| Summary: | Our study focuses on reconstruction root zone soil moisture (RZSM) in the Kulunda plain, a representative dry steppe area in southern Western Siberia, using remote sensing data (RSD) and machine learning techniques. We employed modern machine learning methods with soil surface layer moisture data from the AMSR2 passive microwave radiometer as the primary predictor. Additionally, we incorporated data from local meteorological and soil hydrological stations, as well as gravity lysimeter data for 2015–2017. This choice of predictors was based on the extensive time series of continuous observations and the availability of selected meteorological parameters. Among the machine learning models we evaluated, Random Forest (RF) and Extreme Gradient Boosting (XGW) yielded the best results, achieving statistical metrics of R-squared (R2) values of 0.96 and 0.94, respectively, with corresponding root mean square error (RMSE) values of 0.34 and 0.41.
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