Summary of the SGLI products
Validation results(Ver. 2.00)
Earth Observation Research Center
Japan Aerospace Exploration Agency
29 June 2020

S-1

History
23 Dec. 2017
1 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

S-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-up 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

Level/Area

Standard accuracy
Target accuracy*

1 (+1) 4 (+3) 5 (+1) 5 (+2) 2 (+1) 17 (+8)
002

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.

Eight products achieved newly standard accuracy through version-up validation.
S-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

Revise of BRDF estimation and QA
Update of the calibration coefficient
Add the Solar radiation in 8 bits
NA
Brush up the estimation coefficient and validation
methodLand PI

Shadow Index

Vegetation roughness index

Ver.2 Major changes
NA (bug fixed)

Vegetation Index

Above-ground biomass

Ver.2 Major changes
Correction for sensor sensitivity aging
Reduction of linear noise (VNR) and horizontal stripes (TIR)Land PIs

Land PIs

Revise of LUT and coefficient
Add the past observation for QA and data complement

Land PIs

Revise of LUT and coefficient
Revise of Understory LAI

Land PI

JAXA, Land PI
Land PI
Algorithm
Atmosphere PIs

Validation
JAXA,
Atmosphere PIs

Revise of cloud screening using CLFG

Ver.2主な変更点
NA

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

Atmosphere PIs

NA

Ice cloud optical thickness

Aerosol over the oceanLand aerosol by near ultra-violet
Aerosol by polarization

JAXA,
Atmosphere PIs

Atmosphere PIs

Revised inversion method
Using data assimilation model result as initial value
Revised cloud and snow screening

S-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
Ocean PIs,
JAXA

CryosphereVer.2 Major changes
Revised algorithm and QAs

JAXA, Ocean PI
JAXA

Ocean PIs

NA

Suspended solid concentration

Ocean PI

Revised coefficients and QAs

Colored dissolved organic matter

Ocean PI

Revised coefficients and QAs

Sea surface temperature (incl. clouddetection)-

Validation

Product

JAXA
Algorithm

Revised cloud detection
Validation

Snow and ice covered area (incl. clouddetection)
Okhotsk sea-ice distribution
Snow and ice surface temperature

Snow grain size of shallow layer

Ver.2 revision
Revised cloud detection algorithm

Cryosphere PI

Cryosphere PI
Updated emissivity table
Revised neural-network algorithmS-5

Accuracy requirements of SGLI standard products and Current Evaluation Status
Products

Ver.2 Accuracy

Release Accuracy

VNR/SW:5%(abs.),1%(rel.)

VNR/SW:5%(abs.)
TIR: 0.24 K (@300K)
Geometric acc.< 1 pixel
Geometric acc.< 0.43 pixel

L1 Level-1

Standard Accuracy

Target Accuracy

VNR/SW:5%(abs.),1%(rel.)
TIR: 0.5 K (@300K)
Geometric acc.< 0.5 pixel

VNR/SW:3%(abs.),0.5%(rel.)
TIR: 0.5 K (@300K)
Geometric acc.< 0.3 pixel

Precise geometric correction

0.15～0.43 pixel<1pixel<0.5pixel<0.25pixel

Atmospheric correctedreflectance (incl. clouddetection)

0.02 (<=443nm)
0.04 (>443nm)

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

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

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

Grass: 20%, Forest: 15%

Grass: 10%, Forest: 10%

20%

10%

Grass：30%, Forest：50%

Grass：10%, Forest：20%

20%

10%

Vegetation Index* NDVI Grass:12%, Forest:10%
Grass: 25%, Forest: 20%
Grass:16%, Forest:20%
EVI

Land

26.3%

Shadow Index

30%

Grass:30%, Forest:52% Grass： 50%, Forest： 100%

Above-ground biomass
Vegetation roughness index

20.3%

40%

fAPAR

Grass:16%, Forest:17% Grass: 50%, Forest: 50%

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

Leaf area Index

Grass:27%, Forest:36% Grass: 50%, Forest: 50%

Grass: 30%, Forest: 30%

Grass: 20%, Forest: 20%

3.0 K

2.5 K

1.5 K

Atmosphere

Land surface temperature

2.1 K

Cloud flag/Classification-

10% (with whole-sky camera)

Incl. below cloud amount

Incl. below cloud amountClassified cloud fraction-

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-

10%/30% (CloutOT/raduis)

100% (as cloud liquid water) 50%/20%

Ice cloud optical thickness

-

30%

70%

20%

Aerosol over the ocean

0.09**

0.1 (monthly ta_670,865)

0.1 (Scene ta_670,865)

0.05 (Scene ta_670,865)

Land aerosol by near UV

0.13

0.15 (monthly ta_380)

0.15 (scene ta_380)

0.1 (scene ta_380)

Aerosol by polarization

0.14

0.15 (monthly ta_670,865)

0.15 (scene ta_670,865)

0.1 (scene ta_670,865)

Accuracy achievedon Ver.1

Accuracy achievedon Ver.2

Version Up algorithms*The vegetation index is evaluated with the accuracy of both NDVI and EVI. Therefore,
the vegetation index was not achieved standard accuracy.
**The number of validation samples for Ocean AOT was small. Therefore, Ocean AOT was
not judged to achieved standard accuracy.

S-6

Accuracy requirements of SGLI standard products and Current Evaluation Status

Ocean

Products

Ver.2 Accuracy

Normalized waterleaving radiance (incl.
cloud detection)

19~47% (<600nm)
0.9W/m2/sr/um(>600nm)

Atmospheric correction
parameters
Photosynthetically available
radiation

Release AccuracyStandard Accuracy
50% (<600nm)

Target Accuracy
30% (<600nm)

60% (443～565nm)

0.5W/m /str/um(>600nm)79%

80% (ta_865nm)

50% (ta_865nm)

30% (ta_865nm)

9.5%

20% (10km/month)

15% (10km/month)10%(10km/month)2

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

not achieved >670nm

Chlorophyll-aconcentration-58～+140%-60～+150% (offshore)-60～+150%-35～+50% (offshore)-50～+100% (coast)

Suspended solid
concentration-58～+138%-60～+150% (offshore)-60～+150%-50～+100%

Colored dissolved
organic matter-58～+139%

60% (443～565nm)-60～+150%-50～+100%

0.8K (daytime)

0.8K (day/night)

CryosphereSea surface temperature(incl. cloud detection)

day:0.4K,night:0.7K

Snow and Ice covered area(incl. cloud detection)

8.5%

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

7%

5%

Okhotsk sea-ice distribution

9.0%

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

5%

3%

Snow and ice surface
Temperature

2K

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

2K

1K

Snow grain size ofshallow layer

34%

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

50%

30%

Version Up algorithms

Accuracy achievedon Ver.1

0.6K (day/night)

not achieved night-time

Accuracy achievedon Ver.2*Version numbers of all products including version unchanged algorithms will be changed to “2xxx”.
The accuracy number is defined by the root mean square error (RMSE), which has the same unit as the physical quantities. The accuracy ratio(%) is evaluated by the ratio of RMSE and the average value of the in-situ data. For flag type products, the false determination rate (%) isstatistically evaluated using in-situ data.

