Abstract
The release of high-spatiotemporal-resolution Sentinel-1 Synthetic Aperture Radar (SAR) data to the public has provided an unprecedented opportunity to map soil moisture at watershed and agricultural field scales. However, the existing retrieval algorithms fail to derive soil moisture with expected accuracy. Insufficient understanding of the effects of soil and vegetation parameters on the backscatters is an important reason for this failure. To this end, we present a Sensitivity Analysis (SA) to quantify the effects of parameters on the dual-polarized backscatters of Sentinel-1 based on a Water Cloud Model (WCM) and multiple global SA methods. The identification of the incidence angle and polarization of Sentinel-1 and the description scheme of vegetation parameters (A, B and α) in WCM are especially emphasized in this analysis towards an optimal estimation of parameters. Multiple SA methods derive identical parameter importance ranks, indicating that a highly reasonable and reliable SA is performed. Comparison between two existing vegetation description schemes shows that the scheme using Vegetation Water Content (VWC) outperforms the scheme combing particle moisture content and VWC. Surface roughness, soil moisture, VWC, and B, are most sensitive on the backscatters. Variation of parameter sensitivity indices with incidence angle at different polarizations indicates that VV- and VH- polarized backscatters at small incidence angles are the optimal options for soil moisture and surface roughness estimation, respectively, while VV-polarized backscatter at larger incidence angles is well-suited for VWC and B estimation and HH-polarized backscatter is well suited for roughness estimation. This analysis improves the understanding of the effects of vegetated surface parameters on multi-angle and multi-polarized backscatters of Sentinel-1 SAR, informing improvement in SAR-based soil moisture retrieval.
Highlights
Retrieving soil moisture using high-spatiotemporal-resolution Synthetic ApertureRadar (SAR) allows for an insight into the spatial distribution of soil moisture details at field scale and temporal variety within a weekly scale, which greatly benefits precision agriculture [1]
In addition to reiterating the previous statement that HH-polarized backscatter is better than VV-polarized backscatter in surface roughness retrieval [26,34,35,36], this paper found that the cross-polarized backscatter at incidence angle less than 37 degrees was more sensitive than HH- polarized backscatter to surface roughness, which indicates that surface roughness can be estimated by VH-polarized backscatter at the smaller incidence angle
The release of Sentinel-1 Synthetic Aperture Radar (SAR) data has alleviated the issue of data unavailability and the introduction of the global Sensitivity Analysis (SA) method to analyzing the sensitivity of bare soil parameters on microwave backscatters has improved our understanding of the backscattering properties over bare soils
Summary
Retrieving soil moisture using high-spatiotemporal-resolution Synthetic ApertureRadar (SAR) allows for an insight into the spatial distribution of soil moisture details at field scale and temporal variety within a weekly scale, which greatly benefits precision agriculture [1]. 2021, 13, 3889 vegetation parameters, and others Under this background, various surface backscattering models, such as the Integral Equation Model (IEM) [4], Advanced IEM [5], and Oh model [6], have been proposed and widely applied in soil moisture retrieval algorithms. Various surface backscattering models, such as the Integral Equation Model (IEM) [4], Advanced IEM [5], and Oh model [6], have been proposed and widely applied in soil moisture retrieval algorithms These models serve as key tools that reproduce the radar observation from the surface parameters, quantitatively interpreting the dependence of SAR observations on multiple surface parameters and providing prior information for soil moisture retrieval. Not all surface parameters of the model contribute to the model outputs due to the complicity of the model structure and distinct contribution of the parameters; quantitatively evaluating the effect of each input parameter into the model output is important to understand the model mechanism and to retrieve these parameters by inverting the models
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