacts on vegetation due to snow on the crowns.
- 75

There is sufficient ground observation data to achieve the required improvement
and it will be necessary to continue intensive observation as well as long-term fixed
point observation.
8)

Soil Moisture
Estimation of the soil moisture has much progressed with the introduction of the
AMSR-E. However, the computation of highly reliable global soil moisture data will
require the development of (i) a flexible algorithm which is capable of dealing withdifferent soil textures, surface roughness and vegetation types, (ii) a method whichis capable of continually computing changes of the soil moisture, starting from thesnow melting process, and (iii) a method to estimate the soil ice content in cold
regions (monitoring of the permafrost zone).

6.1.3

Calibration and Validation

6.1.3.1(1)

Calibration

Pre-Launch Calibration
The sensor characteristics relating to calibration will be identified to make the groundtest and test items reflect such characteristics. If necessary, correction methods will be
examined for their application to the sensor models for brightness temperaturecorrection and geometric correction which determine the inputs to the Level 1(brightness temperature) processing algorithm.

(2)

Validation of Initial Calibration
1)

Calibration of Brightness Temperature
As there is a de facto lack of a method to evaluate the absolute brightness
temperature, the final calibration will be conducted based on the general
assessment of the compatibility of the results of the following calibration evaluation
methods.
①

On-Orbit Calibration
On-orbit calibration will be conducted as two-point calibration using low andhigh temperature calibration source data. Firstly, the characteristics of eachcalibration source data will be established through comparison with various
types of telemetric data. Optimisation will be conducted through theadjustment of the sensor model factors or the introduction of a new model. In
the case of low temperature calibration sources, the contribution by the earth,moon and satellite body, excepting the contribution by deep space, will be
removed. In the case of high altitude calibration sources, adjustments will bemade with a model computing the effective radiant temperature from thetelemetric data on the calibration source temperature.

- 76

②

Deep-Space Calibration
Using the observation result for the deep-space temperature retrieved bydeep-space calibration manoeuvring, low temperature absolute calibration(checking of the compatibility between the main mirror and CSM), evaluationand correction of the scan bias and evaluation of the spill-over amount (TBD)
will be conducted.

③

Multiple/Mutual Calibration
Because of the absence of an ideal target for absolute calibration on the earth,mutual calibration with a group of similar microwave radiometers in operation
during the lifetime of the AMSR2 will be conducted along with comparative
validation using both ground observation data and the observation data ofother sensor and radiative transfer calculation results.

④

Sensor Characteristics and Data Quality Evaluation
The scan bias and inter-scan bias, such sensor characteristics as thedifferences between individual receivers (89 GHz A + B systems, 6.9 – 7.3 GHz,etc.), temperature resolution and stability of gains will be evaluated throughthe statistical processing of the observation, calibration and telemetric data. In
addition, the global distribution and frequency of abnormal values caused byartificial radio waves will be evaluated and an appropriate abnormal values
removal method will be decided.

1)

Geometric Calibration
Evaluation of the geometric accuracy will be conducted by means of comparing theobservation data for each channel and pre-set mapping information on coastal lines,
islands and others. The evaluation results will then be used to adjust the alignment
data of the sensor model and registration parameters between different frequencies.

(3)

Constant Calibration and Validation
In addition to the constant implementation of the initial calibration and validation work,
constant monitoring of the telemetric and calibration data, brightness temperature ofsuch specified areas as tropical forests, ice sheets, oceans and others will be conducted toevaluate the long-term stability of the sensor performance. The trend evaluation resultsof the geophysical quantities may be inversely used for the calibration of the brightness
temperature.

6.1.3.2(1)

Validation

Integrated Water Vapour
Validation of the integrated water vapour will primarily be conducted using the- 77

worldwide radio-sonde network. When judged necessary, validation will also be
conducted using ground-based microwave radiometers on remote islands, ground-based
GPS receivers and LEO GPS occulation observation. A quality survey will also be
conducted by means of comparison with other GPM satellite observation data and theuse of operational numerical weather forecasting system data.
(2)

Cloud Liquid Water
The planned activities include comparative validation with the remote sensing
observation data provided by ground-based microwave radiometers, radars andceilometers to measure the cloud bottom height. Comparison will also be conducted withthe analysis results provided by operational numerical weather forecasting systems.

(3)

Precipitation
Precipitation is characterised by considerable temporal-spatial fluctuations and is one of
the geophysical quantities which is difficult to directly compare with ground observation
data. In principle, the existing operational ground-based radar network and a group ofground-based hydrometers will be used for validation. Comparison of the AMSR2 datawith the data provided by satellite-based precipitation radars if the observation periods
converge will be important because of questions regarding the accuracy and uniformityof ground-based data.

(4)

Sea Surface Temperature
Validation will basically be conducted by comparison with the water temperatureobservation data supplied by globally deployed fixed and drifting buoys. Comparison
with the sea surface temperature data simultaneously produced by satellite-based
infrared radiometers will assist the validation, especially in the initial observation
period, of the global sea surface temperature and evaluation of the characteristics anderror distribution of different datasets.

(5)

Sea Surface Wind Speed
Validation will primarily be conducted by comparison with the wind speed data suppliedby globally deployed fixed buoys. If a satellite-based microwave scatterometer isoperational during the same observation period, comparison with its data will also be
important.

(6)

Sea Ice Concentration
Validation data based on various types of sea ice, seasons and sea areas will be required
to adjust the algorithm parameters and to validate the AMSR2 observation data. In
particular, validation of the data for the Sea of Okhotsk will be important to validate
data for thin ice areas. To be more precise, validation datasets starting from ground levelmust be developed through synchronised multi-stage observation involving ships,
airplanes and satellites. The comparison of satellite data with a high resolution ofseveral to several tens of meters will be particularly important.
- 78

(7)

Snow Depth
Validation must be conducted under three different conditions to improve and validate
the algorithm as mentioned earlier in the section explaining the Level 2 algorithm forsnow depth. Firstly, highly accurate physical validation will be conducted throughobservation by ground-based microwave radiometers at sites where the snow coverconditions, characteristics of the bottom face of the snow cover and vegetation conditions
can be quantitatively established. Next, the AMSR2 data will be actually validated
through the intensive observation of the snow parameters in satellite footprint size
validation areas. Finally, the observation will move to the long-term validation phase
while continuing observation at sites of different climate, vegetation and soil types.

