GCOM-C Mission design

Hiroshi Murakami
Earth Observation Research Center,
Japan Aerospace Exploration Agency

11. Mission Concept of GCOM-C
1.1 JAXA global earth observation missions• GCOM-C for the global surface radiation budget and carbon cycle②

GCOM-C (2014)

Water cycle

Carbon cycle
and land cover

EarthCARE/CPR(2013)④

Spatial distribution ofcloud and aerosols①

GCOM-W(2012)

Atmosphere

3D structure of cloud
and aerosols

Water vapor

Water cycle

Radiative forcing

GOSAT Jan. 2009-③

Green house
gases

ALOS Jan. 2006-

C

n
bo
r
a

e
cl
y
c

Green housegasesdischarge

sink-source

Vegetation
Land coverproduction Soil moisture⑥
High resolution
mapping

Land

Cloud

Aerosol
Aerosol
Ca
rb
on

⑤

Precipitationcy
cle
sink-source

Snow Ice Ocean primary

production
Coastal environment

Water cycle

Sea-surfacewind
Sea-surface
temperature

Ocean

Deep ocean circulationPH, nutrients, dissolved CO2

3D structure of
precipitation

GPM/DPR(Jul. 2013)

Other variables:
Ozone
Sea level
Salinity
Sea state...

① GCOM-C:
Long-term observation of the horizontal distribution of aerosol, cloud, and ecosystem CO2 absorption and discharge② GCOM-W:
Long-term observation of water-cycle such as the snow/ice coverage, water vapor, and SST③ GOSAT:
Observation of distribution and flux of the atmospheric greenhouse gases, CO2 and CH4④ EarthCARE/CPR: Observation of vertical structure of clouds and aerosols⑤ GPM/DPR:
Accurate and frequent observation of precipitation with active and passive sensors⑥ ALOS:
Fine resolution mapping by optical and SAR instruments2

1. Mission Concept of GCOM-C
1.2 Radiation budget by the atmosphere-surface system
Today’s the most significant factor:
atmospheric CO2

GCOM-C
Global/horizontal
distribution of cloud
and aerosol

radiative forcingchangeCloud

EarthCARE/CPR
3D structure ofcloud and aerosol
Aerosol

Monitoring and process investigation aboutcloud and aerosol by GCOM-C & EarthCARE

Improvement

Evaluation of model outputs
and process parameterization

Climate model prediction
present and future cloud andaerosol roles in the global
warming scenarios

Today’s the most significant
uncertainty of radiative
forcing is direct/indirect roleof cloud-aerosol system
3

1. Mission Concept of GCOM-C
1.3 Carbon cycle in the Land and Ocean
Today’s Carbon budget

Today’s the mostsignificant factor

Atmosphere CO2 +3.2 0.1

increase

Net of Land -1.0 0.6

Future Carbon cycle

CO2 increase andGlobal warming

Global environmental change

Several tens of %uncertainty in the global
warming prediction(irradiance, temperature,
CO2, precipitation）

Future ecosystem
CO2 sink and poolLand

Monitoring ofprimary production
Contribution by GCOM-C

Process study
and long-term
diagnosis withmodel researches•Photosynthesis
production•Photosynthesis
production- Vegetation index- Leaf area index- Primary production- Above-ground biomass- Phytoplankton chl-a- Sea surface temperature- PAR- Dissolved organic matter•Land cover/use•Soil respiration•CO2 solution, pH•Sedimentation

Feedback

Modified from the 4th
IPCC report FAQ7.1

Ocean

Change of atmosphere CO24

1. Mission Concept of GCOM-C
1.4 Long-term observation• Climate monitoring and research need long-term
consistent observation• GCOM-W/C observe more than 10 years by threesatellites with one-year overlap.
5-yearOperation extension

GCOM-W1

1-year overlap

GCOM-W2
GCOM-W3

LaunchJFY 2011

GCOM-C1
GCOM-C2
LaunchGCOM-C35

1.5 Social needs of ocean area
Social needs about oceanography• Future risk evaluation of the ocean-climate change• Sustainable use of marine environment and biological resources

