Soil moisture (SM) data can be reliably obtained at high spatial resolution through the disaggregation of passive microwave-derived SM. Optical/thermal data are classically used as fine scale information in such a disaggregation procedure. The point is that optical data are unavailable under cloudy conditions and their relationship with SM is strongly affected by vegetation cover. Alternatively, Sentinel-1 provides radar data at an unprecedented spatio- temporal resolution, despite the complex relationship between backscatter and SM that often requires ancillary data for calibration. Hence the idea of combining disaggregation- and Sentinel-1-based SM retrieval approaches on clear sky days and to run the so-calibrated radar-based algorithm irrespective of weather conditions. This study undertakes preliminary analyses before both approaches can be efficiently merged. It consists of 1) extending the applicability of DISPATCH (disaggregation) algorithm to full-vegetated areas, in order to optimize the spatial match between disaggregation- and radar-based approaches and 2) providing a first comparative assessment of DISPATCH and the recently released Copernicus 1 km resolution SM data sets over a contrasted area including dryland and irrigated crops. Results indicate significant differences between both data sets, thus highlighting the potential of synergies between them.