Soil moisture inversion in highland areas based on Sentinel-1 and Landsat 8 remote sensing data
Soil moisture is a crucial parameter for agricultural production,water resource management,global climate,and other related fields.Synthetic aperture radar(SAR)serves as a significant mean for acquiring soil moisture,among which,vegetation and surface roughness are two key influencing factors.Therefore,this paper focuses on several aspects.Firstly,using Sentinel-1 radar data to obtain overall backscattering coefficients and which of vegetation are separated utilizing three vegetation indices(NDVI,NDWI,MSAVI)deriving from Landsat 8 optical data using the water-cloud model.Subsequently,the Dobson model is employed in conjunction with the advanced integral equation model(AIEM)to establish a table of backscattering coefficients lacking surface roughness,determining optimal roughness parameters through a minimum-cost function.Finally,a least squares method is used to determine the coefficients of the empirical equation for soil moisture inversion.Experimental results demonstrate that in highland areas,the model's inversion results exhibit good consistency with ground-truth measurements.Among these,the use of the normalized difference water index(NDWI2)as input for the water-cloud model combined with optimal roughness inversion yields the best results,with a fitting coefficient of 0.840 2 and a root mean square error of 0.027 21 cm3/cm3.
万方数据
维普
