Machine Learning-Based Soil Moisture Inversion in Agro-Pastoral Areas Using Sentinel Data

Soil moisture is a critical regulator in Earth’s energy cycle, exerting substantial influence on hydrological, ecological, and thermal processes. However, the spatio-temporal heterogeneity of soil moisture poses significant challenges for remote-sensing inversion, and conventional methods are constrained under complex surface conditions. In this study, Sentinel-1 and Sentinel-2 imagery were integrated with terrain data, and machine learning algorithms were employed to develop a soil moisture estimation model for the agro-pastoral transition zone. The primary objectives were to evaluate the accuracy of machine learning approaches for remote-sensing estimation of soil moisture under complex surface conditions, and to assess the suitability of different machine learning algorithms for this application. The results showed that ensemble methods, specifically boosting approaches, delivered low inversion errors and high R2 values, whereas linear models showed limited accuracy. The vertically polarized microwave backscattering coefficients emerged as the parameters most sensitive to soil moisture variations, with optical bands and topographic factors also making substantial contributions. The overall inversion yielded a root mean square error (RMSE) of 0.066 cm3.cm−3. This study provides an effective approach for soil moisture inversion on complex surfaces, simplifies the modeling process, and offers a tool for soil moisture monitoring across large spatial scales.