S-7

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

Cal for the Ver.3

Cal. coef.
Determination

Ver.2 processing

Ver.1 processing
Proc.test

Algo. Improvement for Ver.1
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.
fixingProc.test

Proc.test
Past data
re-processing

Past data re-processing

Algo. Improvement for Ver.2

Ver.3 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

Current - 2020.06.29
S-8

Ver2. Land Products0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 Methods

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

Grass:50%,
forest: 100%

Grass:30%,
forest:50%

Grass:10%,
forest:20%○

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

Grass &
Grass & forest:
forest:20%
40% (scene)(scene)Grass &
Grass & forest:
forest:20%
30% (scene)(scene)Grass &
forest:10%(scene)Grass &
forest:10%(scene)○Comparison with other satellite data.

○

Comparison with in-situ observations.

fAPAR

Grass:50%,
forest: 50%

Grass:30%,
forest:20%

Grass:20%,
forest:10%○⇒◎

Comparison with in-situ observation and other satellite data.

Leaf area index

Grass:50%,
forest: 50%

Grass:30%,
forest:30%

Grass:20%,
forest:20%○

Comparison with in-situ observation and other satellite data.

Surfacetemperature<3.0 K (scene) <2.5 K (scene) <1.5 K (scene)○⇒◎

Comparison with in-situ observation and other satellite data.

Vegetation index
Above-groundbiomass
Vegetation
roughness index
Shadow index*1 Symbols denote as follows; 〇: the release threshold achieved, ◎: the standard accuracy achieved, ☆: the target accuracy achieved.

1

Atmospherically corrected reflectance（RSRF）
1) BRDF calculation✓ Bug fix about relative zenith angle (should be abs() in BRDF kernel c1✓ PI02/01 and VN11P/08P samples are used for the BRDF regression of VN11P/08P

Ver. 1 RSRFQ RGB

VN11-c1

VN11-c2

BRDF kernel 1

BRDF kernel 2

VN11-c1

VN11-c2

BRDF kernel 1

BRDF kernel 2

Ver. 2 RSRFQ RGB

Cloud areas where BRDF
is available are recoveredby the BRDF (flagged bybit-14 of QA_flag)

2

Atmospherically corrected reflectance（RSRF）
2) Cloud screening and aerosol correction of the slant-view telescopes
Ver.1 PI02 2019/08/04 T0911

Ver.2 PI02 2019/08/04 T0911✓ Adding cloud tests byradiance intensity (I)of Pol data
✓ Aerosol is estimatedby PI01 for PI01/02
correction (AE is from
non-pol data)✓ Improve agreementwith MODIS BRDF
(bellows)
Improvement of clouds screening (some are remaining)

Ver. 1 2018/08/01 daily data

Ver. 2 2018/08/01 daily data

X-axis values are calculated by MODIS BRDF product with SGLI geometry3

Atmospherically corrected reflectance（RSRF）

Ver.1 results3) Increase of validation samples
Target accuracy✓ About 550 samples
for Ver.2 (about
120 samples forVer.1）

Validation result at
the Ver.1 release
Sample N~120
0.019(<=443nm)
0.084 (>443nm)

O2A absorption

Worst value (PI02)

Standard accuracy

Release accuracyStandard accuracy✓ Takayama (PEN),
Fuji-hokuroku (PEN),
RadCalNet (Namibia,
France, US), RRV (PIMoriyama), Mongolia(PI Nasahara), tottori(PI Susaki)

The accuracy targets aredefined as error ratios of “solar
zenith angle<30 deg, smoothsurface of reflectance ~0.2”
Polarization telescopes

Validation results
0.02 (<=443nm)
0.07 (>443nm)

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

Worst value (VN09)

0.1 (<=443nm) →50%
0.05 (>443nm) →25%

0.05 (<=443nm) →25%
0.025 (>443nm) →13%4

Atmospherically corrected reflectance（RSRF）

Ver.2 results

Target accuracy

3) Increase of validation samples

O2A absorption

Standard accuracy

Release accuracy

Target accuracy✓ Channel number
achieving target
accuracy is increased
by improvement of
L1B calibration andQA (cloud screening)

Polarization telescopes

Validation results
0.02 (<=443nm)
0.06 (>443nm)

Error ratio ofreflectance~0.2

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

Worst value (PI02)

0.1 (<=443nm) →50%
0.05 (>443nm) →25%

0.05 (<=443nm) →25%
0.025 (>443nm) →13%5

Atmospherically corrected reflectance（RSRF）

Standard accuracyStandard accuracy

Target accuracy

Target accuracy

3) Increase of validation samples

Ver.2 results with BRDF correction

0.021
27%

0.019
20%

0.018
16%

0.020
14%

0.020
12%

0.021
15%

0.024
10%

0.024
10%

0.039
13%

0.032
10%

0.032
10%

0.025
7%

0.034
8%

0.026
8%

0.020
8%

0.030
9%

偏光鏡筒

Validation results

Release accuracy (L+1yr)

0.02 (<=443nm)
0.04 (>443nm)

0.3 (<=443nm) →150%
0.2 (>443nm) →100%✓ All CH achieve thestandard accuracy
and CH number
achieving target
accuracy is increased
with conversion to
the in-situ
observation angles(nadir) by derived
BRDF✓ All CH except forCH1 can achievetarget accuracy byfollowing thevalidation definition,“solar zenith
angle<30 deg,smooth surface ofreflectance ~0.2”

Error ratio ofreflectance~0.2

Standard accuracy (L+5yr) Target accuracy (L+5yr)
0.1 (<=443nm) →50%
0.05 (>443nm) →25%

0.05 (<=443nm) →25%
0.025 (>443nm) →13%

6

Atmospherically corrected reflectance（RSRF）(4) Addition of shortwave radiation (SWR)
Daily RMS/AVG=27%

8-day RMS/AVG=18%

Monthly RMS/AVG=14%

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
✓ 14% does not achieve the SWR target accuracy (monthly 10W/m2); the daily change should beconsidered in the future version✓ Anomaly is useful for applications such as agriculture and long-term weather monitoring

7

T2A/VeGetation Index（VGI)：NDVI✓ NDVI became slightly higher than ver.1
by the improvement of QA.

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

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

Ver.2

1 others

Ver.1/NDVI（MVC）[2019/8/29-9/13]

Ver.1

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 east part of Mongol

8

T2A/ VGI：NDVI
Validation method- SGLI/NDVI is evaluated using reference NDVI at validation sites for forests and grasslands. The reference NDVI is calculated using spectral reflectancedata collected at validation sites.
- The SGLI data is not used when the quality flag indicates cloud shadow or probably cloud, or the pixel is adjacent to cloud pixels.
- The effect of observation tower is corrected for TKY, FHK and PFRR. It is simply corrected for TSE using the data of TKY and FHK.
- The in-situ spectral reflectance data for PFRR and MSE sites are upscaled to the SGLI scale using the Sentinel-2A/B Multispectral Imager(MSI) Level1C/2A data (Band4 and 8b). The reference NDVI is calculated using the upscaled data and used as the validation.

