Combination of Physical and Geostatistical Models for Assessing Surface Moisture in Semiarid Agricultural Soils with Sentinel-1 Through Remote Sensing

Estimating surface soil moisture in semi-arid regions is challenging due to its high spatial and temporal variability, the scarcity of in-situ measurements, and the limitations of optical sensors in the presence of cloud cover and vegetation cover. Synthetic Aperture Radar (SAR) sensors, such as Sentinel-1, overcome these constraints by operating in the microwave domain and providing high-resolution data regardless of atmospheric conditions or daylight availability. This enables the application of inverse semi-empirical models, notably the Hallikainen model for the soil dielectric constant and the Dubois model for backscattering. This study proposes an integrated methodology applied to the municipality of Villaconejos (Madrid, Spain) over the period 2015–2025. The approach was initially calibrated on a pilot plot near Balcón del Tajo using field measurements of soil moisture and soil texture data (sand and clay content) obtained from the SoilGrids platform. Starting from Sentinel-1 VV and VH backscatter coefficients, the combined Hallikainen–Dubois model is inverted through an iterative search over a range of volumetric soil moisture values (0.02–0.45 m* m*) and surface roughness values (0.85–2 cm), selecting the parameter pair that minimises the difference between modelled and observed backscatter. The calibrated methodology is then extrapolated across the entire municipality of Villaconejos using Empirical Bayesian Kriging Regression Prediction (EBK-RP), incorporating topographic covariates (digital elevation model, slope, aspect), hydrological covariates (Topographic Wetness Index, TWI), and vegetation covariates (NDVI). The results include annual and seasonal maps of near-surface volumetric soil moisture (0–5 cm depth) at 10 m resolution and, after a geostatistical downscaling procedure, at 2 m resolution. Additional outputs comprise analyses of temporal variations between wet and dry periods and spatial patterns related to land use and topography. The developed methodology provides continuous, high-resolution, operational, and low-cost soil moisture estimates, representing a valuable tool for water resource management and agro-environmental monitoring in semi-arid regions.