(8)

Soil Moisture
The validation of soil moisture data must consider three points for the purpose of
improving and validating the relevant algorithm. Firstly, the validation must take themicrowave radiation transmission characteristics which differ depending on the soil
moisture conditions, ranging from dry to wet, into consideration. Next, the validationmust consider the different soil types (ground surface roughness and texture). The
difference in roughness greatly affects the ground surface scattering while the differencein texture greatly affects the volumetric scattering in soil. Finally, the validation must
consider the different vegetation conditions (amount of vegetation, type of vegetation andunevenness of vegetation cover). Evaluation of the vegetation conditions is extremely
important as the AMSR2 uses the C band which is susceptible to the state of vegetation.

- 79

6.2

SGLI (Hiroshi Murakami: Japan Aerospace Exploration Agency)

6.2.1

Objective of the Development of GCOM-C/SGLI Algorithms

The primary objective of the GCOM-C/SGLI is to produce products which will be used as
input data to contribute to the understanding of the physical processes and prediction ofclimate change as part of the GCOM’s overall objective of monitoring and understanding
climate change. These products are also essential for various monitoring activities, including
the retrieval of highly accurate data on the physical quantities of vegetation, determination of
the global distribution of aerosols over land, detailed observation of rapidly changing coastal
areas and detection of signals for climate change emerging in the cryosphere. For this reason,emphasis will be placed on the long-term continual observation of the changing values of theparameters for global observation, such as the global radiation balance and plant production
which affects the carbon cycle and food production, and also on observation of the impacts ofhuman activities as well as climate change utilising such characteristics of the SGLI as thewide observation wavelength range from near ultraviolet to thermal infrared, spatial
resolution of 250 m and multi-directional observation. The objective of the development of
GCOM-C/SGLI algorithms is to produce the above-mentioned products with sufficientaccuracy (the target accuracy level is described in the Calibration and Validation Plan
document) for the application purposes.
6.2.1.1

Algorithm Development Policies• The target algorithms will be efficiently developed utilising the development outcomes andexperience of the ADEOS-II/GLI.
- Actual satellite data from the ADEOS and ADEOS-II and the relevant data processing
algorithms will be utilised to efficiently develop algorithms for the GCOM-C/SGLI,
including improvement of the existing algorithms. Advance research using ground-based
observation data and simulation data will be conducted in relation to the development of
new algorithms.
- A system capable of analysing, modifying and making improvement proposals will beestablished within the JAXA to ensure efficient algorithm development, achievement of
the required accuracy and feedback for the development of products in the future(similar to the GLI Algorithm Integration Team (GAIT) of the ADEOS-II mission).
• External researchers will be recruited as principal investigators (PIs) so that efficient joint
R & D work with researchers of the JAXA will achieve the development of new algorithms,
application of existing algorithms to the SGLI and improvement of the product accuracy inan assured and effective manner.
• For the monitoring of and research on climate change, the developed algorithms will havetaken into consideration the need to establish uniform, stable and highly accurate datasets
covering a long period with GCOM (three generations) data at the core.
- For the establishment of the datasets mentioned above, the calibration and validation
results, particularly the mutual calibration and validation results for data supplied by- 80

three generations of GCOM satellites and related satellites, will be fed back to improve
the algorithms.
• Algorithms will be developed with the practical use of products in mind.
- The integrated use of products will be facilitated in consideration of their generalcontribution to the GEOSS-GEO information system and others. To be more precise, thedevelopment efforts will include compounding with data supplied by related satellites
using GCOM data as the core data, definition of and data structure for physicalquantities in consideration of compatibility with numerical models and intimate
information sharing with organizations developing models and development of interfaces
for such information sharing.
- Various activities will be conducted to facilitate the practical use of products. These will
include improvement of the data reliability, supply of detailed data to organizations
using products for operational purposes, development of interfaces with suchorganizations and proposal for advanced product application methods.
- The feasibility of the practical use of satellite data will be demonstrated using actual
examples of the use of data for the prediction of climate change and other purposes. The
identified needs and obtained knowledge through such demonstration will be fed backfor algorithm development.
- The user confidence in the datasets will be enhanced through the continual preparationof datasets.
• Processing software capable of conducting steady processing will be prepared for thesmooth distribution of data for swift analysis.
• New data analysis and utilisation methods will be developed to expand the application
scope of satellite remote sensing in the future observation of the global environment.
• As the accuracy and quality of products reflect the overall performance of the process fromsatellite-based data gathering to processing on the ground, algorithms reflecting the designand development results of satellite-based sensors will be developed if necessary. The
development outcomes of such algorithms may be fed back to the development process ofsatellite-based sensors.
6.2.1.2(1)

Algorithm Development Procedure

Basic Procedure
There are three types of algorithms at present. The first type determines output valuesby empirically linking input data and output data (first generation of algorithms). The
second type determines output values using the radiation process in a positive manner(such as a radiation transmission model) (second generation of algorithms). The third
type determines output values by making the physical, biological and chemical processes
into a formula or model (third generation of algorithms). While the weight of an- 81

individual step of algorithm development differs depending on the type of algorithm, analgorithm is generally developed in accordance with the steps described below. In linewith the progress of algorithm development, an algorithm theoretical basis document(ATBD) will be prepared and revised.
1)

Creation of Basic Database
The data for quantities to be developed as products will be directly observed in-situ
and will be linked to the radiance of each wavelength channel. When the in-situ
measurement of a target quantity is difficult (cloud characteristics, etc.), the direct
satellite remote sensing of the radiance will be used as a substitute observation
method. When dealing with such a complicated system as the ecosystem, multiple
steps may be combined based on a theoretical model or a numerical model may be
used. At this step, the feasibility of the algorithm under development and the likely
level of accuracy of the satellite products can be confirmed to a certain extent.

