Retrieving zinc concentrations in topsoil with reflectance spectroscopy at Opencast Coal Mine sites

Abstract

Heavy metals contaminations in mining areas aroused wide concerns globally. Efficient evaluation of its pollution status is a basis for further soil reclamation. Visible and near–infrared reflectance (Vis–NIR) spectroscopy has been diffusely used for retrieving heavy metals concentrations. However, the reliability and feasibility of calibrated models were still doubtful. The present study estimated zinc (Zn) concentrations via the random forest (RF) and partial least squares regression (PLSR) using ground in-situ Zn concentrations as well as soil spectral reflectance at an Opencast Coal Mine of Ordos, China in February 2020. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and the ratio of performance to deviation (RPD) were selected to assess the robustness of the methods in estimating Zn contents. Moreover, the characteristic bands were chosen by Pearson correlation analysis and Boruta Algorithm. Finally, the comparison between RF and PLSR combined with eight spectral reflectance transformation methods was conducted for four concentration groups to determine the optimal model. The results indicated that: (1) Zn contents represented a skewed distribution (coefficient of variation (CV) = 33%); (2) the spectral reflectance tended to decrease with the increase of Zn contents during 580–1850 nm based on Savitzky–Golay smoothing (SG); (3) the continuous wavelet transform (CWT) demonstrated higher effectiveness than other spectral reflectance transformation methods in enhancing spectral responses, the R2 between Zn contents and the soil spectral reflectance achieved the highest (R2 = 0.71) by using CWT; (4) the RF combined with CWT exhibited the best performance than other methods in the current study (R2 = 0.97, RPD = 3.39, RMSE = 1.05 mg kg−1, MAE = 0.79 mg kg−1). The current study supplied a scientific scheme and theoretical support for predicting heavy metals concentrations via the Vis–NIR spectral method in possible contaminated areas such as coal mines and metallic mineral deposit areas.

Mapping the Mine: Combining Portable X-ray Fluorescence, Spectroradiometry, UAV, and Sentinel-2 Images to Identify Contaminated Soils—Application to the Mostardeira Mine (Portugal)

“…A thorough literature review reveals that the methodologies used in this study are often used either individually-e.g., [7,9,11,14]-or, less often, combined-e.g., [14][15][16]-to identify and characterize soils and minerals from contaminated areas.…”

“…As these soils are dominated by the presence of Zn, this element must be influencing the spectral behaviour. In fact, [15] states that the spectral reflectance tended to decrease with the increase of Zn contents in wavelengths in the range of 580-1850 nm. It is recognized that iron oxides and hydroxides such as hematite and goethite, respectively, have distinct absorption features in the vis-NIR wavelengths [16].…”

“…Other studies relate chemical composition with the spectral response of materials commonly in the Visible-near-infra-red regions, e.g., [12,13], and identifying spectral signatures of materials with implications in the characterization of the soils and mine tailings. The use of soil spectroradiometry for identifying the characteristics of soils is widespread in the literature; e.g., [14][15][16].…”

Identifying Population Hollowing Out Regions and Their Dynamic Characteristics across Central China

“…The RF algorithm, based on regression tree (CART) analysis and classification, is a bagging method [62]. The variable selection number (mtry) when branching, the number of trees of the classification tree (ntree), and the size of the leaf (node size) were necessary for fitting the RF model [63,64]. The detailed information of the RF model can be found in the published paper [65].…”

Improving the Accuracy of Soil Organic Carbon Estimation: CWT-Random Frog-XGBoost as a Prerequisite Technique for In Situ Hyperspectral Analysis

“…Previous studies have reported good results using in situ spectroscopy combined with particle swarm optimization (PSO), ant colony optimization (ACO), and simulated annealing (SA) for estimating the soil organic matter content in the desert areas of southern Xinjiang [16]. The successive projections algorithm (SPA) and Boruta algorithm eliminate a certain amount of redundancy in spectral information and effectively preserve the integrity of the spectral data and the physical meaning of the original bands [17]. The random frog algorithm, which has been shown to be a better variable selection algorithm, simulates a smoothly distributed Markov chain in the feature space and calculates the probability of each variable being selected to perform the selection of important variables [18].…”