Water-energy
Warming DisasterCoastalFishery
Sea IceLand water

Meteorologicaldisaster• Frequency• Intensity

Wind
Heat
SalinityAir-sea water andenergy interaction• Precipitation• Evaporation• Turbulent flux
• Temperature distribution• Atmosphere convection

Scattero-meterGPM

GCOM-W

Radiation budget
Global warming•Air temperature • Atmospheric CO

Sea surface
temperature2

Potosyntheticallyavailable radiation

Meteorologicalsatellites

Ocean circulation / vertical profile• Wind-driven circulation• Thermohaline circulation• Upwelling• Cloud• Aerosol• Irradiance• Water vapor/ Mixing, Stratification/ Temperature profile

GOSAT

GCOM-C

Nutrient

Grazing

EarthCARE• Absorption and discharge of O2 and CO2• Aerosol and cloud condensation nuclei
Physical andthrough sea spray and DMS
chemical processes • Iron supply

RedtideCarbon fixationby photosynthesis

Suspendedmatter,CDOM

Nutrient

Ocean
primary
production

Air-sea substance exchange
Argo

Food
production• Fisheries• Cultivation• pollution

DOM, pH, Iron

Marineecosystem
production

Altimeter

Ship

Coastalenvironment• Eutrophication• Temperature• Sea levelabsorption
scattering

In-water
opticalenvironment
GrowthpCO2, pH, Fe..

Phytoplankton
Chlorophyll-a/functional typePhysical andSediment
Bio-chemical process Decomp
Sedimentation
osition

Effective ocean-color-ecosystem research for the social needs

Marine ecosystem6

1.6 Science Targets
Global warming

Coastalenvironment

Fishery

Water-energy(heat) cycle

Ocean science targets(1) Ocean primary production

O

O

O

(2) Coastal ocean eutrophication and land-sea
substance exchange

O

O

O

Social needs(3) Marine biological resources(4) Ocean-climate system by effective use ofsatellite-ground and ocean-land-atmosphereobservations(5) Satellite sensor & data product improvement

O
O

O

O

O

O

O

O

They will also contribute to the implement plan and targets of GEOSS.

7

(1) Ocean Primary Production• The ocean primary production canbe a sink of the atmospheric CO2
regulating the global warming.

Estimation of Ocean net primary
production using satellite data
Global distribution and change– Global trend and distribution• The estimation needs integrative
analysis of ocean colour with relatedb) Improve oceanc) model –parameters, such as solar irradiance a) model
input
photosynthetic
satellite(PAR), nutrient, water temperature,parameterscomparison
and a vertical mixing/stratification.
Ocean ecosystem production model• Solutions:global ocean color observation by GCOM-C
ONPP, grazing, transport, decomposition,

On
going

Begin
ning

– Improvement of satellite ONPP
algorithm– Improvement of input parameters(CHL, SST, PAR, nLw, IOP, species..)– Sample number and accuracy of insitu observation– Combination with ocean ecosystemproduction models

sedimentation, pCO2, pH, ocean circulation...

Today's and future abilities of
CO2 uptake by the ocean
increase

Net of Land-1.0 0.6

Atmosphere CO2+3.2 0.1
Modified fromthe 4th IPCC
report FAQ7.1

8

(2) Coastal Ocean• Coastal ocean has large productivity
and strong relation to the humanactivities.
• Estimation of coastal primary
production needs special considerations
about irregular light, nutrient, plankton
species and their vertical profiles due
to inland-water and substance(including carbon).
• Solutions : 250m observation by GCOM-C
On
going

Begin
ning

– Local characterization/ standardizationof plankton taxa and optical properties– Land aerosol correction– Sunglint correction (coastal area)– High spatial resolution sensor (<250m)– Adjacent scattering from bright surface– Land water inflow by cooperation withhydrology and land surface model

Chlorophyll-a concentration east of
Hokkaido Japan on 24 Sep. 2003 estimatedby ADEOS-II/GLI 250m channels.

