Summary of the SGLI products
Validation results(Ver. 3.00)
Earth Observation Research Center
Japan Aerospace Exploration Agency
25 Nov. 2021

1

History
23 Dec. 2017
01 Jan. 2018
28 Mar. 201820 Dec. 2018

GCOM-C (SHIKISAI) launched from Tanegashima Space Center
Release of the SGLI First-light images
Initial function verification completed
Public release of first version Level- 1 and Level- 2 SGLI products

Aug. 2019
Jun. 2020

GCOM-C Science team decided version-up products
Public release of second version Level- 1 and Level- 2 SGLI products

30 Aug. 2021

Online mini-workshop

07 Sep. 2021
29 Nov. 2021

SGLI user committee meeting
Public release of third version Level- 2 SGLI products

Late 2022

Final review for the full/extra success of GCOM-C mission

2

Validation summary of the SGLI L1/L2 products
GCOM-C Success criteria (data production aspect only)
Success
Level

Minimum Success[L + 1 yr]

Full Success[L + 5 yr]

Extra Success[L + 5 yr]

Standard
Products

Complete the Cal. & Val. phaseand start data distribution of
more than 20 products
achieving the release accuracy
thresholds

Achieve standard accuracy
thresholds of all standard
products

Achieve target
accuracy thresholds of
one or more standard
products

Results of the version-upgrade validation[The number of products]

L1
[1]

Land
[9]

Atmosphere
[8]

Ocean
[7]

Cryosphere
[4]

Total
[29]

Release accuracy

1

9

8

7

4

29

Standard accuracy

1

Target accuracy*

0

Level/Area

9 (+5) 8 (+3) 7 (+2) 4 (+2) 29 (+12)
02

1 (0)0

3 (0)*the number of products achieved standard and target accuracy threshold
The numbers in parentheses are the differences of achieved product number from Ver.1.
Confirmation of achievement of standards and target accuracy will take place five years after launch.

Twelve products achieved newly standard accuracy through version-upgrade validation.

3

Standard products version up summary

L1-

Product

Algorithm

Validation
JAXA

Level-1-

Product

Algorithm

Validation
JAXA

Precise geometric correction
Atmospheric correctedreflectance (incl. clouddetection)

JAXA

Land PIs

Land

fAPAR
Leaf area Index
Land surface temperature-

Product
Cloud flag/Classification

Brush up the estimation coefficient and validation
method and added solar altitude data

Land PI
Land PIs

Land PIs
Land PI

JAXA, Land PI

Land PIs

Land PI
Algorithm
Atmosphere PIs

Update LUTs based on GEDI’s observed data
NA

Validation
JAXA,
Atmosphere PIs

Classified cloud fraction

Atmosphere

Revise of BRDF estimation and update QA
NA

Shadow Index

Vegetation roughness index

Ver.3 Major changes
NA

Vegetation Index

Above-ground biomass

Ver.3 Major changes
Correction for sensor sensitivity aging
Reduction of linear noise (VNR) and horizontal stripes (TIR)

Update of forest structure map using SGLI
Revise of cloud screening using CLFG

Ver.3 Major changes
Add the cloud and heavy aerosol screening
using machine learning method focused on
the snow region and night-time

Cloud top temp/height
Water cloud optical
thickness/effective radiusAtmosphere PIs

Atmosphere PIs

JAXA,
Atmosphere PIs

Atmosphere PIs

NA

Ice cloud optical thickness
Aerosol over the oceanLand aerosol by near ultra-violet
Aerosol by polarization

Integrated of aerosol retrieval algorithms using
polarization channels and non-polarization channels

4Standard products version up summary-

Product
Normalized water leaving radiance(incl. cloud detection)
Atmospheric correction parameters

Ocean

Photosynthetically available radiation
Chlorophyll-a concentration

Algorithm

CryosphereVer.3 Major changes
Algorithm and QA improvements, including
modification of aerosol model and additionof underwater model.

Ocean PIs,
JAXA
JAXA, Ocean PI
JAXA

Ocean Pis

NA

Suspended solid concentration

Ocean PI

NA

Colored dissolved organic matter

Ocean PINA

Sea surface temperature (incl. clouddetection)-

Validation

Product

JAXA
Algorithm

Revised cloud detection
Validation

Snow and ice covered area (incl. clouddetection)

Ver.3 Major changes
Revised training data set

Okhotsk sea-ice distribution
Snow and ice surface temperature
Snow grain size of shallow layer

Cryosphere PI

Cryosphere PI

Updated emissivity table
Revised training data set
Add the Snow Albedo as a research
product

5

Evaluation Status(1/2)
Products

Ver.1/Ver.2 already achieved

Ver.3 Accuracy

Release AccuracyStandard Accuracy

Target Accuracy

Precise geometric correction

VNR:0.15-0.21,IRS:0.15-0.29<1pixel<0.5pixel<0.25pixel

Atmospheric correctedreflectance (incl. clouddetection)

0.022 (<=443nm)
0.035 (>443nm)

0.3 (<=443nm),
0.2 (>443nm)

0.1 (<=443nm),
0.05 (>443nm)

0.05 (<=443nm),
0.025 (>443nm)

Vegetation Index* NDVI Grass:8.4%, Forest:11.8%
Grass: 25%, Forest: 20%
EVI Grass:16.0%, Forest:14.7%

Land

14.0%

Shadow Index
Above-ground biomass
Vegetation roughness index

30%

20%

10%

Grass：30%, Forest：50%

Grass：10%, Forest：20%

40%

20%

10%

Grass: 50%, Forest: 50%

Grass: 30%, Forest: 20%

Grass: 20%, Forest: 10%

Grass:18.2%, Forest:31.9% Grass： 50%, Forest： 100%

18.5%

Grass: 20%, Forest: 15% Grass: 10%, Forest: 10%

fAPAR

Grass:26.1%, Forest:8.5%

Leaf area Index

Grass:28.5%, Forest:28.8% Grass: 50%, Forest: 50%

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

Atmosphere

Land surface temperature

1.996 K

3.0 K

2.5 K

Cloud flag/Classification

10.2％

10% (with whole-sky camera)

Incl. below cloud amount Incl. below cloud amountClassified cloud fraction

10.2％

20% (on solar irradiance)

15% (on solar irradiance) 10% (on solar irradiance)

Cloud top temp/height-

1K

3K/2km1.5K/1km

Water cloud optical
thickness/effective radius

82%

10%/30% (CloutOT/raduis)

100% (as cloud liquidwater)

50%/20%Ice cloud optical thickness

56%

30%

70%

20%

Aerosol over the ocean

670nm:0.072
865nm:0.051

0.1 (monthly ta_670,865)

0.1 (Scene ta_670,865)

0.05 (Scene ta_670,865)

Land aerosol by near UV

0.137

0.15 (monthly ta_380)

0.15 (scene ta_380)

0.1 (scene ta_380)

Aerosol by polarization

0.137

0.15
(monthly ta_670,865)

0.15 (scene ta_670,865) 0.1 (scene ta_670,865)

V3 updated algorithms

1.5 K

Ver.3 newly achieved

In principle, the numerical values of accuracy targets are defined in terms of root mean square error (RMSE), which has the same units as physical quantities. Note that theaccuracy value described in the unit of ratio (%) is evaluated by the ratio between RMSE and the average value of field data. For the flag type product (cloud flag / type), theerror rate (%) of the flag is statistically evaluated using the in-situ.

6AEvaluation Status(1/2)
Ver.1/Ver.2 already achieved
Products
Normalized water leaving
radiance (incl. clouddetection)

Ver.3 Accuracy

Release Accuracy

23~45%(<600nm)
0.50W/m2/sr/um( 60% (443～565nm)>600nm)Standard Accuracy

Target Accuracy

50% (<600nm)

30% (<600nm)

0.5W/m2/str/um(>600nm)

0.25W/m2/str/um(>600nm)

Ocean

Atmospheric correction
parameters

46%

80% (ta_865nm)

50% (ta_865nm)

30% (ta_865nm)

Photosynthetically available
radiation

8.9%

20% (10km/month)

15% (10km/month)10%(10km/month)

Cryosphere

Chlorophyll-a concentration-55〜+121%-60～+150% (offshore)-60〜+150%-35～+50% (offshore)-50～+100% (coast)

Suspended solid
concentration-59-+141%-60～+150% (offshore)-60〜+150%-50～+100%

Colored dissolved organic
matter-54〜+119%60% (443～565nm)-60〜+150%-50～+100%

Sea surface temperature (incl.
cloud detection)

Day:0.4 K,Night:0.4 K

0.8K (daytime)

0.8K (day/night)

0.6K (day/night)

Snow and Ice covered area (incl.
cloud detection)

6.5%

10% (vicarious val. with other sat. data)

7%

5%

Okhotsk sea-ice distribution

5.0%

10% (vicarious val. with other sat. data)

5%

3%

Snow and ice surface
Temperature

1.6 K

5K (vicarious val. with other sat. data andclimatology data)

2K

1K

Snow grain size of shallow layer

50%

100% (vicarious val. with climatology
between temp-size)

50%

30%

V3 updated algorithms

Ver.3 newly achieved

In principle, the numerical values of accuracy targets are defined in terms of root mean square error (RMSE), which has the same units as physical quantities. Note that theaccuracy value described in the unit of ratio (%) is evaluated by the ratio between RMSE and the average value of field data. For the flag type product (cloud flag / type), theerror rate (%) of the flag is statistically evaluated using the in-situ.

