河南理工大学学报（自然科学版）2024，Vol.43Issue(3)：91-100,10.DOI:10.16186/j.cnki.1673-9787.2023030004
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
Hyperspectral estimation of coal-derived carbon mass fraction in mine soil based on the CWT-CARS-CNN integrated method
摘要
Abstract
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.关键词
煤源碳/碳固存/高光谱估测/深度学习/矿区土壤Key words
coal-derived carbon/carbon sequestration/hyperspectral estimation/deep learning/mine soil分类
农业科技引用本文复制引用
聂小军,洪雯雯,GILL Ammara,于海洋,陈晓东..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[J].河南理工大学学报（自然科学版）,2024,43(3):91-100,10.基金项目
国家自然科学基金资助项目(41977284) （41977284）
河南省科技攻关项目(222102320032) （222102320032）