Validation data and period- RSRFQ [ver.2] is used as the input data.
- The data is used from April 1, 2018 to November 30, 2018 for TKY and FHK, from April 26, 2018 to October 31, 2018 for TOC, from May 2, 2018 toSeptember 2, 2018 for TSE, from August 30, 2018 to September 30, 2018 for Lambir and PFRR.
- The data at MSE site during summer season (from July 11, 2018 to August 10 and from August 1, 2019 to August 31, 2019) is used as the data for grassland.
- The data at observation date ± 10 days is used for MBA, MBU and WTR regarding the NDVI that stay consistent during this period.

< Validation results >Estimated errors
12.2→11.7%
11.6→10.2 %[for reference]
Results by the input of RSRFQ [ver.1]< validation sites >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 number of Validation data was increased.
✓ The accuracy became higher than ver.1 inputs by the improvement of QC.

9

T2A/VGI：EVI
Key updates✓ No modification for the algorithm.
✓ Input data [RSRFQ] was changed.
✓ Validation data was increased.

✓ EVI became slightly higher than ver.1 by
the improvement of QA.

EVIVer.2 EVI（ndvi_MVC, input: Ver.2）[2018/8/29-9/13]

Ver.1 EVI（ndvi_MVC）[2018/8/29-9/13]

0

1 others

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

Around Japan

Around the east part of Mongol

10

T2A/ VGI：EVI
Validation method- SGLI/EVI is evaluated using reference EVI at validation sites for forests and grasslands. The reference EVI is calculated using spectralreflectance data collected at validation sites.
- Only the data collected at cloud-free days are used for the validation. Cloud-free days are identified using the images taken by digital
camera and incoming sky radiation taken by spectroradiometers.
- The SGLI data is not used when the quality flag indicates cloud shadow or probably cloud, or the pixel is adjacent to cloud pixels.
- The effect of observation tower is corrected for TKY, FHK and PFRR. It is simply corrected for TSE using the data of TKY and FHK.
- The in-situ spectral reflectance data for PFRR and MSE sites are upscaled to the SGLI scale using the Sentinel-2A/B Multispectral
Imager(MSI) Level-1C/2A data (Band4 and 8b). The reference EVI is calculated using the upscaled data and used as the validation.

< validation sites >Validation data and period- RSRFQ [ver.2] is used as the input data.
- The data is used from April 1, 2018 to November 30, 2018 for TKY and FHK, from April 26, 2018 to October 31, 2018 for TOC, fromMay 2, 2018 to September 2, 2018 for TSE, from August 30, 2018 to September 30, 2018 for Lambir and PFRR.
- The data at MSE site during summer season (from July 11, 2018 to August 10 and from August 1, 2019 to August 31, 2019) is used as thedata for grassland.
- The data at observation date ± 10 days is used for MBA, MBU and WTR regarding the EVI that stay consistent during this period.

< Validation results >Data at cloud-free days

[for reference]
Results by the input of ver.1 RSRFQ.
< Data at cloud-free days >< All data >Release threshold

Standard accuracy

Target accuracy

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

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

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

All data※All data was used for Lambir/WTR/MBU/MBA.

Estimated errors
21.8→20.3%
21.5→16.4 %✓ The number of Validation data was increased.
✓ The accuracy became higher than ver.1 inputs by the improvement of QC.

11

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• 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, Japan12

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., (in press). Large-scale ecological field data for satellite validation in deciduous forests and grasslands.
Ecological Research.
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)

13

T2A/ShaDow Index（SDI）
Key updates✓✓

Minor revision of coefficients for estimating SDI.
Modification of validation method.

Validation method- SGLI/SDI is evaluated by the shadow areas calculated using the in-situ observation data.
- The in-situ shadow areas are calculated from the DSM data of USGS 3 Dimensional Elevation Programme (3DEP) and Solar Zenith Angle(SZA). SGLI/SDI is compared with the in-situ shadow areas correspond to each SGLI pixel (250[m]). The validation was conducted atMichigan, USA.
- The pixels with satellite zenith angle < 8 deg. and NDVI < 0.65 are validated. The pixels adjacent to cloud or probably cloud are excluded
from validation.
Shadow areas correspond
Validation period< Validation result >to each SGLI pixel※ Validation result for ver.1 alg.
- November 23, 2018.
#: 3242, Bias: -0.0172, RMSE: 0.102,(truth)AVG. SDI: 0.426, Relative error: 24.0 %DSM

Shadow areas
comparison

Target areas in Michigan（DSM on GMTED）

Comparison between ver.1 and ver.2

Water areas
SGLI_ver.2 SDI

Estimated errors

Release threshold

Standard accuracy

Target accuracy

30.0→ 24.0%

30% (Forest, Grassland) Scene

20% (Forest, Grassland) Scene

10% (Forest, Grassland) Scene※The accuracy was improved (the possibility increased to achieve standard accuracy).

14

T2B/Leaf Area Index (LAI）
Key updates✓ ver.1: LAI [Forest: Overstory_LAI, Non-forest: Total_LAI], Understory_NDVI [Forest: Understory_NDVI, Non-forest: 0]⇒ ver.2: LAI [Total_LAI] 、Overstory_LAI [Overstory_LAI]← The definition become independent to land cover on the BaseMap. LAI is estimated properly when BaseMap misclassified the land cover.
✓ Modification of LUTs. Use of the reflectance data for VN08, VN11, PI01 and PI02.
← The accuracy is improved and the subdivision of LUTs become possible for later versions.
✓ Use of multiple LUTs for each land cover. LAI estimation at sparse vegetation areas.
← LAI is retrieved properly when the Base Map misclassified the land cover.
LAI [m2/m2] 0others✓ Increase of the number of validation data.
8

LAI is estimated for sparse vegetation.

Ver.2 T2B/LAI (total LAI) [2019/09/01-10] （input: Ver.1）

The accuracy of overstory LAI was improved

Ver.2 T2B/Overstory LAI [2019/09/01-10] （input: Ver.1）
The overestimation of LAI at boreal forest was improved.

Total_LAI is equivalent to other products.

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

Ver. 1 T2B/LAI (F: overstory, NF: total) [2019/09/1-10]

15

T2B/ LAI
Validation method- SGLI/LAI is evaluated using reference LAI data at validation sites for forests and grasslands. The reference
LAI is calculated using in-situ observation data.
- The daily SGLI/LAI is averaged for 11 days and compared with reference LAI.
- The SGLI data is not used when the quality flag indicates cloud shadow, probably cloud, pol. cloud or
backup algorithm.
- RSRFQ [ver.2] is used as the input data for the sites in Japan and Mongolia.
- RSRFQ [ver.1] is used as the input data for the other sites.

