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Herein we present RegreSSM, a software tool that enables the downscaling of SMAP L3 Surface Soil Moisture (SSM) operational product from 36 km to 1 km by the fusion of optical and thermal data retrieved from Sentinel-3 platform. The downscaling method is based on the well-established properties of the Ts/VI feature space. Most of the existing soil moisture downscaling methods are computationally complex, require advanced expertise, and lack standalone tools suitable for operational or non-expert use. To address these limitations, this study proposes a simple and accessible framework for generating high-resolution SSM maps using only land surface temperature and vegetation cover as inputs. The tool has been developed in python programming language as a stand-alone application and can be executed in any operational system. The application offers automated and reproducible workflows for spatiotemporal matching and processing of SMAP L3 SSM products and Sentinel-3 dataset. The software tool's practical application is demonstrated over the Iberian Peninsula, where validation of the SMAP L3 product performed for all calendar year 2022 using in-situ observations from the REMEDHUS operational network stations. Results showed a satisfactory retrieval of SSM with a small average bias of 0.01 m 3 /m 3 , a MAD of 0.06 m 3 /m 3 , a RMSD of 0.07 m 3 /m 3 , and a satisfactory R 2 of 0.63, confirming the ability of the proposed downscaling framework and RegreSSM to retrieve SSM at the 1 km spatial resolution. Results obtained herein were also compared to the validation metrics reported for operational RS-based SSM products, with typically reported uncertainty of 0.04 m 3 /m 3 . The availability of RegreSSM to the SSM users' community consists an important step towards the standardization of downscaling procedures as well as bridging the spatial gap of existing operational SM products to the requirements of the fine-scale applications. It also contributes towards advancing the deployment of geo-processing tools utilizing the synergies between state-of-the-art methods and RS data available today from the most sophisticated satellites in orbit. • An open-source Python tool for SMAP operational product automated downscaling. • Spatiotemporal fusion of Sentinel-3 optical/thermal data for generating 1 km soil moisture products. • Practical demonstration of REGRESSM in REMEDHUS network in-situ data.
Petropoulos et al. (Thu,) studied this question.