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Soil moisture content estimation based on Sentinel-1 and auxiliary earth observation products. A hydrological approach

Alexakis Dimitrios, Mexis Filippos-Dimitrios, Vozinaki Anthi-Eirini, Daliakopoulos Ioannis, Tsanis Giannis

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URIhttp://purl.tuc.gr/dl/dias/E7A0126C-2725-46AE-8574-04A2E39BE7C7-
Identifierhttp://www.mdpi.com/1424-8220/17/6/1455-
Identifierhttps://doi.org/10.3390/s17061455-
Languageen-
TitleSoil moisture content estimation based on Sentinel-1 and auxiliary earth observation products. A hydrological approachen
CreatorAlexakis Dimitriosen
CreatorΑλεξακης Δημητριοςel
CreatorMexis Filippos-Dimitriosen
CreatorΜεξης Φιλιππος-Δημητριοςel
CreatorVozinaki Anthi-Eirinien
CreatorΒοζινακη Ανθη-Ειρηνηel
CreatorDaliakopoulos Ioannisen
CreatorΔαλιακοπουλος Ιωαννηςel
CreatorTsanis Giannisen
CreatorΤσανης Γιαννηςel
PublisherMDPIen
Content SummaryA 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.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2018-05-08-
Date of Publication2017-
SubjectArtificial neural networken
SubjectCreteen
SubjectHEC-HMSen
SubjectLandsat 8en
SubjectSentinel-1en
SubjectSoil moisture contenten
Bibliographic CitationD. D. Alexakis, F.-D. K. Mexis, A.-E. K. Vozinaki, I. N. Daliakopoulos and I. K. Tsanis, "Soil moisture content estimation based on Sentinel-1 and auxiliary earth observation products. A hydrological approach," Sensors, vol. 17, no. 6, Jun. 2017. doi: 10.3390/s17061455en

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