Study on hyperspectral inversion of chromium content in soil around the West Sichuan copper mine
The extraction of mineral resources has brought many environmental problems to the surrounding soil,and it is particularly important to quickly screen for soil pollution in the mining area.This article takes the soil around a copper mine in western Sichuan as the research object.Using LASSO algorithm to screen feature bands for SG smoothed hyperspectral data RSG and spectral data after multivariate scattering correction(MSC),first-order differentiation(FD),and reciprocal transformation(RT).Perform inversion using five models:Partial Least Squares Regression(PLSR),Random Forest(RF),Support Vector Machine Regression(SVR),eXtreme Gradient Boosting(XGBoost),and Backpropagation Neural Network(BPNN).The results show that the feature bands selected by RSG,MSC,and FD are concentrated in the near-infrared region,while the feature bands selected by RT are concentrated in the visible light region;the R2,RMSE,and RPD of the MSC-SVR model are 0.763,6.745,and 2.06,respectively,with the highest accuracy among all models.This model can be used for rapid monitoring of chromium in the study area.
hyperspectral inversionsoil heavy metal contentspectral analysisWest Sichuan copper minechromium content
万方数据
维普
