The importance of soil moisture on many scientific fields like hydrology, meteorology, crop growth or soil erosion has been addressed frequently. Its characterisation has been a difficult task because of its high spatial and temporal variability. Several point based measurement techniques have been developed with different degree of success, but their conversion to spatially distributed values depends on complex geostatistical techniques. Furthermore, sensor installation and maintenance can be quite tedious. In this background, SAR remote sensing sensors provide valuable information on land surface parameters. The backscattering of the SAR signal depends amongst others on the dielectric constant of the observed surface, which is mainly related to the soil surface water content. It also gives spatially distributed information with a resolution adequate for different spatial scales: from medium or small watersheds to agricultural fields. Its periodicity can be appropriate for calibrating, on a monthly basis, the simulations of distributed hydrologic modelling tools. The present paper reports the first results of an ongoing research of which the main objective is the development of a simple methodology for the calibration of the soil moisture component of distributed hydrological models using SAR data. Five RADARSAT-1 images, acquired between 27/02/2003 and 02/04/2003 over the Navarre region (Northern Spain) have been processed. The calculated backscattering values have been compared to soil moisture and surface roughness ground measurements. Empirical linear regression models have been fitted at three different scales: point scale, field scale and catchment scale, showing acceptable correlation between calculated backscattering values and ground measured soil moisture specially at field and watershed scale. However, consistent trends have not been found probably due to differing local conditions such as surface roughness or vegetation cover. Seeking for a more consistent approach, the physically based Integral Equation Method (IEM) model has been applied. Yet, simulations run by the IEM have not been completely successful probably due to an inadequate characterisation of surface roughness.