ESTIMATION OF LAND SURFACE ALBEDO FROM GCOM-C/SGLI SURFACE REFLECTANCE
This paper examines algorithms for estimating terrestrial albedo from the products of the Global Change Observation Mission – Climate (GCOM-C) / Second-generation Global Imager (SGLI), which was launched in December 2017 by the Japan Aerospace Exploration Agency. We selected two algorithms: one base...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2021-06-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2021/227/2021/isprs-annals-V-3-2021-227-2021.pdf |
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| author | J. Susaki H. Sato A. Kuriki K. Kajiwara Y. Honda |
| author_facet | J. Susaki H. Sato A. Kuriki K. Kajiwara Y. Honda |
| author_sort | J. Susaki |
| collection | DOAJ |
| description | This paper examines algorithms for estimating terrestrial albedo from the products of the Global Change Observation Mission – Climate (GCOM-C) / Second-generation Global Imager (SGLI), which was launched in December 2017 by the Japan Aerospace Exploration Agency. We selected two algorithms: one based on a bidirectional reflectance distribution function (BRDF) model and one based on multi-regression models. The former determines kernel-driven BRDF model parameters from multiple sets of reflectance and estimates the land surface albedo from those parameters. The latter estimates the land surface albedo from a single set of reflectance with multi-regression models. The multi-regression models are derived for an arbitrary geometry from datasets of simulated albedo and multi-angular reflectance. In experiments using in situ multi-temporal data for barren land, deciduous broadleaf forests, and paddy fields, the albedos estimated by the BRDF-based and multi-regression-based algorithms achieve reasonable root-mean-square errors. However, the latter algorithm requires information about the land cover of the pixel of interest, and the variance of its estimated albedo is sensitive to the observation geometry. We therefore conclude that the BRDF-based algorithm is more robust and can be applied to SGLI operational albedo products for various applications, including climate-change research. |
| first_indexed | 2024-12-21T19:56:42Z |
| format | Article |
| id | doaj.art-519f77062af744638b8c953883ad83e7 |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-12-21T19:56:42Z |
| publishDate | 2021-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-519f77062af744638b8c953883ad83e72022-12-21T18:52:05ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502021-06-01V-3-202122723410.5194/isprs-annals-V-3-2021-227-2021ESTIMATION OF LAND SURFACE ALBEDO FROM GCOM-C/SGLI SURFACE REFLECTANCEJ. Susaki0H. Sato1A. Kuriki2K. Kajiwara3Y. Honda4Graduate School of Engineering, Kyoto University, C1-2-332, Kyotodaigakukatsura, Nishikyo-ku, Kyoto 615-8540, JapanGraduate School of Advanced Integrated Studies in Human Survivability, Kyoto University, 1 Nakaadachi-cho, Yoshida, Sakyo-ku, Kyoto 606-8306, JapanGraduate School of Engineering, Kyoto University, C1-2-332, Kyotodaigakukatsura, Nishikyo-ku, Kyoto 615-8540, JapanCenter for Environmental Remote Sensing, Chiba University, 1-33, Yayoicho, Inage-ku, Chiba-shi, Chiba, 263-8522 JapanCenter for Environmental Remote Sensing, Chiba University, 1-33, Yayoicho, Inage-ku, Chiba-shi, Chiba, 263-8522 JapanThis paper examines algorithms for estimating terrestrial albedo from the products of the Global Change Observation Mission – Climate (GCOM-C) / Second-generation Global Imager (SGLI), which was launched in December 2017 by the Japan Aerospace Exploration Agency. We selected two algorithms: one based on a bidirectional reflectance distribution function (BRDF) model and one based on multi-regression models. The former determines kernel-driven BRDF model parameters from multiple sets of reflectance and estimates the land surface albedo from those parameters. The latter estimates the land surface albedo from a single set of reflectance with multi-regression models. The multi-regression models are derived for an arbitrary geometry from datasets of simulated albedo and multi-angular reflectance. In experiments using in situ multi-temporal data for barren land, deciduous broadleaf forests, and paddy fields, the albedos estimated by the BRDF-based and multi-regression-based algorithms achieve reasonable root-mean-square errors. However, the latter algorithm requires information about the land cover of the pixel of interest, and the variance of its estimated albedo is sensitive to the observation geometry. We therefore conclude that the BRDF-based algorithm is more robust and can be applied to SGLI operational albedo products for various applications, including climate-change research.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2021/227/2021/isprs-annals-V-3-2021-227-2021.pdf |
| spellingShingle | J. Susaki H. Sato A. Kuriki K. Kajiwara Y. Honda ESTIMATION OF LAND SURFACE ALBEDO FROM GCOM-C/SGLI SURFACE REFLECTANCE ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | ESTIMATION OF LAND SURFACE ALBEDO FROM GCOM-C/SGLI SURFACE REFLECTANCE |
| title_full | ESTIMATION OF LAND SURFACE ALBEDO FROM GCOM-C/SGLI SURFACE REFLECTANCE |
| title_fullStr | ESTIMATION OF LAND SURFACE ALBEDO FROM GCOM-C/SGLI SURFACE REFLECTANCE |
| title_full_unstemmed | ESTIMATION OF LAND SURFACE ALBEDO FROM GCOM-C/SGLI SURFACE REFLECTANCE |
| title_short | ESTIMATION OF LAND SURFACE ALBEDO FROM GCOM-C/SGLI SURFACE REFLECTANCE |
| title_sort | estimation of land surface albedo from gcom c sgli surface reflectance |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2021/227/2021/isprs-annals-V-3-2021-227-2021.pdf |
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