APPENDIX 1
OVERVIEW OF THE GLOBAL CHANGEOBSERVATION MISSION (GCOM)

1-1

1.

Introduction

Comprehensive observation, understanding, assessment, and prediction of global climate change arecommon and important issues for all mankind. This is also identified as one of the important socioeconomic benefits by the 10-year implementation plan for Earth Observation that was adopted by the Third
Earth Observation Summit to achieve the Global Earth Observation System of Systems (GEOSS).
International efforts to comprehensively monitor the Earth by integrating various satellites, in-situ
measurements, and models are gaining importance. As a contribution to this activity, the Japan Aerospace
Exploration Agency (JAXA) plans to develop the Global Change Observation Mission (GCOM). GCOM
will take over the mission of the Advanced Earth Observing Satellite-II (ADEOS-II) and develop into longterm monitoring of the Earth.
As mentioned in the fourth assessment report of the Intergovernmental Panel on Climate Change (IPCC),
warming of the climate system is unequivocal as is now evident from observations of increases in global
average air and ocean temperatures and widespread melting of snow and ice. However, climate change
signals are generally small and modulated by natural variability, and are not necessarily uniform over the
Earth. Therefore, the observing system of the climate variability should be stable, and should cover a longterm over the entire Earth.
To satisfy these needs, GCOM consists of two medium-size, polar-orbiting satellite series and multiple
generations (e.g., three generations) with one-year overlaps between consecutive generations for intercalibration. The two satellite series are GCOM-W (Water) and GCOM-C (Climate). Two instruments wereselected to cover a wide range of geophysical parameters: the Advanced Microwave Scanning Radiometer2 (AMSR2) on GCOM-W and the Second-generation Global Imager (SGLI) on GCOM-C. The AMSR2
instrument will perform observations related to the global water and energy cycle, while the SGLI willconduct surface and atmospheric measurements related to the carbon cycle and radiation budget. This
chapter presents an overview of the mission objectives, observing systems, and data products of GCOM.
2.

Mission Objectives

The major objectives of GCOM can be summarized as follows.
- Establish and demonstrate a global, long-term Earth-observing system for understanding climate
variability and the water-energy cycle.
- Enhance the capability of climate prediction and provide information to policy makers throughprocess studies and model improvements in concert with climate model research institutions.
- Construct a comprehensive data system integrating GCOM products, other satellite data, and in-situ
measurements.
- Contribute to operational users including weather forecasting, fishery, and maritime agencies by
providing near-real-time data.
- Investigate and develop advanced products valuable for understanding of climate change and water
cycle studies.
Detailed explanations of the objectives are as follows.
(1) Understanding global environment changes
A) Establish and demonstrate a global, long-term Earth-observing system that is able to observe
valuable geophysical parameters for understanding global climate variability and water cycle
mechanisms.
B) Contribute to improving climate prediction models by providing accurate values of model
1-2

parameters.
C) Clarify sinks and sources of greenhouse gases.
D) Contribute to validating and improving climate prediction models by forming a collaborative
framework with climate model institutions and providing long-term geophysical datasets tothem.
E) Detect trends of global environment changes (e.g., global warming, vegetation changes,
desertification, variation of atmospheric constituents, wide area air pollution, and depletionof ozone layers) from long-term variability of geophysical parameters by extracting shortterm (three- to six-year) natural variability.
F) Advance process studies of Earth environmental changes using observation data.
G) Estimate radiative forcing, energy and carbon fluxes, and albedo by combining satellite
geophysical parameters, ground in-situ measurements, and models.
H) Advance the understanding of the Earth’s system through the activities above.
I) Contribute to an international environmental strategy utilizing the results above.
(2) Direct contribution to improving people’s lives
A) Improvement of weather forecast accuracy (particularly typhoon track prediction, localized
severe rain, etc.).
B) Improvement of forecast accuracy for unusual weather and climate.
C) Improvement of water-route and maritime information.
D) Provision of fishery information.
E) Efficient coastal monitoring.
F) Improved yield prediction of agricultural products.
G) Monitoring and forecasting air pollution including yellow dust.
H) Observation of volcanic smoke and prediction of the extent of the impact.
I) Detection of forest fires.
3.

Observing Systems

3.1. Overall concept
As mentioned in the previous section, the entire GCOM will consist of two satellite series spanning three
generations. However, a budget will be approved for each satellite. Currently, only the GCOM-W satellite
has been launched as the first satellite in the GCOM series. Both GCOM-W and GCOM-C satellites will
be medium-size platforms that are smaller than the ADEOS-II satellite. This is to reduce the risk associatedwith large platforms having valuable and multiple observing instruments. Also, since the ADEOS-II
problem was related to the solar paddle, a dual solar-paddle design was adopted for both satellites. To
assure data continuity and consistent calibration, follow-on satellites will be launched so as to overlap thepreceding satellite by one year. The concept is summarized in Fig. 1.
May 18 2012

1-year
GCOM-W
GCOM-W2

LaunchGCOM-W3
GCOM-CGCOM-C2

LaunchGCOM-C3

Figure 1: GCOM Concept

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3.2. GCOM-W and AMSR2 instrument
Figure 2 presents an overview of the GCOM-W satellite; its major characteristics are listed in Table
1. GCOM-W will carry AMSR2 as the sole onboard mission instrument. The satellite will orbit at analtitude of about 700km and will have an ascending node local time of 13:30, to maintain consistency
with Aqua/AMSR-E observations.

Figure 2: Overview of GCOM-W Satellite

Instrument
Orbit
Size

Table 1: Major Characteristics of GCOM-W Satellite
Advanced Microwave Scanning Radiometer-2 (AMSR2)
Sun-synchronous orbit
Altitude: 700km (over the equator)
5.1m (X) * 17.5m (Y) * 3.4m (Z) (on-orbit)

Mass

1991kgPower

More than 3880W (EOL)

LaunchMay 18, 2012 by H-IIA Rocket

Design Life

5 years

StatusPost-Mission Phase since Nov. 2018

Figure 1 presents an overview of the AMSR2 instrument in two different conditions. Also, basic
characteristics including center frequency, bandwidth, polarization, instantaneous field of view(FOV), and sampling interval are indicated in Table 2. The basic concept is almost identical to that of
AMSR-E: a conical scanning system with a large offset parabolic antenna, feed horn cluster to realize
multi-frequency observation, external calibration with two temperature standards, and total-power
radiometer systems. The 2.0m diameter antenna, which is larger than that of AMSR-E, provides better
spatial resolution at the same orbit altitude of around 700km. The antenna will be developed basedon the experience gained from the 2.0m diameter antenna for ADEOS-II AMSR except the
deployment mechanism. For the C-band receiver, we adopted additional 7.3GHz channels forpossible mitigation of radio-frequency interference. An incidence angle of 55 degrees (over theequator) was selected to maintain consistency with AMSR-E. The swath width of 1450km and theselected satellite orbit will provide almost complete coverage of the entire Earth’s surface within twodays independently for ascending and descending observations.
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Figure 3: Sensor Unit of AMSR2 Instrument in Deployed (left) and Stowed (right) Conditions.
Table 2: Major Characteristics of AMSR2 Instrument
Parameter

Performance and characteristics

Center Frequency (GHz)6.925/7.3

10.65

18.7

23.8

36.5

89.0

350

100

200

400

1000

3000

Bandwidth (MHz)Polarization
NET (K)

Vertical and Horizontal polarization1< 0.34/0.43< 0.70< 0.70

Dynamic range (K)< 0.70< 1.20/1.40 2

2.7 to 340

Nominal incidence angle (deg.)
Beam width (deg.)
IFOV (km) Cross-track< 0.60x along-track

55.0/54.5 2

55.0
1.8

1.2

0.65

0.75

0.35

0.15

35x62

24x42

14x22

15x26

7x12

3x5

Approximate sampling interval (km)10

5Swath width (km)> 1450Digital quantization (bits)

12

Scan rate (rpm)40

3.3. GCOM-C and SGLI instrument
Figure 4 gives an overview of the GCOM-C satellite; its major characteristics are listed in Table 3.
GCOM-C will carry SGLI as the sole mission onboard instrument. The satellite will orbit at an altitude ofabout 800km; the descending node local time will be 10:30, to maintain a wide observation swath andreduce cloud interference over land.

1-5Figure 4: Overview of GCOM-C Satellite

Instrument
Orbit
Size

Table 3: Major Characteristics of GCOM-C Satellite
Second-generation Global Imager (SGLI)
Sun-synchronous orbit
Altitude: 798km (over the equator)
4.6m (X) * 16.3m (Y) * 2.8m (Z) (on orbit)

Mass

2093kgPower

More than 4000W (EOL)

LaunchDec. 23 2017 by H-IIA Rocket

Design Life

5 years

StatusPhase-DThe SGLI instrument has two major new features: 250m spatial resolution for most of the visible
channels and polarization/multidirectional observation capabilities. The 250m resolution will provide
enhanced observation capability over land and coastal areas where the influences of human activity
are most obvious. The polarization and multidirectional observations will enable us to retrieve aerosol
information over land. Precise observation of global aerosol distribution is a key for improving
climate prediction models.
SGLI consists of two major components: the Infrared Scanner (IRS) and the Visible and Nearinfrared Radiometer (VNR). An overview of the SGLI instrument is shown in Fig. 5 for the entireradiometer layout, IRS, and VNR components. Also, requirements for sensor performance are listed
in Tables 4 and 5. VNR can be further divided into two components: VNR-Non Polarized (VNR-NP)
and VNR-Polarized (VNR-P). VNR-NP and VNR-P are the 11-channel multi-band radiometer andthe polarimeter with three polarization angles (0, 60, and 120 degrees). VNR-P has a tilting function
to meet the scatter angle requirement from aerosol observation. The IRS is an infrared radiometercovering wavelengths from 1μm to 12μm. It consists of short infrared (SWI; 1.05 to 2.21μm) andthermal infrared (TIR 10.8 and 12.0μm) sensors. It employs a scanning mirror system with a 45degree tilted flat mirror rotating continuously to realize an 80-degree observation swath andcalibration measurement in every scan.
Through intensive discussions and optimizing studies, the number of SGLI channels was decreased
from the 36 channels of GLI aboard ADEOS-II to 19 channels, while the number of SGLI standard
products will increase compared to those of GLI.
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Figure 5: Overview of SGLI Radiometer Layout (upper), IRS Instrument (lower-left), and VNR Radiometers(lower-right).

Table 4: SGLI Major Performance Requirements
Item
Spectral Bands

Scan Angle
Swath width
Instantaneous field of view
(IFOV) at nadir
Observing direction
Quantization
Absolute Calibration Accuracy
Lifetime

Requirement
VNR-NP : 11CH 380-865nm
VNR-P: 2CH 673.5, 868.5nm / 0, 60, 120deg Polarization
IRS SWI : 4CH 1.05-2.21μm
IRS TIR : 2CH 10.8, 12.0μm
VNR-NP: 70deg (Push broom scanning)
VNR-P: 55deg (Push broom scanning)
IRS SWI/TIR : 80deg (45deg rotation mirror scanning)
1150km for VNR-NP/P
1400km for IRS SWI/TIR
VNR-NP: 250mVNR-P: 1000m
IRS SWI: 250m(SW3CH), 1000m(SW1,2,4CH)
IRS TIR: 500m (250m: option)±45 degrees in along track direction for VNR-P
Nadir for VNR-NP, IRS SWI, and IRS TIR
12bit
VNR : ≤3% IRS : ≤5% TIR : ≤0.5K
5 Years

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Table 5: SGLI Observation Requirement Details
CH
VNR-NPVNR-PIRS SWIIRS TIR

4.

VN1
VN2
VN3
VN4
VN5
VN6
VN7
VN8
VN9
VN10
VN11
P1
P2
SW1
SW2
SW3
SW4
T1
T2

λ
Δλ
nm: VNR, IRS SWI
μm: IRS TIR
380
10
412
10
443
10
490
10
530
20
565
20
673.5
20
673.5
20
763
12
868.5
20
868.5
20
673.5
20
868.5
20
1050
20
1380
20
1630
200
2210
50
10.8
0.74
12.0
0.74

IFOV
m
250
250
250
250
250
250
250
250
250
250
250
1000
1000
1000
1000
250
1000
1000/500/250
1000/500/250

SNR
SNR: VNR, IRS SWINEΔT(K): IRS TIR
250
400
300
400
250
400
400
250
1200 (@1km IFOV)
400
200
250
250
500
150
57
211
0.2 (@500m IFOV)
0.2 (@500m IFOV)

L (for SNR)
W/m2/sr/μm
60
75
64
53
41
33
23
25
40
8
30
25
30
57
8
3
1.9
300 (K)300 (K)

Products

Geophysical products made available by GCOM-W and GCOM-C are listed in Tables 6, 7, and 8.
There are two categories of data products: standard product and research product. A “standard”
product is defined as a product with proven accuracy that is to be operationally processed anddistributed. In contrast, a “research” product is a prototype for a standard product and is processed ona research basis. Both tables indicate standard products with shading.

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Table 6: Standard Geophysical Products of GCOM-W
Accuracy 1

Product

Areas

Resolution(km)

ReleasethresholdStandard

Goal

Integrated
water vapor

Global,over ocean

15±3.5 kg/m2±3.5 kg/m2±2.0 kg/m2

0-70 kg/m2

Vertically integrated (columnar) water vapor
amount. Except sea ice and precipitating areas.

Integrated
cloud liquid
water

Global,over ocean

15±0.10 kg/m2±0.05 kg/m2±0.02 kg/m2

0-1.0 kg/m2

Vertically integrated (columnar) cloud liquid water.
Except sea ice and precipitating areas.

Precipitation

Global, except
cold latitudes

15

Ocean ±50 % Ocean ±50% Ocean ±20%Land ±120 % Land ±120 % Land ±80 %0-20 mm/h

Surface precipitation rate. Accuracy is defined as
relative error (ratio of root-mean-square error toaverage precipitation rate) in 50km grid average.

Sea surface
temperature

Global,over ocean

50±0.8 °C±0.5 °C±0.2 °C-2-35 °C

Except sea ice and precipitating areas. Goal
accuracy is defined as monthly mean bias error in
10 degrees latitudes.

Sea surface
wind speed

Global,over ocean

15±1.5 m/s±1.0 m/s±1.0 m/s

0-30 m/s

Sea iceconcentration

Polar region,over ocean

15

±10 %

±10 %

±5 %

0-100 %Snow depth

Land

30

±20 cm±20 cm±10 cm0-100 cm

Except ice sheets and dense forest areas. Accuracy
is expressed in snow depth and defined as mean
absolute error of instantaneous observations.

