Soil moisture is a very important parameter influencing many hydrological and climatic processes. It is also a key factor in agriculture, determining crop yields and thus influencing food security. It is crucial to model this variable for large areas with high spatial and temporal resolution and good accuracy. The aim of this study is to develop a soil moisture model for bare soils from Sentinel-1 SAR data that would be characterized by high spatial resolution and would be universal enough to be applicable to large areas of various soil types, textures, and large ranges of roughness. Over 800 soil moisture measurements from five study areas located in different parts of Poland were used. The work was performed on Sentinel-1 data registered between March 2024 and March 2025 using both backscattering and polarimetric analysis. The soil data were obtained from a 1:5000 scale soil map available online for Poland through the soil-agricultural geoportal. The results of machine learning modeling of soil moisture based on backscattering were relatively poor, with R2 = 0.49 and 6.65% accuracy of volumetric water content in the soils. In the case of polarimetric channels, results were more or less the same. The best results were obtained by taking the silt and clay content (particles < 0.02 mm) in the soil into account. Volumetric water content accuracy of 5.27% with R2 = 0.69 was thus achieved. The proposed solution seems to be a good alternative to soil moisture studies that take soil roughness into account due to its simplicity, good accuracy, and relatively easy availability of data necessary for model inversion. The analyses carried out showed that it can be used for exposed soils of very diverse roughness.
Ziółkowski et al. (Wed,) studied this question.