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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).
Highlights
Petroleum hydrocarbons are a complex mixture of hydrocarbons containing various hydrocarbons (n-alkanes, branched alkanes, cycloalkane, and aromatics) and a small amount of other organics [1]
We combined leave-one-out cross-validation to establish partial least squares regression (PLSR), and selected the optimal estimation model of petroleum hydrocarbon contents (PHCs) by comparing the accuracy of the spectral prediction models of different methods to provide a feasible analysis for the subsequent inversion of large-scale soil petroleum hydrocarbon pollution
By observing initial reflection spectra of the soil samples with different PHCs (Figure 4), we found that the morphological characteristics of these spectral curves were basically the same
Summary
Petroleum hydrocarbons are a complex mixture of hydrocarbons containing various hydrocarbons (n-alkanes, branched alkanes, cycloalkane, and aromatics) and a small amount of other organics [1]. Scholars have used spectral data processing methods, including continuum removal, standard normal energy transform, differential derivation, and principal component analysis to extract soil petroleum hydrocarbon information, amplify the spectral absorption characteristics of petroleum hydrocarbon, and accurately predict the PHCs [13,14,15,16]. Ren [12] chose the 360–600 nm band spectrum for pretreatment and used the first derivative as spectral pretreatment in building models for predicting soil petroleum hydrocarbon concentration on the basis of visible–near-infrared spectroscopy and spectral analysis (coefficient of determination (R2) = 0.65, root mean square error (RMSE) of prediction = 60.58 g/kg). We combined leave-one-out cross-validation to establish PLSR, and selected the optimal estimation model of PHCs by comparing the accuracy of the spectral prediction models of different methods to provide a feasible analysis for the subsequent inversion of large-scale soil petroleum hydrocarbon pollution
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