0-40 %

Volumetric water content over global land areas
including arid and cold regions, except areas
covered by vegetation with 2kg/m2 waterequivalent. Accuracy is defined as mean absolute
error of instantaneous observations.

Soil moisture

Land

50

±10 %

±10 %

±5 %

Range

Except sea ice and precipitating areas.

Accuracy is expressed in absolute value of sea ice
concentration (%).

1 Accuracy is defined as root-mean-square error of instantaneous values unless otherwise stated. Assumed validation methodologies
are not explained here.

Table 7: Research Products of GCOM-W
Ocean

Resolution
(km)
60

Ocean

30

Africa,Australia

25

Land surface temperature

Land

15

Vegetation water content

Land
Ocean in highlatitudeOkhotsk sea
Ocean in highlatitude10

soil moisture: ± 8%
vegetation water:± 1 kg/m2
forest area: ± 3 °C
nondense vegetation: ± 4 °C
± 1 kg/m2

5±1%

0 - 100 %15± 80 %
2 components:
3 cm/sN/A

Products
All-weather sea surface wind speed
High-resolution (10-GHz)
sea surface temperature
Soil moisture and vegetation watercontent based on the land data
assimilation

High resolution sea ice concentration
Thin ice detection
Sea ice moving vector

Area

50

1-9

Target accuracyRange± 7 m/s

0 - 70 m/s± 0.8 °C

9 - 35 °C
soil moisture: 0 - 100 %vegetation water: 0 - 2 kg/m2
0 - 50 °C
0 - 4 kg/m2

0 - 40 cm/s

Table 8: Geophysical Products of GCOM-C (1/3)
Area Group

Product

common

TOA radiance(including
system Standard
geometric correction)

Radiance

Category Developer

Precisegeometric
Standard
correction
Surface
Atmospheric correctedreflectancereflectance (incl. cloudStandard
detection)
Vegetation index

Standard

fAPAR

Standard

Land

Vegetation
Leaf area index
and carbon
cycle
Above-ground biomass

Day/night

Temperature Surface temperatureLand
netproduction

primary

Water stress trend

JAXA

Both

Tile, Global (mosaic
250m
1, 8 days, month)

JAXA

DaytimeTile , Global (1, 8days, month)

250m

JAXA

DaytimeTile , Global (1, 8days, month)

250m

JAXA/PI

DaytimeTile , Global (1, 8250m
days, month)PI

DaytimeTile , Global (1, 8days, month)Standard
Standard

Atmosphere

Cloud

Aerosol

1km
1km

Standard

PI

DaytimeStandard

PI

Both

Tile , Global (1, 8days, month)
Tile , Global (1, 8days, month)

ResearchPI

DaytimeGlobal (month, year) 1km
Tile , Global (1, 8days, month)
Scene or Tile
Global (month,season)Tile , Global (1, 8days, month)
Tile , Global（1, 8day, month）

PI

N/A

ResearchPI

Both*12Land cover typeResearch

PI/JAXA

DaytimeLand surface albedo

Research

JAXA/PI

N/A

Cloud flag/Classification

Standard

BothClassified cloud fraction

Standard

DaytimeGlobal （1, 8 day,month）

Cloud top temp/heightStandard

Both

Tile , Global （1, 8day, month）

Water cloud OT/effective
radiusStandard

DaytimeTile , Global （1, 8day, month）

Ice cloud optical thickness Standard

DaytimePI

Water cloud geometrical
thickness

ResearchPI

DaytimeAerosol over the oceanStandard

JAXA/PI

DaytimeTile , Global（1, 8day, month）
Tile , Global （1, 8day, month）
Tile , Global （1, 8day, month）

Releasethreshold*2

VNR,SWI Land/coast:
Radiometric 5%250m,
offshore: 1km,
(absolute)*3
polarimetory:1km
Geometric<1
TIR Land/coast: 500m, pixel
offshore: 1km

JAXA

Research

Application Fire detection index

Grid size

TIR and land
2.2m: both,
Other VNR, SWI: Scene
daytime (+special
operation)

Vegetation roughness index Standard
Shadow index

Production unit

250m, 1km
500m

<1pixelStandard accuracy*2

Target accuracy*2

VNR,SWI: 5% (absolute),
VNR,SWI: 3% (absolute), 0.5% (relative) *3
1% (relative) *3
TIR: 0.5K (@300K)
TIR: 0.5K (@300K)
Geometric<0.3 pixel
Geometric<0.5 pixel<0.5pixel<0.25pixel

0.3 (<=443nm),
0.1 (<=443nm),
0.05 (<=443nm),
0.2(>443nm)0.05 (>443nm) (scene) *7 0.025 (>443nm) (scene)*7*7(scene)Grass:
25%,
Grass: 20%, forest: 15%
forest:20%
Grass: 10%, forest: 10% (scene)(scene)(scene)Grass:
50%,
Grass: 30%, forest:20% Grass: 20%, forest: 10%
forest: 50%
Grass:
50%,
Grass: 30%, forest:30% Grass: 20%, forest: 20%
forest: 50%
Grass:
50%,
Grass: 30%, forest: 50% Grass: 10%, forest: 20%
forest: 100%
Grass and forest: Grass and forest: 20%
Grass and forest: 10% (scene)
40% (scene)(scene)Grass and forest: Grass and forest: 20%
Grass and forest: 10% (scene)
30% (scene)(scene)<3.0K (scene)<2.5K (scene)<1.5K (scene)

N/A

N/A

30% (yearly)

500m

N/A

N/A

10% *13 (error judgment rate)

500m

N/A

N/A

20% *14 (error judgment rate)

250m

N/A

N/A

30% (error judgment rate)

1km

N/A

N/A

10%

1km

1km (Tile),
0.1deg (global)

1C-10

10% (with wholeIncl. below cloud amountsky camera)
20% (on solar
15% (on solar
irradiance)*9
irradiance)*9
3K/2km (top
1K*4
temp/height)*5
10%/30% (Cloud
100% as CLW*7
OT/radius)*6

Incl. below cloud amount
10% (on solar irradiance)*9
1.5K/1km (temp/height)*5
50%*7 / 20%*8

30%*6

70%*8

20%*8

N/A

N/A

300m

0.1 (Monthly

0.1(scene a_670,865)*10 0.05 (scene a_670,865)

Land aerosol by near UV
Aerosol by Polarization
RadiationbudgetStandard
Standard

PI

DaytimeTile , Global （1, 8day, month）

DaytimeTile , Global （1, 8day, month）

Long-wave radiation flux

Research

TBD

DaytimeShort-wave radiation flux

Research

JAXA/PI

Daytimea_670,865)*10
0.15 (Monthly
0.15 (scene a_380)*10
a_380)*10
0.15 (Monthly
0.15 (scenea_670,865)*10 a_670,865)*10

Tile , Global （1, 8day, month）
Tile , Global （1, 8day, month）

1C-11

0. 1(scene a_380 )
0.1 (scene a_670,865)

N/A

N/A

Downward 10W/m2, upward 15W/m2 (monthly)

N/A

N/A

Downward 13W/m2, upward 10W/m2

Table 8: Geophysical Products of GCOM-C (2/3)
Area Group

Product
Category Developer Day/night
Normalized water-leaving
radiance (incl. cloudStandard
detection)
PI
DaytimeAtmospheric correction
Standard
Ocean color parameter
Photosynthetically available
Standard JAXA/ PI Daytime
radiation
Euphotic zone depth

Research

Chlorophyll-a concentration Standard

In-water
Ocean

Total suspended matter
concentration
Colored dissolved organic
matterInherent optical properties

Temperature Sea-surface temperature
Ocean net primary
productivity

ApplicationPI

Grid sizePI

Standard

PI

Research

PIStandard

JAXA
PI

Daytime80% (AOT@865nm)
Scene, Global (1, 8 days,
month)
Scene, Global (1, 8 days,
month)

Daytime

Scene, Global (1, 8 days,
month)

Both

Scene, Global (1, 8 days,
month)

Daytime

Scene, Global (1, 8 days,
month)PI

Daytime

Scene, Global (1, 8 days,
month)

Red tide

ResearchPI

Daytime

Scene, Global (1, 8 days,
month)

Cryosphere

multi sensor merged ocean
Research JAXA/PI Daytime
color
multi sensor merged SST
Research
TBD
Both
Snow and Ice covered area
Standard
Daytime(incl. cloud detection)
PI/JAXA
Okhotsk sea-ice distribution Standard
DaytimeArea/
distribution Snow and ice classification Research PI/JAXA Daytime
Snow covered area in forests
Research JAXA Daytime
and mountains
Snow and ice surface
Standard
DaytimeTemperature
Snow grain size of shallow
Standard
Daytimelayer
PI
SurfaceSnow grain size ofproperties
Research
Daytimesubsurface layer
ResearchPI

N/A
60 to +150% (offshore)60 to +150% (offshore)60 to +150% (offshore)

Research

Snow and ice albedo

20% (10km/month)
Coast: 250m
Offshore: 1km
Global: 4-9km

Scene, Global (1, 8 days,
month)

Phytoplankton functional
type

Snow grain size of top layer Research

Release threshold*2
60% (443~565nm)

Scene, Global (1, 8 days,
month)JAXA

Standard

Research

Daytime

Production unit

Area, Global (1, 8 days, month)

Coast: 500m
Others: Same as above
Coast: 500m
Others: Same as above

Coast: 250m
Others: Same as above

Coast: 250m
Offshore: 1km

250m (Tile), 1km
Tile, Global (1, 8 days, month)(global)Area (1day)
250m
Global (8 days, month)
1km
250m

Area (1, 8 days)

Standard accuracy*2
Target accuracy*2
50% (<600nm)
30% (<600nm)
0.5W/m2/str/um (>600nm) 0.25W/m2/str/um (>600nm)
50% (AOT@865nm)
15% (10km/month)

30% (AOT@865nm)
10% (10km/month)

N/A

30%

60 to +150%

35 to +50% (offshore), 50
to +100% (coast)60 to +150%
60 to +150%

50 to +100%
50 to +100%

N/A

N/A

a (440): RMSE<0.25, bbp(550): RMSE<0.25

0.8K (daytime)

0.8K (day & night time)

0.6K (day and night time)

N/A

N/A

70% (monthly)

N/A

N/A

error judgment rate of large/
small phytoplankton
dominance<20%; or error
judgment rate of the dominantphytoplankton functionalgroup <40%

N/A

N/A

error judgment rate <20%

N/A

N/A

N/A

N/A

35 to +50% (offshore), 50 to+100% (coast)
0.8K (day & night time)

10%
10%
N/A
N/A

(vicarious val
other sat. data)with 7%

5%

5%
N/A

3%
10%

N/A

30%

500m (Tile), 1km(global)250m (Tile), 1km
Tile, Global (1, 8 days, month)(global)

5K (vicarious val with other sat.
2K
data and climatology)
100% (vicarious val. with50%
climatology between temp-size)

Tile, Global (1, 8 days, month) 1km

N/A

N/A

50%

N/A

N/A

50%

N/A

N/A

7%

Tile, Global (1, 8 days, month)DaytimeTile, Global (1, 8 days, month)DaytimeGlobal (1, 8 days, month)

250m (Tile), 1km(global)
1km

1C-12

1K
30%

Table 8: Geophysical Products of GCOM-C (3/3)
Area Group
Cryosphere

Surfaceproperties
Boundary

Product

Category Developer Day/night

Production unit

Snow impurity

ResearchPI

DaytimeIce sheet surface roughness Research
Ice sheet boundary
ResearchmonitoringPI

Daytime

Grid size
250m (Tile), 1km
Tile, Global (1, 8 days, month)(global)Area (Season)
1km

JAXA

DaytimeArea (Season)

250m

Release threshold*2Standard accuracy*2

Target accuracy*2

N/A

N/A

50%

N/A

N/A

0.05 *15

N/A

N/A

<500m

Common notes:*1. Heritage levels from ADEOS-II/GLI study are shown by A-C; A: high heritage, B: Remaining issues, C: new or many issues remaining to be resolved*2. The “release threshold” is minimum levels for the first data release at one year from launch. The "standard" and "research" accuracies correspond to full and extra success criteria of the mission. Accuracies
are basically shown by RMSE.
Radiance data notes:*3. Absolute error is defined as offset + noise; relative error is defined as relative errors among channels, FOV, and so on. Release threshold of radiance is defined as estimated errors from vicarious, onboard solar
diffuser, and onboard blackbody calibration because of lack of long-term moon samples
Atmosphere notes:*4. Vicarious val. on sea-surface temperature and comparison with objective analysis data
*5. Inter comparison with airplane remote sensing on water clouds of middle optical thickness*6. Release threshold is defined by vicarious val. with other satellite data (e.g., global monthly statistics in the mid-low latitudes)*7. Comparison with cloud liquid water by in-situ microwave radiometer*8. Comparison with optical thickness by sky-radiometer (the difference can be large due to time-space inconsistence and large error of the ground measurements)*9. Comparison with in-situ observation on monthly 0.1-degree*10. Estimated by experience of aerosol products by GLI and POLDER
Land data notes:*11. Defined with land reflectance~0.2, solar zenith<30deg, and flat surface. Release threshold is defined with AOT@500nm<0.25*12. Night time 250m product can be produced by special observation requests of 1.6m channel*13. Evaluate in semiarid regions (steppe climate, etc.)*14. Fires >1000K occupying >1/1000 on 1km pixel at night (using 2.2um of 1 km and thermal infrared channels)
Cryosphere notes:*15. Defined as height/width of the surface structures

1C-13

APPENDIX 1-C
SUPPLEMENTAL TABLES FOR THE PRODUCTVALIDATIONS OF THE GLOBAL CHANGEOBSERVATION MISSION-CLIMATE (GCOM-C)

1C-0

Table C1 Definition and validation method of GCOM-C L1B and L2 products
Category

Product [Definition・Unit]

Accuracy*16

CommonReleaseSatellite-observed radiance(Data(Level-1B）
releaseDef.:
Satellite-observaedthreshradianceswhich
are old)

radiometricallyandgeometrically corrected withinter-bandregistration. StanCalibration information is dard
added.
Unit: W/m2/str/m

Land

Goal

Precise geometric corrected Releaseradiance (LTOA)
Def.: This product contains 1)
PGCP parameters whichindicate geometric biases Stanestimated using GCP, and 2) dardradiance images which areprojected
tosinusoidal Goal
projection plane with thecenter longitude of 0 degree
after the correction of thegeometric biases using the
PGCP.
Unit: W/m2/str/m
Land atmospheric corrected Releasereflectance (LSRF)
Def.: Land surface reflectancecorrected for the effects ofatmospheric scattering and Stanabsorption. Correction of dard
directional anisotropic effects Goal
are also made for 8-day andmonthly composite products.
Unit: none
Release
Vegetation index (VGI)
Def.: Indices indicating
vegetation cover and activity
such as NDVI and EVI
Unit: none
Standard
Goal

Above-ground biomass (AGB)
Def.: Dry weight of aboveground vegetation
Unit: t/ha

ReleaseStandard
Goal

Cal/Val Method

5% (absolute*11)
geometric accr.<1pixel

Accuracy of radiance is evaluated as RMS error basedon vicarious calibration, on-board calibrations withsolar diffuser and blackbody and so on.
Geometrical accuracy is evaluated using GCP as RMS
error of pixel position after systematic geometriccorrection.
except TIR: 5%(abs.*11), Accuracy of radiance is evaluated as RMS error based
1% (relative)on vicarious calibration, on-board calibrations with
TIR: 0.5K (@300K)
solar diffuser and blackbody, and maneuver
geometric accr.<0.5pixel operations for moon calibration and inter-band
calibration (yaw-direction maneuver).
Except TIR: 3%(abs.*11),
Geometricalaccuracy is evaluated using GCP as RMS
0.5% (relative)
error of pixel position after systematic geometric
TIR: 0.5K (@300K)
geometric accr.<0.3pixel correction.
<1pixelAccuracy of precise geometric correction is evaluatedas RMS error of pixel position using GCPs.