7ASchedule for the GCOM-C mission

2016

2017

2018

FY2017H29

FY2016
H28

2019

FY2018
H30

2020

2021

FY2020H32

FY2019H312022

FY2021
H33

2023

FY2022
H34

10-11-12 1 - 2 - 3 4 - 5 - 6 7 - 8 - 9 10-11-12 1 - 2 - 3 4 - 5 - 6 7 - 8 - 9 10-11-12 1 - 2 - 3 4 - 5 - 6 7 - 8 - 9 10-11-12 1 - 2 - 3 4 - 5 - 6 7 - 8 - 9 10-11-12 1 - 2 - 3 4 - 5 - 6 7 - 8 - 9 10-11-12 1 - 2 - 3 4 - 5 - 6 7 - 8 - 9 10-11-12 1 - 2 - 3Pre-launch algorithm
development

GCOM-C launch

Sensor Operation
Cal.

InitialCheck-out

Sensor performance
Evaluation

Val.

Review for the Ver. 1
data release and minimum success

Review for the Ver. 2
data release

Ver.1

Ver.2

Initial Cal.

Ver.3

Post-normal

Operation

Vi- cal.
obs. data

Cal. for the Ver.2

Algo. Improvement for Ver.1

Cal for the Ver.3

Cal. coef.
Determination

Ver.2 processing

Ver.1 processing
Proc.test

Accuracy
Evaluation

Review for the Ver. 3
data release
Final review for the
full/extra success

Normal Operation

Cal. for the Ver.1

Ground System

Algorithm
Development &Improvement

Algorithm improvement and validation

Initial Cal./Val.

Review for thedevelopment Completion First Light

Postnormal
operation

Coef.
fixingVer.3 processing
Proc.test

Proc.test
Past data
re-processing

Algo. Improvement for Ver.2

Past data
re-processing

Past data
re-processing

Algo. Improvement for Ver.3

Coef.
TuringVal. for the Ver.1

Val. for the Ver.2

Val. for the Ver.3

Val. for the Final review

Ground truth data

In-situ
Observation

Field campaigns for the
Ver.1 data release

Field campaigns for the Ver.2/Ver.3 data release

Field campaigns for the
Final Review

Ver.3 Release - 2021.11.29
8

Ver.3 Land Products

9Evaluation Summary
Product

ReleasethresholdStandard
accuracy

Targetaccuracy

Precise<1 pixel<0.5 pixel<0.25 pixel
geometriccorrection
Atmospheric
0.05(<=443nm),
0.3(<=443nm)
0.1(<=443nm)
corrected
0.2 (>443nm) 0.05 (>443nm) 0.025
reflectance(incl. (scene)(>443nm)(scene)(scene)cloud detection)

Status*1◎

Evaluation of geolocation accuracies with GCPs prepared using
AVNIR-2 data.

◎

Comparison with in-situ observed reflectance.

Grass:25%(scene),forest:20%(scene)Grass:20%(scene),forest:15%(scene)Grass:10%(scene),forest:10%(scene)Grass:50%,
forest: 100%

Grass:30%,
forest:50%

Grass:10%,
forest:20%

Grass &
Grass & forest:
forest:20%
40% (scene)(scene)Grass &
Grass & forest:
forest:20%
30% (scene)(scene)Grass &
forest:10%(scene)Grass &
forest:10%(scene)fAPAR

Grass:50%,
forest: 50%

Grass:30%,
forest:20%

Grass:20%,
forest:10%◎

Leaf area index

Grass:50%,
forest: 50%

Grass:30%,
forest:30%

Grass:20%,
forest:20%○⇒◎

Surfacetemperature<3.0 K (scene) <2.5 K (scene) <1.5 K (scene)

Vegetation index
Above-groundbiomass
Vegetation
roughness index
Shadow index

Evaluation Methods○⇒◎

Comparison with in-situ observation and other satellite data.

○⇒◎

Comparison with in-situ observation (incl. the data from theliteratures).

○⇒◎

Comparison with in-situ observations.

○⇒◎Comparison with in-situ observations.

◎Comparison with in-situ observation and other satellite data.

Comparison with in-situ observation.
Comparison with in-situ observation and other satellite data.

*1 Symbols denote as follows; 〇: the release threshold achieved, ◎: the standard accuracy achieved, ☆: the target accuracy achieved.

10Atmospheric corrected surface reflectance (RSRF)✓ Key revisions for Ver.3(1) Improvement of cloud and shadow screening(2) Add polarization reflectance of PL1 and PL2(3) Ocean surface aerosol LUT over the ocean area
(4) Add correction of radiometric offset calibration(5) Reduce processing time(6) Bug fix (sample number count of POL, uninitialized value..)✓ The first guess and vicarious calibration gains are not changed forthe consistency to the Ver.2 data

✓ More improvement will be needed for BRDF especially in NUV-bluebands, and remaining contamination of the cloud shadow✓ All CH achieve the standard accuracy (10CHs achieved the target
accuracy for the nadir observations, and 12CHs achieve with BRDF
correction in the total 16CHs)

11

Standard accuracy

Validation results of Ver. 2 (RSRF)✓ Takayama (PEN), Fujihokuroku (PEN), RadCalNet(Namibia, France, US), RRV(PI Moriyama), Mongolia (PINasahara), tottori (PI Susaki)✓ Saz is limited (saz<20deg
except for POL✓ smooth surface: Std of 3x3
RSRF is less than 0.01

Target accuracy

Polarization telescopes

Validation results
0.023, 27% (<=443nm)
0.059, 23% (>443nm)

Standard accuracy

O2A
absorption

The accuracy targets aredefined as error ratios of “solar
zenith angle<30 deg, smoothsurface of reflectance ~0.2”
Convert to error% by RMS/0.2

Release accuracy (L+1yr) Standard accuracy (L+5yr) Target accuracy (L+5yr)
0.3 (<=443nm) →150%
0.2 (>443nm) →100%

Worst value (VN01, PI02, PI01)

0.1 (<=443nm) →50%
0.05 (>443nm) →25%

0.05 (<=443nm) →25%
0.025 (>443nm) →12.5%

12Standard accuracy

Validation results of Ver. 3 (RSRF)✓ Takayama (PEN), Fujihokuroku (PEN), RadCalNet(Namibia, France, US), RRV(PI Moriyama), Mongolia (PINasahara), tottori (PI Susaki)✓ In-situ is nadir observation:
Saz is limited (saz<20deg
except for POL)

O2A
absorption
Polarization telescopes

Validation results
0.023, 28% (<=443nm)
0.056, 21% (>443nm)

Standard accuracy

Target accuracy✓ smooth surface: Std of 3x3
RSRF is less than 0.01

The accuracy targets aredefined as error ratios of “solar
zenith angle<30 deg, smoothsurface of reflectance ~0.2”
Convert to error% by RMS/0.2

Release accuracy (L+1yr) Standard accuracy (L+5yr) Target accuracy (L+5yr)
0.3 (<=443nm) →150%
0.2 (>443nm) →100%

Worst value (VN01, PI02, PI01)

0.1 (<=443nm) →50%
0.05 (>443nm) →25%

0.05 (<=443nm) →25%
0.025 (>443nm) →12.5%

13

Standard accuracy

Validation results of Ver. 3 (corr by BRDF)✓ Takayama (PEN), Fujihokuroku (PEN), RadCalNet(Namibia, France, US), RRV(PI Moriyama), Mongolia (PINasahara), tottori (PI Susaki)✓ In-situ is nadir observation:
Saz (0-51 or 53-63deg) iscorrected to the nadir by
BRDF

Target accuracy✓ smooth surface: Std of 3x3
RSRF is less than 0.01

O2A
absorption

Polarization telescopes

Validation results
0.022, 28% (<=443nm)
0.035, 16% (>443nm)

The accuracy targets aredefined as error ratios of “solar
zenith angle<30 deg, smoothsurface of reflectance ~0.2”
Convert to error% by RMS/0.2

Release accuracy (L+1yr) Standard accuracy (L+5yr) Target accuracy (L+5yr)
0.3 (<=443nm) →150%
0.2 (>443nm) →100%

Worst value (VN01, VN09, VN04)

0.1 (<=443nm) →50%
0.05 (>443nm) →25%

0.05 (<=443nm) →25%
0.025 (>443nm) →12.5%

14

(Appendix) RSRF: examples of the V2-V3 difference
2021/04/22 T0529 Tokyo

2021/06/19 T0620 Northeast of Saharan Des.
越谷

手賀沼

Siwa

Siwa

舎人

皇居

Ver. 2

Ver. 3✓ Error of cloud mask due to the short term TIR gain anomaly isrecoveredVer. 2In-situ (PEN) data

RSRF

Ver. 2

Ver. 3✓ Unrealistic reflectance gap between urban and vegetation
areas is decreased

PEN data is provided by PI K. Nasahara

VN11
VN01

Ver. 3✓ Temporal instability is improved

15

(Appendix) Add polarization reflectance of PL1 and PL2polarization reflectance of PL2 (865nm)

Future research are required
over the land polarization

Influence of sunglint or
dense aerosol?

Strong polarization over ocean in thehigher latitude due to the along-track tilt

16

(Appendix) Reference: Intensity of PL02

17

(Appendix) Reference: RGB image (for reference)

18

(Appendix) (Research product) Shortwave radiation (by TAO/PIRATA/RAMA SWR)
Daily RMS/AVG=21%

8-day RMS/AVG=16%Monthly RMS/AVG=11%

Yearly average✓ Comparison with in-situ Daily SWR is provided by NOAA PMEL Global Tropical Moored Buoy Array, TAO/PIRATA/RAMA(https://www.pmel.noaa.gov/gtmba/)✓ 11% does not achieve the SWR target accuracy (monthly 10W/m2); the daily change should be considered in the future version✓ Anomaly is useful for applications such as agriculture and long-term weather monitoring

19

(Appendix) (Research product) Shortwave radiation (by BSRN SWR)
Daily RMS/AVG=28%

8-day RMS/AVG=18%

Monthly RMS/AVG=13%

Yearly average

✓
✓
✓
✓

Daily SWR is estimated by instantaneous transmittance of visible channels and stored by 8 bit (/Image_data/SWR)
Comparison with in-situ measurements by BSRN
13% does not achieve the SWR target accuracy (monthly 10W/m2); the daily change should be considered in the future version
Anomaly is useful for applications such as agriculture and long-term weather monitoring20

T2A/VeGetation Index（VGI)：NDVI
Key updates✓✓✓

No modification for the algorithm.
Input data [RSRFQ] was changed.
NDVI 0
Validation data was increased.

