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Tarim University (2021)

Soil Moisture in the Desert of South Xinjiang Arid Zone Based on Multi-source Remote Sensing Data Remote Sensing Monitoring Research

高琪

Titre : Soil Moisture in the Desert of South Xinjiang Arid Zone Based on Multi-source Remote Sensing Data Remote Sensing Monitoring Research

Auteur : 高琪

Grade : Master 2021

Université : Tarim University

Résumé partiel
Soil moisture in arid regions is one of the main factors affecting land desertification and plays an important role in the process of vegetation restoration and community succession.How to obtain soil moisture information efficiently,non-destructively and accurately,and on this basis,to conduct research on the spatial distribution characteristics and driving influences of soil moisture in the surface layer(0~20 cm)of deserts is a hot issue at present.Therefore,this study uses the advantages of optical remote sensing Landsat 8 OLI/TIRS and microwave remote sensing Sentinel-1 SAR data to calculate several spectral indices by optical remote sensing with the study area of Aksu region in South Xinjiang Kongtailike,and at the same time carries out relevant improvements on the basis of spectral indices to carry out relevant optical remote sensing monitoring studies,and acquires soil multi-polarization backscattering coefficients by microwave data.The study is based on the acquisition of soil multi-polarity backward scattering coefficients from microwave data,and the study is carried out by multiple linear regression(MLR),partial least squares regression(PLSR),support vector machine(SVM),Random Forest(RF),Decision Tree Regression(Cubist),Partition+PLSR and Partition+Cubist models,and other algorithms to construct an integrated inversion model based on optical data(multispectral indices)to study the spatial distribution of desert soil moisture in arid regions.The main findings of the research include the following three points :(1)Remote sensing inversion study of desert soil moisture based on Landsat8 dataThe 26 preferred spectral indices,such as TVDI,NR and GLI,Ts and DEM all reached highly significant correlation with soil moisture and can be used as indicator factors for remote sensing modelling of desert soil moisture in the southern arid zone ;Comparing the three models,the R2of the modelling set and prediction set of the RF model were 0.93 and 0.91 respectively,and the RPD of the prediction set was 3.90,which were the highest in all evaluation indexes.The PLSR model had the second highest accuracy and the SVM model had the lowest accuracy ;Inverting the surface soil moisture in the study area with the RF model,there were obvious differences in the soil moisture distribution characteristics in different land use classifications,especially in the salt crust area.It is shown that the multi-factor and multi-index model using spectral indices,environmental factors and topographic data can invert the desert surface soil moisture in the arid zone with high accuracy,and the results of the study provide a certain theoretical basis and methodological support for remote sensing monitoring of desert soil moisture in the arid zone of South Xinjiang.

Mots clés : desert soil moisture ;Landsat 8 ;Sentinel-1 ;inversion model ;zoning;spatial distribution of soil moisture ;

Présentation (CNKI)

Page publiée le 9 novembre 2021