2)

Correction of Atmospheric Effects
When physical quantities on the ground surface are targeted, the radiationtransmission codes of the RSTAR and 6S will be used to establish links between thein-situ radiance affected by the geometric conditions of observation, atmosphericmodules, aerosols and clouds and the radiance observed by satellite. For theprocessing codes, a look-up table showing the calculation results under thegeometric conditions for all satellites and the sun will have been prepared inadvance and will be referred to when the actual radiance data observed by satellite
is to be processed.

3)

Processing Test
The practicality of the developed algorithms will be tested using existing satellite
data and in-situ observation data as simulation data. The check items will be theresolution, scaling up or down of the processing range, processing time, abnormal
values generated by processing, flag mask and impacts of the sensor characteristics
and any necessary corrections/modifications will be made.

(2)

Development Phases
1)

Initial Development Phase• The feasibility of developing new algorithms will be examined along withimprovement of any problems encountered by the existing algorithms to be
inherited from the ADEOS-II/GLI.
• Algorithms will be developed using ADEOS-II and other satellite data and in-situ
observation data already available.

- 82• In regard to the satellite sensor design outcomes, a study on the impacts on
product quality and measures to ensure the product quality will be conducted if
necessary.
2)

Performance Development Phase• All of the targeted SGLI algorithms will be developed.
• The necessary basic data will be gathered.
• Development, validation and actual usage demonstrations will be conductedusing data from other satellites and the above-mentioned basic data.
• Based on the outcomes of the demonstrations, a pre-launch implementation
test-1 (PLI-1) will be conducted to evaluate the performance of each algorithm
proper and the applicability of satellite data.
• In regard to the estimated satellite sensor performance, a study on the impacts
on the product quality and measures to improve the product quality will be
conducted if necessary.
• Suitable algorithms will be evaluated and selected in accordance with thefollowing sequence.

3)①

As part of the First Research Announcement (RA #1) at this phase,researchers will be invited to submit an algorithm proposal document(equivalent to a provisional ATBD). The principal investigator (PI) to leadthe development of each selected algorithm will be selected based on thetheoretical validity and past performance in data processing of the proposed
algorithm.

②

When more than one algorithm is selected for the same product, theperformance (theoretical validity, estimation accuracy using the test dataand operational stability of the algorithm) will be evaluated at this phase.
The evaluation results will be discussed at Science Workshop #4 and will bereflected in the Second Research Announcement (RA #2).

③

In principle, one standard algorithm will be selected for each standard
product in preparation for the implementation of an operational algorithm.
When it is judged to be preferable to keep more than one candidate
algorithm, selection will be made based on the evaluation results at the timeof pre-launch or at the time of the release of Ver. 1.

Operational Algorithm Development Phase• Based on the PLI-1 results, the coding, evaluation and improvement of the
pre-launch algorithms will be conducted to match the brightness data (including- 83

sensor characteristics) to be observed by the SGLI and the SGLI data processing
system.
• The pre-launch implementation test 2 (PLI-2) will be conducted to reproduce theflow of processing work at the launch and to check the operational performance of
the algorithm.
• The ATBD Ver. 0 will be prepared.
• The evaluation of the performance of the research algorithms and examination of
their potential upgrading to standard algorithms will be continued.
4)

Post-Launch Development and Improvement Phase• An algorithm evaluation, validation, improvement and utilisation test will be
conducted using real GLI data in view of the upgrading of the version and thecorrection of algorithm errors at an appropriate time.
• The basic data and validation data required for algorithm improvement will begathered.
• The improved algorithms will be used for a follow-up sensor. After the launch of
the second GCOM-C satellite, the products of the GCOM-C1 satellite will becompared with the products of the GCOM-C2 satellite.
• The development, validation and demonstration of the research products will be
conducted and new applications for the products will be clarified.

6.2.1.3

Product List and Classification of Products

The target products for development will be those listed in the GCOM Requirements
Document (a table of the products and required accuracy and a table of correspondence
between the products and the SGLI channels to be used are added to this document as
Attached Table 1 and Attached Table 2 for reference purposes).
Products can be classified in various ways, such as  importance for the GCOM,  feasibilityof algorithm development and  mode of data processing and supply. In connection with themission objectives, some products are part of such mission objectives while new knowledge tobe obtained through algorithm development can also be a mission objective. For the SGLI, aneffective development system and modes of data processing and supply (A: production anddistribution in accordance with the retrieval of satellite data; B: publication after theprocessing and evaluation of data for a certain period of time; C: publication as researchoutcomes) to achieve the mission objectives based on the experience of product development
and data supply in the ADEOS and ADEOS-II mission are necessary.
The distinction between standard products and research products will be based on threecriteria, i.e. importance, feasibility and effective data processing and supply mode. For
example, standard products include cloud/aerosol characteristics, sea surface temperature,- 84

sea colour, snow depth and vegetation, all of which are demanded by a GCOS, which areimportant to achieve the mission objectives, the feasibility of which has been fully confirmedby the ADEOS-II mission and others and which are suitable for the systematic supply of data.
In contrast, the research products so far identified are those at the research stage ofdevelopment or practical use or those of which the importance is high but of which theoutcome is acquired knowledge rather than the systematic supply of data. The productprocessing and supply system must be efficient and effective in consideration of the three
aspects of the products.
6.2.1.4

Practical Use of Data

Algorithm development must take the following issues into consideration along with thetrends of the GEO information system and guarantee of accuracy.
• Combined use with the GCOM framework• Combined use with other satellite data
• Consideration of long-term fluctuation analysis (calibration; analysis of past data)• Application for numerical models
6.2.1.5

Publicity and Diffusion

It is necessary for the SGLI products to be used by as many researchers and organizations as
possible and for their outcomes to be known by as many people as possible to successfully
achieve the mission. The following matters are very important in this regard.
(1)

Maintenance and Improvement of Product Quality
The product accuracy must be sufficient in the light of the planned product use and the
product quality must be equivalent to or even better than that of comparable sensor
products produced abroad. To ensure this, pre-launch sensor calibration on the ground,algorithm development and post-launch calibration and validation will be thoroughly
implemented. If a quality flag attached to each pixel of a product is effective from theviewpoint of securing data reliability, such flags will be attached to the product.