<0.5pixel<0.25pixel

0.3 (<=443nm), 0.2(>443nm) (scene)*8

0.1 (<=443nm), 0.05(>443nm) (scene)*8
0.05 (<=443nm), 0.025(>443nm) (scene)*8

RMS error between satellite-derived reflectances andground truth measurements is estimated at a region
where aerosol optical thickness at 500nm is less than
0.25.
RMS error between satellite-derived reflectances andground truth measurements is estimated.

RMS error is evaluated comparing SGLI-derived VI
with in-situ measured VI derived fromspectroradiometer data at JaLTER, JapanFlux, PEN,
Yatsuga-take tower site etc. and also with othersatellite VI products.
Grass land: 20% (scene), RMS error is evaluated comparing SGLI-derived VI
with in-situ measured VI derived from
Forest： 15% (scene)
spectroradiometer data at JaLTER, JapanFlux, PEN,
Grass land： 10%
Yatsuga-take tower site etc.
(scene),Forest: 10% (scene)
RMS error is evaluated comparing SGLI-derived AGBIO
Grass land： 50%,
with in-situ measured AGBIO at JaLTER, JapanFlux,
Forest： 100%
PEN, Yatsuga-take tower site etc. (derived from direct
measurements of dry weight of grass at grass land,
indirect estimation with allometry equation asfunctions of tree diameter at brest height (DBH) andtree height, or 3-D laser scanner measurements atforest), and also with AGBIO derived from othersatellites and numerical ecosystem models.
RMS error is evaluated comparing SGLI-derived AGBIO
Grass land：30%,
with in-situ measured AGBIO at JaLTER, JapanFlux,
Forest：50%
PEN, Yatsuga-take tower site etc.
Grass land：10%,
Grass land： 25%(scene),Forest： 20% (scene)

Forest：20%

1C-1

Vegetation roughness index(VRI)
Def.: An index indicating plantvertical structure observed
from multi-angle directions.
Unit: none

ReleaseShadow index (SI)
Def.: An index indicating
shadow fraction ofvegetation area inferredfrom spectral reflectance.
Unit: none

ReleaseFraction of absorbed PAR(FAPAR)
Def.: Fraction ofphotosynthetically active
radiation absorbed byvegetation
Unit: none

Leaf area index (LAI)
Def.: The sum of the one sided
green leaf area per unit
ground area.
Unit: none

Grass land･Forest：40%(scene)Grass land・Forest：20%(scene)Grass land・Forest：10%(scene)RMS error is evaluated comparing SGLI-derived VRIwith in-situ measured VRI at JaLTER, JapanFlux, PEN,
Yatsuga-take tower site etc. (derived from spectoral
reflectance data acquired using tower and RC
helicopter and so on).

Grass land・Forest：
30% (scene)
Grass land・Forest：
20% (scene)
Grass land・Forest：
10% (scene)

RMS error is evaluated comparing SGLI-derived VRIwith in-situ measured SI at JaLTER, JapanFlux, PEN,
Yatsuga-take tower site etc. (derived from spectoral
reflectance data acquired using tower and RC
helicopter and so on), or comparing with SI inferredfrom data of high spatial resolution optical sensor.

ReleaseGrass land： 50%,
Forest： 50%

Standard
Goal

Grass land：30%,
Forest：20%
Grass land：20%,
Forest：10%

ReleaseGrass land： 50%,
Forest： 50%

Stan- dard

Grass land：30%,
Forest：30%
Grass land：20%,
Forest：20%

Standard
Goal

Standard
Goal

ReleaseLess than 3.0K (scene)

Stan- dardLess than 2.5K (scene)

Goal

Less than 1.5K (scene)

Land net primary production(LNPP)
Def.: Net primary productivity
which is how much carbon
dioxide vegetation takes induring photosynthesis (GPP)
minus how much carbon
dioxide the plants releaseduring respiration or decay.
Unit: gC/m2/year
Water stress trend (WST)
Def.: An index to understandthe droughty state ofvegetation.
Unit: none

ReleaseN/A

RMS error is evaluated comparing SGLI-derived FAPAR
with in-situ measured FAPAR at JaLTER, JapanFlux,
PEN, Yatsuga-take tower site etc. (derived from data
of PAR meter or spectroradiometer data measuring
upward and downward PAR at forest canopy andfloor.), and with other satellite FAPAR products.
RMS error is evaluated comparing SGLI-derived FAPAR
with in-situ measured FAPAR at JaLTER, JapanFlux,
PEN, Yatsuga-take tower site etc. (derived from data
of PAR meter or spectroradiometer data measuring
upward and downward PAR at forest canopy andfloor.).
RMS error is evaluated comparing SGLI-derived LAI
with in-situ measured LAI at JaLTER, JapanFlux, PEN,
Yatsuga-take tower site etc. (derived from data oflitter trap or LAI-2000 and spectroradiometer datameasuring downward radiant flux etc. at forest floor.),and with other satellite LAI products.
RMS error is evaluated comparing SGLI-derived LAI
with in-situ measured LAI at JaLTER, JapanFlux, PEN,
Yatsuga-take tower site etc. (derived from data oflitter trap or LAI-2000 and spectroradiometer datameasuring downward radiant flux etc. at forest floor.).
RMS error is evaluated comparing SGLI-derived LSTwith in-situ measured LST at the ground surface withuniform land cover and also comparing with othersatellite LST products.
RMS error is evaluated comparing SGLI-derived LSTwith in-situ measured LST at the ground surface withuniform land cover (TBD).
N/A

Stan- dard

N/A

N/A

Goal

30％(annual ave.)

RMS error is evaluated comparing SGLI-derived LNPP
with in-situ measured LNPP at JaLTER, JapanFlux, PEN
sites and also comparing with other satellite LNPP
products.

ReleaseStan- dard

N/A
N/A

N/A
N/A

Goal

10%(as classification
error)*13

Fire detection index (FDI)
Def.: Location of fire hot spots
detected using thermal andshortwave infrared bands.
Unit: none

ReleaseStan- dard
Goal

N/A
N/A
20% (as classification
error)*14

Land cover type (LCT)

ReleaseN/AClassification error is evaluated comparing SGLI
derived WST with in-situ measured latent heattransport at flux sites (TBD).
N/A
N/AClassification error is evaluated comparing SGLI
derived FDI with that derived from high spatial
resolution optical sensors which has shortwave andthermal infrared bands.
N/A

Goal

Land

Land surface temperature (LST)
Def.: Temperature ofterrestrical land surface.
Unit: Kelvin

1C-2

Def.: Land cover type classified
using vegetation indices andland reflectance.
Unit: none

Stan- dard

N/A

N/A

Goal

30% (as classification
error)

Land surface albedo (LALB)
Def.: Ratio of upward reflectedenergy to downward solar
radiation energy.
Unit: none

ReleaseStan- dard

N/A
N/A

Classification error is evaluated comparing SGLI
derived LCT with the ground truth derived from
Degree Confluence Project data on a global scale, andalso comparing with regional LCT (such as Japan area)
derived from high spatial resolution sensors.
N/A
N/A

Goal

10%

Cloud flag (CLFG)
Def.: Cloud discrimination flagincluding the classification ofcloud type and phase(liquid/solid).
Unit: none

Release10% (comparison with
sky-camera binary
image)

Stan- dard

Release

Evaluated as the cloud
fraction products.
Evaluated as the cloud
fraction products.
20% (as solar
radiation)*615%( as solar
radiation)*6
10%（as solar
radiation)*6
1K *1

Stan- dard3K *2 /2km *2

Goal

1.5K *2 /1km*2Classified cloud fraction(CLFR)
Def.: Cloud fractions for 9 cloudtypes which are classified
based on the ISCCP
classification rule.
Unit: percent
Cloud top temp/height (CLTTH)
Def.: Temperature and height ofcloud top layer.
Unit: Kelvin for temperature,
km for height

ReleaseStan- dard
Goal

Water-cloud optical thickness & Release
effective radius (CLOTER_W)
Def.: Optical thickness andeffective radius of water
Stan- dard
cloud droplets
Unit: none for thickness, m forradiusIce-cloud optical thickness(CLOT_I)
Def.: Optical thickness of ice
cloud.
Unit: none

Atmosphere

Atmosphere

Goal

Aerosol over the ocean (ARNP)
Def.: Optical thickness,Ångström exponent, andclassification of aerosol over

10%/30% (optial
thickess/radius) *3

100% (as cloud liquidwater*4)

Goal

50% *4 /20% *5

Release30% *3Stan- dard

70% *5

Goal
Release20 % *50.1(monthly ave. ofa_670, 865)

1C-3

RMS error is evaluated comparing SGLI-derived LALB
with in-situ measured LALB derived fromspectroradiometer data at JaLTER, JapanFlux, PEN,
Yatsuga-take tower site etc. (derived from spectoral
reflectance data acquired using tower and RC
helicopter and so on) and also with other satellite
LALB products.
Overall classification error is evaluated comparing
SGLI derived CLFG with those derived from othersatellite sensors, cloud amounts collected through
GTS (The Global Telecommunication System), andskycamera images.
Same as the classified cloud fraction.
Same as the classified cloud fraction.
Overall classification error is evaluated comparing SGLI
derived solar radiation which is monthly average forevery 0.1 degree global grid with in-situ measured solar
radiation, skycamera images, and existing cloud
fraction climatology datasets such as ISCCP(theInternational Satellite Cloud Climatology Project).
The release criterion shown in the left column
indicates a threshold for SGLI TIR band brightness
temperature by which the ability to sense cloud top
temperature is evaluated indirectly. The accuracy of
TIR band is assessed through the product evaluation
process of sea surface temperature etc. Also
confirmed is the consistency of SGLI derived cloud top
temperature with object analysis data of air
temperature profile over ocean in daytime.
RMS error is evaluated comparing SGLI derived CLTTH
with those derived from airbone and satellite borne
lidar and radiometer etc. for uniform liquid clouds
with moderate optical thickness.
RMS error is evaluated comparing SGLI derived
CLOTER_W with those from other satellite sensors forclouds of mid- to low latitude regions (monthly
average).
RMS error is evaluated comparing cloud liquid water
converted from SGLI derived CLOTER_W with thosemeasured with microwave radiometer on the ground.
Overall RMS error is evaluated comparing SGLI
derived CLOTER_W with those derived frommicrowave radiometer and skyradiometer (for optical
thickness) and other satellite sensors (both param.).
RMS error is evaluated comparing SGLI derived
CLOT_I with those from other satellite sensors forclouds of mid- to low latitude regions (monthly
average). .
RMS error is evaluated comparing SGLI derived
CLOT_I with those from skyradiometers at groundobservation network and other satellite sensors.
Overall RMS error is evaluated comparing SGLI
derived ARV with those from other satellite sensors
and climatology based on the past satellite
observations (monthly average).

Stan- dard

0.1(scene’s a_670,865)*70.05(scene’s a_670,
865)
0.15(monthly ave.
ofa_380)0.15(scene’s a_380) *7

Goal0.1(scene’s a_380 )Release0.15(monthly ave. ofa_670, 865)0.15(scene’s a_670,
865) *70.1(scene’s a_670,
865)

RMS error is evaluated comparing SGLI derived ARP
with those from skyradiometers at groundobservation network (Skynet, Aeronet) and othersatellite sensors for fine mode particles.

ReleaseStan- dard

N/A
N/A

N/A
N/A

Goal

300m

ReleaseN/A

RMS error is evaluated comparing SGLI derived
CLGT_W with those measured at the ground and fromspace (satellite) with cloud radar and lidar
instruments.
N/A

Stan- dard

N/A

N/A

Goal

Downward flux:
10W/m2, Upward flux:
15W/m2 (0.1 deg.,monthly ave.）

RMS error is evaluated comparing SGLI derived
monthly averaged LWRF with those from groundradiation observation network (ARM, BSRN), groundobservation network (JaLTER, JapanFLux, PEN, Fluxnetetc.), and other satellite sensors.

Release

N/A
N/A

N/A
N/A

ocean estimated using visible
and near infrared band.
Unit: none

Stan- dard

Land aerosol by near-UV (ARNP)
Def.: Optical thickness and lightabsorption coefficient ofaerosol over land estimatedusing near-ultraviolet band.
Unit: none
Aerosol by Polarization (ARPL)
Def.: Optical thickness,Ångström exponent, andclassification of aerosol
estimated using polarization
bands.
Unit: none
Water cloud geometrical
thickness (CLGT_W)
Def.: Geometrical thickness ofwater cloud.
Unit: mReleaseLong-wave radiation flux
(LWRF)
Def.: Longwave radiation flux at
the ground including
downward longwave
radiation flux and upward
longwave radiation flux.
Unit: W/m2
Short-wave radiation flux
(SWRF)
Def.: Shortwave radiation flux
at the ground including
downward shortwave
radiation flux and upward
shortwave radiation flux.
Unit: W/m2
Normalized water leaving
radiance (NWLR)
Def.: The upwelling radiancejust above the sea surface.
Unit: W/m2/str/um or 1/sr

Goal

Stan- dard
Goal

Stan- dard

Photosynthetically Available
Radiation (PAR)Def.: Photon flux density within
the visible wavelength range(400 to 700 nm) over ocean
which is potencially available
to plant for photosynthesis.