< validation sites >Validation period- Lambir, Lambir Oil Palm, TMM: From August 1, 2019 to August 10, 2019
- Other sites: Observation date ± 5 days.

< Validation results >Total LAI < Forest >Overstory LAI < Forest >Total LAI < Grassland >[for reference]
Validation results for ver.1 alg.
Total LAI < Forest >Cropland was assigned
for LC in BaseMap

Estimated errors
Forest (Total LAI): 79.1→35.9%
Forest (Overstory LAI): 112.6%→44.6%
Grassland: 34.7→27.0%

Overstory LAI < Forest > Total LAI < Grassland >The accuracy is low insparse grasslands.

Release threshold

Standard accuracy

Target accuracy

50% (Forest),
50% (Grassland)

30% (Forest),
30% (Grassland)

20% (Forest),
20% (Grassland)※ The accuracy was improved especially for Overstory LAI.

16

T2B/Fraction of Absorbed Photosynthetically
Active Radiation（FAPAR）
Key updates✓✓✓

Modification of LUTs. Use of the reflectance data for VN08, VN11, PI01 and PI02.
← The accuracy is improved and the subdivision of LUTs become possible for later versions.
Use of multiple LUTs for each land cover. FAPAR estimation at sparse vegetation areas.
← FAPAR is retrieved properly when the Base Map misclassified the land cover.
FAPAR 0
Increase of the number of validation data.。

1othersFAPAR is retrieved atsparse vegetation➢

The spatial distribution of retrieved FAPAR is equivalent
to other products.
CGLS/GIOGL1_FAPAR300_V1.0.1 [2019/09/01-10]
Ver.2 T2B/FAPAR [2019/09/01-10] （input: Ver.1）
The overestimation of FAPAR at boreal
forest was improved.

Ver. 1 T2B/FAPAR [2019/09/01-10]

Ver.2 T2B/FAPAR daily (input: Ver.2) SENTINEL-3A daily FAPAR[2019/10/10, resol. 300m][2019/10/10]

17T2B/ FAPAR
Validation method< validation sites >- SGLI/FAPAR (Green FAPAR) is evaluated using reference FAPAR (Total FAPAR) data at validation sites
for forests and grasslands. The reference FAPAR (Total FAPAR) is calculated from in-situ observation data.
- Total FAPAR in forests is re-estimated from SGLI data using the same algorithm with T2B, and also
compared with reference FAPAR (Total FAPAR) for the direct evaluation with the same definition.
- The daily SGLI/FAPAR is averaged for 11 days and compared with reference FAPAR.
- The SGLI data is not used when the quality flag indicates cloud shadow, probably cloud, pol. cloud or
backup algorithm.

Validation data and period- RSRFQ [ver.2] is used as input data for URY, TOS, WTR and FHK.
- RSRFQ [ver.1] is used as input data for PFRR.
- Validation period is observation date ± 5 days.

< Validation results >Forest*1（Green FAPAR）

Forest*2（Total FAPAR）

Grassland[for reference]
Validation results for ver.1 alg.
Forest*1 （Green FAPAR）

GrasslandOver-estimation atsparse forest.

※FAPAR by canopy was used as the definition in PFRR.

Estimated errors
Forest: 33.3→16.9%*1
Grassland: 6.3→7.1%

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.

※ Standard accuracy is achieved.

18

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• 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, Japan19

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., (in press). Large-scale ecological field data for satellite validation in deciduous forests and grasslands.
Ecological Research.
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).

20

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

Modification of coefficients for retrieving AGB.
← The accuracy was improved by retrieving AGB using coefficient maps.
Use of RSRFQ and AGB for past 2 months, and NDVI for one year.
← QA flag was set for the possibility of land cover change.
Revision of QA information.

Ver.2 T3A/AGB （input: ver.1） [2019/09/01-30]

AGB [t/ha] 0

400 others

Ver.1 T3A/AGB[2019/09/01-30]

Artificial changes in biomass are seen at the specific latitude. These are caused by theinadequate coefficient map, and will be improved at ver.3 algorithm.
※ The data with the QA information of low quality, probably cloud or bad geometry was not used in these maps.

Improved point
The accuracy was improvedfor boreal forest.

< Detail map for the part of USA >Improved point
Ver.2 AGB (input: Ver.1)

Ver.1 AGB

The overestimation of biomass in cropland
was improved.

21

T3A/AGB
Validation method- SGLI/AGB is evaluated using reference AGB data at validation sites for forests and grasslands. The reference AGB data is calculated using in-situ observation data.
- Forests: The daily SGLI/AGB is averaged for one month and compared with the reference AGB regarding the forest biomass as that stay consistent for one month.
- Grasslands: The daily SGLI/AGB is averaged for the date ± 10 days when the in-situ observation data was collected. The averaged SGLI data is compared withreference AGB.
- The SGLI data is not used when the quality flag indicates low quality, probably cloud or bad geometry.

Validation data and period< validation sites >- Forests✓ The data from September 1, 2019 to September 30, 2019 are used.
✓ RSRFQ [ver.2] is used as the input data for TOS, FHK and URY sites.
✓ RSRFQ [ver.1] is used as the input data for other sites.
- Grasslands✓ The data for the observation date ± 10 days are used.
✓ RSRFQ [ver.2] is used as input data.

< Validation result >※Grassland only[for reference]
Validation result for ver.1 alg.

The accuracy was improvedfor WTR site (grassland).

Estimated errors

Release threshold

Standard accuracy

Target accuracy

Forest: 94.0→80.9%
Grassland: 406.7→30.8%

100% (Forest),
50% (Grassland)

50% (Forest),
30% (Grassland)

20% (Forest),
10% (Grassland)※The accuracy was improved (the possibility increased to achieve standard accuracy).

22

T3A/Vegetation Roughness Index（VRI）
Key updates✓ No modification for the equation for retrieving VRI.
✓ Modification by the revision of AGB algorithm and input RSRFQ.
⇒ The accuracy was improved and the cloud was strictly identified.

VRI

1

Ver.1 T3A/VRI

Ver.2 T3A/VRI

Average for 2019/09/01-30 (input: ver.1)

02019/09/06 (input: ver.2)

Average for 2019/09/01-30

2019/09/06

Validation method- SGLI/ VRI is evaluated using reference VRI which is calculated from in-situ observation data (spectral directional reflectance factor). In-situ observation datais 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 data and 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.
[for reference]
Validation result for ver.1 alg.

< Validation result >: Standard accuracy※The accuracy was improved.
※The number of validation data is not enough.

