Articles | Volume 12, issue 4
https://doi.org/10.5194/essd-12-3247-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-3247-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A global long-term (1981–2000) land surface temperature product for NOAA AVHRR
School of Resources and Environment, Center for Information
Geoscience, University of Electronic Science and Technology of China,
Chengdu 611731, China
Institute of Meteorology and Climate Research, Karlsruhe Institute of
Technology, 76344 Karlsruhe, Germany
Ji Zhou
CORRESPONDING AUTHOR
School of Resources and Environment, Center for Information
Geoscience, University of Electronic Science and Technology of China,
Chengdu 611731, China
Frank-Michael Göttsche
Institute of Meteorology and Climate Research, Karlsruhe Institute of
Technology, 76344 Karlsruhe, Germany
Shunlin Liang
Department of Geographical Sciences, University of Maryland, College
Park, MD 20742, USA
Shaofei Wang
School of Resources and Environment, Center for Information
Geoscience, University of Electronic Science and Technology of China,
Chengdu 611731, China
Mingsong Li
School of Resources and Environment, Center for Information
Geoscience, University of Electronic Science and Technology of China,
Chengdu 611731, China
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Yi Zheng, Ruoque Shen, Yawen Wang, Xiangqian Li, Shuguang Liu, Shunlin Liang, Jing M. Chen, Weimin Ju, Li Zhang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 2725–2746, https://doi.org/10.5194/essd-12-2725-2020, https://doi.org/10.5194/essd-12-2725-2020, 2020
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Accurately reproducing the interannual variations in vegetation gross primary production (GPP) is a major challenge. A global GPP dataset was generated by integrating the regulations of several major environmental variables with long-term changes. The dataset can effectively reproduce the spatial, seasonal, and particularly interannual variations in global GPP. Our study will contribute to accurate carbon flux estimates at long timescales.
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Short summary
Land surface temperature is an important parameter in the research of climate change and many land surface processes. This article describes the development and testing of an algorithm for generating a consistent global long-term land surface temperature product from 20 years of NOAA AVHRR radiance data. The preliminary validation results indicate good accuracy of this new long-term product, which has been designed to simplify applications and support the scientific research community.
Land surface temperature is an important parameter in the research of climate change and many...
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