Soil moisture (SM) is an important physical quantity that can reflect the land surface condition. There are many ways to measure SM, satellite microwave remote sensing is now considered the primary method because it can provide real-time high-resolution data. However, SM data obtained by satellite remote sensing exhibit certain deviation compared with reference data obtained from ground stations. To improve the accuracy of SM forecasts, this study proposed the use of a Bayesian model averaging (BMA) method to integrate multisatellite SM data. First, China was divided into eight regions. Then, SM data observed by satellites (FY3B, SMOS, and WINDSAT) were fused using the BMA method and a traditional averaging method. Finally, SM data were predicted using data from ground observation stations as a reference standard. Following the fusion process, three parameters (standard deviation, correlation coefficient, and root mean square deviation) were used to evaluate the fusion results, which revealed the superiority of the BMA method over the traditional averaging method.
A Mixture-Based Bayesian Model Averaging Method