(2)

Continual Data Supply
Long-term continual data supply is essential for the detection of long-term fluctuations
and also for the operational system developed by each data (product) user using its own
resources. The GCOM aims at the continual supply of data for more than 10 years and it
will be essential to ensure the consist operation of the on-orbit sensors to create the data
products. A feature of GCOM products which allows their combined use with the
products of other sensors should help the acquisition of many users at an early stage
after the launch of the first GCOM satellite as users will be able to use their own
processing and analysis infrastructure to obtain useful data using this feature.

(3)

Easy Access to Data
One effective way of achieving positive outcomes from climate change observation data is- 85

to make the thresholds for access to and use of the data as low as possible so that thedata can be used by many users. For this purpose, a simpler Web/FTP/search/supply
system will be developed to match the characteristics of the data. To facilitate the accessto actual data, there is an additional need for the supply of format descriptions or asample reading tool (for example, binary dump or text output) for easy data use and also
for the spread of techniques to use the data through the distribution of a user guidance
document, etc. (customisation for individual users should be conducted by private
enterprises specialising in data analysis and system technology).
In addition to data access, information disclosure will be required in regard to data
quality information (ATBD, state of calibration and validation and list of referencematerials), data usage (format descriptions and simulation data to check the format),data supply sources, data processing and release schedule and mission schedule(research announcement, launch and data release). Such information will be madeavailable on the EORC’s website in line with the progress of the GCOM.
(4)

Publicity
Academic societies involved in climate change research and international science groups
will be informed of the availability of data/products for practical/research use throughthe communication network of these organizations and meetings to report the researchachievements, etc. Moreover, effective publicity in the press and others will be attempted
at climate change-related events.

6.2.1.6(1)

Development System

Collaboration Between the JAXA and PIs/Data Users(Science Team Management)
The objectives of the GCOM/SGLI mission include items in such wide-ranging fields as
the development of sensors, development of algorithms, calibration and validation, data
analysis and data application (to numerical model, environmental assessment andprediction). The effective division of the work and cooperation with external researchers
and research organizations will be essential to achieve the mission objectives. For thisreason, contracts will be signed to appoint PIs (principal investigators) to be responsible
for algorithm development, verification and applied research in line with each mission
phase. Joint research will be conducted when necessary with external organizations
which are involved in the development of numerical models and the practical applicationof products, etc. in the fisheries and other industries or the combined use of data fromother satellites.
(Roles of the JAXA, PIs and External Organizations)
The JAXA must aim at successfully achieving the mission objectives in terms of the
global observation system through effective collaboration with Japanese and foreignorganizations using the global observation data products of which the production andsupply are the primary objectives of the GCOM. Among the wide-ranging work items,- 86the development and verification of algorithm, where innovation is an important
requirement, and the observation/gathering of in-situ data must be conducted in close
collaboration with the PIs to be publicly recruited for the GCOM, data users andoperational/research organizations constantly conducting in-situ observation in view of
the reliable and effective implementation of this work. The division of the roles between
JAXA staff and the PIs will be determined to ensure “reliable and effective” outcomes,
taking the performance of similar development phases in the past into consideration forthe planned deployment of human resources for the GCOM into consideration.
(2)

Linkage with Calibration and Validation Activities
Algorithm development, calibration and validation will be implemented as an integral
unit. As the in-situ data required for algorithm development falls in practically the same
category as the in-situ data required for post-launch validation, the know-how andequipment for in-situ observation will be fully inherited and used for algorithm
development and validation.

6.2.2
6.2.2.1

Calibration and Validation
Objectives

The SGLI is a multi-purpose, multi-channel optical sensor to be used for the observation ofand research on land, atmosphere, oceans and cryosphere. It has 11 non-polarised visible tonear infrared channels, four short-wave infrared channels, two thermal infrared channels andtwo polarised near infrared channels with three different polarisation azimuth angles andobserves radiance in each channel reaching the on-orbit sensor. Through the combination of
the radiance of multiple channels based on scientific knowledge, products related togeophysical quantities will be created using algorithms developed for each sphere.
The primary objective of the SGLI is to conduct frequent global observation with a view tosupplying global-scale observation data which can contribute to the prediction of long-termclimate change. For this reason, it will be necessary for the SGLI to supply observation data
based on absolute criteria of no regional dependence for geophysical quantities of which thefluctuation values differ from one region or ocean of the earth to another. Moreover, the values
for the geophysical quantities to be supplied must be compatible with similar data/products
supplied by similar optical sensors on other satellites which will be in operation during the
GCOM period.
When satellite observation products are used for the monitoring and analysis of climatechange, their absolute values and changing patterns must be highly accurate along with the
supply of information relating to accuracy. While the accuracy of the radiance observed by thesensor and the accuracy of sensor monitoring (geometric accuracy) are calibrated (measured
and evaluated) on the ground before launch, post-launch checking and modification will be
required for those of which checking can only be conducted on-orbit, those of which theaccuracy cannot be sufficiently determined on the ground and those which can change during
launch and in space while in operation.

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The objectives of the calibration and validation activities are to achieve the necessary
accuracy level for each product to fulfil the relevant requirements under the GCOM and torelease accurate products.
6.2.2. 2(1)

Policies

Product Accuracy(Definitions of Accuracy)
Three definitions of accuracy have been established for the products to be made available
under the GCOM as described below and these are linked to the success criteria for the
mission which will be evaluated approximately one year and five years after launch.
1)

Minimum accuracy for release (evaluation criterion for minimum success which will
be evaluated approximately one year after launch)This refers to the minimum level of accuracy of the data to be released as data
which can contribute to the analysis of climate change. The yardstick is for suchdata/products to be useful for the mission objective (contribution to the monitoringand process analysis of changes of the global environment and improved accuracy ofnumerical models).

2)Standard accuracy (evaluation criterion for full success which will be evaluatedapproximately five years after launch)This refers to the useful as well as standard level of accuracy in view of theperformance of the relevant sensor of the ADEOS-II and other missions. The
yardstick is equivalent or better values (in terms of accuracy, resolution andcoverage of observation) than those of similar data/products.

3)

Target accuracy (evaluation criterion for extra success which will be evaluatedapproximately five years after launch)This refers to the level of accuracy of which the achievement will depend on
progress of the research on improvement of the algorithm performance,
improvement of the calibration accuracy and others.