Estimated errors

34.8→20.3%

Release threshold

Standard accuracy

Target accuracy

23
40% (Forest, Grassland) Scene 20% (Forest, Grassland) Scene 10% Forest, Grassland) Scene

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• 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, Japan24

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., (in press). Large-scale ecological field data for satellite validation in deciduous forests and grasslands.
Ecological Research.
Suzuki, R., Kim, Y., & Ishii, R., 2013. Sensitivity of the backscatter intensity of ALOS/PALSAR to the above-ground biomass
and other biophysical parameters of boreal forest in Alaska. Polar Science, 7(2), pp.100-112.

25

Validation results of Land Surface Temperature (1/2)•

Algorithm Overview•••

Algorithm PI: Dr. Moriyama (Nagasaki University)
Features :• Simultaneous estimation of the LST and emissivity by inputting LTOA data, which
considers the temporal fluctuation of emissivity.
• Development and implementation of radiation transmission code for high speed processing.

Issues in V1
a. Low accuracy of the cloud screening (Not use the cloud flag product)b. Discontinuity of meteorological data profile (Solid line on the right fig.)c. Sparse validation points (Only 3 points at V1 release)•

V2 update① Cloud screening using cloud flag product(← a)② Revision of the meteorological data loading function（← b）③ Adding Flux site data as in-situ data（←c）•

Validation data••••

In-situ：Fuji-hokuroku, Mase, AmeriFLUXMatch-up： time±15min.
QC: Convergence residual<1[K]Period: Jan. 23 2018 to Oct. 14 2019

Meteorological profile discontinuity
Quoted from PI-WS 2020
by Dr. Moriyama

Validation results of Land Surface Temperature (1/2)
V2 Day+NightV1 Day+Night

Improved accuracy due tocloud screening update.

V1 DayV1 Night

V2 DayV2 Night

Matchup score at night
decreased.

Results

Release threshold

Standard accuracy

Target accuracy

RMSE=2.15 [K]（scene）<3.0[K]（scene）<2.5[K]（scene）<1.5[K]（scene）

Conclusion• V2 is more accurate than V1 and achieves standard accuracy.
• In the future, plan to consider adding high-latitude in-situ sites.
• Adjusting the threshold is needed after improving the accuracy of the nighttime cloud flag.

Ver2. Atmosphere Products

1

Evaluation Summary
Product

ReleasethresholdStandard
accuracy

Targetaccuracy

Status*1

Evaluation Methods

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% (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)◎

10%/30%100% (as cloud(CloudOT/radiusliquid water))

50% / 20%○

Comparison with other satellite (MODIS) data.

30%

70%

20%

○

Comparison with other satellite (MODIS) data.

0.1 (Monthly
a_670,865)

0.1 (scenea_670,865)

0.05 (scenea_670,865)○

Comparison with other satellite (MODIS) data.

0.15 (Monthly
a_380)0.15 (scenea_380)0.1 (scenea_380 )Water cloudOT/effective
radiusIce cloud optical
thickness
Aerosol over theocean
Land aerosol bynear ultra violet
Aerosol by
Polarization

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

0.1 (scenea_670,865)

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

◎

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.

2

ARNP
Atmosphere Product- Aerosol over Land and Ocean with Near UV■Changes from Ver. 1
・Aerosol retrieval by inputting model forecast of AHI as an initial value
・Simultaneous estimation of 3 variables with canonical correlation analysis(Optical thickness, Angstrom exponent, Single scattering albedo)■Using channels
・Land ：VN01, VN02, VN03, VN05, VN08
・Ocean：VN10, SW01, SW03, SW04
■Ver. 2 algorithm
SGLI
Observed
brightness

Retrieval

Aerosolestimation( 𝒙𝒐 ’)

Restec / Yoshida

Priori (𝒙𝒑 )AHI
Observed
brightness

Retrieval

Aerosolestimation( 𝒙𝒐 )

Adapt
ation

AnalyzedValue( 𝒙𝒂 )

Fore
cast

Forecast( 𝒙𝒇 )3

ARNP
Validation methods■Validation Method:
・Aerosol over ocean:Standard accuracy: RMS errors are evaluated comparing SGLI derived AOT withthose from other satellite sensors and shipborne in-situ observations(AERONET/Maritime Aerosol Network) (scene by scene).
・Aerosol over land:Standard accuracy: RMS errors are evaluated comparing SGLI derived AOT withthose from other satellite sensors and sky-radiometer at ground observation network(Skynet and AERONET) (scene by scene).
■Validation data and condition etc.:
The SGLI data within 10 km from the in-situ sites were used and averaged forcomparison with the in-situ data.
■Validation period:(1) Land: Sky-radiometer
Source – Irie PI@Chiba univ. (SKYNET), Aoki PI@Toyama univ., JMA,
AERONET
Period – March 2018, June 2018, Feb. 2018, Jan. 2020, Feb. 2020
(2) Ocean: Microtops on ships
Source – AERONET/Maritime Aerosol Network
Period – March 2018, June 2018, Feb. 2019

4

ARNP

ARNP
Validation Results
Aerosol over land with near UV (ARNP_land)
AOT over land（Ver. 1）
Averaged SGLI within 10km vs.
Sky-radiometer at the same time

AOT over land（Ver. 2）
Averaged SGLI within 10km vs.
Sky-radiometer at the same time

R
=0.82
Bias
=0.08
RMSE
=0.16
Data num=37

Product
AOT over land

Estimated RMSE

Standard accuracy

Target accuracy

Ver. 1

Ver. 2

(L+5)

(L+5)

0.16

0.13 (Scene)

0.15 (Scene)

0.10 (Scene)

Standard accuracy is archived.
5

ARNP

ARNP
Validation Results
Aerosol over ocean with near UV (ARNP_ocean)
AOT over ocean（Ver. 1）
Averaged SGLI within 10km vs.
shipborne microtops at the same time

AOT over ocean（Ver. 2）
Averaged SGLI within 10km vs.
shipborne microtops at the same timeAOT(670nm)

AOT(670nm)

R
=0.91
Bias
=0.05
RMSE
=0.12
Data num=8

R
=0.94
Bias
=0.04
RMSE
=0.09
Data num=8

Estimated RMSE

Product
AOT over oceanStandard accuracy

Target accuracy

Ver. 1

Ver. 2

(L+5)

(L+5)

0.12

0.09 (Scene)

0.10 (Scene)

0.05 (Scene)

It has potential to archive standard accuracy.
6

ARNP

ARNP
Validation Results
Aerosol over land with near UV (Monthly ARNP_land)
Ver1

Ver2

Aerosol over ocean with near UV (Monthly ARNP_ocean)
Ver1

Ver2

The cloud contamination reduced over both land and ocean.
7Validation results of Aerosol by Polarization (1/2)•

Algorithm Overview•••

Algorithm PI: Dr. Sonoyo Mukai (The Kyoto College of Graduate Studies for Informatics)
Dr. Itaru Sano (Kindai Univ.)
Features：• Retrieval methods of aerosol properties based on POLDER research.
• Improvements of the precision of aerosol properties by using polarization and near
ultraviolet wavelength.

