The Global Land Surface Satellite (GLASS) Product Suite

Shunlin Liang Department of Geographical Sciences, University of Maryland, College Park, College Park, Maryland

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Jie Cheng State Key Laboratory of Remote Sensing Science, and Beijing Engineering Research Center for Global Land Remote Sensing Products, and Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing, China

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Kun Jia State Key Laboratory of Remote Sensing Science, and Beijing Engineering Research Center for Global Land Remote Sensing Products, and Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing, China

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Bo Jiang State Key Laboratory of Remote Sensing Science, and Beijing Engineering Research Center for Global Land Remote Sensing Products, and Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing, China

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Qiang Liu State Key Laboratory of Remote Sensing Science, and Beijing Engineering Research Center for Global Land Remote Sensing Products, and College of Global Change and Earth System Science, Beijing Normal University, Beijing, China

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Zhiqiang Xiao State Key Laboratory of Remote Sensing Science, and Beijing Engineering Research Center for Global Land Remote Sensing Products, and Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing, China

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Yunjun Yao State Key Laboratory of Remote Sensing Science, and Beijing Engineering Research Center for Global Land Remote Sensing Products, and Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing, China

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Wenping Yuan School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China

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Xiaotong Zhang State Key Laboratory of Remote Sensing Science, and Beijing Engineering Research Center for Global Land Remote Sensing Products, and Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing, China

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Xiang Zhao State Key Laboratory of Remote Sensing Science, and Beijing Engineering Research Center for Global Land Remote Sensing Products, and Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing, China

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Ji Zhou School of Resources and Environment, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu, China

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Abstract:

The Global Land Surface Satellite (GLASS) product suite currently contains 12 products, including leaf area index, fraction of absorbed photosynthetically active radiation, fraction of green vegetation coverage, gross primary production, broadband albedo, broadband longwave emissivity, downward shortwave radiation and photosynthetically active radiation, land surface temperature, downward and upwelling thermal radiation, all-wave net radiation, and evapotranspiration. These products are generated from the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer satellite data. Their unique features include long-term temporal coverage (many from 1981 to the present), high spatial resolutions of the surface radiation products (1 km and 0.05°), spatial continuities without missing pixels, and high quality and accuracy based on extensive validation using in situ measurements and intercomparisons with other existing satellite products. Moreover, the GLASS products are based on robust algorithms that have been published in peer-reviewed literature. Herein, we provide an overview of the algorithm development, product characteristics, and some preliminary applications of these products. We also describe the next steps, such as improving the existing GLASS products, generating more climate data records (CDRs), broadening product dissemination, and fostering their wider utilization. The GLASS products are freely available to the public.

Joint second coauthors, equally contributed to this paper.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Shunlin Liang, sliang@umd.edu

Abstract:

The Global Land Surface Satellite (GLASS) product suite currently contains 12 products, including leaf area index, fraction of absorbed photosynthetically active radiation, fraction of green vegetation coverage, gross primary production, broadband albedo, broadband longwave emissivity, downward shortwave radiation and photosynthetically active radiation, land surface temperature, downward and upwelling thermal radiation, all-wave net radiation, and evapotranspiration. These products are generated from the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer satellite data. Their unique features include long-term temporal coverage (many from 1981 to the present), high spatial resolutions of the surface radiation products (1 km and 0.05°), spatial continuities without missing pixels, and high quality and accuracy based on extensive validation using in situ measurements and intercomparisons with other existing satellite products. Moreover, the GLASS products are based on robust algorithms that have been published in peer-reviewed literature. Herein, we provide an overview of the algorithm development, product characteristics, and some preliminary applications of these products. We also describe the next steps, such as improving the existing GLASS products, generating more climate data records (CDRs), broadening product dissemination, and fostering their wider utilization. The GLASS products are freely available to the public.

Joint second coauthors, equally contributed to this paper.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Shunlin Liang, sliang@umd.edu
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