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Preliminary validation of two temporal parameter-based soil moisture retrieval models using a satellite product and in situ soil moisture measurements over the REMEDHUS network

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This study aims to preliminarily validate two newly developed temporal parameter-based surface soil moisture (SSM) retrieval models, namely the mid-morning model and daytime model, using both microwave satellite soil moisture product and in situ SSM measurements over a well-organized soil moisture network named REd de MEDición de la HUmedad del Suelo (REMEDHUS) in Spain. Ground SSM measurements and geostationary satellite observations were primarily implemented to obtain the model coefficients for the two SSM retrieval models for each cloud-free day. These model coefficients were subsequently used to estimate SSM using the Meteosat Second Generation products over the study area. Preliminary verification using both a satellite product and in situ SSM measurements demonstrated that SSM variation can be well detected by both SSM retrieval models. Specifically, a generally similar accuracy (coefficient of determination R 2: 0.419–0.379, root mean square error: 0.046–0.051 m3 m−3, Bias: −0.020 to −0.025 m3 m−3) was found for the mid-morning model and the daytime model with the microwave missions based climate change initiative SSM product, respectively. Moreover, except for the comparable R 2 (0.614–0.675), a better accuracy (Bias: 0.032–0.044 m3 m−3, RMSE: 0.043–0.050 m3 m−3) are achieved for the daytime model and the mid-morning model with network SSM measurements, respectively. These results indicate that the daytime model exhibited generally comparable or better accuracy than that of the mid-morning model over the study area. This study has strengthened the feasibility of using multi-temporal information derived from the geostationary satellites to estimate SSM in future research.
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Document Type: Research Article

Affiliations: 1: Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China 2: College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China

Publication date: December 16, 2016

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