Global Sensitivity Analysis of a Water Cloud Model toward Soil Moisture Retrieval over Vegetated Agricultural Fields

The release of high-spatiotemporal-resolution Sentinel-1 Synthetic Aperture Radar (SAR) data to the public has provided an unprecedented opportunity map soil moisture at watershed and agricultural field scales. However, existing retrieval algorithms fail derive with expected accuracy. Insufficient understanding effects vegetation parameters on backscatters is important reason for this failure. To end, we present a Sensitivity Analysis (SA) quantify dual-polarized based Water Cloud Model (WCM) multiple global SA methods. identification incidence angle polarization description scheme (A, B α) in WCM are especially emphasized analysis towards optimal estimation parameters. Multiple methods identical parameter importance ranks, indicating that highly reasonable reliable performed. Comparison between two schemes shows using Vegetation Content (VWC) outperforms combing particle content VWC. Surface roughness, moisture, VWC, B, most sensitive backscatters. Variation sensitivity indices different polarizations indicates VV- VH- polarized small angles options surface roughness estimation, respectively, while VV-polarized backscatter larger well-suited VWC HH-polarized well suited estimation. This improves vegetated multi-angle multi-polarized SAR, informing improvement SAR-based retrieval.