Inversion Of Soil Moisture On Vegetation-covered Surface In Arid And Semi-arid Area

Soil moisture plays an important role in the Earth's ecosystem,and soil moisture has a significant impact both in theoretical research and in actual production.Water scarcity in arid and semi-arid regions,soil moisture research in this region plays an important role in local agriculture,ecological environment and economic development.Traditional soil moisture measurement methods cost a lot of money when monitoring large areas,and remote sensing technology provides a new solution for soil moisture measurement in a wide range and in real time.Among them,SAR technology has strong penetrability and sensitivity to soil moisture,which has attracted much attention in the field of soil moisture.When using SAR to invert soil moisture,it is mainly affected by vegetation cover and surface roughness.The current main method is to select the applicable vegetation microwave scattering model to remove the influence of vegetation,and then select the applicable bare ground table microwave scattering model.The mathematical relationship between backscattering coefficient,surface roughness and soil moisture in the model is used to invert soil moisture.In this paper,sensitivity of multispectral data to vegetation information,penetration of radar data and sensitivity to soil moisture are utilized.Synergistic use of optical and radar data to invert the soil moisture of vegetation-covered area.In this paper,Linze grassland and Huazhaizi farmland in the middle reaches of Heihe River are taken as the research area.The soil moisture model was established by using remote sensing data and ground measured data of Linze grassland.And the inversion model was applied to the Huazhaizi farmland,and compared with the traditional method to verify the applicability of the model.In the establishment of soil moisture inversion model,this paper combines the characteristics of the study area to analyze the applicability of the model.Firstly,the water cloud model is selected to remove the influence of vegetation layer on radar backscattering coefficient,and the input of key parameters of the water cloud model is discussed and studied.Secondly,the AIEM model was used to establish the LUT table,and the LUT table method was used to invert the soil moisture in the research area.Through the research,this paper mainly draws the following conclusions and results:(1)The correction of the backscattering coefficient by the water cloud model can effectively remove the influence of vegetation,which is helpful to improve the accuracy of the backscattering coefficient of simulated soil surface.In addition,the soil moisture inversion accuracy of the water cloud model with different input modes of parameters is improved.Among the input parameters of the water cloud model,the vegetation water content has the dominant factor.The value of vegetation parameter A has no significant effect on the result,while the vegetation parameter B has a greater impact on the result.(2)When the vegetation water content is input into the water cloud model,this paper uses the remote sensing vegetation index method to invert.The response relationship between remote sensing index(EVI,NDVI,NDWI,RVI,SAVI)and vegetation water content was established.It was found that the vegetation index had a high correlation with vegetation water content,and the RVI inversion vegetation water content had the highest accuracy.When the vegetation parameters are input into the water cloud model,the parameter input method based on vegetation coverage classification is used,which has a certain improvement in accuracy compared with the traditional input method.(3)When the LUT table method was used to invert soil moisture,different single polarization SAR data sources(whether vegetation influence was removed,different polarities,and different water cloud model parameter input modes)were used,and it was found that the VV polarimetric SAR data with vegetation influence removed by this method had the highest inversion accuracy,and this method can effectively remove the influence of surface roughness.At the same time,it is found that the inversion accuracy of the same polarization data is better than the cross polarization data,and the VV polarization data has the highest accuracy.