Chlorophyceae
Bacillariophyceae
Haptophyceae

SGLI
channels

Absorption spectra of different algae9(3) Marine biological resources• Fishery estimation has been
operated since OCTS
• Prediction and catch managementof the marine resources are
required today• Solutions:Ocean color observation by GCOM-C– Near-real time robust products– Plankton taxa
– Cooperation with ocean
ecosystem/ bioresources model

Ocean current structure and marine ecosystem
activity from GCOM-C observations, sea surface
temperature, chlorophyll-a concentration, lightabsorption and solar radiation (PAR)Monitoring

Marineresources
distribution

Fisheryinquiry

Ocean primary
production
sustaining marine
resources

Marineenvironment
relevant toclimate change

Ocean
ecosystem
model
Prediction of futuremarine resources

Kuroshiowarm current

fisheries

Marine-resource management for sustainable
use (contribution to the policy design)
Cooperation with a operational organization
and a numerical model research institution

Chlorophyll-a concentration in the northwestern Pacificin June 2003 overlaid on fisheries of skipjack and tuna.

10

(4) Ocean-climate system by effective use(4-1) Integrated use of different sensors and parameters• Data merger through sensor cross-calibration and product cross-validation• Sensor cross-calibration in cooperation with CEOS/WGCV/IVOS• Product cross-validation in cooperation with CEOS/OCR-VC, IOCCG..
GCOM-C observation
Algorithm
feedback

SGLI VNR one-day coverage
Surface albedo
and land cover

NPOESS/VIIRS

Surface solar irradiance andPhotosynthetically available radiation

Aerosol amount and properties

syntheticuse of the
global data
Sentinel-3/ OLCI
Geophysicalconnection
Land vegetation,
ocean chlorophyll-a,and primary production

Sea and Land surface temperature

Cloud fraction and properties1111

(4) Ocean-climate system by effective use(4-2) Data/knowledge integration using numerical models
Satellite observation

Main responsibility of JAXA

Model prediction

GCOM observation

GCOM-W
GCOM-C

Prediction results

Climate system model

Radiationbudget albedo•Surface•Snow ice
•Cloud/ aerosol•Temperature

Carbon cycle•Vegetation cover•Primary production•Coastal exchange
Frequent and long-term(>10yer) global observation
system required for earth
environment changemonitoring and prediction

Cooperation with the climate
model research institutions

Water cycle

Input

Improveaccuracy

Comparison
Products orradiance
with• Improvement of parameterization
radiative
about radiation budget and carbon
transfer

cycle, etc. in climate prediction
model.
• Verification and improvement ofprediction of the earth environmentchange including the water cycle bycomparison with the satellite
observation.

•Water vapor, cloud,precipitation•Soil moisture•Sea ice, snow
•SST, wind

•Surface temperature•Sea level•Snow and sea ice area
•Environmental change•Rain/drought
distribution•Extreme weather
frequency•Land cover
Air temperature prediction

3C

Data application

Knowledge

Scenario

Operational use for fishery,sea route, weather forecast,etc.

monitoring and understandingof the earth environmentchangePolicy design throughprediction improvement

Solutions: Cooperation with model research,
Connecting model parameters with satellite observations directly12(5) Satellite sensor & data products(5-1) High accuracy products for the climate research• For every purposes, improvement of satellite products
accuracy is still the most essential• Solutions :– Improvement of in-water algorithms of complicated substancecomposition (e.g., coastal area),– Combination with bio-physical-optical model– Improvement of atmospheric correction (extraction of the waterleaving signals) in conditions of much suspended matter, whitecaps,
sunglint and irregular aerosols including absorptive aerosols– Long-term/inter-sensor continuity & consistency– High sensor performance (SNR and perfect sensor calibration model)13(5) Satellite sensor & data products(5-2) GCOM-C/SGLI• The next Japanese ocean color mission is Global Change Observation
Mission for Climate research (GCOM-C)• The mission focuses the carbon cycle and radiation budget, and willconsist of three satellites for 13 years from early 2014 to contributedetection of the "global change".
• The GCOM-C will carry Second-generation Global Imager (SGLI) which isa radiometer of 380-12000nm wavelength, 1150-1400km swath width,and descending orbit around 10:30am, as a follow-on mission of ADEOSII/GLI.
• Features of SGLI are 250m-spatial resolution (500m for thermal
infrared) and polarization/along-track slant view channels (red andnear-infrared), which will improve coastal ocean, land, and aerosol
observations.
14

