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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Cited articles

Almendra-Martín, L., Martínez-Fernández, J., Piles, M., and González-Zamora, Á.: Comparison of gap-filling techniques applied to the CCI soil moisture database in Southern Europe, Remote Sens. Environ., 258, 112377, https://doi.org/10.1016/j.rse.2021.112377, 2021. 
Amani, M., Salehi, B., Mahdavi, S., Masjedi, A., and Dehnavi, S.: Temperature-Vegetation-soil Moisture Dryness Index (TVMDI), Remote Sens. Environ., 197, 1–14, https://doi.org/10.1016/j.rse.2017.05.026, 2017. 
Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., Dee, D., Dutra, E., Muñoz-Sabater, J., Pappenberger, F., de Rosnay, P., Stockdale, T., and Vitart, F.: ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, https://doi.org/10.5194/hess-19-389-2015, 2015. 
Belgiu, M. and Drãguþ, L.: Random forest in remote sensing: A review of applications and future directions, ISPRS J. Photogramm., 114, 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011, 2016. 
Bessenbacher, V., Gudmundsson, L., and Seneviratne, S. I.: Capturing future soil-moisture droughts from irregularly distributed ground observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8714, https://doi.org/10.5194/egusphere-egu22-8714, 2022a. 
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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.