Articles | Volume 30, issue 13
https://doi.org/10.5194/hess-30-4191-2026
https://doi.org/10.5194/hess-30-4191-2026
Research article
 | 
06 Jul 2026
Research article |  | 06 Jul 2026

Filling data gaps in soil moisture monitoring networks via integrating spatio-temporal contextual information

Weixuan Wang, Yizhuo Meng, Zushuai Wei, Linguang Miao, Hui Wang, and Wen Zhang

Cited articles

Birgé, L. and Massart, P.: Gaussian Model Selection, J. Eur. Math. Soc., 3, 203–268, https://doi.org/10.1007/S100970100031, 2001. 
Chen, S., Wang, X.-Y., Guo, H., Xie, P., and Sirelkhatim, A. M.: Spatial and Temporal Adaptive Gap-Filling Method Producing Daily Cloud-Free NDSI Time Series, IEEE J. Sel. Top. Appl., 13, 2251–2263, https://doi.org/10.1109/JSTARS.2020.2993037, 2020. 
Chhabra, G., Vashisht, V., and Ranjan, J.: A classifier ensemble machine learning approach to improve efficiency for missing value imputation, in: Proceedings of the 2018 International Conference on Computing, Power and Communication Technologies (GUCON), IEEE, 23–27, https://doi.org/10.1109/GUCON.2018.8674904, 2018. 
Decorte, T., Mortier, S., Lembrechts, J. J., Meysman, F. J. R., Latré, S., Mannens, E., and Verdonck, T.: Missing Value Imputation of Wireless Sensor Data for Environmental Monitoring, Sensors, 24, 2416, https://doi.org/10.3390/s24082416, 2024. 
Dhevi, A. S.: Imputing Missing Values Using Inverse Distance Weighted Interpolation for Time Series Data, in: Proceedings of the 2014 Sixth International Conference on Advanced Computing (ICoAC), IEEE, 255–259, https://doi.org/10.1109/ICoAC.2014.7229721, 2014. 
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Short summary
Soil moisture data is vital for climate studies and agriculture, but sensors often have gaps that disrupt data continuity. To address this, we developed ST-GapFill, a new framework that uses information from nearby stations and a special tool to fill in missing data. By selecting the best neighboring stations and capturing how soil moisture changes over time, ST-GapFill can accurately reconstruct soil moisture patterns.
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