Soil moisture is a critical component in many meteorological, hydrological, and agricultural applications, and understanding its spatial and temporal dynamics is vital for the understanding of these processes. Satellite-based remote sensing offers the ability to synoptically capture this spatiotemporal information over large areas, compared to more site-based in-situ field measurements. In this study, we use Sentinel-1 SAR imagery of the River Thames catchment, United Kingdom, over the period 2015 - 2020. A backscatter normalisation process is applied to account for the use of multiple satellite viewing geometries. A change-detection algorithm utilising backscatter power is then applied to the timeseries, to estimate relative surface soil moisture (rSSM) across the study area. To determine information across the large river watershed, smaller sub-catchments, and intra-field scales, the rSSM time series is replicated at multiple spatial scales (1 km, 500m, 250m, and 100m). Although positive biases are present during the growing season of arable farmland, comparison with rainfall data and in-situ soil moisture probes shows there is good agreement with the temporal cycle of soil moisture. These data are being used to evaluate natural flood management by land use and management across a wide area to better understand relationships between surface wetness and water storage in relation to land cover and underlying geology for the Landwise project (Landwise-NFM.org).