Ocean

RMS error is evaluated comparing SGLI derived ARU
with those from skyradiometers at groundobservation network (Skynet, Aeronet) and othersatellite sensors.

Goal

Downward: 13W/m2,
RMS error is evaluated comparing SGLI derived
Upward: 10W/m2
monthly averaged SWRF with those from ground(0.1deg. , monthly ave.） radiation observation network (ARM, BSRN), groundobservation network (JaLTER, JapanFLux, PEN, Fluxnetetc.), and other satellite sensors.

Release60% (443~565nm)

Stan- dard

Release50% (<600nm)
0.5W/m2/str/um(>600nm)
30% (<600nm)
0.25W/m2/str/um(>600nm)
80% (a_865)

Stan- dard

50% (a_865)

Goal

30%

Release20% (10km/month)

Stan- dard

15% (10km/month)

Goal

10% (10km/month)

Goal

Atmospheric correction
param.(ACP)
Def.: Aerosol optical properties
for the atmosphericcorrection over ocean.
Unit: none

RMS error is evaluated comparing SGLI derived ARVwith those from other satellite sensors and shipborne
in-situ observations (AERONET/Maritime Aerosol
Network).

1C-4

RMS error is evaluated comparing SGLI derived NWLR
with in-situ optical measurements conducted during
simultaneous ship observations campaign and also
comparing with other satellite products.
RMS error is evaluated comparing SGLI derived NWLR
with in-situ optical measurements conducted during
simultaneous ship observations campaign.

RMS error is evaluated comparing SGLI derived
aerosol optical thickness with those from in-situ
measurements using radiometers during
simultaneous ship observations campaign and also
comparing with other satellite sensors.
RMS error is evaluated comparing SGLI derived
aerosol optical thickness with those from in-situ
measurements using radiometers during
simultaneous ship observations campaign.
RMS error is evaluated comparing SGLI derived
monthly averaged PAR with those derived frommooring buoy such as NDBC, TAO/TRITON etc. as solar
radiation or PAR.

Unit: Ein/m2/day or mol
photons/m2/day
Chlorophyll-a concentration(CHLA)
Def.: Concentration of thegreen pigment inphytoplankton in sea surface
layer.
Unit: mg/m3

Release-60~+150% (open sea)

Stan- dard

-60~+150%

Goal-35~+50% (open sea),-50~+100% (coastal)

Release-60~+150% (open sea)

Stan- dard

-60~+150%

Goal-50~+100%

Release-60~+150% (open sea)

Stan- dard

-60~+150%

Goal
Release-50~+100%
0.8K (daytime only)

Stan- dard

0.8K

Goal

0.6K

Euphotic zone depth (EZD)
Def.: The sea depth where
photosynthetic available
radiation ( PAR) is 1% of its
surface value.
Unit: mReleaseN/A

Overall RMS error is evaluated comparing SGLI
derived SST with those derived from other satellite
sensors and also comparing with those from buoymeasurements (daytime only) obtained through GTS
and internet.
Overall RMS error is evaluated comparing SGLI
derived SST with those derived from other satellite
sensors and also comparing with those from buoymeasurements obtained through GTS and internet.
N/A

Stan- dard

N/A

N/A

Goal

30% (inferred fromextinction coefficient)

Inherent optical properties(IOP)
Def.: Optical properties of sea
water such as spectral
absorption, scattering, andbackscattering coefﬁcientsfor characterizing the marine
optical environment andremote-sensing applications.
Unit: 1/m
Ocean net primary productivity(ONPP)
Def.: Net primary productivity
which is gross photosynthetic
carbon fixation minus the
carbon respired to support
maintenance requirementsof the whole plant.
Unit: mgC/m2/day
Phytoplankton functional type
(PHFT)

ReleaseN/A

RMS error is evaluated comparing SGLI derived EZD
with those derived from simultaneous measurements
of in-water downward irradiance (in-situ EZD isdetermined from the slope of measured irradiance).
N/A

Stan- dard

N/A

N/A

Goal

Absorption coefficient@440nm: RMSE<0.25
and backscattering
coefficient ofphytoplankton@550nm: RMSE<0.25

RMS error is evaluated comparing SGLI derived IOP
with those derived from simultaneous opticalmeasurements.

Release

N/A

N/A

Stan- dard

N/A

N/A

Goal

70% (monthly ave.)

RMS error is evaluated comparing SGLI derived
monthly averaged ONPP with those derived fromsimultaneous in-situ measurements.

Release

N/A
N/A

N/A
N/A

Total Suspended Matter
concentration (TSM)
Def.: Dry weight of suspended
matter in a unit volume of
surface water which is thesum of organics such as
phytoplankton and inorganics
such as soil.
Unit: g/m3
Colored dissolved organic
matter (CDOM)
Def.: Light absoption coefficientof organics dissolved insurface water.
Unit: 1/m

Ocean

Sea surface temperature (SST)
Def.: Temperature of sea
surface.
Unit: C

Stan- dard

1C-5

RMS error is evaluated comparing SGLI derived CHLA
with those derived from sea water samples byfluorescence method or HPLC analysis and also withother satellite products.
RMS error is evaluated comparing SGLI derived CHLA
with those derived from sea water samples byfluorescence method or HPLC analysis.

RMS error is evaluated comparing SGLI derived SSwith those derived from sea water samples byfiltration method and also with other satellite
products.
RMS error is evaluated comparing SGLI derived SSwith those derived from sea water samples byfiltration method.

RMS error is evaluated comparing SGLI derived CDOM
with those derived from sea water samples by opticalmeasurements and also with other satellite products.
RMS error is evaluated comparing SGLI derived CDOM
with those derived from sea water samples by opticalmeasurements.

GoalDef.: Conceptual groupings ofphytoplankton species, whichhave a ecological
functionality in common suchas nitrogen fixation,
calciﬁcation, siliciﬁcation,
DMS production and so on.
Unit: none
ReleaseRedtide (RTD)
Def.: Detection of a red tide Stan- dard
phenomenon known as an
Goalalgal bloom.
Unit: none

Multi sensor merged ocean
color parameters (MOC)
Def.: Multi-sensor merged
chrollophyl-a concentration
product with higher temporal
resolution than that of SGLI
original product.
Unit: mg/m3
Multi sensor merged sea
surface temperature (MSST)
Def.: Multi-sensor merged
seasurface temperature
product with higher temporal
resolution than that of SGLI
original products.
Unit: C
Snow and Ice covered area
(SICA)
Def.: The extent of global snow
and ice cover.
Unit: none

ReleaseStan- dard

Snow grain size of shallow layer(SNGSL)
Def.: Grain size of snow ice
particle in shallow layer
derived mainly from SGLI
865nm band reflectance.
Unit: m

Cryosphere

N/A

N/A

20％ (as classification
error)Classification error is evaluated comparing SGLI
derived RTD with the occurrence of red tide events
determined by eye during simultaneous shipobservations campaign.
N/A
N/A

N/A
N/A

N/A

-35~+50% (Open sea),-50~+100% (Coastal)Same as the SGLI original product (CHLA).

ReleaseStan- dard

N/A
N/A

N/A
N/A

Goal

0.8K

Same as the SGLI original product (SST).

Release10% (comparison with
Overall classification error is evaluated comparing
other satellite products) SGLI derived SICA with other satellites’ same products
and climatology of related geophysical parameters
derived from the past observations.
7%
Overall classification error is evaluated comparing
SGLI derived SICA with those derived from moderate
and high spatial resolution satellite sensors and also5%
with snow and ice information obtained at groundstations etc.
10% (comparison with
Overall classification error is evaluated comparing
other satellite products) SGLI derived OKID with other satellites’ same products
and climatology of related geophysical parameters
derived from the past observations.
5%
Overall classification error is evaluated comparing
SGLI derived OKID with those derived from moderate
and high spatial resolution satellite sensors and also3%with ice information obtained at ship etc.

Stan- dard

ReleaseStan- dard
Goal

Snow and ice surface
Temperature (SIST)
Def.: Temperature of snow andice surface.
Unit: Kelvin

Classification error is evaluated comparing with SGLI
derived PHFT with the dominant type ofphytoplankton group (such as Bacillariophyceae,
Chlorophyceae, and Haptophyta etc.) determinedfrom the plant pigment analysis of sea water samples
using HPLC.

Goal

Goal

Okhotsk sea-ice distribution(OKID)
Def.: The extent of sea ice inOkhotsk Sea.
Unit: none

Classification error of
dominant/nondominant spesies oflarge/small
phytoplankton: 20%, orclassification error of
dominant functional
type in a phytoplankton
group: 40%
N/A

ReleaseStan- dard

5K (comparison withother satellite products
and meteorologicalmeasurements)
2K

Goal

1K

ReleaseStan- dard

100%(evaluated withclimatology oftemperature-snow
grain size relationship)
50%

Goal

30%

RMS error is evaluated comparing SGLI SNGSL withthose from in-situ radiometer measurements andsnow pit works.

N/A

N/A

Snow and ice classification (SIC) Release

1C-6

Overall RMS error is evaluated comparing SGLI
derived SIST with those from other satellite sensors,
air temperatures from GTS and ice buoys, andclimatology derived from the past observations.
RMS error is evaluated comparing SGLI SIST withthose from in-situ radiometer measurements andsnow pit works, air temperatures from GTS and ice
buoys.
Overall error is evaluated comparing SGLI derived
SNGSL with other satellites’ products and climatology
derived from the past observations.

CryosphereDef.: Classification of snow andice cover types derived using
spectral reflectance andtemperature.
Unit: none

Stan- dard

N/A

N/A

Goal

10%

Snow area in forest andmountain (SCAFM)
Def.: The extent of snow coverin forest and mountaneous
region.
Unit: none

ReleaseN/AClassification error is evaluated comparing SGLI
derived SIC with those derived from other moderate
and high spatial resolution satellite sensors and alsowith snow and ice information obtained at groundstation etc.
N/A

Stan- dard

N/A

N/A

Goal

30%

Snow grain size of subsurface
layer (SNGSS)
Def.: Grain size of snow ice
particle in sub-surface layer
derived mainly from SGLI
1050nm band reflectance.
Unit: m
Snow grain size of top layer(SNGST)
Def.: Grain size of snow ice
particle in top-surface layer
derived mainly from SGLI
1640nm band reflectance.
Unit: m
Snow and ice albedo (SIALB)
Def.: Spectrally integrated
albedo of snow surface.
Unit: none

ReleaseN/AClassification error is evaluated comparing SGLI
derived SCAFM with those derived from othermoderate and high spatial resolution satellite sensors
and also with snow information obtained at groundstation etc.
N/A

Stan- dard

N/A

N/A

Goal

50%

RMS error is evaluated comparing SGLI SNGSS withthose from in-situ radiometer measurements andsnow pit works.

Release

N/A

N/A

Stan- dard

N/A

N/A

Goal

50%

RMS error is evaluated comparing SGLI SNGST withthose from in-situ radiometer measurements andsnow pit works.

Release

N/A

N/A

Stan- dard

N/A

N/A

Goal

7%

Snow impurity (SNIP)
Def.: Mass fraction of snow
impurity mixed in snow layer
which is optically equivalent
to soot.
Unit: ppmw
Ice sheet surface roughness(ISRGH)
Def.: Surface roughness of ice
sheets defined as the ratio ofheight to width of aroughness pattern.
Unit: none

ReleaseN/A

RMS error is evaluated comparing SGLI SIALB withthose from in-situ radiometer measurements andsnow pit works.
N/A

Stan- dard

N/A

N/A

Goal

50%

ReleaseN/A

RMS error is evaluated comparing SGLI SNIP withthose from in-situ radiometer measurements andsnow pit works.
N/A

Stan- dard

N/A

N/A

Goal

0.05 *15

N/A

RMS error is evaluated comparing SGLI derived ISRGH
with those derived from other moderate and highspatial resolution satellite sensors and with numericalsimulation results.
N/A

N/A

N/A

500m 以下

Overall bias of ice sheet boundary line is evaluatedcomparing SGLI derived ISBM with those derived fromother moderate and high spatial resolution satellite
sensors.

Ice sheet boundary monitoring Release(ISBM)
Stan- dard
Def.: Boundary line between ice
Goalsheets and sea surface.
Unit: none

Common notes:*1. Heritage levels from ADEOS-II/GLI study are shown by A-C; A: high heritage, B: Remaining issues, C: new ormany issues remaining to be resolved*2. The “release threshold” is minimum levels for the first data release at one year from launch. The "standard" and"research" accuracies correspond to full and extra success criteria of the mission. Accuracies are basically shown by
RMSE.
Radiance data notes:*3. Absolute error is defined as offset + noise; relative error is defined as relative errors among channels, FOV, and soon. Release threshold of radiance is defined as estimated errors from vicarious, onboard solar diffuser, and onboard
blackbody calibration because of lack of long-term moon samples

1C-7

Atmosphere notes:*4. Vicarious val. on sea-surface temperature and comparison with objective analysis data
*5. Inter comparison with airplane remote sensing on water clouds of middle optical thickness*6. Release threshold is defined by vicarious val. with other satellite data (e.g., global monthly statistics in the mid-low
latitudes)*7. Comparison with cloud liquid water by in-situ microwave radiometer*8. Comparison with optical thickness by sky-radiometer (the difference can be large due to time-space inconsistence
and large error of the ground measurements)*9. Comparison with in-situ observation on monthly 0.1-degree*10. Estimated by experience of aerosol products by GLI and POLDER
Land data notes:*11. Defined with land reflectance~0.2, solar zenith<30deg, and flat surface. Release threshold is defined with
AOT@500nm<0.25*12. Night time 250m product can be produced by special observation requests of 1.6m channel*13. Evaluate in semiarid regions (steppe climate, etc.)*14. Fires >1000K occupying >1/1000 on 1km pixel at night (using 2.2um of 1 km and thermal infrared channels)
Cryosphere notes:*15. Defined as height/width of the surface structures

1C-8

TABLE C2 Expected reference data for the validation of GCOM-C/SGLI standard products
Category

Product [Unit]
Satelliteobserved
radiance (Level1B)[W/m2/str/μm]

Accuracy
Targets

Val. Data Type
(Main/Auxiliary)

Algorithm
PIs

Validation
PIs

Release: 5％(Abs. *11)
Geometric:<1pixelIn-situ & various
cal.data(Main)JAXA

JAXA

In-situ Data

Instruments

Observation Sites

Period, Frquency,
Obs. Cycles

Ground reflectance data,
MOBY data etc.
(cooperation with NOAA)
Onboard calibration data

Spectrometer

CEOS cal sites

Year-round

MODIS(MOD02,MYD02)
CAI(L1,L1B)
ASTER(L1B)

GlobalYear-round

Spectrometer

CEOS cal sites

Year-round

MODIS(MOD02,MYD02)
CAI(L1,L1B)
ASTER(L1B)

GlobalYear-round

GCP database derived from
AVNIR-2 etc.

