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. Issue 24 (16th December 2016)

• Record Type: Journal Article
• Authors: Leng, Pei; Song, Xiaoning; Duan, Si-Bo; Li, Zhao-Liang
• Is Part Of: International journal of remote sensing. Volume 37:Issue 24(2016)
• Page Start: 5902
• Page End: 5917
• Publication Date: 2016-12-16
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2016.1253896
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: 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 m 3 m −3, Bias: −0.020 to −0.025 m 3 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 m 3 m −3, RMSE: 0.043–0.050 m 3 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.