Articles | Volume 12, issue 4
https://doi.org/10.5194/essd-12-2725-2020
© 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-12-2725-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017
Yi Zheng
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Ruoque Shen
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Yawen Wang
Key Laboratory of Physical Oceanography, College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China
Xiangqian Li
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Shuguang Liu
College of Life Science and Technology, Central South University of
Forestry and Technology (CSUFT), Changsha 410004, Hunan, China
Shunlin Liang
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Jing M. Chen
Department of Geography, University of Toronto, Toronto, M5G 3G3 Canada
College of Geographical Science, Fujian Normal University, Fuzhou 3500007, Fujian, China
Weimin Ju
International Institute for Earth System Sciences, Nanjing University,
Nanjing, China
Jiangsu Center for Collaborative Innovation in Geographical
Information Resource Development and Application, Nanjing, China
Li Zhang
Key Laboratory of Digital Earth Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Wenping Yuan
CORRESPONDING AUTHOR
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519000, Guangdong, China
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Latest update: 06 Dec 2024
Short summary
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.
Accurately reproducing the interannual variations in vegetation gross primary production (GPP)...
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