Soil moisture is an important parameter that directly impacts crop productivity. Microwave signals are highly sensitive to soil dielectric constant and so they are used to derive soil moisture. The potential of entropy and alpha (H  /  α) decomposition for moisture estimations at 0- to 5-cm soil depth was assessed. The H  /  α parameters were extracted from the dual-polarimetric Sentinel-1 dataset. Also, we used the gray level co-occurrence matrix (GLCM) texture parameters extracted from Sentinel-1 and tested them for soil moisture retrieval. The generalized regression neural network (GRNN), neural network (NN), and support vector regression (SVR) algorithms were trained and tested for soil moisture estimation. Multiple input features, including Sentinel-1 intensities, GLCM parameters, and H  /  α parameters, were used for training these algorithms. For NN, three activation functions of rectified linear unit (ReLU), tanh, and sigmoid and for SVR three kernel functions of radial basis function (RBF), polynomial, and linear kernel were tested. The ReLU outperformed the other two activation functions with root mean squared error (RMSE) of 0.042  m³ m^−  3 and coefficient of determination (R²) of 0.72. For the SVR algorithm, the highest accuracies derived from the RBF kernel function with RMSE 0.053  m³ m^−  3 and R² of 0.51. Between all the three machine learning algorithms, the GRNN algorithm outperformed the other two algorithms with RMSE of 0.033  m³ m^−  3 and R² of 0.82. These results demonstrated the high potential of using polarimetric synthetic aperture radar data in combination with the machine learning algorithms for surface soil moisture monitoring.