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

Viewed

Total article views: 6,947 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
5,592 1,156 199 6,947 122 236
  • HTML: 5,592
  • PDF: 1,156
  • XML: 199
  • Total: 6,947
  • BibTeX: 122
  • EndNote: 236
Views and downloads (calculated since 10 Jun 2025)
Cumulative views and downloads (calculated since 10 Jun 2025)

Viewed (geographical distribution)

Total article views: 6,947 (including HTML, PDF, and XML) Thereof 6,856 with geography defined and 91 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 16 Aug 2026
Download
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.
Share