Articles | Volume 27, issue 2
https://doi.org/10.5194/hess-27-577-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-577-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Jan 2023
Research article |
| 30 Jan 2023
| 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
Aerospace Information Research Institute, Chinese Academy of Sciences,
Beijing 100094, China
Institute at Brown for Environment and Society, Brown University,
Providence, RI 02912, USA
Shudong Wang
CORRESPONDING AUTHOR
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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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.
Remote sensing has opened opportunities for mapping spatiotemporally continuous soil moisture,...
