Analysis Of Soil Moisture Inversion In The Southern Slope Of Qilian Mountain Based On The MODIS Data

Soil water status is an important indicator for the analysis of soil degradation,which is of great significance to agricultural production.The traditional soil moisture monitoring is the method of weighing and drying to complete the analysis of soil moisture information,this method is generally more suitable for a small range of soil moisture information analysis,although this method to obtain the soil moisture information is more accurate,the use of this method will result in a relatively large human and material resources of the waste.However,with the continuous development of science and technology and extensive application,in recent years began to widely use remote sensing technology to complete the alpine areas of soil moisture monitoring,which is a very important development trend.This thesis firstly focus on analyzing the correlational research literature at home and abroad,and expresses the quantitative inversion of soil moisture with Soil Moisture Remote Sensing Technology.Through the comparative analysis of the different remote sensing models of soil moisture inversion,3 indexes which might meet the topography and climate in the southern slope of Qilian Mountains were selected better in this paper.They were respectively the Vegetation Water Supply Index,the Crop Water Stress Index and the Temperature Vegetation Drought index.On the basis of the MODIS land surface temperature product and vegetation index product in 2015,combining the measured data of 0-10 cm soil moisture,the above three indexes were used to conduct the inversion on the soil moisture in the southern slope of Qilian Mountains.Also,the inversion accuracy was compared.A method suitable the research on the inversion of soil moisture was used to conduct the inversion on the status and spatial distribution of the soil moisture in the southern slope of the Qilian Mountains over the past 15 years.The main conclusions were as follows:(1)The Vegetation Water Index Method was only suitable in the plain and the research region with the dense meteorological stations.The meteorological stations in the research region were sparse,and the difference in the topography and elevation was great,which would lead to the inaccurate spatial interpolation and reduce the accuracy of the model.(2)The suitable models in the research region were Water Supply Index Method and Temperature Vegetation Drought Index Method,but the one with best precision was the Vegetation Temperature Drought Index Method.(3)TS/NDVI feature space was constructed by using MODIS data.On the basis of the optimized value,TVDI model was constructed for the special space.Combining the measured data of soil moisture in 2015,the inversion on the soil moisture in the southern slope of Qilian Mountains was carried out.And the inversion value was compared with the measured value in 2013 or 2014.The average error was about 33%,which showed good accuracy,and met the basic requirements of remote sensing inversion of soil moisture.On the basis,the inversion on the soil moisture in the southern slope of Qilian Mountains in the past 15 years was carried out.The result showed that the drought index of temperature vegetation can accurately invert soil moisture.