Ver.3 NDVI（MVC, input: Ver.2）[2019/8/29-9/13]

SGLI Ver.3
1 others

Ver.2 NDVI（MVC）[2019/8/29-9/13]

SGLI Ver.2

PROBA-V
Similar distribution

MODIS

CGLS/GIOGL1_NDVI300_V1.0.1[2019/09/01-10]

Around Japan

MOD13Q1 [2019/08/29-9/13]

Around the eastern part of Mongolia
21

T2A/ VGI：NDVI
Validation method- The accuracy of SGLI/NDVI was evaluated using the reference NDVI for forests and grasslands. The reference NDVI was derived from the spectral reflectancemeasurements at the field observation sites.
- The effects of observation tower were corrected by shielding devices at TKY, FHK and PFRR sites.
- The data was not used for the evaluation when the "prob. cloud" or "shadow" bits are set or when cloud pixels are adjacent.
- The validation pixel was moved for MSE site because the nearest neighbor pixel includes the river.

Validation data and period- The data obtained at clear weather condition were used for validation, referring to solar radiation and sky cameras synchronized with the ground observation.
However, the data of the day with the maximum NDVI in 20 days were used for WTR, MBU, MBN, DGT or KYM sites because there is no information of
weather condition. The data during snow season were excluded from the evaluation.
- The in-situ spectral reflectance data were upscaled to the SGLI scale using the Sentinel-2A/B Multispectral Imager(MSI) Level 1C/2A data (Band4 and 8b) for
TKY, FHK, PFRR, and MSE sites.
- Validation period: TKY [2018/4/1-2021/7/31], FHK [2018/4/1-2019/12/31], TOC [2018/4/26-10/31], PFRR [2018/9/1-9/30], MSE [7/1-8/31 for 2018, 2019, 2020
and 2021], MBN/MBU/DGT/KYM/WTR [the day with the smallest cloud effect within ± 10 days of field observation]＜ validation sites ＞＜ Validation result ＞＜ Validation result for ver.2 ＞

Estimated accuracy

11.7 → 11.8 %10.2 → 8.4 %Release threshold

Standard accuracy

Target accuracy

20% (Forest) Scene,
25% (Grassland) Scene

15% (Forest) Scene,
20% (Grassland) Scene

10% (Forest) Scene,
10% (Grassland) Scene

The standard accuracy is expected to be achieved.

22

T2A/VeGetation Index（VGI)：EVI
Key updates✓✓✓

No modification for the algorithm.
Input data [RSRFQ] was changed.
Validation data was increased.

Ver.3 EVI（ndvi_MVC, input: Ver.2）[2019/8/29-9/13]

EVI

Ver.2 EVI（ndvi_MVC）[2019/8/29-9/13]

0

1 others

MOD13Q1 [2019/08/29-9/13]

Similar distribution

Around Japan➢ Distribution is similar to MODIS products.

Around the eastern part of Mongolia
23

T2A/ VGI：EVI
Validation method- The accuracy of SGLI/EVI was evaluated using the reference EVI for forests and grasslands. The reference EVI was derived from the spectral reflectancemeasurements at the field observation sites.
- The effects of observation tower were corrected by shielding devices at TKY, FHK and PFRR sites.
- The data was not used for the evaluation when the "prob. cloud" or "shadow" bits are set or when cloud pixels are adjacent. The data was not used for forest sites
when the satellite zenith angle is larger than 30 degrees.
- The validation pixel was moved for MSE site because the nearest neighbor pixel includes the river.

Validation data and period- The data obtained at clear weather condition were used for validation, referring to solar radiation and sky cameras synchronized with the ground observation.
However, the data of the day with the maximum NDVI in 20 days were used for WTR, MBU, MBN, DGT or KYM sites because there is no information of
weather condition. The data during snow season were excluded from the evaluation.
- The in-situ spectral reflectance data were upscaled to the SGLI scale using the Sentinel-2A/B Multispectral Imager(MSI) Level 1C/2A data (Band4 and 8b) for
TKY, FHK, PFRR, and MSE sites.
- Validation period: TKY [2018/4/1-2021/7/31], FHK [2018/4/1-2019/12/31], TOC [2018/4/26-10/31], PFRR [2018/9/1-9/30], MSE [7/1-8/31 for 2018, 2019, 2020
＜ validation sites ＞
and 2021], MBN/MBU/DGT/KYM/WTR [the day with the smallest cloud effect within ± 10 days of field observation]＜ Validation result ＞＜ Validation result for ver.2 ＞

Estimated accuracy

20.3 → 14.7 %16.4 → 16.0 %Release threshold

Standard accuracy

Target accuracy

20% (Forest) Scene,
25% (Grassland) Scene

15% (Forest) Scene,
20% (Grassland) Scene

10% (Forest) Scene,
10% (Grassland) Scene

The standard accuracy is expected to be achieved.

24

The list of validation sites
Forests• FHK: Fuji Hokuroku Flux Observation site, Japan• Lambir: Lambir Hills site, Malaysia• PFRR: Poker Flat Research Range site, USA• TKY: Takayama Deciduous Broadleaf Forest site, Japan• TOC: Tomakomai Crane Site, Japan• TSE: Teshio CC-LaG Experiment site, Japan

Grasslands and paddy fields• DGT: Delgertsogt JAXA Super Site 500, Mongolia• KYM: Khar Yamaat JAXA Super Site 500, Mongolia• MBN: Baganuul JAXA Super Site 500, Mongolia• MBU: Bayan-Unjuul JAXA Super Site 500, Mongolia• MSE: Mase Paddy Flux site, japan• WTR: Watarase JAXA Super Site 500, Japan25

Acknowledgements
✓
✓

✓
✓

The field data were mainly collected by GCOM-C PIs, AKITSU Tomoko, HONDA Yoshiaki, KAJIWARA Koji, KOBAYASHI Hideki,
MORIYAMA Masao, NAGAI Shin, NASAHARA Kenlo Nishida, and SUSAKI Junichi.
The CGLS/GIOGL1_LAI300, FAPAR300 and NDVI300 products were used for the comparison with SGLI products in thisarticle. They were generated by the global component of the Land Service of Copernicus, the Earth Observation program of
the European Commission. The research leading to the current version of the product has received funding from various
European Commission Research and Technical Development programs. The products are based on PROBA-V 333m data ((c)
Belgian Science Policy and distributed by VITO NV).
Sentinel-2A/B Multispectral Imager(MSI) Level-1C/2A data were produced by European Space Agency (ESA). They wereacquired from the Copernicus Open Access Hub of the ESA.
MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006 were acquired from NASA EOSDIS Land
Processes DAAC from https://lpdaac.usgs.gov, maintained by the NASA EOSDIS Land Processes Distributed Active Archive
Center (LP DAAC) at the USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota.

References➢➢➢

Akitsu, T. K., Nakaji, T., Yoshida, T., Sakai, R., Mamiya, W., Terigele, Takagi, K., Honda, Y., Kajiwara K., Nasahara, K. N., 2020.
Field data for satellite validation and forest structure modeling in a pure and sparse forest of Picea glehnii in northern
Hokkaido. Ecological Research, 2020, pp.1–15. doi:10.1111/1440-1703.12114.
Akitsu, T. K., Nakaji, T., Kobayashi, H., Okano, T., Honda, Y., Bayarsaikhan, U., Terigele, Hayashi, M., Hiura, T., Ide, R., Igarashi,S., Kajiwara, K., Kumikawa, S., Matsuoka, Y., Nakano, Ta., Nakano, To., Okuda, A., Sato, T., Tachiiri, K., Takahashi, Y., Uchida,J., Nasahara, K. N., 2020. Large-scale ecological field data for satellite validation in deciduous forests and grasslands.
Ecological Research, 2020, ERDP-2020-16.1.1(http://db.cger.nies.go.jp/JaLTER/metacat/metacat/ERDP-202016.1.1/jalter-en)
Kobayashi, H., Nagai, S., Kim, Y., Yang, W., Ikeda, K., Ikawa, H., Nagano, H. and Suzuki, R., 2018. In situ observations reveal
how spectral reflectance responds to growing season phenology of an open evergreen forest in Alaska. Remote Sensing,10(7), p.1071. (Data are available from Arctic Data archive System (ADS), Japan, https://ads.nipr.ac.jp/dataset/A20181212002)

26

T2A/ ShaDow Index（SDI）
Key updates✓✓✓

Revision of coefficients for estimating SDI.
Addition of “Cosine_of_solor_incidence” calculated from DEM and solar geometry in Geometry data.
Modification of QA flag.

Validation method- SGLI/SDI is evaluated by the shadow-areas truth data calculated using the in-situ observation data.
- The shadow-areas truth data are generated based on the DSM data from USGS 3 Dimensional Elevation Programme (3DEP) and Solar
Zenith Angle (SZA). SGLI/SDI is compared with the shadow-areas truth data correspond to each SGLI pixel (250[m]).
- The land pixels (Landwater=100) with NDVI > 0.75 are validated. The pixels adjacent to cloud or probably cloud are excluded from validation.