2. GCOM‐C/SGLI design15

2.1 GCOM-C observation products
Common• TOA radiance (including system
Radiancegeometric correction)• Radiation budget by the atmosphere-surface system• Carbon cycle in the Land and Ocean

Land

Atmosphere

Ocean

Cryosphere• Precise geometricSurfacecorrection
reflectan•
Atmosphericcorrected
cereflectance• Vegetation index• Above-ground biomass• Vegetation roughness
Vegetati index
on andcarbon • Shadow index• Fraction of Absorbed
cycle
Photosyntheticallyavailable radiation• Leaf area index
Temp.
• Surface temperature
Land net primary
production
Applicati Water stress trend
Fire detection index
onLand cover typeLand surface albedo• Cloudflag/Classification• Classified cloud
fraction• Cloud top temp/height• Water cloud optical
Cloudthickness /effective
radius• Ice cloud optical
thickness
Water cloud geometrical
thickness• Aerosol over the ocean• Land aerosol by near
Aerosolultra violet• Aerosol by Polarization
Radiation Long-wave radiation flux
budget Short-wave radiation flux

• Normalized waterleaving radiance• Atmospheric correction
Oceanparametercolor• Photosyntheticallyavailable radiation
Euphotic zone depth• Chlorophyll-a conc.
• Suspended solid conc.
In-water• Colored dissolved
organic matter
Inherent opticalIn-waterproperties
Temp.
• Sea surface temp.
Ocean net primary
productivity
Phytoplankton functional
Applicati type
Redtideon
multi sensor merged
ocean color
multi sensor merged SST• Snow and Ice coveredarea• OKhotsk sea-ice
Area/
distribution
distributi
Snow and ice
onclassification
Snow covered area inforest and mountain• Snow and ice surface
Temperature• Snow grain size ofshallow layer
Snow grain size ofSurface subsurface layer
propertie
Snow grain size of topslayer
Snow and ice albedo
Snow impurity
Ice sheet surface
roughnessIce sheet boundary
Boundarymonitoring

Blue: standard products
Red: research products

16

2.2 SGLI products and channels
 Lstd-

VN, P: nm
T: m

Lmax

SNR

at Lstd
VN, P:
W/m2/sr/m VN, P: T: Kelvin
T: NET

IFOV*3m

Land

Atmosphere

VN1 380 10 60 210
VN2 412 10 75 250
VN3 443 10 64 400
VN4 490 10 53 120
VN5 530 20 41 350
90
VN6 565 20 33
62
VN7 673.5 20 23
VN8 673.5 20 25 210
VN9 763 12 40 350
30
VN10 868.5 20 8
VN11 868.5 20 30 300
P1 673.5 20 25*1 250*1P2 868.5 20 30*1 300*1

