Research On Soil Moisture Retreval Base On Sentinel Data

Soil moisture is an important surface parameter,which plays a vital role in the global climate,carbon and energy cycle,and the entire ecosystem.Real-time,accurate,and large-scale monitoring of soil moisture is of great value in the fields of agricultural production estimation,disaster prediction and assessment,and natural science research.Synthetic aperture radar has the ability to observe the surface of the earth at all times and all weathers,is not affected by clouds and rain,and has a high degree of attention in soil moisture inversion.The Sentinel 1 SAR data is open to the outside world with high resolution and accuracy,which greatly reduces the development cost.Therefore,the research on soil moisture inversion based on the sentinel data has great practical significance.The work of this article is mainly divided into the following three aspects:(1)First of all,this paper obtains the surface roughness parameters of the threedimensional point cloud data obtained by the Lidar scanner.It is found through experiments that the RMS height obtained by using point cloud data is 6.4% to 10.1% higher than that of the traditional roughness measuring plate method.Aiming at the problem that the length of the 2D surface profile collected by the traditional method is not enough and the correlation length calculation result is too small,this paper proposes a method to extend the measurement length by randomly combining the 2D surface profile.Compared with the traditional method,the accuracy is improved by 10.2 % ~ 20.8%.Accurate acquisition of surface roughness can establish a more accurate surface scattering model.(2)Combined with the extracted surface roughness parameters,a semi-empirical soil moisture inversion algorithm was constructed using the AIEM model.Firstly,the water content of vegetation obtained by using water cloud model combined with Sentinel 2 optical data removes the effect of vegetation cover on the backscatter coefficient,and obtains the backscatter coefficient set of bare soil.Then,using the AIEM model,a data set of backscatter coefficients under different surface parameters is established,and the influence of the surface parameters on the backscatter coefficient is studied.A semiempirical model of backscatter coefficient,combined roughness,and soil moisture under bare soil is established by combining the data set established by the model with the measured surface parameters.Applying it to the study area,and comparing the obtained soil moisture inversion value with the measured data,the overall inversion accuracy is 78.54%.(3)The change detection algorithm can estimate soil moisture without relying on surface parameters,but it has the disadvantage of ignoring changes in surface roughness.In this paper,the change detection algorithm is improved,and the concept of normalized roughness parameter is introduced.The difference between the backscattering coefficients of co-polarization and cross-polarization is used to eliminate the effect of roughness changes in the same plot at different timings.Combined with the soil moisture product at a resolution of 9 km from the SMAP satellite,the relative water content is converted into absolute water content.Finally,comparing the inverted soil moisture with the measured data,the accuracy of the improved change detection algorithm in this paper can reach 80.67%,which is higher than the traditional change detection algorithm of 64.24%,which shows that the improvement of the change detection algorithm is reasonable and effective.