＜ Estimated SDI ＞＜ Validation ＞

SDI by Ver.3 algorithm [2020/08/01]

Estimated accuracy

Release threshold

Standard accuracy

Target accuracy

26.3 → 14.0 %

30% (Forest・Grassland) Scene

20% (Forest・Grassland) Scene

10% (Forest・Grassland) Scene

The standard accuracy is expected to be achieved.

27

T2B/ Leaf Area Index (LAI）
Key updates✓ Creation of base map using SGLI data and increase of forest cover type from 7 to 17 types → The estimation
accuracy is improved.
✓ Modification of LUTs and reduction of LAI-intervals at low LAI → The estimation accuracy is improved.
✓ Increase of the site for validation: Yona-site (Evergreen broadleaf forest)
LAI [m2/m2] 0.0

CGLS/GIOGL1_LAI300_V1.0.1 (total LAI)
[2019/09/01-10]

The estimation accuracy isimproved in areas with high LAI
such as tropical forests

8.0

othersVer. 2 SGLI/LAI (total LAI)
[2020/08/01-10]

The accuracy of overstory LAI is improved at boreal forests.

Ver.3 SGLI/LAI (total LAI) [2020/08/01-10] （input: Ver.2）

Ver.3 SGLI/Overstory LAI [2020/08/01-10] （input: Ver.2）

The accuracy is improved in areas where forests were misclassified
as grasslands. LAI is retrieved at urban areas.

Ver. 3 SGLI/LAI (overstory LAI) Ver. 2 SGLI/LAI (overstory LAI)

28

T2B/ Leaf Area Index (LAI）
Validation method- The accuracy of SGLI/LAI was evaluated using the reference LAI data at validation sites for forests and grasslands. The reference LAI was derived
from in-situ observation data.

SGLI data used for validation- The daily SGLI/LAI was averaged for 11 days and compared with reference LAI [PFRR/Lambir/TMM/Yamashiro: date of field data acquisition ±5
days in 2020, other sites: date and year of field data acquisition ±5 days.]. The period was extended for another 11 days if there was only one day or lessof valid data within the 11 days.
- RSRFQ ver.2 was used as input data for LAI retrieval.
- The data was not used for the evaluation when the “cloud shadow”, “bad air condition”, “backup algorithm” or “poor quality” bits were set, though thedata with “poor quality” bit were used when all the data were set as “poor quality” for one month.

Total LAI < Forest and grassland >Overstory LAI < Forest >< Validation sites >Validation results for ver.2 algorithm in 2019
Total LAI < Forest >Estimated accuracy
Forest (total LAI): 35.9% → 28.5%、
Forest (overstory LAI): 44.6% → 27.2%
Grassland: 27.0% → 19.1%

Overstory LAI < Forest > Total LAI < Grassland >Release threshold

Standard accuracy

Target accuracy

50% (Forest),
50% (Grassland)

30% (Forest),
30% (Grassland)

20% (Forest),
20% (Grassland)

The standard accuracy is expected to be achieved.

29

T2B/ Fraction of Absorbed Photosynthetically
Active Radiation（FAPAR）
Key updates✓ Creation of base map using SGLI data and increase of forest cover type from 7 to 17 types → The estimation
accuracy is improved.
✓ Modification of LUTs → The estimation accuracy is improved.
FAPAR 0Ver.3 T2B/FAPAR [2020/08/01-10] （input: Ver.2）

1others

CGLS/GIOGL1_FAPAR300_V1.1.1 [2020/08/01-10]
The estimation accuracy was improved.

Ver. 2 T2B/FAPAR [2020/08/01-10]
Around Amazon
Ver.3 T2B/FAPAR
Ver.2 T2B/FAPAR

30

T2B/ FAPAR
Validation method- The accuracy of SGLI/FAPAR was evaluated using the reference FAPAR (total FAPAR) data at validation sites < Validation sites >for forests and grasslands. The reference total FAPAR was derived from in-situ observation data.
- In addition to SGLI/green FAPAR (T2B output value), SGLI/total FAPAR was evaluated to assess with the same
FAPAR definition as the validation data in forests. SGLI/total FAPAR was re-estimated from SGLI data using
the same algorithm with T2B,.

SGLI data used for validation- The daily SGLI/FAPAR was averaged for 11 days and compared with reference FAPAR [PFRR: date of field dataacquisition ±5 days in 2020, other sites: date and year of field data acquisition ±5 days.]. The period was extended
for another 11 days if there was only one day or less of valid data within the 11 days.
- RSRFQ ver.2 was used as input data for FAPAR retrieval.
- The data was not used for the evaluation when the “cloud shadow”, “bad air condition”, “backup algorithm” or“poor quality” bits were set.

Forest*1（green FAPAR）

Forest*2（total FAPAR）

Grassland
Validation results for ver.2 algorithm in 2019
Forest*1 （green FAPAR）

Grassland※FAPAR by canopy was used as the definition at PFRR site.

Estimated accuracy
Forest: 16.9% → 10.4%*1
Grassland: 7.1% → 6.3%

Release threshold

Standard accuracy

Target accuracy

50% (Forest),
50% (Grassland)

20% (Forest),
30% (Grassland)

10% (Forest),
20% (Grassland)*1 Validation for green FAPAR, though the total FAPAR was used as in-situ data
*2 Validation for total FAPAR.

The standard accuracy is expected to be achieved.

31

The list of validation sites
Forests• AK06: AK06 Alaska Forest site, USA• FHK: Fuji Hokuroku Flux Observation site, Japan• Lambir: Lambir Hills site, Malaysia• Lambir Oil Palm: Lambir Oil Palm Plantation site, Malaysia• PFRR: Poker Flat Research Range site, USA• SSP: Spasskaya Pad site, Russia• TKY: Takayama Deciduous Broadleaf Forest site, Japan• TMM: Tokachi Mitsumata JAXA Super Site 500, Japan• TOC: Tomakomai Crane Site, Japan• TOS: Hokkaido University Tomakomai Experimental Forest site, Japan• TSE: Teshio CC-LaG Experiment site, Japan• URY: Hokkaido University Uryu Experimental Forest site, Japan• YNF: Yona Field, Japan• Yamashiro: Yamashiro site, Japan
Grasslands and paddy fields• DGT: Delgertsogt JAXA Super Site 500, Mongolia• KYM: Khar Yamaat JAXA Super Site 500, Mongolia• MBN: Baganuul JAXA Super Site 500, Mongolia• MBU: Bayan-Unjuul JAXA Super Site 500, Mongolia• MSE: Mase Paddy Flux site, japan• WTR: Watarase JAXA Super Site 500, Japan32

Acknowledgements✓✓✓

The field data were mainly collected by GCOM-C PIs, AKITSU Tomoko, HONDA Yoshiaki, KAJIWARA Koji, KOBAYASHI Hideki,
MORIYAMA Masao, NAGAI Shin, NASAHARA Kenlo Nishida, and SUSAKI Junichi.
The CGLS/GIOGL1_LAI300, FAPAR300 and NDVI300 products were used for the comparison with SGLI products in thisarticle. They were generated by the global component of the Land Service of Copernicus, the Earth Observation program of
the European Commission. The research leading to the current version of the product has received funding from various
European Commission Research and Technical Development programs. The products are based on PROBA-V 333m data ((c)
Belgian Science Policy and distributed by VITO NV).
Sentinel-3A daily FAPAR (OLCI Global Vegetal Index (faPAR)) were used for the comparison with SGLI/FAPAR. They were
produced by European Space Agency (ESA) and acquired from the Copernicus Open Access Hub of the ESA.

References
➢
➢

➢

➢

Akitsu, T. K., Nakaji, T., Yoshida, T., Sakai, R., Mamiya, W., Terigele, Takagi, K., Honda, Y., Kajiwara K., Nasahara, K. N., 2020.
Field data for satellite validation and forest structure modeling in a pure and sparse forest of Picea glehnii in northern
Hokkaido. Ecological Research, 2020, pp.1–15. doi:10.1111/1440-1703.12114.
Akitsu, T. K., Nakaji, T., Kobayashi, H., Okano, T., Honda, Y., Bayarsaikhan, U., Terigele, Hayashi, M., Hiura, T., Ide, R., Igarashi,S., Kajiwara, K., Kumikawa, S., Matsuoka, Y., Nakano, Ta., Nakano, To., Okuda, A., Sato, T., Tachiiri, K., Takahashi, Y., Uchida,J., Nasahara, K. N., 2020. Large-scale ecological field data for satellite validation in deciduous forests and grasslands.
Ecological Research, 2020, ERDP-2020-16.1.1(http://db.cger.nies.go.jp/JaLTER/metacat/metacat/ERDP-202016.1.1/jalter-en)
Kobayashi, H., Nagai, S., Kim, Y., Yang, W., Ikeda, K., Ikawa, H., Nagano, H. and Suzuki, R., 2018. In situ observations reveal
how spectral reflectance responds to growing season phenology of an open evergreen forest in Alaska. Remote Sensing,10(7), p.1071. (Data are available from Arctic Data archive System (ADS), Japan, https://ads.nipr.ac.jp/dataset/A20181212002)
Nagai, S., Kotani, A., Sato, T., Sugimoto, A., Maximov, T. C., Nogovitcyn, A., Miyamoto, Y., Kobayashi, H., and Tei, S. (in press).
Direct measurement of leaf area index in a deciduous needle-leaf forest, eastern Siberia, Polar Science, (in press).