Issues in V1

GC1SG1_20190501D01M_D0000_3MSG_AOTLF_1000

a. Large deviation of validation resultsb. False detection of AOT in clear-sky areac. Low AOT in dirty sky area•

V2 update① Cloud screening using polarization bands(←a)② Masking snow cover area (see cloud-flag product)（ ← b）
＊Issue c will continue to be considered for the next version•

Validation data••

In-situ: SKYNET, AERONET
Match-up:• distance±10km(ave.) / time±30min.
• Period: Apr.1st to Dec. 26 2018

Validation results of Aerosol by Polarization (2/2)
AOT＠670nm(V1)

AOT@670nm(V2)
Decreased in thedeviation and false
detection of AOT insnow area.
However, match-up points are notenough in higher AOT area (due tocloud screening, etc.)

2019年5月1日daily全球

False detection of
AOT in clear-sky area

Results
RMSE=0.137（scene）
Conclusion••

Decreased false detectionof AOT

Release threshold

Standard accuracy

Target accuracy

0.15 （monthly average）

0.15 （scene）

0.10 （scene）

Decreased in deviation and false detection of AOT in snow area at V2.
Plan to consider the following for V3:•
Re-examination of cloud screening threshold in high AOT region.
•
Detailed investigation of high AOT event cases. (air pollution, forest fires, etc.)•
Re-examination of radiative transfer. (including LUT)

Ver2. Ocean Products

1

Evaluation Summary
Product

ReleasethresholdStandard
accuracy

Target accuracy

Normalized water
50% (<600nm)
30% (<600nm)
2
0.5W/m/str/um
0.25W/m2/str/um
leaving radiance (incl. 60% (443~565nm)(>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 & night 0.6 K (day & night
time)
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.

2

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

Improved aerosol estimation method, sunglint correction method, in-water model
Applied the vicarious calibration of version 2.0

Validation Method:
Validated the accuracies of predicted NWLR data from the SGLI algorithm comparing with insitu 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 andTaua_865 nm less than 0.15

Period of Validation•

January 1, 2018-February 29, 2020
Reference: Bailey, S.W., and Werdell, P.J. (2006). A multi-sensor approach for the onorbit validation of ocean color satellite data products. Rem. Sens. Environ. 102, 12-23.

Validation Results of Ocean Products:
Normalized Water-Leaving Radiance - NWLR
NWLR_530

Achv. Target

Achv. Standard

Achv. Standard

Achv. Target

Achv. Target

N=113

Accuracy:

28%

N=721

Accuracy:47%

In-situ nLw [W/m2/sr/μm]

In-situ nLw [W/m2/sr/μm]

NWLR_565

NWLR_670

Achv. Target

Not Achv.
Stnd.
n=722

N=722

Accuracy:

22%

N=659

Accuracy:

0.9 W/m2/sr/um

In-situ nLw [W/m2/sr/μm]

In-situ nLw [W/m2/sr/μm]

N=722

Accuracy:32%
In-situ nLw [W/m2/sr/μm]

N=722

Accuracy:

19%
In-situ nLw [W/m2/sr/μm]

SGLI nLw [W/m2/sr/μm]

NWLR_490
SGLI nLw [W/m2/sr/μm]

NWLR_443
SGLI nLw [W/m2/sr/μm]

NWLR_412
SGLI nLw [W/m2/sr/μm]

NWLR_380

SGLI nLw [W/m2/sr/μm]

SGLI nLw [W/m2/sr/μm]

SGLI nLw [W/m2/sr/μm]

・Ver. 2: Validation Results

N=722

Accuracy:22%
In-situ nLw [W/m2/sr/μm]• NWLR(380nm, 490-565nm) achieved the target accuracies,
NWLR(412-443nm) achieved the standard accuracies 。
• NWLR(670 nm) didn't achieved the standard accuracy on coastal
regions. It is a issue to be solved by version 3. However, version 2
accuracy improved than version 1 (1.2→0.9W/m2/sr/um).
• Number of In-situ points increased than the validation result at
version 1 release(ver.1 release: 14-102 points → ver.2 release: 113-722 points).

Validation Result

Release AccuracyStandard Accuracy

Target Accuracy[Ver. 1]30-40 → [Ver. 2] 19-47% (<600nm)

60% (443-565 nm)

50% (<600 nm),

30% (<600 nm)[Ver. 1] 1.2 → [Ver. 2] 0.9W/m /sr/um (>600nm)

N/A

2

0.5W/m2/sr/um (>600nm) 0.25W/m2/sr/um (>600nm)

Achieved the standard accuracies (<600 nm)

Validation Results of Ocean NWLR Products :Atmospheric Correction Parameters - ACP
Major Updates of Algorithm:••

Improved aerosol estimation method, sunglint correction method, in-water model
Applied the vicarious calibration of version 2.0

Validation Method:
Validated the accuracy of predicted aerosol optical thickness at 865 nm (Taua_865) from the
SGLI algorithm comparing with in-situ data of 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 <0.3, solar zenith angle < 70 degrees, NWLR of all channels > 0, CLDAFFCTD,GAMMA-OUT and OVERITER flags aren’t set.
2. Coefficient of variation for Taua_865 nm less than 0.05

Period of Validation•

January 1, 2018-February 29, 2020

Validation Results of Ocean Products:- Atmospheric Correction Parameters: ACPTAUA_865: Ver. 1 [for reference]

TAUA_865: Ver. 2

Achieved
Release Accuracy

Achieved
Release Accuracy

N=98

N=142

Accuracy:
80%

Accuracy:79%✓ Kept the release accuracy✓ Increased the validation points than version 1
Validation Result

Release AccuracyStandard Accuracy

Target Accuracy[Ver. 1] 80%→ 79% (AOT@865nm)

80% (AOT@865nm)

50% (AOT@865nm)

30% (AOT@865nm)

Achieved the release accuracy

Photosynthetically available radiation
Daily RMS/AVG=21%

8-day RMS/AVG=15%Monthly RMS/AVG=9.5%

Colors indicate difference of the buoy

Yearly RMS/AVG=5.6%

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 ratio estimated by Pstar-4 calculation andobjective analysis water vapor data
✓ SGLI PAR will be able to achieve the target accuracy; improvement from Ver.1 is mainly from L1B improvement

Validation result

Release accuracyStandard accuracy

Target accuracy[Ver. 1] 15% (10km/month)→[Ver.2] 9.5% (10km/month)

20% (10km/month)

15% (10km/month)

10% (10km/month)
2.2 - 7

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

None of major updates

Validation Method:
Validated the accuracy of predicted CHLA data from the SGLI algorithm comparing with in-situ
data (High-Performance Liquid Chromatography: HPLC or fluorescence method) of shipobservation

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 pixels
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.

