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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
Document Type: Research Article
Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
Publication date: December 16, 2016
More about this publication?