33

T3A/ Above Ground Biomass（AGB）
Key updates✓✓✓

AGB values estimated from GEDI data were used for the determination of the coefficients of LUT in evergreen broadleaved
forests and mixed forest. The GEDI spot values were spatially extended for this purpose. → The AGB underestimation has been
improved mainly in tropical forests.
The coefficients of LUT was assigned differently for each latitude, mainly north of 40°N. → AGB gaps were resolved.
All validation data were reviewed, and the data inappropriate for validation were excluded.

ver.3 AGB (input: RSRFQ ver.2) [2020/08/01-31]

AGB [t/ha] 0

500 othersver.2 AGB (input: RSRFQ ver.2)[2020/08/01-31]

Large AGB gaps at specific latitudes
have been improved.

※ The data with the QA information of low quality, probably cloud or bad geometry was not used in these maps.

< Detailed ma for South America>
The AGB underestimation of ver. 2 algorithm in tropicalforests has been improved.

ver.3

ver.2

34

T3A/ AGB
Validation method- The accuracy of SGLI/AGB was evaluated using the reference AGB data at validation sites for forests andgrasslands. The reference AGB was derived from in-situ observation data.
- The validation data was selected by considering spatial uniformity, vegetation type, and topography, etc.

SGLI data used for validation- Forests: The daily SGLI/AGB from August 1, 2020 to August 31, 2020 were averaged and compared with thereference AGB regarding the forest biomass as that stay consistent for one month.
- Grasslands: The daily SGLI/AGB was averaged for 21 days [date and year of field data acquisition ±10 days] andcompared with the reference AGB.
- RSRFQ ver.2 was used as input data.
- The data was not used for the evaluation when the “low quality”, “probably cloud” or “bad geometry” bits were set.

Validation result < Forest >< Validation sites >Validation result < Grassland >Validation results for ver.2

The estimation accuracy was improved in tropical forest.

Estimated accuracy

Forest: 48.4% → 31.9%
Grassland: 47.6% → 18.2%

Release threshold

Standard accuracy

Target accuracy

100% (Forest),
50% (Grassland)

50% (Forest),
30% (Grassland)

20% (Forest),
10% (Grassland)

The standard accuracy is expected to be achieved.

35

T3A/ Vegetation Roughness Index（VRI）
No updates

Ver.3 VRI [input: ver.2]

2020/08/19 （around Japan)

Average for 2020/08/01-31

VRI

0

1

Validation method- SGLI/ VRI was evaluated using reference VRI. Reference VRI was calculated from the same equation with T3A using in-situ observed directional surface
reflectance factor data which was collected for the same sun-target-satellite geometry with SGLI data. .
- The SGLI data is not used when the quality flag indicates cloud, probably cloud or pol. cloud.

Validation period- SGLI/VRI: May 1, 2018 to June 10, 2018 (daily data). Only the data with ±10°of the view zenith angle for the in-situ data is used.
- Reference VRI: May 21, 2018 at WTR site

: standard accuracy

The standard accuracy is expected to be achieved.
Estimated accuracy

Release threshold

Standard accuracy

Target accuracy

18.5%

40% (Forest, Grassland)scene

20% (Forest, Grassland)scene

10% (Forest, Grassland)
scene
36

Acknowledgements✓

The validation data were mainly collected from literature survey by NAGAI Shin, GCOM-C PI. The field data were collected by
GCOM-C PI and AKITSU Tomoko.

References
➢
➢

➢
➢
➢
➢
➢
➢
➢

➢

Akitsu, T. K., Nakaji, T., Yoshida, T., Sakai, R., Mamiya, W., Terigele, Takagi, K., Honda, Y., Kajiwara K., Nasahara, K. N., 2020.
Field data for satellite validation and forest structure modeling in a pure and sparse forest of Picea glehnii in northern
Hokkaido. Ecological Research, 2020, pp.1–15. doi:10.1111/1440-1703.12114.
Akitsu, T. K., Nakaji, T., Kobayashi, H., Okano, T., Honda, Y., Bayarsaikhan, U., Terigele, Hayashi, M., Hiura, T., Ide, R., Igarashi, S.,
Kajiwara, K., Kumikawa, S., Matsuoka, Y., Nakano, Ta., Nakano, To., Okuda, A., Sato, T., Tachiiri, K., Takahashi, Y., Uchida, J.,
Nasahara, K. N., 2020. Large-scale ecological field data for satellite validation in deciduous forests and grasslands. EcologicalResearch, 2020, ERDP-2020-16.1.1(http://db.cger.nies.go.jp/JaLTER/metacat/metacat/ERDP-2020-16.1.1/jalter-en)
Motohka Takeshi, 2012. Measurement of biomass to verify forest carbon stocks in Riau province, Sumatora, Indonesia. Phase 2.
Baker, T. R., et al., 2004. Increasing biomass in Amazonian forest plots. Phil. Trans. R. Soc. Lond. B, 359, pp.353-365.
10.1098/rstb.2003.1422. Dataset is in ForC-db, https://github.com/forc-db/ForC
Lewis, S. L., et al., 2009. Increasing carbon storage in intact African tropical forests. Nature, 457, pp.1003-1006.
10.1038/nature07771. Dataset is in ForC-db, https://github.com/forc-db/ForC
Chave, J., et al., 2003. Spatial and temporal variation of biomass in a tropical forest: results from a large census plot in Panama.
The Journal of ecology, 91(2), pp.240-252. 10.1046/j.1365-2745.2003.00757.x
Liu, L. B., et al., 2016. Forest biomass and net primary productivity in southwestern China: A meta-analysis focusing on
environmental driving factors. forests, 7(8), 173. DOI: 10.3390/f7080173
Slik, J. W. F., et al., 2009. Environmental correlates of tree biomass, basal area, wood specific gravity and stem density
gradients in Borneo's tropical forests. Global Ecology and Biogeography, 19, pp.50-60. DOI: 10.1111/j.1466-8238.2009.00489.x
Lourens, P., et al., 2016. Biomass resilience of Neotropical secondary forests. Nature, 530, pp211-214.
doi:10.1038/nature16512
Akiyama, T. 2006. Analysing water shed vegetation changes along space and time by remote sensing.

37

Ver.3 Atmosphere Products

38

Evaluation Summary
Product

ReleasethresholdStandard
accuracy

Targetaccuracy

Status*1

10% (with
Cloud
whole-skyflag/Classification camera)

Incl. below cloud Incl. below cloudamountamount☆

Classified cloud
fraction

20% (on solar
irradiance)

15% (on solar
irradiance)

10% (on solar
irradiance)☆

Cloud top
temp/height

1K

3 K/2 km (toptemp/height)

1.5 K/1 km(temp/height)◎

Water cloudOT/effective
radiusIce cloud optical
thickness
Aerosol overthe
oceanLand aerosol bynear ultra violet

Comparison with in-situ observation (sky-camera images)
for release threshold. Evaluations for standard and target
accuracies were performed as the Classified cloud fraction
products.
Comparison of SGLI-derived solar irradiance using cloud
products including cloud flag, cloud fraction etc. withground-measured solar irradiance.
Evaluation was made as vi-cal. of SGLI TIR bands for the
release threshold. In addition, comparison with othersatellite data for evaluating the achievement of thestandard accuracy.

10%/30%(CloudOT/radius 100% (as cloudliquid water))

50% / 20%○ → ◎ Comparison with in-situ observation.

30%

70%

20%

○ → ◎ Comparison with in-situ observation.

0.1 (Monthly
a_670,865)

0.1 (scenea_670,865)

0.05 (scenea_670,865)○ → ◎ Comparison with in-situ observation.

0.15 (Monthly
a_380)0.15 (scenea_380)0.1 (scenea_380 )◎→Aerosol overthe landAerosol by
Polarization→Merged to
ARNP

Evaluation Methods

0.15
0.15 (scene(Monthlya_670,
a_670,865)
865)

0.1 (scenea_670,865)◎

Comparison with in-situ observation.

Comparison with in-situ observation and other satellite(MODIS) data.

*1 Symbols denote as follows; 〇: the release threshold achieved, ◎: the standard accuracy achieved, ☆: the target accuracy achieved.

39

CLFG (CLoud FlaG)■Algorithm PI : Takashi Nakajima■Implement + V3 update : Kazuhisa Tanada■Problems of V2 CLFG
Misclassification

Impact①day： snow（〇）→land clear（×）

Overestimation in ARNP/AOT②day： heavy aerosol（〇）→cloud（×）

Decreasing effective pixels in ARNP

③night： snow、clear（〇）→cloud（×）

Decreasing effective pixels in LST■Method of V3 CLFG
Method：DNN（Deep Neural Network）
Ground truth：made by checking the RGB & SIPR images
Input data：
day…lat,VN01,03,04,06,09,11,
SW03,TI01,02,elavation,date (12 data)night…lat,TI01,02,elavation,date (5 data)
Output labels：
day…land, ocean, cloud, aerosol, snow (5 labels)night…land&ocean, cloud, aerosol, snow (4 labels)

LTOA

Input

CLAUDIACurrent
algorithm

DNN

Added
algorithm

V3 CLFG

Output40

CLFG (CLoud FlaG)■Training dataset■Input parameter selection
Spectrum

We used 500,000 pixels, various locations and periods.

■Training results
Regression
Coefficient

night
day
We chose the SGLI channel which has large coefficient.

