In the last decade, many SAR missions have been launched to reinforce the all-weather observation capacity of the Earth. The precise modeling of radar signals becomes crucial in order to translate them into essential biophysical parameters for the management of natural resources (water, biomass and energy). The objective of this study was to demonstrate the capabilities of two statistical algorithms (i.e., multiple linear regression (MLR) and random forest (RF)) to accurately simulate the backscattering coefficients observed over bare agricultural soil surfaces. This study was based on satellite and ground data collected on bare soil surfaces over an agricultural region located in southwestern France near Toulouse. Multi-configuration backscattering coefficients were acquired by TerraSAR-X and Radarsat-2 in the X- and C-bands, in co-(abbreviated σ0HH and σ0VV) and cross-polarization states (abbreviated σ0HV and σ0VH) and at incidence angles ranging from 24° to 53°. Models were independently calibrated and validated using a ground dataset covering a wide range of soil conditions, including the topsoil moisture (range: 2.4–35.3%), root-mean-square height (range: 0.5–7.9cm) and clay fraction (range: 9–58%). Higher-magnitude correlations (r) and lower errors (RMSE) were obtained when using RF (r values ranging from 0.69 to 0.86 and RMSE from 1.95 to 1.00 dB, depending on the considered signal configuration) compared to MLR (r values ranging from 0.58 to 0.77 and RMSE from 2.22 to 1.24 dB). Both surpass the performance presented in previous studies based on either empirical, semi-empirical or physical models. In the linear approach, the information is mainly provided by the surface moisture and the angle of incidence (especially in the case of co-polarized signals, regardless of the frequency), while the influence of roughness or texture becomes significant for cross-polarized signals in the C-band. On the contrary, all the surface descriptors contribute in the approach based on RF. In future work, the use of the RF algorithm developed in this paper should improve the estimation of soil parameters.