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Chang’an University (长安大学) 2021

Radar Remote Sensing Research on Soil Moisture in Arid Areas Using Machine Learning and Polarization Feature Optimization

苏志强

Titre : Radar Remote Sensing Research on Soil Moisture in Arid Areas Using Machine Learning and Polarization Feature Optimization

Auteur : 苏志强

Grade : Master 2021

Université : Chang’an University (长安大学)

Résumé partiel
Soil moisture is an important parameter of the earth’s water cycle process,which affects the exchange of energy and water in processes such as ecology,climate,and agriculture.Microwave remote sensing has penetrating characteristics,will not be disturbed by weather factors,can monitor soil moisture in real time,and is highly sensitive to soil moisture.Among them,synthetic aperture radar(SAR)technology has developed rapidly in recent years,from single polarization to multi-polarization and full polarization,and fully polarized synthetic aperture radar(Pol SAR)includes with more polarization information,it has been widely used in research fields such as ground object classification,target recognition,and inversion of ground parameters.However,the polarization scattering characteristics of different ground objects are different,and under different test conditions,the effect of polarization characteristics on soil moisture retrieval is also different.Therefore,it is of great significance to study the potential relationship between polarization characteristic parameters and soil moisture.However,existing studies mostly use the traditional method of simple linear regression to retrieve soil moisture through a limited number of polarization parameters,so it is impossible to comprehensively and systematically measure the role of each polarization characteristic parameter in soil moisture retrieval.Compared with traditional regression methods,machine learning is not limited by the number and types of input parameters,and can learn nonlinear and complex mapping relationships.This paper will use machine learning model combined with polarized SAR characteristic parameters to carry out soil moisture retrieval.This research has important significance and reference value for the ecological environment protection,sustainable economic development and drought monitoring research in Juyanze area.In this paper,Juyanze,a typical arid area,is used as the research area.Based on the fully polarized Radarsat-2 data and combined with the measured data of soil moisture in the research area,a machine learning soil moisture inversion model in arid areas with different combinations of characteristic parameters is constructed to explore the machine learning mode

Mots clés : soil moisture ;Radarsat-2 ;polarization characteristic parameters ;machine learning model ;arid area ;

Présentation (CNKI)

Page publiée le 18 mars 2022