(2)

Required Accuracy Levels
The standard accuracy and target accuracy of the SGLI have been set throughdiscussions involving organizations to use the data/products for research or operational
purposes and the GCOM Committee based on the past results of the use of satellite
sensor data, including those for the GLI, and product validation while taking the future
prospects of algorithm improvement into consideration. The product accuracy isdetermined as the combination of (i) calibration error of the brightness data, (ii) error
due to the inclusion of various observation targets within the pixels, (iii) error
originating from the theoretical assumptions used for the algorithms and error inherent- 88

in atmospheric pressure and other supplementary data and (iv) error contained in thein-situ data used for comparison. The degree of contribution of each error depends on the
product (algorithm) and observation conditions. It has, therefore, been decided that“values based on past results” will be used as indicators which can be evaluated todetermine the standard accuracy and target accuracy as these past results containvarious error elements. The theoretical errors were checked as part of the GLI validation
work using algorithms and simulation exercises.
The specifications of the SGLI sensor have been determined to achieve thestandard/target accuracy explained above.
(3)

Calibration Policies and Accuracy Targets
Brightness calibration is a type of work to accurately establish the values of thespectral-radiance reaching the sensor opening based on the sensor output values. This
work involves evaluation of the general response characteristics of each observation
component (mirror tube for each VNI channel, mirror tube for each polarised channel
and IRS), each wavelength channel and each pixel, the effects of the incidence angle on
the scanning mirror reflectance and the polarisation sensitivity characteristics from thedesign and prototype stages for the purpose of improving and maintaining the accuracy(absolute accuracy and relative accuracy) of the observation data. The target brightness
calibration accuracy is set for each mission phase as a system-calibrated satellite
observed radiance product (Level-1B).
There are various means to calibrate the brightness, including pre-launch ground-based
calibration (measurement and deduction of parameters for character evaluation and forthe preparation and calibration of a sensor model), on-orbit calibration using internallamp, black body, moon, deep space, dark period and night observation data, vicarious
calibration using the SGLI’s global observation data, ground-based spectral radianceobservation data and observation data of other satellites and analysis of the Yaw
manoeuvre for the correction of sensitivity deviation. As each of these offers excellent
accuracy for some items but requires some assumptions for other, sufficiently reliableaccuracy cannot be determined by a single means. To rectify this situation, cooperation
will be sought with sensor development and ground system operating divisions forcomprehensive analysis of the calibration results to evaluate, improve and maintain theaccuracy of the products on radiance observed by the SGLI in operation.
Geometric system calibration means the decision on the position of each earth
observation pixel using information on the position and attitude of the satellite andsensor alignment. The target geometric accuracy has been determined for each mission
phase as system-calibrated radiance products of satellite observation. Positional error
can be caused by the on-orbit thermal distortion of the satellite and sensor, seculardistortion and error in the decisions relating to the position or attitude. Short-term (up to
one year) fluctuations will be corrected by geometric system calibration. In the case ofcomponents of long-term (longer than one year) periodic fluctuations, these components
will be statistically retrieved as mean values for each period of a certain length by meanof matching the GCP and SGLI observation image patterns for correction through theLevel-1 processing. Such geometric correction work will be conducted in collaboration- 89

with the satellite, sensor and ground system development divisions.
(4)

Validation Policies and Accuracy Targets
Validation means the work to validate the accuracy of the geophysical quantity which isestimated from the spectral radiance value for each channel. The target accuracy level isset for each geophysical quantity in each mission phase. In general, there are two typesof accuracy validation methods. One is based on the in-situ observation data supplied byground stations, ships and others. The other is based on theoretical values estimated inview of the inherent temporal-spatial fluctuation characteristics of each geophysicalquantity or empirical values (including meteorological values).
Validation based on in-situ observation data will be conducted mainly using match-up
data sets which consist of in-situ observation data on the same points at the same orsimilar times and SGLI cut-out data. Although the method, period and frequency of
in-situ observation vary depending on the targeted physical quantity, the accuracy of ahigher SGLI product will ultimately be computed through comparison between thevalues of the physical property in question calculated from these two types of data.
In regard to those physical quantities of which ground observation is difficult, therelationship between the target physical quantity and the observed radiance or anothergeophysical quantity not subject to validation will be estimated in advance based ontheoretical computation, a numerical model or past observation data by airplane orsatellite so that the accuracy of the target physical quantity can be estimated on thebasis of the accuracy of the radiance (based on vicarious calibration, etc.) or anotherphysical quantity.
Based on this estimated accuracy, the higher algorithm used for the retrieval of aphysical quantity will be revised if necessary.

(5)

Combined Use of Data
For the combined use of data, the accuracy of each data must be sufficiently high. It will
also be necessary to announce the evaluation results along with the evaluation methodor to make it possible to compare the evaluation results of different sensors (for example,
use of common in-situ data and partial integration of the computation process to produce
numerical values which can be used as accuracy evaluation results). For these purposes,
such existing international frameworks as the GEOSS and CEOS/WGCV will be utilised.

6.2.2.3(1)

Systems

Calibration System
Calibration of the SGLI, including on-orbit brightness calibration and lunar calibration,
will primarily be conducted by those responsible for the SGLI in the JAXA’s satellite
sensor development, mission management and applied research divisions with theparticipation of representatives of the equipment/instrument manufacturers and othercontractors and also the PIs who will be recruited through the research announcements.
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In consideration of the nature of calibration where various factors affect the data quality
in a complex manner, an interdisciplinary group will be formed within the JAXA to deal
with specific themes if necessary. The actual calibration work will be conducted byindividual divisions of the JAXA in coordination with this group. The JAXA-SGLI
calibration committee will be established as an umbrella body and the SGLI-Cal working
group will be introduced as a calibration-related group of PIs. The GCOM-C1 mission
manager will have ultimate responsibility for calibration as indicated in theconfiguration of the GCOM-C1 Project.
The themes identified through the calibration work of the GLI are listed below.
1)
2)
3)
4)
5)
6)