Accuracy
pixelsCorrected pixels / Total---------------------------------------------------------------------------------------------------------------

Total Acc = 99.78% 5226469/5238201
land Acc = 99.67% 1230057/1234083
ocean Acc = 99.93% 811784/812343
cloud Acc = 99.84% 1362311/1364443
aerosol Acc = 99.70% 958309/961198
snow Acc = 99.75% 864008/866134

41

CLFG (CLoud FlaG)■Results
We partially solved these issues of V2 CLFG and improved it.
The CLFG product is expected to remain on the target accuracy, because V2 CLAUDIA algorithm iscontinuously used for the area where there are no misclassifications.
RGB

Fire smoke(heavy aerosol)snow

V2CLFG
clear

cloud

snow

aerosol

V3CLFG

Validation result

Release accuracy[Ver.1/Ver.2] 10.2 %10%(with whole-sky camera)

Standard accuracy

Target accuracy

15%
10%(Evaluate with cloud fraction on (Evaluate with cloud fraction
solar irradiance)on solar irradiance)

The standard accuracy is expected to be achieved.

42

(Appendix) CLFG at night
Clear pixels increase by ~ 20% at night (however, still few at high latitudes)

V2 V3
20200101A

20200401A

20200701A

20201001A

V2

V3

43

CLPR (CLoud PRopertis)
・Major changes- Algorithm has not been changed- Screening the validation data with strictly quality check・Validation method- Comparing with the in-situ observation of sky radiometers- Using the average values of SGLI’s 5x5 pixels around the in-situ site
- Only using the in-situ data observed within +/-30 minutes of SGLI observation time.
- Data period is July 2019 – December 2020

Water Clouds

Ice Clouds

Validation result

Release accuracyStandard accuracy

Target accuracy

Water cloud
Optical thickness: 8-9%(vs. MODIS)
Effective radius: 2-5%(vs. MODIS)
Cloud liquid water: 82.07%(in-situ)（with other satellite)
10%
30%
-

(with in-situ)
100%（with in-situ）
20%
50%

Ice cloudOptical thickness: 55.99%(in-situ)（with other satellite）
30%（with in-situ）
70%（with in-situ）
20%

The standard accuracy is expected to be achieved.

44

ARNP
(AeRosol properties using Numerical Prediction)
Atmosphere Product-Aerosol over Land and Ocean
 Algorithm (RESTEC Yoshida)• Use the forecast from aerosol assimilation system of Himawari8/AHI as a priori estimate of retrieval.
 Major improvement from Ver.2• Utilize both polarization and non-polarization observation• Consider the error covariance of surface reflectance• Apply V3 cloud flag (retrieval of heavy aerosol)• Add the estimated uncertainties of aerosol optical thickness,
angstrom exponent, and single scattering albedo• Integrate output over ocean and land
 Channels used
• Land：VN01, VN02, VN03, VN05, VN08, P02• Ocean：VN10, SW01, SW03, SW04, P02
45

ARNP: Validation methods• Method:• Standard accuracy: RMS errors are evaluated comparing SGLI derived AOT
with those from In-situ data (scene by scene)• In-situ data（１）Land：Sky-radiometer• Source: Irie PI@Chiba univ. (SKYNET), Aoki PI@Toyama univ., JMA, AERONET*1• Period：March 27, 2018 ～ November 30, 2020
（V2: March 2018, June 2018, Feb. 2018, Jan. 2020, Feb. 2020）（２）Ocean: Microtops on ships• Source：AERONET - Maritime Aerosol Network*1• Period： March 27, 2018 ～ July 1, 2020
QA flags in V3（V2: March 2018, June 2018, Feb. 2019）*1 Aerosol Robotic Network(Giles et al., 2019; O’Neill et al., 2003),
https://aeronet.gsfc.nasa.gov/• Matchup conditions• Observed within 30 minutes• SGI AOT is averaged within 10km fromin-situ site, where the valid pixel islarger than 50%, and the standard
deviation is less than 0.1• QA_flag is considered for bit
0,2,3,5,9,10,12,13,15

46

ARNP: Validation results over land
AOT over land@380nm（Ver. 2）

AOT over land@380nm（Ver. 3）

Averaged SGLI within 10km vs. Sky
radiometer at the same time

Averaged SGLI within 10km vs. Sky
radiometer at the same timeR
=0.862Bias=0.092
RMSE =0.137
Data num=1058

Product

Estimated RMSE

Standard accuracy(L+5)

Target accuracy(L+5)

AOT over land

0.137（Scene）

0.15 （Scene）

0.10 （Scene）• More validation sample number than V2• Standard accuracy will be archived
47

ARNP: Validation results over ocean
AOT over ocean@670nm（Ver. 2）

AOT over ocean@670nm and 865nm（Ver. 3）

Averaged SGLI within 10km vs. shipborne
microtops at the same time

Averaged SGLI within 10km vs. shipborne
microtops at the same time

865nm

670nm

670nm

R
=0.942Bias=0.048RMSE =0.072
Data num=30

Product

Estimated RMSE

Standard accuracy(L+5)

Target accuracy(L+5)

AOT overocean

0.072@670nm
0.051@865nm（Scene）

0.1 （Scene）

0.05 （Scene）

R
=0.9
Bias
=0.0
RMSE =0.051
Data num=30• More validation sample number than V2• Standard accuracy will be archived
48

ARNP: Summary

Evaluation
MethodProduct

PeriodAOT_ocean@670,865

2018/3/27～
2020/11/30

In-situ(Scene)AOT_land＠380

2018/3/27～
2020/11/30

In-situ(Scene)Estimated
RMSE

ReleaseStandard

0.072@670nm
0.051@865nm 0.1 （Month） 0.1（Scene）（Scene）
0.137（Scene）

0.15 (Month）

0.15（Scene）

Target0.05（Scene）

0.10（Scene）

The standard accuracy is expected to be achieved.

49

(Appendix) Site maps of ARNP validation data

Blue: Ocean
Red: Land

50

(Appendix) ARNP Image (global)
Sep. 7 2020 (California Forest fire)
V2（ARNPF）
ocean
land
AOT

AOT

AE

AE

SSA

V3（ARNPF）

P2 polarization reflectance
SSA

51

(Appendix) ARNP Image (Tile)
Sep. 7 2020 (California Forest fire)
AOT

AE

SSA

V2
（Land）V3（ARNPK）

52

Ver.3 Ocean Products

53Evaluation Summary
Product

ReleasethresholdStandard
accuracy

Target accuracy

Normalized water
50% (<600nm)
30% (<600nm)
leaving radiance (incl. 60% (443~565nm) 0.5W/m2/str/um 0.25W/m2/str/um(>600nm)(>600nm)cloud detection)
Atmospheric
80%
50%
30%
correction(AOT@865nm)(AOT@865nm)(AOT@865nm)
parameters
Photosynthetically20% (10km/month) 15% (10km/month) 10% (10km/month)available radiation−35~+50%
Chlorophyll-a−60~+150%(offshore),−60~+150%(offshore)concentration−50~+100% (coast)

Evaluation Methods◎

Comparison with in-situ observation data.

○⇒◎

Comparison with in-situ observation data.

☆

Comparison with in-situ observation data.

◎

Comparison with in-situ observation data.

−60~+150%−50~+100%◎Comparison with other satellite data
(GOCI).

−60~+150%(offshore)−60~+150%−50~+100%◎Comparison with in-situ observation andother satellite data (MODIS).

0.8 K (daytime)

0.8 K (day & nighttime)

0.6 K (day & night
time)

☆

Comparison with in-situ observation data.

Total suspended−60~+150%
matter concentration (offshore)

Colored dissolved
organic matterSea surface
temperature

Status*1*1 Symbols denote as follows; 〇: the release threshold achieved, ◎: the standard accuracy achieved, ☆: the target accuracy achieved.

54

Validation Results of Ocean NWLR Products:
Normalized Water-Leaving Radiance - NWLR
Major Updates of Algorithm:
•

•
•
•
•

Added water leaving reflectance estimation model in the red(VN07) and near-infrared(VN10)
bands using Linear combination index (LCI)
Applied the vicarious calibration
Improved sunglint correction method
Added negative NWLR correction
Revision of QA flags and Mask for statistics

Validation Method:
Validated the accuracies of predicted NWLR data from the SGLI algorithm comparing within-situ data: ship observation, buoy(MOBY and BOUSSOLE) and AERONET-OC

Quality Control:••

In-situ data: time difference in ±3 hours to SGLI observation
SGLI data: average of the data passed the following conditions within a 5 by 5 pixel
centered the in-situ point (for details refer to Bailey et al, 2006)
1. 13 or more pixels which satisfies the following conditions: aerosol optical
thickness < 0.5, solar zenith angle < 70 degrees, NWLR of all channels > 0,
CLDAFFCTD flag isn’t set.
2. Median CV (coefficient of variation) computed from NWLR_380-565nm
and Taua_865nm less than 0.15

Period of Validation:•

January 1, 2018 - July 31, 2021
55

Validation Results of Ocean NWLR Products:
Normalized Water-Leaving Radiance - NWLR
380nm

412nm

443nm

490nm

530nm

Achv. Standard

Achv. Standard

Achv. Standard

Achv. Target

Achv. Standard

N=693
Accuracy
46.1%

N=133
Accuracy
45.1%
565nm

673.5nmAchv. Target

Not Achv. Stnd

N=690
Accuracy
27.5%

N=692
Accuracy
41.4%
673.5nm

Remove in-situ
data on lakes

N=662
Accuracy:
0.63 W/m2/sr/um

Achv. Standard

N=691
Accuracy
33.1%

N=692
Accuracy
24.4%

•

N=646
Accuracy:
0.499 W/m2/sr/um

NWLR(673.5nm) didn’t
achieved the standard
accuracy on coastal regions.
→ Standard accuracy isachieved when in-situ data onlakes, which are not originallysubject to validation, areexcluded.

•

NWLR(490nm, 565nm) achieved the target accuracies, NWLR(380-443nm, 530nm, 673.5nm)
achieved the standard accuracies.