Period of Validation•

January 1, 2018-February 29, 2020

Validation Results of Ocean IWPR Products:
Chlorophyll-a Concentration - CHLA
CHLA: Ver. 1 [for reference]

CHLA: Ver. 2

Achieved
Standard Accuracy

Achieved
Standard Accuracy

N=135

N=108

Accuracy:-59~142%

Accuracy:-58~140%✓ Increased the validation points(at time of Ver. 1 release: 9 points → 135 points)✓ Achieved the standard accuracy (include the coastal area)
Validation Result

Release AccuracyStandard Accuracy

Target Accuracy[Ver. 1] -59~142%→[Ver. 2] -58~140%-60~+150% (offshore)-60~+150%-35~+50% (offshore)-50~+100% (coastal)

Achieved the standard accuracy

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

Improved the empirical formula

Validation Method:
Validated the accuracy of predicted TSM data from the SGLI algorithm comparing with in-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 pixels
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.

Period of Validation•

January 1, 2018-February 29, 2020

Validation Results of Ocean IWPR Products:
Total Suspended Matter - TSM
TSM: Ver. 1 [for reference]

TSM: Ver. 2

Not Achieved
Standard Accuracy

Achieved
Standard Accuracy

N=34

N=19

Accuracy:-65~182%

Accuracy:-58~138%✓ Validated for in-situ data (at time of version 1 release, in-situ data comparison
was not possible)✓ Achieved the standard accuracy (issue: continue to validate with wider range
data)
Validation Result

Release AccuracyStandard Accuracy

Target Accuracy[Ver. 1] -65~182%→[Ver.2] -58~138%-60~+150% (offshore)-60~+150%-50~+100%

Achieved the standard accuracy

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

Improved the empirical formula

Validation Method:
Validated the accuracy of predicted CDOM data from the SGLI algorithm comparing with in-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 (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 andTaua_865 nm less than 0.05.

Period of Validation•

January 1, 2018-February 29, 2020
Reference: Bailey, S.W., and Werdell, P.J. (2006). A multi-sensor approach for the onorbit validation of ocean color satellite data products. Rem. Sens. Environ. 102, 12-23.

Validation Results of Ocean IWPR Products:
Colored Dissolved Organic Matter - CDOM
CDOM: Ver. 1 [for reference]

CDOM: Ver. 2

Achieved
Standard Accuracy

Achieved
Standard Accuracy

N=8

The underestimation bias is cased by theaccuracies of the input NWLR. The bias
doesn’t occur when the in-situ NWLR is
inputted, as shown below.
Ver. 2 CDOM
inputted in-situ NWLR

N=14Accuracy:-55~121%

Accuracy:-58~139%✓ Increased the validation points(at time of Ver. 1 release: 5points →14 points)✓ Achieved the standard accuracy (include the coastal area)

Accuracy:-47~88%

Validation Result

Release AccuracyStandard Accuracy

Target Accuracy[Ver. 1] -55~121%→[Ver.2] -58~139%-60~+150% (offshore)-60~+150%-50~+100%

Achieved the standard accuracy

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

Improved the cloud mask algorithm (modified cloud mask on coast and inland waters）
Modified the probability density function of cloud mask

Validation Method:
Validated the accuracy of predicted SST data from the SGLI algorithm comparing with in-situ
data of iQuam (Ship and buoy observations)

Quality Control:••

In-situ data: bouy data from NOAA/ iQuam passed the following conditions:
1. quality control level on iQuam: iquam_flag=0, quality_level=5
2. time difference in ±30 minutes to SGLI observation
SGLI data: average of the 1km data passed the following conditions within a 5 by 5 pixels(3 km) centered the in-situ point:
1. Standard deviation of the 5 by 5 pixels < 1.0 degrees C
2. Minimum and maximum temperature differences of the 5 by 5 pixels < 3.0 degrees C
3. Temperature difference between SGLI and iQuam < 5.0 degrees C
4. Flag of quality assurance: Good or Acceptable

Period of Validation•

January 1, 2018-Decenber 31, 2019

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

SST (Nighttime)

Achv. Target

Achv. Standard

Accuracy:
0.4℃

Accuracy:
0.7℃*) In the night time, more pixels on coastal areas became available bymodified cloud detection algorithm, whereas the accuracy deteriorated by
the slight increase of cloud affected pixels.

Ver. 1

Ver. 2On coastal area, pixels masked on version 1
as shown green of the left figure became
available as shown red of the right figure.

Validation Result

Release AccuracyStandard Accuracy

Target Accuracy[Ver.1] 0.4 → [Ver.2] 0.4 ℃ (D)[Ver.1] 0.5 → [Ver.2] 0.7 ℃ (N)*0.8℃ (Daytime)

0.8℃

0.6℃

Achieved the target accuracy in the daytime andthe standard accuracy in the nighttimeVer2. Cryosphere Products

1

Evaluation Summary
Product

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

Snow grain size ofshallow layer

ReleasethresholdStandard
accuracy

Targetaccuracy

10%

7%

10%

5%

5K

100%

2K

50%

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…).

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

○→◎Comparison with climatology (relationshipbetween snow surface temperature andsnow grain size) for the release accuracy
threshold. In addition, comparison with insitu data for the standard and target
accuracy thresholds.

1K

30%

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

2

Validation results of Cryosphere products - SICE
- Version 2 Major changes and validation data details
Major Change for the C1AB - Snow and Ice cover area algorithm- Revised cloud detection/surface classification algorithm from ordinary threshold method to Neuralnetwork machine learning method- All training data were simulated by DISORT radiative transfer model

Major change for the C1C - Okhotsk sea-ice distribution algorithm- Revised cloud detection/surface classification algorithm from ordinary threshold method to Neuralnetwork machine learning method partly.
- All training data using Neural network were simulated by DISORT radiative transfer model
Validation data for the C1AB/ SICE - Snow and Ice cover Extent product (snow/ice fraction > 15%*)- Snow area: MOD10C2 Snow Cover Extent Product- Sea ice area: MOD29E1D Sea Ice Product
Validation data for the C1C/OKID - Okhotsk sea-ice distribution product (sea ice fraction > 15%*)- Sea ice area :MOD29E1D Sea Ice Product*NSIDC defines sea ice exists in case of the ice fraction/ice concentration more than 15%.

