| Soil moisture(m_v)is an important physical parameter on the earth’s surface,which plays a vital role in the research of agriculture,hydrology,meteorology,etc.It is also a factor that must be considered when modeling the atmosphere and hydrosphere.Due to the strong temporal and spatial variability of soil moisture,it is important to find a method that can rapidly and widely measure soil moisture with high resolution.To achieve this goal,optical remote sensing,passive microwave radiometry,and synthetic aperture radar(SAR)have been developed to measure large-scale soil moisture.Among them,synthetic aperture radar has become the most promising technology for soil moisture inversion due to its strong penetrating ability,all-weather,all-weather observation,and high spatial resolution.There are two main problems in using SAR data to retrieve the surface soil moisture:1)On the bare surface,the empirical models,semi-empirical models and polarization decomposition methods in the existing methods require full polarization SAR data,the theoretical model need to input the measured surface parameters,and the methods using multitemporal SAR should meet the requirement of no effective rainfall during the data acquisition period.These conditions limit the feasibility of existing methods for fast,large-scale soil moisture inversion;2)In the area covered by vegetation,the scattering contribution of vegetation reduces the sensitivity of SAR backscattering to soil moisture.Existing methods often use vegetation descriptors and input measured ground data to train a semi-empirical inversion model,which reduces the applicability of these methods.Therefore,in order to carry out research on the above two aspects,the main research contents and innovations of this paper are as follows:(1)A method of combining neighborhood pixel data and juxtaposed possibility curve to invert soil moisture is proposed,which solves the limitations of relying on multi-temporal,multi-polarization SAR data when using the Integral Equation Model(IEM).Since the existing methods require full-polarization SAR data,time series data,or ground-measured parameters over bare soil surface,it is difficult to use single-polarization SAR data to invert soil moisture,which reduces the feasibility of fast and large-scale soil moisture inversion.The feasibility of fast and large-scale soil moisture inversion is reduced.Hence,the mean real part and the rms height of the neighborhood pixels were obtained by using the neighborhood pixels from the single-polarization SAR data and the juxtaposed neighborhood possible curve.The algorithm is verified by using the airborne SAR data of Agri SAR 2006 and SARSense 2019.The experimental results show that the VV polarization achieves the best inversion effect at the L-band,the RMSE is equal to 0.036 cm~3/cm~3,and the correlation coefficient is 0.84,which met the standard for practical use.The method provides a possibility to meet the production and living needs of rapid and large-scale measurement of soil moisture.(2)An algorithm based on the radar vegetation descriptor and Water Cloud Model(WCM)to obtain soil moisture in the area covered by vegetation is proposed,which effectively reduces the effect of vegetation scattering on the inversion of soil moisture.Since vegetation scattering will interfere with the functional relationship between backscattering and soil moisture,the radar vegetation descriptors and WCM were used to characterize the scattering contribution of vegetation.Then,the inversion method used single-polarization SAR data to obtain soil moisture in the area covered by vegetation.The algorithm is verified by Agri SAR 2006airborne data.The experimental results show that the method can effectively reduce the influence of vegetation scattering,and the correlation coefficient of VV polarization at L-band reaches 0.53,which proves the feasibility of the experimental method.This method provides a feasible solution for retrieving soil moisture on a global scale using spaceborne dual-polarization SAR data. |
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