Articles | Volume 30, issue 15
https://doi.org/10.5194/hess-30-4909-2026
© Author(s) 2026. 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-30-4909-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Aug 2026
Research article |
| 04 Aug 2026
| 04 Aug 2026
Field-scale soil moisture retrieval from drone-based L-band radiometry with optical and thermal infrared priors
Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
Yan Li
Information Center (Hydrology Monitor and Forecast Center), Ministry of Water Resources, Beijing 100053, China
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
Fujian Key Laboratory of Severe Weather & Key Laboratory of Straits Severe Weather, China Meteorological Administration, Fuzhou 350008, China
Peizhe Cheng
Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
Fuqiang Tian
Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
Fujian Key Laboratory of Severe Weather & Key Laboratory of Straits Severe Weather, China Meteorological Administration, Fuzhou 350008, China
Yao Zhuang
Fujian Key Laboratory of Severe Weather & Key Laboratory of Straits Severe Weather, China Meteorological Administration, Fuzhou 350008, China
Fujian Meteorological Administration, Fuzhou 350008, China
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Yi Nan, Zhihua He, Fuqiang Tian, Zhongwang Wei, and Lide Tian
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Kunbiao Li, Fuqiang Tian, Mohd Yawar Ali Khan, Ran Xu, Zhihua He, Long Yang, Hui Lu, and Yingzhao Ma
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Short summary
Satellite soil moisture is too coarse and ground measurements are too sparse to describe field conditions. Drone microwave sensing helps fill this gap, but mixed signals from vegetation and surface variability reduce accuracy. We combine drone microwave, optical, and thermal data in a Bayesian framework to improve soil moisture estimates and quantify uncertainty. Field tests in China show higher accuracy, lower bias, and highlight small-scale heterogeneity as a key source of uncertainty.
Satellite soil moisture is too coarse and ground measurements are too sparse to describe field...
