A COMPARISON OF TREE-BASED REGRESSION MODELS FOR SOIL MOISTURE ESTIMATION USING SAR DATA

Abstract. Soil moisture content plays a pivotal role in biomass development of vegetation coverage at various growth stages. Moisture content of the soil is considered as a crucial parameter for agricultural studies which directly leads to higher fertility rate. Remote sensing techniques, specifically Synthetic Aperture Radar (SAR) sensors, provides suitable opportunity for continuous soil moisture monitoring at various spatial and temporal resolutions. In this study, field campaigns conducted to measure soil surface parameters, including soil moisture and roughness, synchronized with Sentinel-1 pass over an agricultural region near Mohammadshahr, Iran. Fieldwork for soil moisture sampling have done during plants’ (canola and winter wheat) growth stages. The Gradient Boosted Regression Tree (GBRT), eXtreme Gradient Boosted (XGB), and Random Forest (RF) machine learning algorithms were employed to model the relationship between the ground measured soil moisture and polarimetric SAR derived features from Sentinel-1 imageries. The results showed promising results obtained for soil moisture estimation using the dual-polarized SAR dataset over crop-covered agricultural fields with R² = 0.95 and RMSE = 0.023 m³ m⁻³ using the GBRT regression model.