Solar calibration and lamp calibration (on-orbit calibration of visible andshort-wave infrared data)
Black body calibration and deep space calibration (on-orbit calibration of thermal
infrared data)
Geometric calibration (evaluation of geometric accuracy using the GCP and
modification of the alignment table)
Sensor characteristics and image quality evaluation (strip pattern noise, stray light,
transient response and saturation)
Vicarious calibration and mutual calibration (vicarious calibration using in-situ
data and mutual calibration with MODIS or SeaWiFS data)
LI processing software (reflection of methods and coefficients obtained by the above
calibration exercises for improvement on products)

In addition, lunar calibration and analysis of the polarised observation performance will
be conducted with the SGLI.
(2)

Validation System
Validation of the SGLI products will be conducted through collaboration between JAXA
staff working for application and research projects and the PIs. JAXA staff and the PIs
will form four groups in charge of land, atmosphere, oceans and cryosphere respectively
and will conduct the actual validation work within each group. If necessary, the
SGLI-Val working group will be set up to coordinate the work of the four different
spheres.
Important decisions on calibration and validation which will affect the entire science
project will be made at meetings of the SGLI science team consisting of JAXA staff and
the PIs.

(3)

Announcement of Calibration and Validation Results(4)

The calibrated and validated accuracy of each product will be disclosed on the science
research project’s website and will also be externally presented through lectures andacademic papers.
Linkage with Algorithm Development
The higher algorithms used for the retrieval of physical quantities will be revised if- 91necessary based on the estimated accuracy of each physical quantity by the validation
work.
(5)

Required Manpower, Computers and In-Situ Observation Equipment
The necessary manpower, computers and in-situ observation equipment for due
operation of the SGLI will be fully in place.

6.2.2.4

Observation Requirements for Calibration and Validation

When observation using modes which differ from the standard SGLI observation modes isrequired for the calibration/validation observation to be conducted by the PIs and/or JAXA
staff members, the requirements will be adjusted and put together within the research projectframework for inclusion in the mission operating system approximately every three months.
6.2.2.5

Schedule

Observation on the ground, etc. prior to the launch of the SGLI will be conducted as part of
the algorithm development. The calibration and validation phase of the SGLI products will
start with the launch. Table 5-1 shows the major milestones for the SGLI-related calibrationand validation activities.
6.2.2. 6(1)

Calibration Plan

Sensor Design and Prototype Phase
In line with the design and prototype fabrication of the sensor, the sensor characteristics(brightness and geometry) believed to affect the product accuracy and quality will beidentified for examination of their possible correction if necessary. This work will
simultaneously take place with the sensor development. The examination results will bereflected on the basic brightness and geometric conversion models to be primarily
developed by the sensor development division in the BBM phase.

(2)

Sensor Performance Evaluation Phase
Based on the performance evaluation results of the actual sensor, the sensor
characteristics (brightness and geometry) believed to affect the product accuracy andquality will be identified and their correction methods will be examined if necessary. The
examination results will be reflected on the Level-1 algorithms to be used for the
post-launch initial processing. This work will be jointly conducted with the sensor
development and ground processing development divisions. This phase will correspondto the EM and PFM phases of sensor development. The sensor development, groundprocessing development and research divisions will cooperate to conduct thedevelopment of the Level-1 algorithms as part of the ground processing of the SGLI datain the EM phase. In the PFM phase, a PFM table (database) will be prepared for its use
for algorithm improvement and post-launch initial processing by means of analysing thesensor PFM calibration test data in combination with the relevant algorithms.

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(3)

Initial Calibration and Validation Phase
1)

On-Orbit Brightness Calibration
On-orbit calibration (dark period, night-time, black body, solar diffusion andinternal lamp calibration) can be expected to produce stable calibration results asknown light sources will be used as the target data. Using such data, correction
coefficients will be prepared if necessary by means of evaluating the temporalfluctuations of the overall sensitivity. The brightness range and geometricconditions for observation will, however, be restricted because of the need forseveral assumptions in terms of the light intensity of the internal lamps after
launch, reflectance of the diffuser plate and others. For this reason, the calibration
reliability will be improved through the complementary use of lunar calibration,
vicarious calibration using earth observation data and/or mutual calibration. This
work will be jointly conducted with the sensor development and ground processing
development divisions.

2)

Evaluation of Sensor Characteristics and Image Quality
The relative accuracy of sensitivity deviations between pixels, channels and mirror
tubes and of the scan angle will be improved using on-orbit calibration and the
SGLI’s earth observation data. The sensitivity deviation between pixels, channels
and mirror tubes under specific observation conditions will be estimated very
accurately using such on-orbit calibration sources as black body, diffuser plate andinternal lamps. In regard to other observation conditions, suitable earth
observation data and data of other satellites will be used to evaluate thecharacteristics and to determine the correction coefficients. Although individual
samples of earth observation data have a high level of error due to changes of theatmosphere and ground surface, such relative sensitivity as differences between the
mirror tubes, angle of view deviation and scan angle dependency of the IRs can bestatistically evaluated by means of using a large volume of observation data forsuch relatively stable ground surface areas as snow and ice covered area, desert andopen ocean in the required brightness range. Because of the existence of
uncertainties regarding the calibration accuracy of deviation in the field of view ofother satellite data, atmospheric conditions and bidirectional reflectance
distribution function (BRDF) of the ground surface reflectance, evaluation must be
conducted in combination with fixed point observation through manoeuvring. The
sensor characteristics and image quality evaluation will also feature stray light,
transitional response, MTF and noise. If necessary and possible, development of thecorrection methods will be conducted for their application to Level-1 processing. The
work described here will be jointly conducted with the sensor development andground processing development divisions.