MODIS（MCD43C4)CAIAVNIR-2(Defined in GCP
library)

GCP database derived from
AVNIR-2 etc.

MODIS（MCD43C4)

Other satellite data (TBD)

CommonPrecisegeometriccorrected
radiance[W/m2/str/μm]

Standard: VISSWIR: 5％(Abs.*11), 1％(Relative)
TIR: 0.5K(@300K)
Geometric:<0.5pixel
Goal: VIS-SWIR :
3％ (Abs.*11),
0.5% (Relative)
TIR: 0.5K(@300K)
Geometric:<0.3pixel
Release: <1pixel

LandStandard:<0.5pixel
Goal: <0.25pixel

In-situ & various
cal.data(Main)

Ground reflectance data,
MOBY data etc.
(cooperation with NOAA)
Onboard calibration data
Other satellite data (TBD)

Other satellites(Main)

Other satellites(Main)JAXA(RESTEC,
Tokai U.)

JAXA

SGLI

SGLI

CAI

1C-9

Year-roundLand
atmosphericcorrected
reflectance [―]Vegetation
index
[―]

Release :0.3 (<=443nm),
0.2 (>443nm)(scene)(*8)

In-situ(Main)JAXA

HondaKajiwara

Spectral reflectance (incl.
BRDF) data measured from
UAV

FieldSpec, MS-720
Hyperspectral Camera

Yatsugatake

Campaign

Nasahara

Spectral data measured
from Tower
Spectral data measured
from UAV (combined withBiRS simulations foruniform surfaces)
L2 atmospheric correctedreflectance product(MOD09, MYD09)

MS-700MS-720JaLTER, JapanFlux,
PEN sites

Year-round

MODIS of Terra & Aqua

Global but every
typical LCC (TBD)

Year-round or
Seasonally

Other satellites(Auxiliary)

JAXA

Standard: 0.1(<=443nm), 0.05(>443nm)(scene)(*8)
Goal: 0.05(<=443nm),
0.025 (>443nm)(scene)(*8)

In-situ(Main)

HondaKajiwara

Spectral data measured
from UAV

FieldSpec, MS-720
Hyperspectral Camera

Yatsugatake

Campaign

Nasahara

Spectral data measured
from Tower
Spectral data measured
from UAV

MS-700MS-720JaLTER, JapanFlux,
PEN sites

Year-round

Release:grass： 25%(scene),forest： 20%(scene)In-situ(Main)

HondaKajiwara

Spectral data measured
from UAV

FieldSpec, MS-720
Hyperspectral Camera

forest：Yatsugatake

Campaign

Spectral data measured
from Tower
Spectral data measured
from UAV
L2 VI products(MOD13,MYD13)

MS-700MS-720grass・forest：
JaLTER, JapanFlux,
PEN sites

Year-roundNasahara

MODIS of Terra & Aqua
JASMESCAI

GlobalYear-round

Other satellites(Main)

JAXA

JAXA

1C-10

Above-groundbiomass[t/ha]

Standard:
grass： 20%(scene),forest： 15%(scene)Goal:
grass： 10%(scene),forest： 10%(scene)Release:grass： 50%,
forest： 100%In-situ(Main)

HondaKajiwara

Spectral data measured
from UAV

FieldSpec, MS-720
Hyperspectral Camera

forest：Yatsugatake

Campaign

Nasahara

Spectral data measured
from Tower
Spectral data measured
from UAV

MS-700MS-720grass, forest：
JaLTER, JapanFlux,
PEN sites

Year-round

HondaKajiwaraNasahara

AGBIO estimated from
Every Tree Measurements(DBH, Tree Hight, Tree
Density etc.)

Tree (direct) measurements

Campaign

HondaKajiwaraJAXA

AGBIO estimated from 3DLaser Scanner datameasured at ground

3D-Laser Scanner

forest：Yatsugatake,grass・forest：
JaLTER, JapanFlux,
PEN sites
Mine site (GOSAT2)
of Australia、Fujihokuroku,
Tomakomai, Uryuu,
Mase, Alaska(boreal, 200m sq
scale), Pasoh/
Malaysia (Tropicalrain)
forest：Yatsugatake,grass・forest：
JaLTER, JapanFlux,
PEN sites

Other satellites(Main)

HondaKajiwara

PALSAR-2, ISS/MOLI (L+5yr),
ISS/GEDI (L+5yr)

model(Main)

Sasai

L2-L3 AGBIO products
derived from satellite borne
lider and SAR
Output of eco-systemmodel

In-situ(Main)

Kajiwara

1C-11

BEAMS (Sasai)

Campaign

Year-round

Global but every
typical LCC (TBD)

Year-roundStandard:grass：30%,
forest：50%
Goal:grass：10%,
forest：20%

Vegetation
roughness
index
[―]

Shadow index[―]

In-situ(Main)

Release: grass･
forest： 40%(scene)In-situ(Main)

Standard: grass・
forest： 20%(scene)Goal: grass・
forest： 10%(scene)Release: grass･
forest： 30%(scene)In-situ(Main)

In-situ(Main)

KajiwaraMoriyama

HondaKajiwaraNasahara

AGBIO estimated from
Every Tree Measurements(DBH, Tree Hight, Tree
Density)

Tree (direct) measurements

forest：Yatsugatake,grass・forest：
JaLTER, JapanFlux,
PEN sites
Mine site (GOSAT2)
of Australia、Fujihokuroku,
Tomakomai, Uryuu,
Mase, Alaska(boreal, 200m sq
scale), Pasoh/
Malaysia (Tropicalrain)
forest：Yatsugatake,grass・forest：
JaLTER, JapanFlux,
PEN sites
forest：Yatsugatake,grass・forest：
JaLTER, JapanFlux,
PEN sites
forest：Yatsugatake,grass・forest：
JaLTER, JapanFlux,
PEN sites

Campaign

HondaKajiwaraJAXA

AGBIO estimated from 3DLaser Scanner datameasured at grou

3D-Laser Scanner

HondaKajiwara

VRI derived from 3D-Laser
Scanner data measured
from UAV or near surface(Tower)3D-Laser Scanner

HondaKajiwara

VRI derived from 3D-Laser
Scanner data measured
from UAV or near surface(Tower)3D-Laser Scanner

HondaKajiwara

Spectral reflectance from
UAV
3D-Laser Scanner data &images from UAV

FieldSpec, MS-720
3D-Laser Scanner
Digital camera etc.

forest：Yatsugatake,
Gotograss・forest：
JaLTER, JapanFlux,
PEN sites

Campaign

Spectral reflectance from
Tower
Spectral data measured
from UAV

MS-700MS-720L1 radiance of high-rsol.
satellite imagers

Landsat8

forest：Yatsugatake,
Gotograss・forest：
JaLTER, JapanFlux,
PEN sites
grass：
forest：Goto Is.

Nasahara

Other satellites(Auxiliary)Moriyama

Campaign

Campaign

Campaign

Campaign

1C-12

Year-roundStandard:grass・forest：
20% (scene)Goal:grass･forest：
10% (scene)

In-situ(Main)

HondaKajiwara

Release:grass： 50%,
forest： 50%

Standard:
grass：30%、
forest：20%
Goal:
grass：20%,
forest：10%

FieldSpec, MS-720
3D-Laser Scanner
Digital camera etc.

forest：Yatsugatake,
Gotograss・forest：
JaLTER, JapanFlux,
PEN sites

Campaign

Spectral reflectance from
Tower
Spectral data measured
from UAV

MS-700MS-720Campaign

Moriyama

L1 radiance of high-rsol.
satellite imagers

Landsat8

forest：Yatsugatake,
Gotograss・forest：
JaLTER, JapanFlux,
PEN sites
grass：
forest：Goto Is.

HondaKajiwaraNasahara

PAR derived with PAR meteror spectrometer (incident,
reflercted, transmitted PAR)measured from Towers

PAR meters
MS700 Spectrometer

Year-round

Combine canopy model. <=Ground LIDER+
Heli DSM

3D-Laser Scanner
Digital camera etc.

forest：Yatsugatake,grass・forest：
JaLTER, JapanFlux,
PEN sites, Australia,
Fuji-hokuroku,
Tomakomai, Uryuu,
Mase
500m square sites
GlobalNasahara

Other satellites(Auxiliary)
Fraction ofabsorbed
photosynthetic
ally active
radiation (fPAR)[―]

Spectral reflectance from
UAV
3D-Laser Scanner data &images from UAV

In-situ(Main)

HondaKajiwara(JAXA
Kobayashi)

Other satellites(Main)JAXA

L2 FPAR products (MOD15,
MYD15)

MODIS of Terra & Aqua

In-situ(Main)

HondaKajiwaraNasahara

PAR derived with PAR meteror spectrometer (incident,
reflercted, transmitted PAR)measured from Towers

PAR meters
MS700 Spectrometer

Combine canopy model. <=Ground LIDER+
Heli DSM

3D-Laser Scanner
Digital camera etc.

forest：Yatsugatake,grass・forest：
JaLTER, JapanFlux,
PEN sites
Australia, Fujihokuroku,
Tomakomai, Uryuu,
Mase
500m square sites

1C-13

Year-roundYear-roundYear-roundLeaf area index[―]

Land surface
temperature [K]Release:grass： 50%,
forest： 50%

In-situ(Main)

HondaKajiwara(JAXA
Kobayashi)

HondaKajiwaraNasahara

In-situ measured LAI (frominstrument (indirect) or
grass cutting (direct)
method)LAI-2000
Litter trap etc.

Other satellites(Main)JAXA

L2 LAI products (MODIS)MODIS of Terra & Aqua

Standard:
grass：30%、
forest：30%
Goal:
grass：20%,
forest：20%

In-situ(Main)

HondaKajiwaraNasahara

In-situ measured LAI (frominstrument (indirect) or
grass cutting (direct)
method)LAI-2000
Litter trap etc.

Release:
3.0K 以下(scene)In-situ(Main)Moriyama

in situ BT measured fromgroundIR thermometer

HondaKajiwara

In-situ BT measured fromUAV

IR thermometer

Nasahara

In-situ BT measured from
Tower

JAXA

LST converted from Tairobtained at Fluxsite, GTS,
GSOD or other sites

Moriyama

1C-14

forest：Yatsugatake,grass・forest：
JaLTER, JapanFlux,
PEN sites
Australia, Fujihokuroku,
Tomakomai, Uryuu,
Mase Alaska(boreal, 200m sq
scale), Pasoh/
Malaysia (Tropicalrain)(500m square sites)
Global but every
typical LCC (TBD)

Campaign

forest：Yatsugatake,grass・forest：
JaLTER, JapanFlux,
PEN sites
Australia, Fujihokuroku,
Tomakomai, Uryuu,
Mase Alaska(boreal, 200m sq
scale), Pasoh/
Malaysia (Tropicalrain)(500m square sites)
Railroad Valley, ND& Ivanpah playa, CA
Yatsugatake

Campaign

IR thermometer

JaLTER, JapanFlux,
PEN sites

Year-round

Thermometer

Fluxnet, GTS sites

Year-roundYear-roundCampaign
Campaign

Standard:
2.5K 以下(scene)Goal:
1.5K 以下(scene)Cloud flag [―]AtmosphereClassified cloud
fraction [%]Release: 10%(comparisonwit
h sky-camera
binary image)Standard&Goal:
Eavaluated as
the cloudfraction
products
Release: 20% (assolarradiation)(*6)

Other satellites(Main)JAXA

L2 LST products (MOD11,
MYD11)
L2 LST products from
Sentinel-3
in situ BT measured fromgroundMODIS of Terra & Aqua
Sentinel-3

Global but every
typical LCC (TBD)

Year-roundIn-situ(Main)Moriyama

IR thermometer

Railroad Valley, ND& Ivanpah playa, CA
Yatsugatake

Campaign

HondaKajiwara

In-situ BT measured fromUAV

IR thermometer

Nasahara

In-situ BT measured fromTower

IR thermometer

JaLTER, JapanFlux,
PEN sites
GTS sites

Year-round

JAXA

Tair obtained from Fluxsite,
GTS, GSOD or other sites
Cloud amount derived fromskycamera

Thermometer

GTS cloudiness

Human-eyeOther satellites(Main)

L2 cloud flag product(MOD35, MYD35)MODIS
VIIRS etc.

In-situ(Main)

same as CLFR

same as CLFR

BSRN solar radiation data
Whole sky image data

In-situ(Main)

In-situ(Main)

Nakajima(main),
Ishimoto,
Riedi

Nakajima(main),
Ishimoto,
Riedi

Irie
Nakajima
Kuji

Hayasaka

1C-15

Sky-Camera

Campaign

Year-roundKumamoto,
Greenland, Abashiri,
Tsukuba, Shirase,
Noto, Yoyogi,
Iriomote, Osaka,
Polar region(Sbalvard, Syowa
St.)
GlobalYear-roundSolar radiation base

BRSR etc.

Year-round

Sky-Camera (supplemental)

Kumamoto,
Greenland, Abashiri,
Tsukuba, Shirase,
Noto, Yoyogi,
Iriomote, Osaka,
Polar region(Sbalvard, Syowa
St.)

Year-roundYear-roundCloud top temp
& height[K], [km]Climatology(Main)

ISCCP climatological dataset

Various satellites

GlobalYear-round

Standard:
15%( as solar
radiation )(*6)
Goal: 10%（ as
solar
radiation )(*6)

In-situ(Main)

BSRN solar radiation data

Solar radiation base

BRSR etc.

