Hyperspectral Inversion of Petroleum Hydrocarbon Contents in Soil Based on Continuum Removal and Wavelet Packet Decomposition

Chaoqun Chen, Qigang Jiang, Zhenchao Zhang, Pengfei Shi, Yan Xu, Bin Liu, Jing Xi and ShouZhi Chang
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Chaoqun Chen: College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
Qigang Jiang: College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
Zhenchao Zhang: College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
Pengfei Shi: College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
Yan Xu: College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
Bin Liu: College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
Jing Xi: College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
ShouZhi Chang: School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China

Sustainability, 2020, vol. 12, issue 10, 1-13

Abstract: Hyperspectral remote sensing is widely used to detect petroleum hydrocarbon pollution in soil monitoring. Different spectral pretreatment methods seriously affect the prediction and analysis of petroleum hydrocarbon contents (PHCs). This study adopted a combined spectral data preprocessing technique that improves the prediction accuracy of petroleum hydrocarbons in soil. We combined continuum removal and wavelet packet decomposition (CR–Daubechies 3 (db3)) to process the hyperspectral reflectance data of 26 soil samples in the oil production work area in China and judged the correlation between spectral reflectance and petroleum hydrocarbons in soil. Partial least squares regression was used to construct an optimal model for the inversion of PHCs in soil and the leave-one-out cross-validation was used to select the best factor number. The best model of soil petroleum hydrocarbon inversion was determined by comprehensively comparing the initial spectrum, db3 to high-frequency spectrum, db3 to low-frequency spectrum, after-continuum removal spectrum, CR-db3 to high-frequency spectrum, and CR-db3 to low-frequency spectrum comprehensively. The main contributions of this study are as follows: (1) three-layer decomposition with CR-db3 can improve the correlation between spectral reflectance and PHCs and effectively improve the sensitivity of the spectrum to PHCs; (2) the prediction accuracy of the high-frequency spectrum of wavelet packet decomposition for PHCs in soil is higher than that of low-frequency information; (3) the proposed petroleum hydrocarbon prediction model based on CR-db3 processed spectra to obtain high-frequency information is optimal (coefficient of determination = 0.977, root mean square error of calibration = 3.078, root mean square error of cross-validation = 4.727, root mean square error of prediction = 4.498, ratio of performance to deviation = 6.12).

Keywords: hyperspectral inversion; petroleum hydrocarbon content; CR-db3; wavelet packet decomposition (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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