Simulating carbon and water fluxes using a coupled process-based terrestrial biosphere model and joint assimilation of leaf area index and surface soil moisture
Sinan Li,Li Zhang,Jingfeng Xiao,Rui Ma,Xiangjun Tian,and Min Yan
Sinan Li
Key Laboratory of Digital Earth Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy
of Sciences, No. 19A Yuquan Road, Beijing 100049, China
Key Laboratory of Digital Earth Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China
International Research Center of Big Data for Sustainable
Development Goals, Beijing 100094, China
Earth Systems Research Center, Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, Durham, New Hampshire 03824, USA
Rui Ma
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Xiangjun Tian
International Center for Climate and Environment Sciences (ICCES),
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing
100029, China
Min Yan
Key Laboratory of Digital Earth Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China
International Research Center of Big Data for Sustainable
Development Goals, Beijing 100094, China
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Accurate estimation for global GPP and ET is important in climate change studies. In this study, the GLASS LAI, SMOS, and SMAP datasets were assimilated jointly and separately in a coupled model. The results show that the performance of joint assimilation for GPP and ET is better than that of separate assimilation. The joint assimilation in water-limited regions performed better than in humid regions, and the global assimilation results had higher accuracy than other products.
Accurate estimation for global GPP and ET is important in climate change studies. In this study,...