Year-round

Whole sky image data

Sky-Camera (supplemental)

Year-round

Release: 1K(*1)

Climatology(Main)JAXA

ISCCP climatological dataset

Various satellites

Kumamoto,
Greenland, Abashiri,
Tsukuba, Shirase,
Noto, Yoyogi,
Iriomote, Osaka,
Polar region(Sbalvard, Syowa
St.)
GlobalStandard:
3K(*2)/2km(*2)
Goal:
1.5K(*2)/1km(*
2)

In-situ(Main)

Irie(Takano)Data measured withground-based radar

FALCON(radar)

Chiba, etc., Nieolson(Contact to
Shiobara-san (Irie))

Year-round

Data measured withairborne lidar

NASA Airborne lidar

Flight courses (TBD,
Shinozuka-san (CI ofSano PI) has info.)

Campaigns

Data measured withsatellite-borne lidar

Satellite borne lidar

GlobalYear-roundL2 Cloud effective radius
prd. (MOD06, MYD06)

MODIS of Terra & Aqua

Mid- to Low latitude
area

Year-round

Cloud liquid water datafrom Ground based passive
microwave radiometer(PMR)

Microwave radiometer

Fukue, Hedo, Chiba(Skynet supersites)

Year-round

Nakajima(main),
Ishimoto,
Riedi

Other satellites(Main)
Water-cloudopticalthickness &effective radius[―], [μm]Release:10%/30%( optial
thickess/radius )(*3)
Standard: 100%( as cloud liquidwater: *4)

Other satellites(Main)

In-situ(Main)JAXA
Nakajima(main),
Ishimoto,
Riedi

Irie

1C-16

PMR by NICT＠Okinawa (TBD)

Year-roundGoal: 50% (*4)/20% (*5)

In-situ(Main)

Other satellites(Main)
Ice-cloudopticalthickness[―]

Release:30%(*3)

Other satellites(Main)

Standard:
70%(*5)
Goal: 20 %(*5)

In-situ(Main)

Nakajima(main),
Ishimoto,
Riedi

Irie

Other satellites(Main)
Aerosol over
the ocean[―]Release:0.1( monthly
ave. of τa_670,
865)

In-situ(Auxiliary)Inoue(main),
Ishimoto,
Riedi

Aoki, K.,
Kobayashi,
NASA
Sano
(Shinozuka)

Standard:
0.1(scene’sτa_670, 865)(*7)
Goal:0.05(scene’sτa_670, 865)

Cloud liquid water datafrom ground based passive
microwave radiometer(PMR)
Cloud optical thickness datafrom skyradiometer
L2 Cloud effective radius
prd. (MOD06, MYD06)

Microwave radiometer

Fukue, Hedo, Chiba(Skynet supersites)
Thai, Gouhi, Chiba,
Fukue, Hedo

MODIS of Terra & Aqua

GlobalYear-roundL2 Cloud optical thickness
prd. (MOD06, MYD06)

MODIS of Terra & Aqua

Mid- to Low latitude
area

Year-round

SKYNET data

Skyradiometer

Year-roundL2 Cloud optical thickness
prd. (MOD06, MYD06)

MODIS of Terra & Aqua

Thai, Gouhi, Chiba,
Fukue, Hedo (Skynet
super sites)
GlobalSkyradiometer data on
Mirai, Shirase etc.

Skyradiometer

Cruise route ofMirai, Shirase etc.
Various sites

Campaign

Microtops data from
Maritime Aerosol Network

Microtops

Airbone Sunphoto

Flight courses (TBD,
Shinozuka-san (CI ofSano PI) has info.)

Campaigns

Airborne Sunphoto data by
NASA Ames

Skyradiometer

Year-roundYear-roundYear-roundCampaign

Other satellites(Main)JAXA

L2 Aersosol products(MOD04, MYD04)

MODIS of Terra & Aqua
CAI
VIIRS

GlobalYear-roundIn-situ(Main)

Aoki, K.,
Kobayashi,
NASA

Skyradiometer data on
Mirai, Shirase etc.

Skyradiometer

Cruise route ofMirai, Shirase etc.
Various sites

Campaign

Microtops data from
Maritime Aerosol Network

Microtops

Airbone Sunphoto

Flight courses (TBD,
Shinozuka-san (CI ofSano PI) has info.)

Campaigns

Airborne Sunphoto data by
NASA Ames

Sano(Shinozuka
)

1C-17

Campaign

Other satellites(Main)

Land aerosol bynear-UV [―]Release:0.15( monthly
ave. of τa_380)

Standard:
0.15(scene’sτa_380) (*7)
Goal:0.1(scene’sτa_380 )Aerosol by
Polarization[―]

Release:0.15( monthly
ave. of τa_670,
865)(JAXA)

L2 Aersosol products(MOD04, MYD04)

MODIS of Terra & Aqua
CAI
VIIRS

GlobalYear-round

Aoki K.
Sano
Yamazaki
Various
PI/CI
Sano(Shinozuka)

SKYNET (Aoki),
AERONET (Sano),Skyradiometer (Yamazaki,
etc.)
Microtops data
Airborne Sunphoto data by
NASA Ames

Skyradiometer
AeronetSkyradiometer
Microtops
Airbone Sunphoto

Year-roundYear-roundYear-roundCampaigns
Campaigns

Other satellites(Main)JAXA

L2 Aersosol products(MOD04, MYD04)

MODIS of Terra & Aqua
CAI
VIIRS

Many Skynet sites(<100)
Many Aeronet sites(<100)
MRI sites
Various sites
Flight courses (TBD,
Shinozuka-san (CI ofSano PI) has info.)
GlobalIn-situ(Main)

Aoki K.
Sano
Yamazaki
Various
PI/CI
Sano(Shinozuka)

SKYNET (Aoki),
AERONET (Sano),Skyradiometer (Yamazaki,
etc.)
Microtops data
Airborne Sunphoto data by
NASA Ames

Skyradiometer
AeronetSkyradiometer
Microtops
Airbone Sunphoto

Year-roundYear-roundYear-roundCampaigns
Campaigns

Other satellites(Main)JAXA

L2 Aersosol products(MOD04, MYD04)

MODIS of Terra & Aqua
CAI
VIIRS

Many Skynet sites(<100)
Many Aeronet sites(<100)
MRI sites
Various sites
Flight courses (TBD,
Shinozuka-san (CI ofSano PI) has info.)
GlobalAoki K.
Sano
Yamazaki
Various
PI/CI
Sano(Shinozuka)

SKYNET (Aoki),
AERONET (Sano),Skyradiometer (Yamazaki,
etc.)
Microtops data
Airborne Sunphoto data by
NASA Ames

Skyradiometer
AeronetSkyradiometer
Microtops
Airbone Sunphoto

Year-roundYear-roundYear-roundCampaigns
Campaigns

JAXA

L2 aersosol products(MOD04, MYD04)

MODIS of Terra & Aqua
CAI
VIIRS

Many Skynet sites(<100)
Many Aeronet sites(<100)
MRI sites
Various sites
Flight courses (TBD,
Shinozuka-san (CI ofSano PI) has info.)
GlobalIn-situ(Main)

In-situ(Main)

Other satellites(Main)

Inoue(main),
Ishimoto,
Riedi

Sano
(main),
Riedi

1C-18

Year-roundYear-roundYear-roundStandard:
0.15(scene’sτa_670, 865)(*7)
Goal:0.1(scene’sτa_670, 865)

Normalized
water leaving
radiance[W/m2/str/μm
or 1/sr]

Ocean
Atmosphericcorrection
param.
[―]

In-situ(Main)

Aoki K.
Sano
Yamazaki
Various
PI/CI
Sano(Shinozuka)

SKYNET (Aoki),
AERONET (Sano),Skyradiometer (Yamazaki,
etc.)
Microtops data
Airborne Sunphoto data by
NASA Ames

Skyradiometer
AeronetSkyradiometer
Microtops
Airbone Sunphoto

Other satellites(Main)JAXA

L2 aersosol products(MOD04, MYD04)

MODIS of Terra & Aqua
CAI
VIIRS

Hirawake,
Ishizaka,
Suzuki,
Kobayashi,
Hirata,Saikaiku,
Tohoku,SEABASS

In-situ measured optical
data

Other satellites(Main)JAXA

Standard: 50%(<600nm)
0.5W/m2/str/um (>600nm)
Goal: 30%(<600nm)
0.25W/m2/str/um (>600nm)

In-situ(Main)

Release: 80%(τa_865)

In-situ(Main)

Release: 60%(443~565nm)

In-situ(Main)

Other satellites(Main)Toratani

Toratani,
FrouinMany Skynet sites(<100)
Many Aeronet sites(<100)
MRI sites
Various sites
Flight courses (TBD,
Shinozuka-san (CI ofSano PI) has info.)
GlobalYear-roundYear-roundYear-roundCampaigns
Campaigns

PRR (Hirawake, etc.)
TRIOS (Ishizaka, etc.)
C-OPS (Suzuki)

ECS, A-line, O-line,
Funka-bay, Tokyobay,Seto Inland sea,
Chukchi Sea, Bering
Sea, North Pacific,
Ise-bay, Akkeshibay, Toyama-Bay

Campaign

MOD18

AQUA/MODIS,NPP/VIIRS

GlobalYear-round

Hirawake,
Ishizaka,
Suzuki,
Kobayashi,
Hirata,Saikaiku,
Tohoku,SEABASS

In-situ measured opticaldata

PRR (Hirawake, etc.)
TRIOS (Ishizaka, etc.)
C-OPS (Suzuki)
Aeronet-OC

ECS, A-line, O-line,
Funka-bay, Tokyobay,Seto Inland sea,
Chukchi Sea, Bering
Sea, North Pacific,
Ise-bay, Akkeshibay, Toyama-Bay

Campaign

Kobayashi
TorataniNASA

Aerosol optical thickness
data
Aerosol optical thickness
data
Aerosol optical thickness
data

Skyradiometer
AERONET/maritime(NASA)

Cruise track of
Shirase etc.
Many Aeronet sites(<100)
GlobalCampaign

JAXA

1C-19

AQUA/MODIS、
NPP/VIIRS

Year-roundYear-roundStandard: 50%(τa_865)
Goal: 30%

In-situ(Main)

Photosynthetic
ally Available
Radiation[Ein/m2/day or
molphotons/m2/day]Release: 20%(10km/month)In-situ(Main)

Standard: 15%(10km/month)
Goal: 10%(10km/month)In-situ(Main)

Chlorophyll-aconcentration[mg/m3]

Release: 60~+150%(open sea)In-situ(Main)

Standard: 60~+150%
Goal: -35~+50%(open sea),-50~+100%(coastal)

Kobayashi,
Toratani,
etc.
NASA，
Cooperati
on withAtmos. Gr.
Hirawake,
Ishizaka,
Suzuki,
Hirata,Saikaiku,
Tohoku,SEABASS

Aerosol optical thickness
data

Skyradiometer
AERONET/maritime(NASA),
SKYNET

Cruise track of
Shirase etc.
Many Aeronet sites(<100)

Campaign

Buoy: NDBC, TAO/TRITON
etc.
Ship: PRR data

PRR

Buoy sites
ECS, A-line, O-line,
Funka-bay, Tokyobay, Coast of Oita,
Chukchi Sea, Bering
Sea, North PacificYear-roundCampaign

Buoy: NDBC, TAO/TRITON
etc.
Ship: PRR data

PRR

Buoy sites
ECS, A-line, O-line,
Funka-bay, Tokyobay, Coast of Oita,
Chukchi Sea, Bering
Sea, North PacificYear-roundCampaign

Hirawake,
Ishizaka,
Suzuki,
Kobayashi,
Saikaiku,
Tohoku,SEABASS

Pigment concentration datameasured with fluorescense
method and HPLC at Ship

Fluorescense method, HPLC

ECS, A-line, O-line,
Funka-bay, Tokyobay,Seto Inland sea,
Chukchi Sea, Bering
Sea, North Pacific,
Ise-bay, Akkeshibay, Toyama-Bay

Campaign

Other satellites(Main)

JAXA

MOD20, MOD21

AQUA/MODIS、
NPP/VIIRS

GlobalYear-roundIn-situ(Main)

Hirawake,
Ishizaka,
Suzuki,
Kobayashi,
Saikaiku,
Tohoku,SEABASS

Pigment concentration datameasured with fluorescense
method and HPLC at Ship

Fluorescense method, HPLC

ECS, A-line, O-line,
Funka-bay, Tokyobay,Seto Inland sea,
Chukchi Sea, Bering
Sea, North Pacific,
Ise-bay, Akkeshibay, Toyama-Bay

Campaign

JAXA &
FrouinJAXA,Toratani,
Hirata1C-20

Totalsuspendedmatterconcentration[g/m3]

Coloreddissolved
organic matter[m-1]

Sea surface
temperature[℃]

Release: 60~+150%(open sea)In-situ(Main)JAXA,
Toratani,
HirataIshizaka,
Kobayashi,
SeaBASSDry weight of filtered SS
sampled at Ship

Sampling and filtering

ECS, Tokyo-bay

Campaign

Other satellites(Main)

JAXA

MOD23

AQUA/MODIS、
NPP/VIIRS

GlobalYear-round

Standard: 60~+150%
Goal: 50~+100%

In-situ(Main)

Ishizaka,
Kobayashi,
SeaBASSDry weight of filtered SS
sampled at Ship

Sampling and filtering

ECS, Ariake, Tokyobay

Campaign

Release: 60~+150%(open sea)In-situ(Main)

Hirawake,
Ishizaka,
Kobayashi,
Saikaiku,
SeaBASS

Ship: Absorption data ofsampling water

Absorption meter

ECS, Ariake, Tokyobay, Ise-bay,
Chukchi Sea, Bering
Sea, Akkeshi-bay

Campaign

Other satellites(Main)

JAXA

MOD24

AQUA/MODIS

GlobalYear-round

Standard: 60~+150%
Goal: 50~+100%

In-situ(Main)

Hirawake,
Ishizaka,
Kobayashi,
Saikaiku,
SeaBASS

Ship: Absorption data ofsampling water

Absorption meter

ECS, Tokyo-bay, Isebay, Chukchi Sea,
Bering Sea, Akkeshibay

Campaign

Release: 0.8K
(daytime only)

In-situ(Main)JAXA

GTS
iQuam (buoy data for
AMSR2 val)
In-situ measured SST
Bucket SST, Nautical SST,
Argo float SST, etc.

