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Updated: Feb 14, 2026

In Situ Soil Moisture Sensors in Undisturbed Soils
Published on: November 18, 2022
Global daily 9 km remotely sensed soil moisture (2015-2025) with microwave radiative transfer-guided learning
Sijia Feng1, Aoyang Li2,3,4,5, Rui Zhou2,3,4,5
1Pioneer Center Land-CRAFT, Department of Agroecology, Aarhus University, Aarhus, 8000, Denmark.
None:
Accurate estimation of surface soil moisture (SM) in terrestrial ecosystems is essential for understanding hydroclimate dynamics. The L-band Soil Moisture Active Passive (SMAP) mission provides 9-km global daily surface SM by using a microwave radiative transfer model (RTM)-based algorithm. However, the accuracy of SMAP SM is limited in regions with dense vegetation cover and complex surface conditions, due to the empirical parameterization and oversimplified radiative transfer processes. To overcome the limitations, we developed a Process-Guided Machine Learning (PGML) framework to integrate RTM theories and deep learning to predict global daily surface 9-km SM from April 2015 to June 2025. Informed by domain knowledge, we developed the PGML model structure using RTM and hydrological theories, designed a Kling-Gupta efficiency-based cost function, pretrained it with RTM simulations, and fine-tuned it with in-situ measurements. The independent validation shows that PGML SM has strong agreement with in-situ measurements (R = 0.868 and unbiased RMSE = 0.054 m3/m3). This study highlights the potential of PGML to enhance the accuracy of satellite SM, thereby supporting improved water resources and ecosystem management.
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