Multi-spatial scale assessment and multi-dataset fusion of global terrestrial evapotranspiration datasets
Yi Wu,Chiyuan Miao,Yiying Wang,Qi Zhang,Jiachen Ji,and Yuanfang Chai
Yi Wu
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Yiying Wang
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Qi Zhang
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Jiachen Ji
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Yuanfang Chai
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
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Total article views: 3,294 (including HTML, PDF, and XML)
Thereof 3,253 with geography defined
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Total article views: 1,937 (including HTML, PDF, and XML)
Thereof 1,897 with geography defined
and 40 with unknown origin.
Total article views: 1,357 (including HTML, PDF, and XML)
Thereof 1,356 with geography defined
and 1 with unknown origin.
Our study introduces BMA-ET, a novel multi-dataset fusion product. Spanning 1980 to 2020 with spatial resolution of 0.5° and 1°, BMA-ET uses Bayesian model averaging (BMA) to combine thirty evapotranspiration (ET) datasets. A key innovation is dynamic weighting scheme, which adjusts for vegetation types and non-common coverage years among ET datasets. BMA-ET provides a comprehensive resource for understanding global ET patterns and trends, addressing the limitation of prior ET fusion efforts.
Our study introduces BMA-ET, a novel multi-dataset fusion product. Spanning 1980 to 2020 with...