The Influence of Soil Texture and Roughness on Soil Moisture Modeling Using Backscattering Coefficient and Polarimetric Decompositions Derived from Sentinel-1 Data

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 Random Forest modeling of soil moisture based on backscattering were relatively poor with R2=0.46 and 7.15% soil moisture accuracy. In the case of polarimetric channels results were slightly better only. The best results were obtained taking into account the clay content (particles < 0.002mm) in the soil. The soil moisture accuracy of 5.16% with R2=0.71 was achieved.