School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China
Qinghai Provincial Key Laboratory of Plateau Climate Change and Corresponding Ecological and Environmental Effects, Qinghai University of Science and Technology, Xining 810016, China
School of Geographical Sciences, Qinghai Normal University, Xining 810008, Qinghai, China
Xiaodong Li
Qinghai Institute of Meteorological Sciences, Xining 810008, Qinghai, China
Yuanfang Chai
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Shengjie Zhou
School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China
Renjie Guo
Faculty of Geographical Science, Beijing Normal University, Beijing, China
Jie Dai
School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China
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Total article views: 2,527 (including HTML, PDF, and XML)
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1,886
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2,527
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Supplement: 98
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415
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Total article views: 3,109 (including HTML, PDF, and XML)
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Total article views: 2,527 (including HTML, PDF, and XML)
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Total article views: 582 (including HTML, PDF, and XML)
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We provide an ensemble-model-based GPP dataset (ERF_GPP) that explains 85.1 % of the monthly variation in GPP across 170 sites, which is higher than other GPP estimate models. In addition, ERF_GPP improves the phenomenon of “high-value underestimation and low-value overestimation” in GPP estimation to some extent. Overall, ERF_GPP provides a more reliable estimate of global GPP and will facilitate further development of carbon cycle research.
We provide an ensemble-model-based GPP dataset (ERF_GPP) that explains 85.1 % of the monthly...