250
400
300
400
250
400
400
250
1200
400
200
250*1
250*1

250
250
250
250
250
250
250
250
1000
250
250
1000
1000

SW1
SW2
SW3
SW4

1050 20 57
1380 20 81630 200 32210 50 1.9

500
150
57
211

1000
1000
250 U T
1000 U T

T1

10.8*2 0.7*2 300 180~340

0.2

500*4 U

U

U U

U C U

T2

12.0

0.2

500*4 U

M

U U

U C U

*2

0.7

*2

248
103
50
20

300 180~340

Ocean

Cryosphere

Ice Sheet Boundary Monitoring
Ice Sheet surface RouGHness
SNow ImPurity
Snow and Ice ALBedo
SNow Grain Size of Top layer
SNow Grain Size of Subsurface layer
SNow Grain Size of shaLlow layer
Snow and Ice Surface Temperature
Snow Covered Area in Forest and Mountain
Snow and Ice Classification
OKhotsk sea-Ice Distribution
Snow and Ice Covered Area
multi sensor Merged Sea Surface Temperature
multi sensor Merged Ocean Color parameters
Red TiDe
PHytoplankton Functional Type
Ocean Net Primary Productivity
Sea Surface Temperature
Inherent Optical Properties
absorption coefficient of Colored Dissolved Organic Matter
Suspended Solid concentration
CHLorophyll-A concentration
Euphotic Zone Depth
Ocean Photosynthetically Available Radiation
Atmospheric Correction Parameters
Normalized Water Leaving Radiance
ShortWave Radiation Flux
LongWave Radiation Flux
AeRosol by Polarization
Land AeRosol over the land by near Ultra violet
AeRosol over the ocean by Visible and near infrared
Water Cloud Geometrical Thickness
Ice Cloud Optical Thickness
Water Cloud Optical Thickness and Particle Effective Radius
Cloud Top Temperature and HeightClassified CLoud Fraction
CLoud FlaG including Cloud Classification and Phase
Land surface ALBedo
Land Cover TypeFire Detection Index
Water Stress trend
Land Net Primary Production
Above-Ground BIOmass
Leaf Area Index
Fraction of Absorbed Photosynthetically Active Radiation
Land Surface Temperature
Shadow Index
Vegetation Roughness Index including BSI_P and BSI_V
Vegetation Index including NDVI and EVI
Atmospherically Corrected Land surface Reflectance
Precise Geometrically Corrected Image

CH

U U
U T U
U T U

U U I
U U I

I
I

I U
I U

U U
U U

U T

U U I

I

I U

U U U C C C C C C C C

E T M M U U III E

U T U U U U I
U U
U R
I
U U
U R
I

I
I
I

I U
I R
I R

U
U

U
U E
U

I
I
I

U
I
E I U C C C C C C C CMIUIU U U C C C C C C C C
R R
E
R R
E

M C C M C UUU R U E U U C C
C
U U
M
U

I T E
I I U
I T
U
E
I T
U U E
E
T
U M U
T
U U E
T
U M E UI T M
I I E IR UIIM
I I I I
R U
R R
R

U
I R
U
I R
U
I U
U
I U
U C I R
U
I U
U
I R
C I

R
R
U
U
R
U
R

I
C I

I

I

R C

I

I

I

R
R

U U

I

I

M I

U

R R

E

U
U
U
U
U
U C U
U
U
R C U
R
U
R C U

U I

U M
R R
U U U S U U S U M S U
U U U S S S S E S S U

U U U S S S S U S R U

U U U S M S S U U R U
RRRRU U U U S S R S U S S U
M U C C C C C C C C C
M U U U C C C R U C C UUURM U U U U M S S S S S S
E U

R

M

M: Most essential, E: essential, U: used channel, T: correction targets, R: future research, I: indirect use, C: cloud detection, S: Snow detection*1: defined as intensity of non-polarized light, *2 :Unit is m, *3: 1km in the open ocean, *4: 250m mode possibility
Green: Succession of GLI standard products, Red: New standard products, and White: research products.

17

2.3 Swath width of SGLI VNR and TIR
Higher (250-m)
resolution, multiband and frequent
observation
350km

SCAN-SAR area
(Jan. 28 2009)

70km

AVNIR-2 area
(Feb. 24 2009)

SGLI-VNR width

1150km(Simulated by MODIS dataon Jan. 28 2009)

Optimized for
detecting seasonalchange of land coverand vegetation

Daily coverage of SGLI VNR(Simulated by GLI data on 20
March 2003)

1400km
SGLI-IRS width(Simulated by MODIS dataon Jan. 28 2009)18
18

2.4 Special resolution of SGLI VNR (1)250m resolution to detect finer structure in the coastal area such as
river outflow, regional blooms, and small current.
250m Ocean colour product simulated using GLI 250m channels(a) GLI 1km Osaka Bay(1 Oct. 2003, CHL by LCI)(b) GLI 250m Osaka Bay(1 Oct. 2003, CHL by LCI)
19

2.4 Special resolution of SGLI VNR (2)250m resolution and 1150-km swath for regular monitoring of theland and coastal environment such as redtide
1km resolution

250m resolution

1-km and 250-m resolution RGB image simulated using AVNIR-2.
Light red filaments in the 250m image were the Noctilca redtide on 19 April 2009 in
Wakasa-Bay.
20

2.5 Special resolution of SGLI thermal infrared
500/250-m thermal bands and 1400-km swath for regular monitoring of
the land and coastal heat condition influenced by the land cover andriver outflow
1km resolution

250m resolution Ara Riv.
Sumida Riv
Edo Riv

High watertemperature near
the landTama Riv.

