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Tarim University (2022)

Hyperspectral Inversion of Inorganic Carbon in Salinized Soil in Southern Xinjiang Desert

郝梦洁

Titre : Hyperspectral Inversion of Inorganic Carbon in Salinized Soil in Southern Xinjiang Desert

Auteur : 郝梦洁

Grade : Master 2022

Université : Tarim University

Résumé partiel
Soil inorganic carbon(SIC)is a key index to maintain the carbon cycle in fragile ecosystems in arid and semi-arid regions,which is related to the balance of carbon cycle and carbon sequestration rate in terrestrial ecosystems.Since the industrial Revolution,people’s production and living behaviors have discharged a large amount of greenhouse gases to the atmosphere and accelerated the global warming rate,which is not conducive to the sustainable development of the environment,and has a great negative impact on the absorption of atmospheric carbon dioxide by soil inorganic carbon to maintain the carbon cycle stability of terrestrial ecosystems.Therefore,in the context of global warming,rapid detection of soil inorganic carbon content in arid and semi-arid areas is of great significance for the development of precision agriculture,maintenance of fragile ecosystems and environmental stability in arid areas of China.Traditional laboratory methods to determine soil inorganic carbon content are time-consuming,laborious and environmentally unfriendly.With the development of remote sensing technology and computer technology,hyperspectral technology and machine learning algorithm have been vigorously promoted in the study of soil spectrum and soil physical and chemical properties.This study investigated four data after pretreatment of soil spectral reflectance data and on the basis of using two spectral dimension reduction method for dimension reduction band respectively set up four kinds of soil inorganic carbon quantitative estimates of the model,a total of 48 models for predicting the soil inorganic carbon,choose the most accurate model as the soil inorganic carbon inversion model.The main research results are as follows :(1)Considering the obvious noise in the original spectrum,the spectral reflectance of 350-399 nm and2401-2500 nm is removed,and the spectral reflectance data of 400-2400 nm wavelength is retained.Multivariate scattering correction,Savitzky-Golay smoothing,first-order differentiation and maximum and minimum normalization pretreatment can effectively reduce spectral signal noise,which is conducive to the subsequent modeling of soil inorganic carbon content.There was a significant correlation between hyperspectral data and soil inorganic carbon content after different spectral data pretreatment methods.(2)The continuous projection algorithm can extract 4,8,3 and 8 sensitive bands respectively from the soil spectral data after multiple scattering correction,Savitzky-Golay smoothing,first-order differentiation and maximum and minimum normalization,indicating that the continuous projection algorithm has the ability to effectively reduce the spectral dimension.The ratio of variance of the first three principal components to variance and of the ten principal components screened by principal component analysis were all higher than 0.85,indicating that principal component analysis can effectively reduce the dimension of soil inorganic carbon spectrum

Mots clés : Soil inorganic carbon ;hyperspectral;inversion ;machine learning ;

Présentation (CNKI)

Page publiée le 3 mai 2023