Articles | Volume 27, issue 2
https://doi.org/10.5194/hess-27-577-2023
https://doi.org/10.5194/hess-27-577-2023
Research article
 | 
30 Jan 2023
Research article |  | 30 Jan 2023

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

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Latest update: 14 Jul 2024
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Short summary
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.