Bansyu tidal flats

1-km and 250-m thermal images on 4 August 2003 simulated using ASTER 11um data21

2.6 Summary of the GCOM-C orbit
and SGLI specification• The SGLI features are finer spatial resolution (250m (VNI) and 500m (T)) andpolarization/along-track slant view channels (P), which will improve land,
250m over the Land or coastal
coastal, and aerosol observations.
area, and 1km over offshore

GCOM-C SGLI characteristics (Current baseline)
Sun-synchronous (descending local time:Orbit
10:30)
Altitude: 798km, Inclination: 98.6deg
Launch Date Jan. 2014 (HII-A)
Mission Life
5 years (3 satellites; total 13 years)
Push-broom electric scan (VNR: VN & P)
Scan
Wisk-broom mechanical scan (IRS: SW & T)
1150km cross track (VNR: VN & P)
Scan width
1400km cross track (IRS: SW & T)
Digitalization 12bit
Multi-angle
obs. for
Polarization
3 polarization angles for P
674nm and
Along track
Nadir for VN, SW and T,
869nm
direction+45 deg and -45 deg for P
VN: Solar diffuser, Internal lamp (PD), Lunarby pitch maneuvers, and dark current bymasked pixels and nighttime obs.
On-board
SW: Solar diffuser, Internal lamp, Lunar,calibrationand dark current by deep space window
T: Black body and dark current by deep
space window
All: Electric calibration

SGLI channels
Lstd
Lmax
SNR at Lstd
VN, P:CH
VN, P, SW: nm
VN, P, SW: W/m2/sr/m
T: m
T: NET
T: Kelvin
VN1
380
10
60
210
250
VN2
412
10
75
250
400
VN3
443
10
64
400
300
VN4
490
10
53
120
400
VN5
530
20
41
350
250
VN6
565
20
33
90
400
VN7
673.5
20
23
62
400
VN8
673.5
20
25
210
250
VN9
763
12
40
350
1200
VN10 868.5
20
8
30
400
VN11 868.5
20
30
300
200
P1
673.5
20
25
250
250
P2
868.5
20
30
300
250
SW1
1050
20
57
248
500
SW2
1380
20
8
103
150
SW3
1630
200
3
50
57
SW4
2210
50
1.9
20
211
T1
10.8
0.7
300
340
0.2
T2
12.0
0.7
300
340
0.2
250m-mode possibility ~15min /path (TBC)




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

22

2.7 GCOM-C products accuracy targets• Product definition and accuracy targets of GCOM‐C have been set by GCOM advisorycommittee basing GLI achievements and current and future requirements fromapplication organizations• The accuracy value includes errors from
(1) Calibration of sensor observed radiance(2) Sub‐pixel (IFOV) mixing due to the spatial variation of observation targets(3) Algorithm theory and input ancillary data
(4) In‐situ observation for the validation (comparison)
We used practical accuracy values of current achievements, because these error sources
vary with each observation condition.
• SGLI specification is set to achieve the accuracy targets of products within realistic
sensor design and costs• Target accuracy of SGLI ocean color products is set as same level of the GLI products*(by Mean Percent Difference), but try to do by 250m resolution(* Murakami et al., 2006: Validation of ADEOS‐GLI ocean color products using in‐situ
observations, J. Oceanography, 62, 373‐393)
23

2.7 GCOM-C products accuracy targets (Standard-1)
Area group

Target accuracy*1

VNR,SWI
Land/coast: 250m,
offshore: 1km,
polarimetory:1km
Other VNR,SWI:
TIR
daytime (+special
Land/coast: 500m,
operation)
offshore: 1km