3)

Vicarious Calibration and Mutual Calibration
Vicarious calibration using ground targets and mutual calibration between satellite
sensors mean estimation of the ground surface reflectance using in-situ reflectanceobservation values or partially calibrated data from other satellites (a previous- 93generation GCOM satellite can serve as such a satellite for the next generation
GCOM satellite) and its comparison with the satellite observation values for thetargets. Because of the need to consider the characteristics of the ground surface
and the radiative transfer process in the atmosphere (atmospheric molecules andaerosols), this vicarious calibration will primarily be conducted by the researchdivision. This method has such advantages as evaluation with various observation
conditions in terms of brightness range and scan angle because of its ability to use
various targets and also the gathering of many samples in a short period of timeafter the commencement of operation when using data from other satellites.
However, there is a problem in regard to the estimated error of the ground surface
reflectance (question of the accuracy of brightness data supplied by other satellites
and question of representativeness of the ground observation points for comparison
targets). Moreover, calculation errors of the atmospheric influence (estimation
errors regarding aerosols, atmospheric pressure, temperature and water vapour)can constitute estimation errors. It is, therefore, essential for absolute calibration to
improve the evaluation results in general and the reliability of the correction
methods, taking the advantages and disadvantages of the individual calibration
methods outlined above into consideration.
In-situ data is not necessarily required for mutual calibration with the observation
data of other satellites on points which are spatio-temporally stable. However, toobtain results which are not dependent on the accuracy of other satellite data,
vicarious calibration requiring in-situ brightness (reflectance) observation for datacomparison purposes is necessary.
In the GCOM, vicarious calibration based on validated geophysical quantity dataand higher level algorithms will be conducted to check the data accuracy under
different observation conditions (Murakami, H., Yoshida, M., Tanaka, K.,
Fukushima, H., Toratani, M., Tanaka, A. and Senga, Y., Vicarious Calibration of
ADEOS-2 GLI Visible to Short-Wave Infrared Bands Using Global Database, IEEE
Transactions on Geoscience and Remote Sensing, Vol. 43, No. 7, pp. 1571 – 1584,
2005). For mutual calibration, the international frameworks listed in (7) below will
be used. Details of the in-situ observation plan will be described in the calibration
plan. A rough calibration plan will be prepared some two years before the planned
launch and more details of the calibration work will have been determined
approximately one year before the planned launch taking the trends of othermissions into consideration. These details will be incorporated in the calibration
plan.
4)

Lunar Calibration
Using lunar data obtained through lunar calibration pitch manoeuvring, theabsolute accuracy and its changes in time will be evaluated to develop correction
coefficients if necessary. While hardly any uncertain factors are believed to exist forlunar calibration, it is predicted that the continual evaluation of fluctuations causedby the aspect of and distance to the moon will be necessary for a period of three tofive years to ascertain the reliability of this relatively new calibration method.
Mutual calibration through comparison will be conducted if lunar observation data- 94

of other earth observation satellites for the same period is available. MTF and straylight will also be evaluated using the edge of the moon. This work will be jointly
conducted with the sensor development division.
5)

Post-Launch Geometric Correction
The geometric system accuracy will be evaluated through comparison of theobservation data of each mirror tube/channel and GCP data. The registration
accuracy between wavelengths and between mirror tubes will be evaluated using
the observation data. Based on the evaluation results, geometric changes with arepetition cycle of one year or longer will be corrected through modification of thealignment table and others. This work will be conducted in concert with thevalidation of the precise geometrically corrected reflectance and the work to
determine the position and attitude of the on-orbit satellite sensor system.

6)

Modification of Level-1 Processing Software
To supply highly accurate products, it will be necessary to efficiently and swiftlyreflect the calibration results, coefficients and correction methods proposed throughthe series of work described above on the Level-1 processing. For this reason, thework described in  through  above will be conducted in consideration of theapplication of their results to the Level-1 processing (formatting of the correction
coefficients, correction methods corresponding to the processing flow and others).

(4)Constant Evaluation Phase
The accuracy of long-term data will be secured by means of constantly evaluating thematters referred to in  through  of 3) above and applying the evaluation results todata processing. In particular, changes of the sensitivity in time, for which data isrequired for the analysis of climate change, will be evaluated by combining detailed
analysis of the beta effect of solar calibration, the effect of lunar age and vicarious
calibration. If the next generation GCOM/SGLI is on orbit, mutual calibration will be
conducted with the GCOM-C1/SGLI. The evaluation and correction of fluctuations ofnoise and relative sensitivity will be continually conducted. While it is predicted that
evaluation of the polarisation sensitivity of the polarisation observation function will bedifficult on orbit, evaluation using a vicarious calibration technique using theobservation results for temporal-spatial changes by a higher algorithm (aerosols) will beconsidered. Such evaluation and correction methods of the sensor characteristics will beinherited by subsequent climate change observation missions.

6.2.2.7

Cooperation with International Calibration and Validation Frameworks

The following international frameworks will be utilised for effective mutual calibration andthe exchange of vital information.
• CEOS/IVOS• RSL/APEX- 95

Definitions of GCOM-C Products (Baseline)
(1)

Land

1)

Standard Products• Precise Geometrically Corrected Image (PGCI): radiance data of which the pixel
positions are determined using the ground control points for each processing unit
taking the pixel altitude into consideration• Atmospherically Corrected Land-Surface Reflectance (ACLR): estimated groundsurface reflectance after the correction of atmospheric effects, such as scattering
affecting the radiance data
• Vegetation Index (VI): index parameter to express the density and activity of green
vegetation, including the Normalised Difference Vegetation Index (NDVI) calculated
from the red and near-infrared channel reflectances and the Enhanced Vegetation
Index (EVI) using the visible channel reflectance• Vegetation Roughness Index (VRI): index indicating the three-dimensional structureof vegetation based on “differences of the observed light between different observation
angles” as derived from multi-angle observation• Shadow Index (SI): estimated “fraction of vegetation shadow resulting from its
three-dimensional structure” using the wavelength characteristics of observed light• Fraction of Absorbed Photosynthetically Active Radiation (FAPAR): fraction ofphotosynthetically active radiation of 400 to 700 nm absorbed by canopies• Leaf Area Index (LAI): ratio of the total upper leaf surface of vegetation to the surface
area of the land on which the said vegetation is growing• Above-Ground Biomass (AGBIO): dry weight of above-ground biomass• Land-Surface Temperature (LST): temperature of the land surface

2)

