doi:10.16186/j.cnki.1673-9787.2023030004

Received:2023-03-02

Revised:2023-05-05

Online Date: 2023-10-12

Hyperspectral estimation of coal-derived carbon content in mine soils based onthe CWT-CARS-CNN integrated method（Online）

NIE Xiaojun, HONG Wenwen, GILL Ammara, YU Haiyang, CHEN Xiaodong

School of Surveying and Mapping Land Information Engineering，Henan Polytechnic University，Jiaozuo 454000，Henan，China

Abstract:As a high-carbon （C） organic matter，coal particles diffused in the soils can lead to apparent overestimation of soil organic C stocks.However，there is a shortage of reliable methods in quantitating coal- derived C in soils. In this study，soil samples from cultivated lands of Jiaozuo mining area were collected， 249 artificial mixtures of soil and coal samples were prepared to form different contents of coal-derived C， and spectral data of the samples were obtained by ASD FieldSpec4. The Continuous Wavelet Transform （CWT）-Competitive Adaptive Reweighted Sampling（CARS）-Convolutional Neural Network（CNN） method was used to estimate coal-derived C content in soil.The estimation effect of coal-derived C content between the CWT-CARS-CNN and traditional spectral index models，was compared，and the applicability of the CWT-CARS-CNN model was also tested.It was found that in the range of 350-2500nm，the hyperspectralcharacteristics between coal and soil are largely different. The spectral reflectance of coal-contained soilsamples decreases with increasing coal-derived C content.The CWT method improved the sensitivity of thespectrum to the coal-derived C content in soil.The number of feature waveband of coal-derived C extractedby the CARS was obviously increased.In general，accuracies of coal-derived C estimation models based onthe CWT-CARS-CNN integrated method were significantly higher than those based on traditional spectral in⁃dex method.Especially，the CWT-CARS-CNN model constructed with L8 decomposition scale exhibited thehighest accuracy，showing R²=0.999 1 and RPD=40.308 1 for its validation set. In addition，the estimationmodel showed good applicability to coal-contained soils under different land use types.Our study suggests that hyperspectral estimation based on the CWT-CARS-CNN integrated method can provide method support for accurate assessment of soil C sequestration and fertility in coal mining area under the “Double C” background.

Key words:coal-derived carbon;carbon sequestration;hyperspectral estimation;deep learning;mine soils

CLC：S153.6;TP183;O657.3