Radiometric 5%(absolute*3)*5
Geometric<1pixel

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

VNR,SWI: 3% (absolute*3),
0.5% (relative*4)
TIR: 0.5K (@300K)
Geometric<0.3pixel

both

250m

<1pixel*6<0.5pixel*6<0.25pixel*6

Atmospheric correctedreflectance (incl. clouddetection)

250m

0.3 (<=443nm),
0.2 (>443nm) (scene) *7

0.1 (<=443nm),
0.05 (>443nm) (scene) *7

0.05 (<=443nm),
0.025 (>443nm) (scene)*7

Vegetation index

250m

Grass:25%(scene),
forest:20%(scene)

Grass:20%(scene),
forest:15%(scene)

Grass:10%(scene),
forest:10%(scene)

1km

Grass:50%, forest: 100%

Grass:30%, forest:50%

Grass:10%, forest:20%

TOA radiance(including system geometriccorrection)

Surfacereflectance

Precise geometric correction

Vegetation andcarbon cycle

LandStandard accuracy*1

radiance

CommonRelease threshold*1

Product

Above-ground biomass

Day/night

Grid size

TIR and land
2.2m: both

Daytime1% (relative*4)

Vegetation roughness index

1km

Grass&forest: 40% (scene) Grass& forest:20% (scene) Grass&forest:10% (scene)

Shadow index

250m, 1km

Grass&forest: 30% (scene) Grass& forest:20% (scene) Grass&forest:10% (scene)

fAPAR

250m

Grass:50%, forest: 50%

Grass:30%, forest:20%

Grass:20%, forest:10%

Leaf area index

250m

Grass:50%, forest: 50%

Grass:30%, forest:30%

Grass:20%, forest:20%

500m<3.0K (scene)<2.5K (scene)<1.5K (scene)

temper
Surface temperature
ature

Both

Common note:*1: 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 respectively. Accuracies are shown by RMSE basically.
Radiance data note:*2: TOA radiance is derived from sensor output with the sensor characteristics, and other products are physical parameters estimated using
algorithms including knowledge of physical, biological and optical processes*3: absolute error is defined as offset + noise*4: relative error is defined as relative errors among channels, FOV, and so on.
*5: Release threshold of radiance is defined as estimated errors from vicarious, onboard solar diffuser, and onboard blackbody calibration becauseof lack of long-term moon samples
Land data note:*6: Defined as RMSD from GCP*7: Defined with land reflectance~0.2, solar zenith<30deg, and flat surface. Release threshold is defined with AOT@500nm<0.25

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2.7 GCOM-C products accuracy targets (Standard-2)
Area Group

Atmosphere

Cloudaerosol

Ocean
color
Ocean

Product
Cloud flag/Classification
Classified cloud fraction
Cloud top temp/height
Water cloud OT/effective radius
Ice cloud optical thickness
Aerosol over the oceanLand aerosol by near ultra violet
Aerosol by Polarization
Normalized water leaving
radiance (incl. cloud detection)
Atmospheric correction param
Photosynthetically available
radiatioin

Daytime60% (443~565nm)

Daytime

Chlorophyll-a concentration
In-water

Cryosphere

Snow and Ice covered area (incl.
Area/
distributi cloud detection)on
OKhotsk sea-ice distribution
Snow and ice surface
SurfaceTemperature
properties
Snow grain size of shallow layer

80% (AOT@865nm)
250m (coast)
20% (10km/month)
1km (offshore)
4~9km (global)
60~+150% (offshore)
60~+150% (offshore)
60~+150% (offshore)

Suspended solid concentration
Colored dissolved organic mattertemperaSea surface temperature
ture

Release threshold*1
10% (with whole-sky camera)
20% (on solar irradiance)*81K*910%/30% (CloudOT/radius) *11
1km (scene),
30%*11
0.1deg (global)
0.1(Monthly a_670,865)*14
0.15(Monthly a_380)*14
0.15(Monthlya_670,865)*14

