Estimation of surface soil moisture in alpine areas based on medium spatial resolution SAR time-series and upscaled in-situ measurements

Abstract

The goal of this study was to assess the applicability of medium resolution SAR time-series, in combination with in-situ point measurements and machine learning, for the estimation of soil moisture content (SMC). One of the main challenges was the combination of SMC point measurements and satellite data. Due to the high spatial variability of soil moisture a direct linkage can be inappropriate. Data used in this study were a combination of in-situ data, satellite data and modelled SMC from the hydrological model GEOtop. To relate the point measurements with the satellite pixel footprint resolution, a spatial upscaling method was developed. It was found that both temporal and spatial SMC patterns obtained from various data sources (ASAR WS, GEOtop and meteorological stations) show similar behaviors. Furthermore, it was possible to increase the absolute accuracy of the estimated SMC through spatial upscaling of the obtained in-situ data. Introducing information on the temporal behavior of the SAR signal proves to be a promising method to increase the confidence and accuracy of SMC estimations. Following steps were identified as critical for the retrieval process: the topographic correction and geocoding of SAR data, the calibration of the meteorological stations and the spatial upscaling.