Evaluation of Satellite-Based Soil Moisture Products over Four Different Continental In-Situ Measurements: Global, near-real-time satellite-based soil moisture (SM) datasets have been developed over recent decades. However, there has been a lack of comparison among different passing times, retrieving algorithms, and sensors between SM products over various regions. In this study, we assessed seven types of SM products (AMSR_A, AMSR_D, ECV_A, ECV_C, ECV_P, SMOS_A, and SMOS_D) over four different continental in-situ networks in North America, the Tibetan Plateau, Western Europe, and Southeastern Australia. Bias, R, root mean square error (RMSE), unbiased root mean square difference (ubRMSD), anomalies, and anomalies R were calculated to explore the agreement between satellite-based SM and in-situ measurements. Taylor diagrams were drawn for an inter-comparison. The results showed that (1) ECV_C was superior both in characterizing the SM temporal variation tendency and absolute value, while ECV_A produced numerous abnormal values over all validation regions. ECV_P was able to basically express the SM variation tendency, except for a few overestimations and underestimations. (2) The ascending data (AMSR_A, SMOS_A) generally outperformed the corresponding descending data (AMSR_D, SMOS_D). (3) AMSR exceeded SMOS in terms of the coefficient of correlation. (4) The validation result of SMOS_D over the NAN and OZN networks was unsatisfactory, with a rather poor correlation for both original data and anomalies.
Numerous satellite-based SM products have appeared and have been broadly utilized in surface hydrology analysis for decades. The diversity in satellite passing time, sensor working mode, band combination, and retrieving algorithms, and the complexity of land cover, soil texture, and hydrothermal condition have co-contributed to the uncertainty in satellite retrieved SM products. Consequently, we validated seven currently frequently-used SM products (AMSR_A, AMSR_D, SMOS_A, SMOS_D, ECV_A, ECV_C, ECV_P) over four varied continental regions.
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).