A robust gap-filling approach for European Space Agency Climate Change Initiative (ESA CCI) soil moisture integrating satellite observations, model-driven knowledge, and spatiotemporal machine learning
Kai Liu,Xueke Li,Shudong Wang,and Hongyan Zhang
Kai Liu
Aerospace Information Research Institute, Chinese Academy of Sciences,
Beijing 100094, China
Aerospace Information Research Institute, Chinese Academy of Sciences,
Beijing 100094, China
Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science & Technology, Nanjing 210044, China
Hongyan Zhang
Aerospace Information Research Institute, Chinese Academy of Sciences,
Beijing 100094, China
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Total article views: 4,804 (including HTML, PDF, and XML)
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Total article views: 3,396 (including HTML, PDF, and XML)
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Total article views: 1,408 (including HTML, PDF, and XML)
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Remote sensing has opened opportunities for mapping spatiotemporally continuous soil moisture, but it is hampered by data gaps. We propose a robust gap-filling approach to reconstruct daily satellite soil moisture. The merit of our approach is to integrate satellite observations, model-driven knowledge, and spatiotemporal machine learning. We also apply the developed approach to long-term datasets. Our study provides a potential avenue for hydrological applications.
Remote sensing has opened opportunities for mapping spatiotemporally continuous soil moisture,...
