基于CWT-CARS-CNN综合方法的矿区土壤煤源碳质量分数高光谱估测OA北大核心CSTPCD

Objectives There is a shortage of reliable methods to quantitatively identify the coal-derived in soil.Methods In this study,soil samples from cultivated lands in Jiaozuo mining area were collected,249 coal-derived source carbon soil samples with different mass fraction were prepared,and spectral data of the samples were obtained by ASD FieldSpec4,the continuous wavelet transform(CWT)-competitive adaptive re-weighted sampling(CARS)-convolutional neural network(CNN)method was used to estimate coal-derived mass fraction in soil,the estimation effect of coal-derived carbon mass fraction between the CWT-CARS-CNN and traditional spectral index modelswas compared,and the applicability of the CWT-CARS-CNN model was also tested.Results The results showed that in the range of 350～2 500 nm,the hyperspectral characteristics between coal and soil were completely different.The spectral reflectance of coal-contained soil samples decreased with increasing coal-derived carbon mass fraction.The CWT method improved the sensitivity of the spectrum to the coal-derived carbon mass fraction in soil,the number of feature waveband of coal-derived carbon mass fraction extracted by the CARS was obviously increased.In general,accuracies of coal-derived carbon mass fraction estimation models based on the CWT-CARS-CNN integrated method were significantly higher than those based on traditional spectral index method.Especially,the CWT-CARS-CNN model constructed with L8 decomposition scale exhibited the highest accuracy,showing R2=0.999 3 and RPD=40.308 1 for itsvalidation set.Conclusions The study suggests that hyperspectral estimation based on the CWT-CARS-CNN integrated method can accurately estimate the coal-derived carbon mass fraction in soil under different land use types in mining areas,providing reference for accurate assessment of carbon se-questration and fertility in mine soil under the"Double C"background.