State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Fei Chen
National Center for Atmospheric Research, Boulder, CO, USA
State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Zhixing Peng
State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Chen Zheng
Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing, China
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Total article views: 3,149 (including HTML, PDF, and XML)
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2,483
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3,149
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BibTeX: 104
EndNote: 114
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Total article views: 4,345 (including HTML, PDF, and XML)
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Total article views: 3,149 (including HTML, PDF, and XML)
Thereof 3,070 with geography defined
and 79 with unknown origin.
Total article views: 1,196 (including HTML, PDF, and XML)
Thereof 1,151 with geography defined
and 45 with unknown origin.
This study highlights the role of integrating vegetation and multi-source soil moisture observations in regional climate models via a hybrid data assimilation and machine learning method. In particular, we show that this approach can improve land surface fluxes, near-surface atmospheric conditions, and land–atmosphere interactions by implementing detailed land characterization information in basins with complex underlying surfaces.
This study highlights the role of integrating vegetation and multi-source soil moisture...
