Ver.3 Land Products

1

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.

1

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)

3

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%

4

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%

5

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%

6

(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

7

(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

8

(Appendix) Reference: Intensity of PL02

9

(Appendix) Reference: RGB image (for reference)10

(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

11

(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 monitoring

12

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
13

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.

14

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
15

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.

16

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, Japan17

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)

18

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.

19

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)

20

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.

21

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

22

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.

23

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, Japan24

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).

25

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

26

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.

27

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
28

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.

29