Abstract
Accurate retrieval of soil moisture is important for understanding regional environmental changes and sustainable development in arid regions. Through numerical simulation and regression analysis based on advanced integral equation model (AIEM), the study aims to establish a multiangle soil moisture retrieval model based on RADARSAT-2 image in arid Juyanze. A combined roughness parameter Rs was established, and then the influences of roughness and soil moisture on the backscattering simulations were discussed. Finally, the empirical multiangle soil moisture retrieval model was implemented and validated in Juyanze. Inversion results show that the model has favorable validity. The coefficient of determination R2 between the inferred and measured soil moisture is 0.775 with a root-mean-square error (rmse) of 0.626%, implying better retrieval accuracy. Soil moisture varies from about 0.1% to 25% and is no more than 10% in most parts of this region, which is in reasonable agreement with the factual circumstances. The model directly relates the Fresnel reflection coefficient and soil moisture and is independent of ground roughness measurements. With a wider angular range, it has great potential for soil moisture evaluation in arid regions.
Highlights
Soil moisture is a crucial state variable within the fields of hydrology, climatology, ecology, and agriculture.[1,2,3,4] In arid and semiarid regions, soil moisture is the most sensitive environmental factor
The advanced integral equation model (AIEM) input parameters were determined according to RADARSAT-2 specifications and the measured data
The reliability test above clearly suggests that simulated values from the empirical model and AIEM compare fairly well at a specific incidence angle
Summary
Soil moisture is a crucial state variable within the fields of hydrology, climatology, ecology, and agriculture.[1,2,3,4] In arid and semiarid regions, soil moisture is the most sensitive environmental factor. Accurate estimation of soil moisture in arid and semiarid regions improves our understanding on the regional hydroclimatic processes and provides basic data for solutions to many environmental and ecological problems. The advent of satellite-based remote sensing has greatly facilitated the acquisition of various land surface parameters at a variety of scales in wide ranges and with high accuracy without expensive in-situ monitoring networks.[5] Advances in remote sensing technology have shown that soil moisture at the surface layer (i.e., 0 to 5 cm of the land surface) can be measured to some degree by all regions of the electromagnetic radiation spectrum.[6] Microwave remote sensors can operate day and night in most weather conditions and have been extensively utilized to retrieve soil moisture across a broad range of scales. This allows SAR to monitor soil moisture with relatively high prediction accuracy
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