Accurate spatiotemporal monitoring and modeling of soil moisture (SM) is of paramount importance for various applications ranging from food production to climate change adaptation. This study deals with modeling SM with the Random Forest (RF) algorithm using datasets comprising multispectral data from Sentinel-2, Landsat-8/9, and hyperspectral data from a sensor (SpectroCam) mounted on an unmanned aerial vehicle (UAV) in Iran. The model included nine bands from Landsat-8/9, 11 bands from Sentinel-2, and 1252 bands from SpectroCam (covering the wavelength range between 420 and 850 nm). The relative feature importance and band sensitivity to SM variation were analyzed. In addition, four indices, including perpendicular index (PI), ratio index (RI), difference index (DI), and normalized difference index (NDI) were calculated from the different bands of the datasets and their sensitivity to SM was evaluated. The results showed that the PI index exhibited the highest sensitivity to SM changes in all datasets among the four indices considered. Comparisons of the performance of the datasets in SM estimation emphasized the superior performance of the UAV hyperspectral data (R2 = 0.87), while the Sentinel-2 and Landsat-8/9 data showed lower accuracy (R2 = 0.49 and 0.66, respectively). The robust performance of the SpectroCam data is likely due to its superior spatial and spectral resolution as well as the application of pre-processing techniques such as noise reduction and smoothing filters. The lower accuracy of the multispectral data from Sentinel-2 and Landsat-8/9 can also be attributed to their relatively coarse spatial resolution compared to SpectroCam, which leads to pixel non-uniformities and impurities. Therefore, employing SpectroCam on a UAV proves to be a valuable technology providing an effective link between satellite observations and ground measurements.