Validation results of Cryosphere products - SICE
- Snow and Ice cover extent product validation results using other satellite products
Ver.2 sample image
2019.05.09 - 2019.05.24

Validation result of Ver. 1
Snow Fraction > 10 %2018.03.14 - 2018.08.20
8 days composite

Validation result of Ver. 2
Snow Fraction > 15 %2018.03.22 - 2019.12.31
16 days composite

N = 21r = 0.99 (p < 0.01)
Rel. Err. = 9.4 %Bias = 744,065 km2

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

1. Validation period was expanded: 0.5 year to 1.5 year.
2. Composite period and valid snow/ice fraction were revised: 8 day to 16 days, 10% to 15% .
Validation result

Release accuracyStandard accuracy

Target accuracy

Ver.1: 9.4 % (Mar. 2018 - Aug. 2018)
Ver.2: 8.5 % (Mar. 2018 - Dec. 2019)

10 %

7%

5%

Accuracy improved and SICE product is likely to achieve the standard accuracy

Validation results of Cryosphere products - OKID
- Version 2 Major changes and validation data details
Major Change for the C1AB - Snow and Ice cover area algorithm- Revised cloud detection/surface classification algorithm from ordinary threshold method to Neuralnetwork machine learning method- All training data were simulated by DISORT radiative transfer model

Major change for the C1C - Okhotsk sea-ice distribution algorithm- Revised cloud detection/surface classification algorithm from ordinary threshold method to Neuralnetwork machine learning method partly- All training data using Neural network were simulated by DISORT radiative transfer model
Validation data for the C1AB/ SICE - Snow and Ice cover Extent product (snow/ice fraction > 15%*)- Snow area: MOD10C2 Snow Cover Extent Product- Sea ice area: MOD29E1D Sea Ice Product
Validation data for the C1C/OKID - Okhotsk sea-ice distribution product (sea ice fraction > 15%*)- Sea ice area :MOD29E1D Sea Ice Product*NSIDC defines sea ice exists in case of the ice fraction/ice concentration more than 15%.

Validation results of Cryosphere products - OKID-

Okhotsk sea-ice distribution product validation results using other satellite products

False Color Image

Validation result of Ver. 2

Validation result of Ver. 1

Ver.2 sample image
2020.02.27
OKID classification
snow

Ice Fraction > 10 %2018.03.14 - 2018.08.20
8 days composite

Ice Fraction > 15 %2018.02.10 - 2020.03.05
8 days composite

cloud

sea ice

land
ocean

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

N = 21r = 0.99 (p < 0.01)
Rel. Err. = 9.1 %Bias = 91,081 km2

1. Validation period was expanded: 0.5 year to 2 year.
2. Surface classification was improved from visual evaluation compared with False color image.
Validation result

Release accuracyStandard accuracy

Target accuracy

Ver.1: 9.1 % (Mar. 2018 - Aug. 2018)
Ver.2: 9.0 % (Feb. 2018 - Mar. 2020)

10 %

5%

3%

OKID product needs more detail evaluation using high resolution satellite images

Validation results of Cryosphere products - SIPR
- Version 2 Major changes and validation data details
Major Change for the C2AB - Snow grain size of shallow layer- Revised snow grain size estimation algorithm using Neural network machine learning method- Add the layer numbers of Neural-net: improved the processing speed- Revised the training data set (BRDF data set) using Neural-net: improved inversion accuracy

Major change for the C2AB - Snow and Ice surface temperature- Revised the emissivity table
Validation data for the C2AB/ SGSL - Snow grain size of shallow layer- Field campaign carried out on the Greenland Ice Sheet East-GRIP site (Jul. 2018) andJapan/Hokkaido Nakasatsunai site (Feb. 2020)- Surface Specific Area (SSA) measured by IceCube and HISSGraS and converted to optical equivalentsnow grain size
- All data match-up conditions are in 10 minutes and 250 meters from nearest point of satellites
Validation data for the C2AB/ SIST - Snow and Ice surface temperature- Automatic Weather Station (AWS) installed by PROMICE (Fausto and van As, 2019) have measured
the Upward/Downward Longwave Radiation Flux
- Ground surface temperature was converted from Longwave radiation Flux observation- All data match-up conditions are in 10 minutes and 250 meters from nearest point of satellites
Fausto, R.S. and van As, D., (2019). Programme for monitoring of the Greenland ice sheet (PROMICE): Automatic weather station data.
Version: v03, Dataset published via Geological Survey of Denmark and Greenland. DOI: https://doi.org/10.22008/promice/data/aws

Validation results of Cryosphere products - SIPR
Snow grain size of shallow layer product validation results using in-situ observation data (only E-GRIP)
Ver.2 sample image
2019.05.09 - 2019.05.24

Snow grain size estimation accuracy evaluation by comparison between
Ver.1 and Ver.2 results in same quality control condition using Ver.2 SICE result
500

EGRIP

★
EGRIP

SGLI Snow Grain Size (µm)

-

Ver.1

400

100%

Ver.2

50%
50%

300

100%
200

N=8
r = 0.43 -> 0.31
Rel. Er. = 44.9% -> 28.5%
bias = 19.5 -> 12.4 µm

100

0
0

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

500

Validation result

Release accuracyStandard accuracy

Target accuracy

Ver.1: 45 % (E-GRIP, QC: Ver.2)
Ver.2: 29 % (E-GRIP, QC: Ver.2)

100 %

50 %

30 %

Quality control was improved by cloud detection algorithm revision of SICE.
Snow grain size estimation accuracy improved by revision of C2 algorithm.

Validation results of Cryosphere products - SIPR
Snow grain size of shallow layer product validation results using in-situ observation data (wide-range)
Ver.2 sample image
2019.05.09 - 2019.05.24

Adding the in-situ data measured in Nakasatsunai, Hokkaido, JapanSGSL was evaluated in a wide-range of arctic and mid-latitude region
500
Ver.2 EGRIP

★
EGRIP

SGLI Snow Grain Size (µm)

-

400

100%

Ver.2 NSN

50%
50%

300

100%
200

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

100

0
0

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

500

Validation result

Release accuracyStandard accuracy

Target accuracy

Ver.1: 45 % (only arctic, QC: Ver.2)
Ver.2: 34 % (wide-range, QC: Ver.2)

100 %

50 %

30 %

Accuracy improved and SGSL product achieved the standard accuracy

Validation results of Cryosphere products - SIPR-

Snow and Ice surface temperature product validation results using AWS observation data (only E-GRIP)
Ver.2 sample image
2019.05.09 - 2019.05.24

Snow and Ice surface temperature estimation accuracy evaluation bycomparison between Ver.1 and Ver.2 results using E-GRIP observation data

EGRIP

★
EGRIP
N = 33r = 0.97 (p < 0.01)
RMSE = 1.5 K -> 1.4 K
bias = 1.2 K -> 1.0 K

Validation result

Release accuracyStandard accuracy

Target accuracy

Ver.1: 1.5 K (EGRIP)
Ver.2: 1.4 K (EGRIP) & 2 K (GrIS, next page)

5K

2K

1K

Accuracy improved and SIST product achieved the standard accuracy

Validation results of Cryosphere products - SIPR-

Snow and Ice surface temperature product validation results using AWS observation data (whole GrIS)
Snow and Ice surface temperature estimation accuracy evaluation
using all AWSs installed whole region of the Greenland Ice Sheet

ALL

KPC_U

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

RMSE = 1.39 K

SCO_U
THU_U
RMSE = 1.00 K

RMSE = 0.90 K

KAN_U

UPE_U

SIST: R/S/G = 5K/2K/1K
RMSE = 1.57 K

RMSE = 4.38 K