Fusing Active and Passive Remotely Sensed Soil Moisture Products Using an Improved Double Instrumental Variable Method

High-quality soil moisture (SM) is significant for hydrological, meteorological, and agricultural applications. At present, active and passive remote sensing are the only two ways to monitor SM directly at the regional scale. However, the quality of a single satellite-based SM product is insufficient to meet the requirements of these applications. Hence, fusing these two SM products to improve their quality of change capture ability and accuracy is a necessary and challenging work. This study proposes an improved double instrumental variable method to fuse active and passive SM products. First, the method is improved in finding the best instrumental variables in time series based on the correlation coefficient. Second, fused weights of input SM products are estimated using the improved method. Finally, fused SM products are obtained with higher change capture ability and higher accuracy. The Tibetan Plateau (TP) was selected as the study area to test the algorithm using both of the climate change initiative (CCI) active and passive SM products from the European Space Agency (ESA). The ground validation results show that, compared with the original SM products, the change capture ability, expressed by the correlation coefficient ( $R$ ), and the accuracy, expressed by the unbiased root mean square deviation (ubRMSD), have both been improved by about 10% on average. Compared with the original method, the $R$ and ubRMSD have been improved by more than 30% on average. This study indicates that the proposed fusion method can effectively improve the quality of SM products to further understand the global changing water cycle.