Research Products• Land Net Primary Production (LNPP): net carbon amount fixed by land vegetation as
the difference between photosynthetic production and vegetation respiration• Water Stress Trend (WST): estimated “degree of impediment of water supply tovegetation” using the diurnal variation of the surface temperature• Fire Detection Index (FDI): positions of fires detected using radiation data in thethermal and short-wave infrared bands- 96• Land Surface Albedo (LALB): “ratio of upward reflected radiation energy to downward
solar radiation energy” estimated from the type of land cover and surface reflectancein each spectral band of the SGLI
• Land Cover Type (LCT): estimated state of land cover using vegetation indices and thesurface reflectance in each channel(2)

Atmosphere

1)Standard Products• Cloud Flag (CLFG): cloud/clear discrimination and type/phase of cloud cover for eachpixel
• Classified Cloud Fraction (CLFR): statistical fraction of appearance of cloud-pixels bytype of cloud• Cloud-Top Temperature and Height (CLTTH): cloud-top temperature and height
estimated from brightness temperature data in the thermal infrared channels• Water Cloud Optical Thickness and Particle Effective Radius (CLOTER_W): optical
thickness of water cloud and size of cloud droplets• Ice Cloud Optical Thickness (CLOT_I): optical thickness of ice cloud• Aerosols Over the Ocean (ARV): optical amount of aerosols (optical thickness ofaerosols), particle size (expressed in angstrom exponent which indicates the wave
characteristics of aerosols) and aerosol classification (such as soot and sea salt
particles)• Land Aerosols by Near Ultra Violet (ARU): aerosol optical thickness and aerosol
absorption coefficient• Aerosols by Polarisation (ARP): optical thickness and angstrom exponent as derived
from polarisation observation(Multi-Channel Aerosol Products: candidate for a future algorithm unifying ocean andland algorithms to estimate the aerosol size distribution and component ratio)2)

Research Products• Water Cloud Geometrical Thickness (CLGT_W): geometrical thickness of water cloud
expressed in the unit of length; the combined use of this parameter with the cloud-top
height makes it possible to estimate the cloud bottom height, contributing to an
improved retrieval accuracy of the long-wave radiation flux at the ground surface• Long-Wave Radiation Flux (LWRF): downward and upward long-wave radiation flux
at the ground surface, indicating the balance of the earth’s radiation energy; upward- 97

flux in cloudy weather conditions is estimated using the surface temperature data
provided by the AMSR2, etc.
• Short-Wave Radiation Flux (SWRF): downward and upward short-wave radiation flux
at the ground surface, indicating the balance of solar radiation energy
(2)

Ocean

1)

Standard Products• Normalised Water-Leaving Radiance (NWLR): sea colour (radiance of each channel) at
the ocean surface after correction of the atmospheric effects, such as scattering, on theradiance observed by the SGLI
• Atmospheric Correction Parameters (ACP): aerosol properties, etc. necessary forcorrection of the atmospheric effects, including scattering• Photosynthetically Available Radiation (PAR): downward radiation at wavelengths of
400 to 700 nm at the ocean surface that is available for phytoplankton• Chlorophyll-a Concentration (CHLA): concentration of phytoplankton chlorophyll-a, amain pigment in photosynthesis• Suspended Solid Concentration (SS): dry weight of suspended solids which are notdissolved in water; defined as the total of organic matters, such as plankton, andinorganic matters, such as soil
• Coloured Dissolved Organic Matters (CDOM): attenuation coefficient (unit: 1/m) of
coloured dissolved organic matters in the ocean surface layer• Sea Surface Temperature (SST): bulk sea surface temperature

2)

Research Products• Euphotic Zone Depth (EZD): depth of an euphotic zone suitable for plant growth• Inherent Optical Properties (IOP): optical properties of ocean water, such as theabsorption coefficients of plankton pigment, SS and CDOM, and the scattering
coefficients of SS, all of which are estimated using the normalised water-leaving
radiances• Ocean Net Primary Productivity (ONPP): net primary productivity of phytoplankton
for the absorption of carbon produced by ocean phytoplankton minus the carbon
consumed by respiration• Phytoplankton Functional Type (PHFT): phytoplankton existence rate for every
function, such as nitrogen fixation, silicon fixation and carbon dioxide discharge, inconnection with the NWLR
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• Red Tide (RTD): discrimination of red tide using the characteristics of ocean colour• Multi-Sensor Merged Ocean Colour Parameters (MOC): data sets which combine theproducts of other sea colour sensors, such as the NPOESS/VIIRS, for the sameobservation period• Multi-Sensor Merged Sea-Surface Temperature (MSST): data sets which combine theproducts of other sensors, such as the AMSR-2 and NPOESS/VIIRS, for the sameobservation period(4)

Cryosphere

1)Standard Products• Snow and Ice-Covered Area (SICA): snow and ice discrimination by each pixel• Okhotsk Sea Ice Distribution (OKID): sea ice distribution in the Sea of Okhotsk inquasi-real time
• Snow and Ice Surface Temperature (SIST): temperature at the snow and ice surface• Snow Grain Size in Shallow Layer (SNGSL): snow grain size retrieved with thereflectance in the 865 nm channel

2)

Research Products• Snow and Ice Classification (SIC): snow and ice cover types, such as new snow and oldsnow or first year ice or multi-year ice• Snow-Covered Area in Forest and Mountain (SCMFM): snow-covered area in forestand mountain regions where the vegetation cover is likely to be mixed with snow
• Snow Grain Size of Sub-Surface Layer (SNGSS): snow grain size retrieved with thereflectance in the 1050 nm channel; the retrieved sizes represent the snow properties
in a shallower layer than that of the SMGSL• Snow Grain Size of Top Layer (SNGST): snow grain size retrieved with the reflectanceof 1640 nm channel; the retrieved sizes represent the snow properties of the top snow
layer
• Snow and Ice Albedo (SIALB): “ratio of upward reflected radiation energy todownward solar radiation energy” estimated using the snow-surface reflectance ineach channel and taking the atmospheric effects suggested by the radiance values into
consideration• Snow Impurity (SNIP): ratio of such impurities as soot and dust in snow

- 99• Ice Sheet Surface Roughness (ISRGH): roughness of an ice sheet defined as the ratio of
the height to the width of the roughness estimated by multi-angle observation• Ice Sheet Boundary Monitoring (ISBM): monitoring data on the positional changes of
specific ice sheet boundaries- 100