Accurate estimates of soil moisture as initial conditions to hydrological models are expected to greatly in-crease the accuracy of flood and drought predictions. As in-situ soil moisture observations are scarce, satellite-based estimates are a suitable alternative. The validation of remotely sensed soil moisture products is generally hampered by the difference in spatial support of in-situ observations and satellite footprints. Unsaturated zone modeling may serve as a valuable validation tool because it could bridge the gap of differ-ent spatial supports. A stochastic, distributed unsaturated zone model (SWAP) was used in which the spatial support was matched to these of the satellite soil moisture retrievals. A comparison between point observa-tions and the SWAP model was performed to enhance understanding of the model and to assure that the SWAP model could be used with confidence for other locations in Spain. A timeseries analysis was performed to compare surface soil moisture from the SWAP model to surface soil moisture retrievals from three differ-ent microwave sensors, including AMSR-E, SMOS and ASCAT. Results suggest that temporal dynamics are best captured by AMSR-E and ASCAT resulting in an averaged correlation coefficient of 0.68 and 0.71, respec-tively. SMOS shows the capability of capturing the long-term trends, however on short timescales the soil moisture signal was not captured as well as by the other sensors, resulting in an averaged correlation coeffi-cient of 0.42. Root mean square errors for the three sensors were found to be very similar (± 0.05 m 3 m −3). The satellite uncertainty is spatially correlated and distinct spatial patterns are found over Spain.