•

Increased number of valid pixels [Version 2]: 117-616 points → [Version3]: 133-693 points
Validation Result

Release AccuracyStandard Accuracy

Target Accuracy[Ver. 2] 21-42%→[Ver. 3] 23-46%

60%(443-565 nm)

50% (≦600 nm)

30% (≦600 nm)[Ver. 2]0.61 →[Ver. 3] 0.499W/m2/sr/um

N/A

0.5W/m2/sr/um (>600 nm) 0.25W/m2/sr/um (>60056
nm)Validation Results of Ocean NWLR Products:
Normalized Water-Leaving Radiance - ACP
Major Updates of Algorithm:
•

•
•
•
•

Added water leaving reflectance estimation model in the red(VN07) and near-infrared(VN10)
bands using Linear combination index (LCI)
Applied the vicarious calibration
Improved sunglint correction method
Added negative NWLR correction
Described correction formula for TAUA_670 and TAUA_865 in HDF attributes

Validation Method:
Validated the accuracies of predicted aerosol optical thickness at 865nm(Taua_865) from the
SGLI algorithm comparing with in-situ data of AERONET-OC and AERONET Maritime
Aerosol Network (MAN)

Quality Control:••

In-situ data: time difference in ±3 hours to SGLI observation
SGLI data: average of the data passed the following conditions within a 5 by 5 pixel
centered the in-situ point (for details refer to Bailey et al, 2006)
1. 13 or more pixels which satisfies the following conditions: aerosol optical
thickness < 0.5, solar zenith angle < 70 degrees, NWLR of all channels > 0,
CLDAFFCTD, GAMMMA-OUT and OVERITER flags aren’t set.
2. Coefficient of variation for TAUA_865 less than 0.05

Period of Validation:•

January 1, 2018 - March 31, 2021
57

Validation Results of Ocean NWLR Products:
Normalized Water-Leaving Radiance - ACPTAUA_865

TAUA_865_corrected

Achieved Release
Accuracy

Acieved Standard
Accuracy

N=530
Accuracy 62.3%

N=530
Accuracy 46.9%

There is a positive bias because the aerosol model used in the atmospheric correction is
optimized for NWLR not AOT estimation.
→ It is possible to obtain values closer to reality and achieve standard accuracy by applying
the correction formula.
Correction formula：TAUA_865_corrected = 0.822 * TAUA_865 + 0.0
Validation Result

Release AccuracyStandard Accuracy

Target Accuracy[Ver. 2] 65%(not corrected)[Ver. 3] 46%(corrected)

80% (AOT@865)

50% (AOT@865)

30% (AOT@865)

Achieved Standard Accuracy

58

Photosynthetically available radiation
Daily RMS/AVG=19%

8-day RMS/AVG=14%Monthly RMS/AVG=8.9%

Yearly RMS/AVG=6.4%

Bias for each buoy：dependency seems small•••

Daily PAR is estimated by instantaneous transmittance from visible channels.
Reference PAR is made from daily SWR observed by buoy, PAR/SWR ration estimated by
Pstar-4 calculation and objective analysis water vapor data
Achieved the target accuracy（No change from ver.2）

Validation result

Release accuracyStandard accuracy

Target accuracy[Ver.2] 9.5%→[Ver.3] 8.9%

20% (10km/month)

15% (10km/month)

10% (10km/month)59

Validation Results of Ocean IWPR Products:
Chlorophyll-a Concentration - CHLA
Major Updates of Algorithm:•

None of major updates

Validation Method:
Validated the accuracies of predicted CHLA data from the SGLI algorithm comparing within-situ data(High Performance Liquid Chromatography: HPLC or fluorescence method) ofship observation.

Quality Control:••

In-situ data: time difference in ±3 hours to SGLI observation
SGLI data: average of the data passed the following conditions within a 5 by 5 pixel
centered the in-situ point
1. 13 or more pixels which satisfies the following conditions: aerosol optical
thickness < 0.5, solar zenith angle < 70 degrees, NWLR of all channels > 0,
CLDAFFCTD flag isn’t set.
2. Median CV (coefficient of variation) computed from NWLR_380-565nm
and Taua_865nm less than 0.15

Period of Validation:•

January 1, 2018 - December 31, 2020
60

Validation Results of Ocean IWPR Products:
Chlorophyll-a Concentration - CHLA
The coast of Japan (2021/05/10)
Ver.2

Ver.3

Valid pixel ratio
28%

Valid pixel ratio
38%

CHLA
Acieved Standard
Accuracy

N=249

Accuracy：-55% ~ 121%
Valid pixel ratio= number of valid pixels/ number of total pixels••

Achieved Standard Accuracy
Increased the number of validation points on coastal and sunglint areas becauseof the improvement of NWLR estimation.

Validation Result

Release AccuracyStandard Accuracy

Target Accuracy[Ver. 2] -55~121%→[Ver. 2] -58~137%-60%~+150%(offshore)-60%~+150%-35%~+50%(offshore)-50%~+100%(coastal)61

Validation Results of Ocean IWPR Products:
Colored Dissolved Organic Matter - CDOM
Major Updates of Algorithm:•

None of major updates

Validation Method:
Validated the accuracies of predicted CDOM data from the SGLI algorithm comparing within-situ data of ship observation.

Quality Control:••

In-situ data: time difference in ±3 hours to SGLI observation
SGLI data: average of the data passed the following conditions within a 5 by 5 pixel
centered the in-situ point
1. 13 or more pixels which satisfies the following conditions: aerosol optical
thickness < 0.5, solar zenith angle < 70 degrees, NWLR of all channels > 0,
CLDAFFCTD flag isn’t set.
2. Median CV (coefficient of variation) computed from NWLR_380-565nm
and Taua_865nm less than 0.05

Period of Validation:•

January 1, 2018 - December 31, 2020
62

Validation Results of Ocean IWPR Products:
Colored Dissolved Organic Matter - CDOM
CDOM
Acieved Standard
Accuracy

N=38
Accuracy：-54% ~ 119%••

Achieved Standard Accuracy
Increased the number of validation points on coastal and sunglint areas becauseof the improvement of NWLR estimation.

Validation Result

Release AccuracyStandard Accuracy

Target Accuracy[Ver. 1] -52~107%→[Ver. 2] -55~121%-60%~+150%(offshore)-60%~+150%-50%~+100%

63

Validation Results of Ocean IWPR Products:
Total Suspended Matter - TSM
Major Updates of Algorithm:•

None of major updates

Validation Method:
Validated the accuracies of predicted TSM data from the SGLI algorithm comparing with insitu data of ship observation.

Quality Control:••

In-situ data: time difference in ±3 hours to SGLI observation
SGLI data: average of the data passed the following conditions within a 5 by 5 pixel
centered the in-situ point
1. 13 or more pixels which satisfies the following conditions: aerosol optical
thickness < 0.5, solar zenith angle < 70 degrees, NWLR of all channels > 0,
CLDAFFCTD flag isn’t set.
2. Median CV (coefficient of variation) computed from NWLR_380-565nm
and Taua_865nm less than 0.15

Period of Validation:•

January 1, 2018 - December 31, 2020
64

Validation Results of Ocean IWPR Products:
Total Suspended Matter - TSM
Achieved Standard
Accuracy

Not Achieved
Standard Accuracy

in-situ data
quality control

N=51

N=45

Accuracy：-71% ~ 247%

Accuracy：-59% ~ 141%

Standard accuracy is achieved except when there are errors in in-situ data or when theTSM concentration is particularly high, as on the English Channel.
(See appendix for details on quality control.)
Validation Result

Release AccuracyStandard Accuracy

Target Accuracy[Ver. 2] -70~232%→[Ver. 3] -63~171%-60%~+150%(offshore)-60%~+150%-50%~+100%

65

Validation Results of Ocean SST Products:
Sea Surface Temperature - SST
Major Updates of Algorithm:•••

Introduction of atmospheric optical thickness climatology to improve atmospheric correction.
Cloud mask method:
1. Improvements by introduction of 1.6 micron data and so on (Daytime) and
2. Readjustment of thresholds for each quality level (Nighttime).
Introduction of a preprocessing to reduce stripes and random noise in L1B data.

Validation Method:
SGLI SST Version 3 was validated by comparing SGLI SSTs with in-situ SSTs.

Quality Control:•

In-situ data: Moored and drifting buoys data provided by NOAA/iQuam (version 2.1).
The data were screened based on the QC result of the iQuam. High qualified
data (iquam flag = 0 and quality level = 5) were used for the validation.

• SGLI data: SGLI SST V3 of the 1-km spatial resolution with the quality assured as
good or acceptable: which are used to calculate L3 statistics. An SGLI SST
nearest to the center was chosen for each 1hr x 3km collocation window
centered on each buoy data and compared with the centered buoy data.

Period of Validation:•

January 1, 2018 - December 31, 201966

Validation Results of Ocean SST Products:
Sea Surface Temperature - SST
SST (Daytime)

Improved cloud mask

SST (Nighttime)Ver. 2

Accuracy:
0.40℃

Accuracy:
0.43℃

Ver. 1••

Ver. 3The increased total number of valid pixels is due to theimproved cloud masking.
Cloud contaminations have been improved at night due toadjusted thresholds.

Ver. 2Modified cloud masking has also
improved cloud masks at and around SST
fronts during the daytime.

Validation Result

Release AccuracyStandard Accuracy

Target Accuracy

0.4(V2) → 0.4℃ (daytime)
0.7(V2) → 0.4℃ (nighttime)

0.8℃ (日中)

0.8℃

0.6℃

Achieved Target Accuracy
67

(Appendix) Validation Results of Ocean IWPR Products:
Total Suspended Matter - TSM
TSM validation results color-coded by sea area.

