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

Liu, Kai; Li, Xueke; Wang, Shudong; Zhang, Hongyan

doi:https://doi.org/10.5194/hess-27-577-2023

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.

Special issue:

Microwave remote sensing for improved understanding of vegetation–water...

https://doi.org/10.5194/hess-27-577-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 27, issue 2

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

Supplement

https://doi.org/10.5194/hess-27-577-2023-supplement

Data sets

Chinese regional ground meteorological dataset Institute of Tibetan Plateau Research, CAS http://data.tpdc.ac.cn

Model code and software

GLEAM v3: satellite-based land evaporation and root-zone soil moisture B. Martens, D. G. Miralles, H. Lievens, R. van der Schalie, R. A. M. de Jeu, D. Fernández-Prieto, H. E. Beck, W. A. Dorigo, and N. E. C. Verhoest https://doi.org/10.5194/gmd-10-1903-2017

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.