GTS sites

Year-round

Bucket, thermometer

ECS, A-line, O-line,
Funka-bay, Tokyobay,Seto Inland sea,
Chukchi Sea, Bering
Sea, North Pacific,
Ise-bay, Akkeshibay, Toyama-Bay

Campaign

MODIS
AMSR2

GlobalYear-round

JAXA,
Toratani,
HirataJAXA

Hirawake,
Ishizaka,
Kobayashi,
Saikaiku,
Tohoku,SEABASS

Other satellites(Main)JAXA

MOD28
SST of AMSR2

1C-21

Standard: 0.8K
Goal: 0.6K

In-situ(Main)JAXA

GTS
iQuam (buoy data for
AMSR2 val)
In-situ measured SST
Bucket SST, Nautical SST,
Argo float SST, etc.

thermometer onboard buoy

GTS sites

Year-round

Bucket, thermometer

ECS, A-line, O-line,
Funka-bay, Tokyobay,Seto Inland sea,
Chukchi Sea, Bering
Sea, North Pacific,
Ise-bay, Akkeshibay, Toyama-Bay

Campaign

JAXA

MOD28
SST of AMSR2

MODIS
AMSR2

GlobalYear-round

JAXA

In-situ snow depth from
WMO(GSOD),
In-situ snow depth fromNOAA(GHCND)
L2 snow cover prd.
(MOD10、MYD10)
L2 snow cover productL1 radiancesupersonic or laser
supersonic or laser

GTS sites
GTS sites

Year-round

MODIS
VIIRS
Landsat8 etc.

GlobalYear-roundIn-situ snow depth from
WMO(GSOD),
In-situ snow depth fromNOAA(GHCND)
L2 snow cover prd.
(MOD10、MYD10)
L2 snow cover productL1 radiancesupersonic or laser
supersonic or laser

GTS sites
GTS sites

Year-round

MODIS
VIIRS
Landsat8 etc.

GlobalJAXA

L2 sea-ice cover product(MOD10、MYD10)
L2 sea-ice cover productL1 radiance

MODIS
VIIRS
Landsat8 etc.

Sea of Okhotsk

Dec.-MayJAXA

Sea ice conc. measured
from ground, airplane etc.

Human-eye, Camera

Sea of Okhotsk

Dec.-May

Hirawake,
Ishizaka,
Kobayashi,
Saikaiku,
Tohoku,SEABASS

Other satellites(Main)Snow and Ice
covered area[―]

In-situ(Auxiliary)

Other satellites
Climatology(Main)JAXA

Standard：7%
Goal：5%In-situ(Auxiliary)

JAXA

Other satellites
Climatology(Main)JAXA

Cryosphere

Release: 10%(comparison
with othersatellites
products)

Okhotsk sea-ice
distribution[―]

Release: 10%(comparison
with othersatellite
products)
Standard：5%
Goal：3%

Other satellites(Main)

In-situ(Auxiliary)

StamnesStamnes

1C-22

Year-roundOther satellites
Climatology(Main)Snow and ice
surfaceTemperature[K]

Release: 5K( comparison
with othersatellite
products andmeteorologicalmeasurements )In-situ(Main)

Stamnes

Other satellites(Main)JAXA

L2 sea-ice cover product(MOD10、MYD10)
L2 sea-ice cover productL1 radiance

MODIS
VIIRS
Landsat8 etc.

Sea of Okhotsk

Dec.-May

Aoki

In-situ Tair obtained from
GTS, GSOD, GC-Net etc.

Thermometer at GTS and
GC-Net sites or ocean bouys

GTS sites etc.
GC-Net sites on
GreenlandYear-round

JAXA

GLI snow surface temp.
(Climatology)
MODIS snow surf. temp.
(Climatology)
VIIRS snow surface temp.
Landsat8 snow surface
temp. (High resol.)

GLI
MODIS
VIIRS
Landsat8 etc.

Global, Greenland,
Antarctica etc.

Year-round

Thermometer at GTS sites
or ocean bouys

GTS sites etc.

Year-round

Thermometer at GTS and
GC-Net sites or ocean bouys
IR thermoeter, FT-IR,
Thermometer etc.

GTS sites etc.
GC-Net sites on
Greenland
Hokkaido,
Greenland,
Antarctica etc.

Year-round

GLI snow grain size
(Climatology)
MODIS snow grain size
(Climatology)
VIIRS snow grain size
Landsat8 snow grain size
(High resol.)
SGLI SIST product

GLI
MODIS
VIIRS
Landsat8 etc.
SGLI

Global, Greenland,
Antarctica etc.

Year-roundIn-situ Tair obtained from
GTS etc
Snow grain size derived
from in-situ snow pit dataand optical measurements(reflectance, SSA etc.)

Thermometer at GTS

GTS sites etc.

Snow Pit Work Tools,
FieldSpecFR, NIR Camera,
IceCube etc.

Hokkaido,
Greenland,
Antarctica etc.

Climatology(Main)

Standard：2K
Goal：1K

Snow grain sizeof shallow layer[μm]

Release: 100%
(evaluated withclimatology oftemperature‐snow grain size
relationship)Standard：50%
Goal：30%

In-situ(Main)

Other satellites
Climatology(Main)

In-situ(Main)

Aoki

Stamnes

JAXA

Aoki

Climatology of Tair etc.
In-situ Tair obtained from
GTS, GSOD, GC-Net etc.
In-situ Tsnow and Tair data

1C-23

Campaign

Campaign

Other satellites
Climatology(Auxiliary)

JAXA

GLI snow grain size
(Climatology)
MODIS snow grain size
(Climatology)
VIIRS snow grain size
Landsat8 snow grain size
(High resol.)

GLI
MODIS
VIIRS
Landsat8 など

Global, Greenland,
Antarctica etc.

Year-round

Common notes:*1. Heritage levels from ADEOS-II/GLI study are shown by A-C; A: high heritage, B: Remaining issues, C: new or many issues remaining to be resolved*2. The “release threshold” is minimum levels for the first data release at one year from launch. The "standard" and "research" accuracies correspond to full and extra success criteria of the mission. Accuracies arebasically shown by RMSE.
Radiance data notes:*3. Absolute error is defined as offset + noise; relative error is defined as relative errors among channels, FOV, and so on. Release threshold of radiance is defined as estimated errors from vicarious, onboard solar
diffuser, and onboard blackbody calibration because of lack of long-term moon samples
Atmosphere notes:*4. Vicarious val. on sea-surface temperature and comparison with objective analysis data
*5. Inter comparison with airplane remote sensing on water clouds of middle optical thickness*6. Release threshold is defined by vicarious val. with other satellite data (e.g., global monthly statistics in the mid-low latitudes)*7. Comparison with cloud liquid water by in-situ microwave radiometer*8. Comparison with optical thickness by sky-radiometer (the difference can be large due to time-space inconsistence and large error of the ground measurements)*9. Comparison with in-situ observation on monthly 0.1-degree*10. Estimated by experience of aerosol products by GLI and POLDER
Land data notes:*11. Defined with land reflectance~0.2, solar zenith<30deg, and flat surface. Release threshold is defined with AOT@500nm<0.25*12. Night time 250m product can be produced by special observation requests of 1.6m channel*13. Evaluate in semiarid regions (steppe climate, etc.)*14. Fires >1000K occupying >1/1000 on 1km pixel at night (using 2.2um of 1 km and thermal infrared channels)
Cryosphere notes:*15. Defined as height/width of the surface structures

1C-24

TABLE C3 Expected reference data for the validation of GCOM-C/SGLI research products
Categor
Product [Unit]yLand net
primary
production[gC/m2/year]

Water stress
trend [―]
Fire detection
index [―]

Accuracy Targets
Goal: 30％
(annual ave.)

Goal: 10% (asclassification
error)(*13)
Goal: 20% (asclassification
error)(*14)

LandLand cover type Goal: 30% (as[―]classification
error)

Val. Data Type
(Main/Auxiliary)In-situ(Main)

Other satellites(Main)
In-situ(Main)

Validation PIs

In-situ Data

Instruments

Observation Sites

Nasahara

Nasahara

LNPP data derived
from various variables
measured at flux tower
sites
LNPP products derived
from other satellites
Latent heat flux
measured at flux towersites
Hotspots data derived
from other satellites

Thermometer,
spectrometer,pyranometer etc.

forest：Yatsugatake,grass・forest：JaLTER,
JapanFlux, PEN sites

MODIS
VIIRS etc.
Eddy Correlation Flux
Measurement System

Global covering every
typical LCT
forest：Yatsugatake,grass・forest：JaLTER,
JapanFlux, PEN sites
Global covering every
typical vegetation typeYear-roundKajiwara

Nasahara

Other satellites(Main)Moriyama
Nakau

Moriyama
Nakau

In-situ(Main)

Fukue
SoyamaTakagiNasahara

Sasai
SoyamaNasahara

Degree Confluence
Project (DCP) data

Global covering every
typical LCT

Every year (TBD)

SoyamaNasahara

L1 radiance data ofLandsat8high resolution satellite AVNIR-2
etc.
Google Earth
Spectral reflectanceSpectometerdata measured atflux tower, RC
helocopter etc

Global covering every
typical LCT

Seasonally (TBD)

forest：Yatsugatake,grass・forest：JaLTER,
JapanFlux, PEN sites

Campaign/Year-round

JAXA

LALB products derived
from other satellites

MODIS
VIIRS
etc.

Global covering every
typical LCT

Year-round

Irie,
Kuji

Cloud profile data

Falcon radar

Falcon sites

Year-round

Cloud bottom heightobtained withceilometer onboard
ShiraseCloud top height dataetc. measured fromspace

Ceilometer

Cruise course of Shirase
between Japan and theAntarctica

Campaign

Calipso etc.

GlobalYear-round

Other satellites(Main)

Land surface
albedo [―]Goal: 10%In-situ(Main)JAXA

Honda-Kajiwara
Nasahara

Other satellites(Main)

Atmosphere

Water cloud
geometrical
thickness[m]

Goal: 300m

Period, Frquency,
Obs. Cycles
Year-round

Algorithm PIs

In-situ(Main)

Other satellites(Main)

Kuji

1C-25

MODIS
Landsat8

Campaign/Year-roundYear-roundLong-wave
radiation flux
[W/m2]

Goal: Downward In-situ
flux: 10W/m2, (Main)
Upward: 15W/m2(0.1deg., monthly
ave)

Hayasaka

Hayasaka

Other satellites
Climatology(Main)

Short-wave
radiation flux
[W/m2]

Goal: Downward: In-situ
13W/m2,(Main)
Upward: 10W/m2(0.1deg., monthly
ave)

Hayasaka

Hayasaka

Other satellites
Climatology(Main)

Euphotic zone
depth
[m]

Goal: 30%
(inferred fromextinction
coefficient)

Ocean

In-situ(Main)

HirataInherent optical Goal: Absorption In-situ
properties
coefficient(Main)[1/m]@440nm:RMSE<0.25 andbackscattering
coefficient ofphytoplankton@
550nm:
RMSE<0.25

HirataHirawake,
Ishizaka,
Suzuki,
Kobayashi,
Saikaiku,
Tohoku,SeaBASS
Hirawake,
Ishizaka,
Suzuki,
Frouin, SeaBASS

Longwave radiation
data from radiation
network
Longwave radiation
data from from flux
networkClouds and aerosol
data
Global radiative flux
data (ISCCP-FD)
Surface Radiation
Budget (GEWEX-SRB)
Longwave radiation
data from radiation
network
Longwave radiation
data from from flux
networkClouds and aerosol
data
Global radiative flux
data (ISCCP-FD)
Surface Radiation
Budget (GEWEX-SRB)
In-situ measured
optical data

Net radiometer etc.

BSRN, Skynet, JMA etc
sites

Year-round

JaLTER, JapanFLux, PEN,
Fluxnet sites
MODIS
ISCCP
GEWEX

GlobalYear-roundNet radiometer etc.

BSRN, Skynet, JMA etc
sites

Year-round

JaLTER, JapanFLux, PEN,
Fluxnet sites
MODIS
ISCCP
GEWEX

GlobalPRR (Hirawake, etc.)
TRIOS (Ishizaka, etc.)
C-OPS (Suzuki)

ECS, A-line, O-line,
Campaigns
Funka-bay, Tokyobay,Seto Inland sea,
Chukchi Sea, Bering Sea,
North Pacific, Ise-bay,
Akkeshi-bay, Toyama-Bay

Pigment concentration Fluorescense method,data measured withHPLC
fluorescense methodand HPLC at Ship

ECS, A-line, O-line,
Campaigns
Funka-bay, Tokyobay,Seto Inland sea,
Chukchi Sea, Bering Sea,
North Pacific, Ise-bay,
Akkeshi-bay, Toyama-Bay

1C-26

Year-roundOcean net
primary
productivity[mgC/m2/day]

Goal: 70%
(monthly ave.)

In-situ(Main)

Hirawake,
Ishizaka

Hirawake,
Ishizaka,
SeaBASS

ONPP derived from insitu measurements

Phytoplankton
functional type
[―]

Goal:
Classification
error ofdominant/non‐dominan t
spesies oflarge/small
phytoplankton:
20%, orclassification
error ofdominant
functional type ina phytoplankton
group: 40%
Goal: 20% (asclassification
Error)

In-situ(Main)

Hirawake,
HirataHirawake,
Ishizaka,
Suzuki

Pigment concentration Fluorescense method,data measured withHPLC
fluorescense methodand HPLC at Ship

In-situ(Main)

Ishizaka

Ishizaka

Existence of red tide
observed by humaneyes

Multi sensor
merged oceancolor
parameters[mg/m3]

Goal:-35~+50%(open sea),-50~+100%(coastal)

In-situ(Main)

JAXA

Hirawake,
Ishizaka,
Kobayashi,
Saikaiku etc.

Pigment concentration Fluorescense method,data measured withHPLC
fluorescense methodand HPLC at Ship

Multi sensor
merged sea
surfacetemperature[℃]

Goal: 0.8K

Other satellites(Main)JAXA

JAXA

L2 SST products

JAXA

Snow and iceclassification[―]

Goal：10%

JAXA

Redtide [―]In-situ(Main)

Cryosphere

Other satellites(Main)

In-situ(Main)

Stamnes

JAXA

FRRF

Campaigns

MODIS, VIIRSFunka-bay, Tokyo-bay,
Coast of Oita, East andwest of tohoku, east
setonai-kai, Ise-bay
ECS, Ariake, A-line, Oline, Funka-bay, Tokyobay, Coast of Oita,
Chukchi Sea, Bering Sea,
North Pacific, Okhotsk,
East and west of tohoku,
east setonai-kai, Ise-bay
GlobalGTS
iQuam (buoy data for
AMSR2 val)

Thermometer

GTS sites

Year-roundL2 snow cover prd.
(MOD10、MYD10)
L2 snow cover productL1 radiance
In-situ photographtaken at Buoy, Ship,

MODIS
VIIRS
Landsat8 etc.