Day/night Grid size
Both1km
DaytimeBothBothDaytimeStandard accuracy*1
Target accuracy*1
Incl. below cloud amount
Incl. below cloud amount
15%(on solar irradiance)*810%(on solar irradiance)*8*10
3K/2km (top temp/height)
1.5K/1km (temp/height)*10*13100% (as cloud liquid water ) 50%*12 / 20%*13
70%*13
20%*1314
0.1(scene a_670,865)*0.05(scene a_670,865)
0.15(scene a_380)*14
0.1(scene a_380 )0.15(scene a_670,865)*14
0.1(scene a_670,865)
50% (<600nm)
30% (<600nm)
0.5W/m2/str/um (>600nm)
0.25W/m2/str/um (>600nm)
50% (AOT@865nm)
30% (AOT@865nm)
15% (10km/month)60~+150%
60~+150%
60~+150%

500m (coast)
1km (offshore) 0.8K (daytime)
0.8K (day&night time)
4~9km (global)
250m (scene)
10%(vicarious val with other 7%
1km (global)
sat. data)
250m
10%
5%
500m (scene) 5K (vicarious val with other sat.
2K
1km (global) data and climatology)
250m (scene) 100%(vicarious val with50%
1km (global) climatology between temp-size)

10% (10km/month)35~+50% (offshore),50~+100% (coast)50~+100%
50~+100%
0.6K (day&night time)
5%
3%
1K
30%

Atmosphere note:*8: Comparison with in-situ observation on monthly 0.1-degree*9: Vicarious val. on sea surface and comparison with objective analysis data
*10: Inter comparison with airplane remote sensing on water clouds of middle optical thickness*11: Release threshold is defined by vicarious val with other satellite data (e.g., global monthly statistics in the mid-low latitudes)*12: Comparison with cloud liquid water by in-situ microwave radiometer*13: Comparison with optical thickness by sky-radiometer (the difference can be large due to time-space inconsistence and large error of the groundmeasurements)*14: Estimated by experience of aerosol products by GLI and POLDER

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2.7 GCOM-C products accuracy targets(Research product)
Area GroupLand

Application

Product

Day/night

Grid size

Release threshold*1

Land net primary production

Daytime1km

30% (yearly)

Water stress trend

N/A

500m

10% *15 (error judgment rate)

Fire detection index

Both

500m

20% *16 (error judgment rate)

Land cover typeLand surface albedo

Atmosphere

Cloud

Water cloud geometrical thickness

RadiationbudgetLong-wave radiation flux

Ocean color

Euphotic zone depth

In-water

Inherent optical properties

Daytime

Ocean
Cryosphere

Surfacepropaties

Boundary10%

1km (scene), 0.1deg (global)

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

Short-wave radiation flux

Downward 13W/m2, upward 10W/m2
250m (coast), 1km (offshore), 30%
4~9km (global)a(440): RMSE<0.25, bbp(550): RMSE<0.25500m (coast), 1km (offshore),
70% (monthly)
4~9km (global)
DaytimeArea/
distribution

30% (error judgment rate)
300m

Daytime

Ocean net primary productivity

Application

250m
1km

Redtideerror judgment rate of large/ small phytoplankton
250m (coast), 1km (offshore), dominance<20%; or error judgment rate of thedominant phytoplankton functional group <40%
4~9km (global)
error judgment rate <20%

multi sensor merged ocean color

250m (coast), 1km (offshore)

35~+50% (offshore), 50~+100% (coast)

Phytoplankton functional typemulti sensor merged SST

Both

500m (coast), 1km (offshore)

0.8K (day&night time)

Snow and ice classification

N/A

1km

10%

Snow covered area in forest and mountain

250m

30%

Snow grain size of subsurface layer

1km

50%

Snow grain size of top layer

250m( scene), 1km (global)

50%

Snow and ice albedo

1km

7%

Snow impurity

250m( scene), 1km (global)

50%

DaytimeIce sheet surface roughness

N/A

1km

0.05 (height/width)

Ice sheet boundary monitoring

N/A

250m

<500m

Research product note:*15: Evaluate in semiarid regions (steppe climate etc.)*16: Fires >1000K occupying >1/1000 on 1km pixel at night (using 2.2um of 1 km and thermal infrared channels)

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