A new physically based disaggregation method is developed to improve the spatial resolution of the surface soil moisture extracted from the Soil Moisture and Ocean Salinity (SMOS) data. The approach combines the 40-km resolutionSMOSmultiangular brightness temperatures and 1-km resolution auxiliary data composed of visible, near-infrared, and thermal infrared remote sensing data and all the surface variables involved in the modeling of land surface–atmosphere interaction available at this scale (soil texture, atmospheric forcing, etc.). The method successively estimates a relative spatial distribution of soil moisture with fine-scale auxiliary data, and normalizes this distribution at SMOS resolution with SMOS data. The main assumption relies on the relationship between the radiometric soil temperature inverted from the thermal infrared and the microwave soil moisture. Based on synthetic data generated with a land surface model, it is shown that the radiometric soil temperature can be used as a tracer of the spatial variability of the 0–5 cm soil moisture. A sensitivity analysis shows that the algorithm remains stable for big uncertainties in auxiliary data and that the uncertainty in SMOS observation seems to be the limiting factor. Finally, a simple application to the SGP97/AVHRR data illustrates the usefulness of the approach.