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.