An Integrated Time-Series Relative Soil Moisture Monitoring Method Based on a SAR Backscattering Model

Time-series monitoring of relative surface soil moisture (RSSM) with remote sensing observation is crucial for guiding agricultural irrigation management and monitoring global climate change. However, the existing synthetic aperture radar (SAR) soil moisture retrieval algorithms suffer from insufficient decoupling of surface scattering characteristics and poor RSSM time-series monitoring capabilities. Therefore, this article proposes an integrated time-series relative soil moisture monitoring method based on a SAR backscattering model (SBM). Initially, the SBM is introduced, categorizing land cover into built-up areas, water bodies, vegetation-covered areas, and soil. Addressing the inconsistency in spatiotemporal resolution between optical vegetation indices and SAR data, we establish a unique SAR water cloud model (SWCM) in conjunction with the dual-polarization SAR vegetation index. By employing the SWCM to eliminate vegetation's influence, a high-quality soil backscatter coefficient is obtained. Ultimately, the dry and wet reference values of soil backscatter are calculated to retrieve the relative RSSM time series. Based on Sentinel-1 data, we select three representative experimental areas, namely the Qarhan Salt Lake in dry regions, the Tibetan Plateau Naqu in high-cold regions, and Inner Mongolia Xilinhot in grassland regions, conducting RSSM spatiotemporal monitoring for three years. The experimental results demonstrate that the RSSM exhibits seasonal variations in these three regions. The correlation coefficient between the RSSM monitoring results and the in situ data exceed 0.64, with a maximum of 0.84. Consequently, the proposed method underscores the advantages of simplicity in parameters, high estimation precision, and robust adaptability, thereby augmenting the potential for large-scale global monitoring applications.