GlobalYear-roundWeb camera etc.

Buoys, Ships, etc.

Year-round

1C-27

Human eye

ECS, A-line, O-line,
Campaigns
Funka-bay, Tokyo-bay,
Coast of Oita, Chukchi
Sea, Bering Sea, North
Pacific
ECS, A-line, O-line,
Campaigns
Funka-bay, Tokyo-bay,
Coast of Oita, Chukchi
Sea, Bering Sea, North
Pacific, Okhotsk, East andwest of tohoku, east
setonai-kai, Ise-bay

Campaigns

Year-roundSnow area inforest andmountain[―]

Goal：30%

Other satellites(Main)JAXA(Stamnes)

In-situ(Auxiliary)

JAXA

JAXA

etc.
Photograph taken from
Airplane
L2 snow cover prd.
MODIS
VIIRS
(MOD10、MYD10)
L2 snow cover product Landsat8 etc.
L1 radiance
In-situ photographtaken at ground sites
etc.
Photograph taken from
Airplane
Snow grain size derived
from in-situ snow pit
data and opticalmeasurements

GlobalYear-round

Mountain and forestsites, etc.

Year-round

Snow Pit Work Tools,
Hokkaido, Greenland,
FieldSpecFR, NIR
Antarctica etc.
Camera, IceCube etc.

Campaign

Web camera etc.

Snow grain size Goal：50%
of subsurface
layer [μm]In-situ(Main)

Stamnes, Aoki

Aoki

Snow grain size Goal：50%
of top layer[μm]

In-situ(Main)

Stamnes, Aoki

Aoki

Snow grain size derived Snow Pit Work Tools,
Hokkaido, Greenland,
from in-situ snow pit
FieldSpecFR, NIR
Antarctica etc.
data and optical
Camera, IceCube etc.
measurements

Campaign

Snow and ice
albedo [―]Goal：7%In-situ(Main)

Stamnes, Aoki

Aoki

Snow Pit Work Tools,
Hokkaido, Greenland,
FieldSpecFR, NIR
Antarctica etc.
Camera, IceCube etc.

Campaign

Snow impurity[ppmw]

Goal：50%In-situ(Main)

Stamnes, Aoki

Aoki

Spectrometer
Hokkaido, Greenland,(FieldSpecFR etc.),
Antarctica etc.
Snow Pit Work Tools,
Snow filteration system

Campaign

Ice sheetsurfaceroughness [―]Goal：0.05 (*15)

Other satellites(Main)Aoki

Aoki

Albedo calculated
based on in-situ
measured optical dataand snow pit work data
Snow impurity
concentration
estimated from in-situ
measured optical data
and also directly
measured by filtering
methodRoughness estimatedfrom other satellite
data

MODIS, MISR, VIIRS
Landsat8 etc.

Greenland, Antarctica

Annually

Roughness estimatedthrough simulations ofsnow BRDF using
radiative transfer code
L1 radiance data

Radiative transfer code Greenland, Antarctica(ARTMASS)
etc.

Annually

MODIS
VIIRS
Landsat8 etc.

Monthly

Model(Main)

Ice sheetboundary

Goal：<500m

Other satellites
Climatology(Auxiliary)

JAXA

JAXA

1C-28

Antarctica etc.

monitoring [―]Common notes:*1. Heritage levels from ADEOS-II/GLI study are shown by A-C; A: high heritage, B: Remaining issues, C: new or many issues remaining to be resolved*2. The “release threshold” is minimum levels for the first data release at one year from launch. The "standard" and "research" accuracies correspond to full and extra success criteria of the mission. Accuracies arebasically shown by RMSE.
Radiance data notes:*3. Absolute error is defined as offset + noise; relative error is defined as relative errors among channels, FOV, and so on. Release threshold of radiance is defined as estimated errors from vicarious, onboard solar
diffuser, and onboard blackbody calibration because of lack of long-term moon samples
Atmosphere notes:*4. Vicarious val. on sea-surface temperature and comparison with objective analysis data
*5. Inter comparison with airplane remote sensing on water clouds of middle optical thickness*6. Release threshold is defined by vicarious val. with other satellite data (e.g., global monthly statistics in the mid-low latitudes)*7. Comparison with cloud liquid water by in-situ microwave radiometer*8. Comparison with optical thickness by sky-radiometer (the difference can be large due to time-space inconsistence and large error of the ground measurements)*9. Comparison with in-situ observation on monthly 0.1-degree*10. Estimated by experience of aerosol products by GLI and POLDER
Land data notes:*11. Defined with land reflectance~0.2, solar zenith<30deg, and flat surface. Release threshold is defined with AOT@500nm<0.25*12. Night time 250m product can be produced by special observation requests of 1.6m channel*13. Evaluate in semiarid regions (steppe climate, etc.)*14. Fires >1000K occupying >1/1000 on 1km pixel at night (using 2.2um of 1 km and thermal infrared channels)
Cryosphere notes:*15. Defined as height/width of the surface structures

1C-29

Table C4 Definition of the disclosure level (DL)

-

-

-

General users

OK-

Registered users

OK-

EarthCARE PI(B1)GCOM related PIs
only

OK

GCOM PI(A)EORC Internal use
only

EORC researchers

Disclosure level(A-D) to be set bydata provider

Usage

-

1) Cal & Val of SGLI products and/or applications for Earth sciences (such as
scatter plots, statistics from which raw data cannot be reproduced) are
possible to be published. It is necessary to describe the use of JAXA’s
database and the organization of data acquisition in theacknowledgement *1
2) Redistribution of the raw data is prohibited.

-

1) Cal & Val of GCOM products and/or applications for Earth sciences are
possible to be published. It is necessary to agree with data provider about
how to acknowledge the favor (e.g., including data provider as a co-author
or in the acknowledgement) and to describe the use of JAXA’s database
and the organization of data acquisition in the acknowledgement*1.
2) Data use beyond the objectives of the GCOM mission is prohibited.
3) Redistribution of the raw data is prohibited.
1) Cal & Val of EarthCARE products and/or applications for Earth sciences are
possible to be published. It is necessary to agree with data provider about
how to acknowledge the favor (e.g., including data provider as a co-author
or in the acknowledgement) and to describe the use of JAXA’s database
and the organization of data acquisition in the acknowledgement *1.
2) Data use beyond the objectives of the EarthCARE & GCOM mission isprohibited.
3) Redistribution of the raw data is prohibited.
1) User registration is required.
2) Applications for Earth sciences are possible to be published. It is necessary
to submit an application form to JAXA prior to the publication. Also, it isnecessary to to describe the use of JAXA’s database and the organizationof data acquisition in the acknowledgement*1.
3) Redistribution of the raw data is prohibited.

(B2)
GCOM &
EarthCARE PIs
only

OK

OK OK

-

-

(C)
Registered users

OK

OK OK OK-

OK OK OK

1) It is necessary to describe the use of JAXA’s database when using the dataand publishing results. It is also necessary to report the results ofOKpublication to JAXA*1.
2) Redistribution of the raw data is prohibited.

(D)
Open to the public OK(no limitation)*1 follow the JAXA’s policy on data use

1C-30

Land

Table C5 GCOM-C PIs in the previous RA
PI_Name
Yoshiaki Honda

Affiliation
Chiba Univ.

Koji Kajiwara

Chiba Univ.

Masao Moriyama

Nagasaki Univ.

Hideki Kobayashi

JAMSTEC

Junichi Susaki

Kyoto Univ.

Kiyonari Fukue

Tokai Univ.

Algorithm development and validation of the land albedo by using the BRDF model
parameters
Global Land Cover Classification Using Surface Reflectance Data

Noriko Soyama

Tenri Univ.

Development of algorithm and the validation scheme of the global land cover productMasahiro Tasumi

Miyazaki Univ.

Development of the global evapotranspiration index algorithm as a GCOM-C land product

Kenlo Nasahara

Tsukuba Univ.

Validation of land biological information from GCOM-C

Shin Nagai

JAMSTEC

Acquisition of ground truth data for mapping of biophysical parameters of forest

Takayuki Kaneko

Tokyo Univ. ERI

Kaoru Tachiiri

JAMSTEC

Construction and operation of the GCOM-C/SGLI real-time active volcano monitoring
system, and eruption analysis
Investigation of the possibility to improve an Earth system model utilizing GCOM-C data

Kazuo Mabuchi

Chiba Univ.

Tomomichi Kato

Hokkaido Univ.

Masataka Takagi

Kochi Univ. of Tech.

Christoph Rüdiger

Monash Univ.

Yi QinCSIRO

Koji Nakau

JAXA SAOC

Validation of land surface variables observed by GCOM-C and evaluation of theircontribution to modelling activities over instrumented field sites in Australia
Simultaneous Aerosol and Surface BRDF Retrieval by Synergistic Utilization of GCOMC/SGLI and Himawari/AHI
Development and validation of the forest fire detection algorithm using SGLI

Reiji Kimura

Tottori Univ.

Development of global desertification map

Akihiko Kotera

Ibaraki Univ.

Takashi Nakajima

Tokai Univ.

Miho Sekiguchi

Development of monitoring system for flood damages in crop production using GCOMC/SGLI time-series data
Global observations of cloud from GCOM-C SGLI for contributing climate change study
and improving cloud science, Part II
Development of remote sensing algorithm and assimilation system of atmospheric aerosols
using SGLI

Makoto Kuji

Tokyo Univ. of
Marine Science andTechnology
The Kyoto College of Improved algorithms for aerosol retrieval from multidirectional perspectives
Graduate Studies for
Informatics
Meteorological Res. Development of ice cloud and aerosol analysis schemes by improved particle scattering
Inst.
model
Nara Women's Univ. Retrieval and validation of cloud geometrical properties

Hitoshi Irie

Chiba Univ.

Kazuma Aoki

Toyama Univ.

Akihiro Yamazaki
Tadahiro Hayasaka

Meteorological Res.
Inst.
Tohoku Univ.

Ryoichi Imasu

Tokyo Univ., AORI

Sonoyo Mukai

Atmosphere

Hiroshi Ishimoto

Improvement of application technology of GCOM-C products by synthetic use of a climate
model and satellite remote sensing data
Environmental effects on photosynthetic activity analyses by the correlationship in theanomalies between SIF and satellite-derived biotic and environmental variables
Mapping of tender green and autumn color by satellite data fusion

Validation of the GCOM-C atmosphere products by the ground remote sensing observation
network, SKYNET
Study of influence of spatial and temporal representativeness of aerosol optical properties
by solar radiation measurements on in-situ validation of GCOM-C/SGLIProvision of validation data for GCOM-C atmosphere product validation from groundradiation measurement network
Study of surface radiation budget product validation

Mitsuhiro Toratani

Validation of aerosol and cloud microphysical properties using Russian AirplaneLaboratory
Tokyo Univ., AORIUse of GCOM-C and other satellite observations for evaluations of cloud processes in
global climate models
Laboratoire d'Optque Remote sensing of clouds and aerosols properties from SGLI on GCOM-C1 Applying
Atmosphériquelessons learned from the A-Train to explore SGLI and EarthCARE
Tokai Univ.
Study of SGLI ocean color atmospheric correction scheme

Taka Hirata

Hokkaido Univ.

Robert Frouin

Scripps Institution of Vicarious calibration, algorithm development, and in situ data collection for SGLI ocean
Oceanographycolor remote sensing
Hokkaido Univ.
Improvement and validation of net primary production and phytoplankton size
distribution algorithms

Kentaroh Suzuki
Jerome Riedi

Ocean

Research title
Validation scheme development of the atmospheric corrected land reflectance, andalgorithm development of LAI and fAPAR
Algorithm development and validation of the global above-ground biomass, vegetation
roughness index, and water-stress trend products
Algorithm development and improvement of the GCOM-C1/SGLI land surface
temperature and the shadow index
Research algorithm development of GCOM-Cl LAI/FAPAR, and NPPToru Hirawake

Calibration, Validation and application of the SGLI/GCOM-C ocean algorithms

1C-31

Joji Ishizaka

Nagoya Univ., ISEE

Koji Suzuki

Hokkaido Univ.

Hiroshi Kobayashi
Tomonori Isada
Victor Kuwahara
David Antoine
Mati Kahru
Atsushi Matsuoka

Joaquim Goes
Menghua Wang
Bryan A. Franz
Lachlan I.W.
McKinna
Yosuke Yamashiki

Cryosphere

Teruo Aoki
Knut Henrik
StamnesAcquisition of validation dataset for GCOM-C coastal products

Highly frequent and accurate observations of marine phytoplankton pigments and light
regimes for the Validation of SGLI/GCOM-Cl data
Yamanashi Univ
In-situ measurements for development of an atmosphere and in-water combined algorithm
basing on classification of coastal and lake water optical property characterization
Hokkaido Univ.
Products validation for inherent and apparent optical properties, phytoplankton pigments,
and net primary productivity derived from SGLI/GCOM-C in coastal waters
Soka Univ.
Validation of Monthly Observations of Spectral Irradiance and Bio-optical Properties in
the Coastal Waters of Sagami Bay
Curtin Univ
Using the long-term BOUSSOLE time series measurements for S-GLI Ocean Colour
System Vicarious Calibration, and validation of geophysical products
Scripps Institution of Improved and merged estimates of ocean bio-optical properties derived with SGLI for the
Oceanography
California Current
Takuvik Joint
Development of DOC/POC algorithms for Arctic water - Global impact of Arctic carbon
International
cycle Laboratory (CNRS-U
Laval)
Columbia University Towards robust estimations of nitrate and nitrate based new production in the global(LDEO)
oceans using compound remote sensing
NOAA NESDIS
Development and Implementation of Atmospheric Correction Algorithm for SGLI/GCOMC Ocean Color Products
NASA GSFC
NASA ocean color processing and data analysis support for SGLIJames Cook
Support for SGLI in NASA's Generalized Inherent Optical Properties Algorithm
University, Australia Framework
Kyoto Univ.
Development of Water-quality conversion algorithm from Satellite information into
Classification of Aquatic Vegetation and Surrounding Catchment to establish Global
Lakes & Reservoir Repository (GLR) with UNESCO-IHP-IIWQ
Okayama Univ.
Improvement of GCOM-C/SGLI
snow/ice algorithm, and validation by in-situ
measurements and a numerical model
Stevens Institute of GCOM/SGLI snow/ice products: Improvements and continued validation with postlaunchTechnology
data

1C-32