Soil moisture content estimation based on Sentinel-1 and auxiliary earth observation products. A hydrological approachSoil moisture content estimation based on Sentinel-1 and auxiliary earth observation products. A hydrological approach Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2018-05-082017enA methodology for elaborating multi-temporal Sentinel-1 and Landsat 8 satellite images for estimating topsoil Soil Moisture Content (SMC) to support hydrological simulation studies is proposed. After pre-processing the remote sensing data, backscattering coefficient, Normalized Difference Vegetation Index (NDVI), thermal infrared temperature and incidence angle parameters are assessed for their potential to infer ground measurements of SMC, collected at the top 5 cm. A non-linear approach using Artificial Neural Networks (ANNs) is tested. The methodology is applied in Western Crete, Greece, where a SMC gauge network was deployed during 2015. The performance of the proposed algorithm is evaluated using leave-one-out cross validation and sensitivity analysis. ANNs prove to be the most efficient in SMC estimation yielding R2 values between 0.7 and 0.9. The proposed methodology is used to support a hydrological simulation with the HEC-HMS model, applied at the Keramianos basin which is ungauged for SMC. Results and model sensitivity highlight the contribution of combining Sentinel-1 SAR and Landsat 8 images for improving SMC estimates and supporting hydrological studies.http://creativecommons.org/licenses/by/4.0/Sensors176 Alexakis Dimitrios Αλεξακης Δημητριος Mexis Filippos-Dimitrios Μεξης Φιλιππος-Δημητριος Vozinaki Anthi-Eirini Βοζινακη Ανθη-Ειρηνη Daliakopoulos Ioannis Δαλιακοπουλος Ιωαννης Tsanis Giannis Τσανης Γιαννης MDPI Artificial neural network Crete HEC-HMS Landsat 8 Sentinel-1 Soil moisture content