Distribution of in-situ TSM by sea area
（ All data, including non-matchup data ）Max

The sea around Japan
Mean：8.68
Std div：9.15

thirdquartile

outlier

40 g/m3

Median40 g/m3

firstquartile
Min※The outlier is the value that is more than 1.5 times thequartile range away from the first and third quartiles.

 TSM on the English Channel in spring are extremely high and rarely exceed 40 g/m3
on the sea around Japan.
 In this validation, in-situ data of TSM > 40g/m3 is removed.

68

(Appendix) Validation Results of Ocean IWPR Products:
Total Suspended Matter - TSM(B) in-situ TSM vs. TSM
estimated from in-situ Rrs
TSM from in-situ Rrs (g/m3)

SGLI TSM (g/m3)(A) in-situ TSM vs. SGLI TSM

TargetIn-situ TSM (g/m3)(C) Rrs comparison of thetarget point

SGLI

565 nmTable 1:Comparison of estimated andin-situ values for TSM and Chl-a
TSM (g/m3)

Target•In-situ TSM (g/m3)(D) Water sampling points
on the same day•

Ise Bay
TSM [g/m3]

490 nmin-situTarget

Chl-a(mg/m3)in-situ

0.28

36.55

SGLI

7.11

15.85

Estimation error of Rrs (NWLR) is unlikely
to be the cause of outlier of TSM estimate
at the target point :➢ TSM estimation from in-situ Rrs at
the TSM sampling point is far off(Figure B).
➢ Rrs at 490 nm and 565 nm, which arenecessary for TSM estimation, areclose to in-situ Rrs (Figure C).
High possibility of measurement error in
in-situ TSM :➢ TSM at the near stations on thesame day (Figure D) varied from 2
to 5 g/m3 , but Chl-a was similar at
21 to 44 mg/m3.
➢ TSM value is too small for Chl-a(Table 1)

Removed as an outlier in this validation69

Ver.3 Cryosphere Products

70Evaluation Summary
Product

Snow and Ice
covered area (incl.
cloud detection)
Okhotsk sea-ice
distribution

ReleasethresholdStandard
accuracy

Targetaccuracy

10%

7%

10%

5%

Status*1

Evaluation Methods

5%

○⇒◎

Comparison with other satellites data (e.g.
MODIS, VIIRS, Sentinel-3…).

3%

○⇒◎

Comparison with other satellites data (e.g.
MODIS, VIIRS, Sensinel-3…).

Snow and ice
surfaceTemperature

5K

2K

1K

◎

Comparison with in-situ observation(Automatic weather station thermal
radiometer data) and other satellites data
(e.g. MODIS, VIIRS Sentinel-3…).

Snow grain sizeof
shallow layer

100%

50%

30%

◎

Comparison with in-situ data for the
standard and target accuracy thresholds.

*1 Symbols denote as follows; 〇: the release threshold achieved, ◎: the standard accuracy achieved, ☆: the target accuracy achieved.

71

Validation results of Cryosphere products – SICE/OKID- Version 3 Major changes and validation details
C1AB/SICE - Snow and Ice cover area algorithm- Revised cloud detection/surface classification training data set using Neural network machinelearning method- All training data were simulated by DISORT radiative transfer model
C1C/OKID - Okhotsk sea-ice distribution algorithm- Revised cloud detection/surface classification training data set using Neural network machinelearning method – communalize with C1AB
- All training data were simulated by DISORT radiative transfer model
Validation data for the C1AB/ SICE
- Snow area:
MOD10C2 Snow Cover Extent Product- Sea ice area*:
MOD29E1D Sea Ice Product

Validation data for the C1C/OKID- Sea ice area* :MOD29E1D Sea Ice Product*NSIDC defines sea ice exists in case of the ice fraction/ice concentration more than 15%.

72

Validation results of Cryosphere products - SICE
- Snow and Ice cover extent product validation results using other satellite products
Ver.3 sample image
2019.05.09 – 2019.05.24

Validation result of Ver. 3

Validation result of Ver. 2
2018.03.22 - 2019.12.31
16 days composite

2018.03.22 - 2021.06.09
16 days composite

N = 40r = 0.99 (p < 0.01)
Rel. Err. = 8.5 %Bias = 462,283 km2

N = 71r = 0.99 (p < 0.01)
Rel. Err. = 6.5 %Bias = -663.057 km2

Validation period was expanded: 1.5 year to 3 year.
Validation result

Release accuracyStandard accuracy

Target accuracy

Ver.2: 8.5 % (Mar. 2018 - Dec. 2019)Ver.3: 6.5 % (Mar. 2018 - Jun. 2021)

10 %

7%

5%

Accuracy improved and SICE product is achieved the standard accuracy
73

Validation results of Cryosphere products - OKID-

Okhotsk sea-ice distribution product validation results using other satellite products
Validation result of Ver. 2

Ver.3 sample image
2021.02.27
False Color ImageOKID classification
snow

Validation result of Ver. 3

Ice Fraction > 15 %2018.02.10 - 2020.03.05
8 days composite

Ice Fraction > 15 %2018.03.06 - 2021.05.17
8 days composite

cloud

sea iceland

N = 73r = 0.99 (p < 0.01)
Rel. Err. = 4.99 %Bias = -5,345 km2

N = 30r = 0.99 (p < 0.01)
Rel. Err. = 9.0 %Bias = -5,986 km2

ocean

1. Validation period was expanded: 2 season to 4 season.
2. Surface classification was improved from visual evaluation compared with False color image.
Validation result

Release accuracyStandard accuracy

Target accuracy

Ver.2: 9.0 % (Feb. 2018 - Mar. 2018)
Ver.3: 5.0 % (Feb. 2018 - May. 2021)

10 %

5%

3%

Accuracy improved and OKID product is achieved the standard accuracy
74

Validation results of Cryosphere products - SIPR
- Version 3 Major changes and validation details
SGSL - Snow grain size of shallow layer-

Revised snow grain size estimation algorithm using Neural network machine learning method- Revised the training data set (BRDF data set) using Neural-net: improved inversion accuracy

SIST - Snow and Ice surface temperature-

Revised the emissivity table

SALB - Broadband blue sky Snow ALBedo-

Add the direct retrieval of broadband blue sky snow albedo product as a research product
Snow albedo estimation using Neural network machine learning algorithm same as snow grain size

Validation data for the SGSL--

Field campaign carried out on the Greenland Ice Sheet East-GRIP site (Jul. 2018), Japan/Hokkaido Nakasatsunai
site (Feb. 2020), and JARE observation data (2019) on the Antarctic Ice Sheet Dome Fuji Site
Surface Specific Area (SSA) measured by IceCube and HISSGraS and converted to optical equivalent snow grain
size
All data match-up conditions are in 10 minutes and 250 meters from nearest point of satellites

Validation data for the SIST-

Ground surface temperature was converted from Longwave radiation Flux observation by Automatic weather
station (PROMICE)
All data match-up conditions are in 10 minutes and 250 meters from nearest point of satellites

Validation data for the SALB-

Surface albedo was calculated from Downward and Upward shortwave radiation flux observation by Automaticweather station (PROMICE)
All data match-up conditions are in 10 minutes and 250 meters from nearest point of satellites

75

Validation results of Cryosphere products - SIPR
Snow grain size of shallow layer product validation results using in-situ observation data
Ver.3 sample image
2019.05.09 - 2019.05.24
500

2000

Ver.2 EGRIP

★

100%

400

Ver.3 EGRIP

Ver.2 NSN

Ver.3 NSN

50%

50%

300

100%
200

N = 11r = 0.50
Rel. Er. = 34.1%
bias = -19.1 µm

100

0
0

50%

Ver.3 JARE

100
200
300
400
In-situ Snow Grain Size (µm)

500

SGLI Snow Grain Size (µm)★
SGLI Snow Grain Size (µm)

-

1500

100%

50%
1000

100%
500

N = 20r = 0.97
Rel. Er. = 50.3%
bias = 56.5 µm

0
0

500
1000
1500
In-situ Snow Grain Size (µm)

2000

Validation sites were added: around Dome Fuji site by JARE.
Validation result

Release accuracyStandard accuracy

Target accuracy

Ver.2: 34 % (Greenland and Japan)
Ver.3: 50 % (Added the Antarctica)

100 %

50 %

30 %

Retrieval became stable on wide region and SGSL product achieved the standard accuracy
76

Validation results of Cryosphere products - SIPR-

Snow and Ice surface temperature product validation results using AWS observation data
Ver.3 sample image
2019.05.09 - 2019.05.24

★
★ ★★
★
★★ ★

N = 135
r = 0.93 ( p < 0.01)
RMSE = 1.98 K
bias = 0.9 K

N = 551
r = 0.98 ( p < 0.01)
RMSE = 1.62 K
bias = 0.95 K

Validation period was expanded: 2 years to 3 years.
Validation result

Release accuracyStandard accuracy

Target accuracy

Ver.2: 2 K (GrIS: 2018 - 2019)Ver.3: 1.6 K (GrIS: 2018 - 2020)

5K

2K

1K

Accuracy improved and SIST product achieved the standard accuracy
77Validation results of Cryosphere products - SIPR-

Snow and Ice surface albedo product validation results using AWS observation data
Sample image
2020.05.09 - 2020.05.24

Sample image
2020.11.17 - 2020.12.02

★
★ ★★
★
★★ ★

N = 744r = 0.80 ( p < 0.01)
RMSE = 0.087
bias = 0.03

Validation result

Release accuracyStandard accuracy

Target accuracy

0.087 (13 %)

-

-

7%

SALB product needs more in-situ data & quality control
78