Monitoring Relative Surface Soil Moisture Using Sentinel-1 Across the River Thames Catchment

Soil moisture is a critical component in many hydrological, agricultural, and meteorological applications and processes, and understanding the spatiotemporal dynamics and changes is critical to further their understanding. They are also an important parameter for use within soil- and land-based Natural Flood Management (NFM) schemes, to determine a relationship between surface wetness and soil water storage. Satellite-based remote sensing offers the ability to capture this spatiotemporal information on soil moisture on the synoptic scale; compared to more site-based in-situ field measurements, made up of numerous national and international soil moisture networks. In this study, we use Sentinel-1 SAR imagery over the course of six water years (from 2016 to 2021), utilizing the TU-Wein change-detection algorithm to calculate the relative Surface Soil Moisture (rSSM) across the River Thames Catchment in Southern England, equating to approximately 11,000 km². As part of this, two pairs of backscatter normalisation factors were considered, in order to negate the impact from varying local incidence angles: a simple direct-slope and a complex multiple regression slope, both calculated annually and monthly. Whilst the monthly normalisation factor does exhibit a seasonal cycle (attributed to the growth and harvest of arable crops within the study area) in both the simple and multiple regression methodology, the impact upon the rSSM, when compared to the traditional annual method is small. In order to assess the spatiotemporal patterns of soil moisture across the River Thames Catchment, the rSSM timeseries was calculated using multiple spatial scales (1km, 500m, 250m, and 100m), to effectively estimate the rSSM across the catchment, sub-catchment, inter-field, and intra-field spatial scales. Comparisons with the Cosmic-ray Soil Moisture Observing System, United Kingdom (COSMOS-UK), show that, although there is an overestimation in rSSM over the summer months during the growing season of Arable farmland, we were able to effectively capture the general temporal dynamics of the relative Surface Soil Moisture across the region, with an average uncertainty of 30%, across both pairs of backscatter normalisation factors, and across all four spatial scales. Having catchment-wide datasets of rSSM such as this would be advantageous for evaluating land- and soil-based NFM measures across catchment and sub-catchment scales and have the potential for further application to improve hydrological model outputs.