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Heavy metal contamination has been a critical environmental issue in mining areas, while accurate environmental remediation still faces a great challenge because of the complex feature of historical left-over soil pollution and its unclear spatial distribution patterns. Hyperspectral-based remote sensing techniques became a hot issue in interdisciplinary research on remote sensing and soil contamination, for it enables economical and efficient contamination detection. However, the unclear spectral response feature of soil heavy metal challenges the widespread of its application. In this study, near standard soil samples (NSS) were employed to extract spectral response feature bands of soil Pb. First, NSS was produced by artificially adding heavy metal ion solution in the background soil. Next, based on the proximal hyperspectral data of NSS, the spectral response feature bands were precisely identified by the Monte Carlo Uninformative Variables Elimination (MC-UVE) method as it achieved the highest predicting accuracy of the Partial Least Squares Regression (PLSR) model. Finally, to verify the reliability of the above extracted spectral response feature bands, 46 naturally contaminated soil samples (NCS) were collected synchronously with field and laboratory spectra in a mining area of Hengyang city, China. Enhanced by the corresponding feature bands of NSS, the NCS predicted models for soil Pb achieved the highest accuracy both for laboratory spectra (and RPD were improved from 0.45 and 1.36 to 0.71 and 1.87, respectively) and Direct standardization (DS)-converted field spectra (and RPD were improved from 0.38 and 1.28 to 0.62 and 1.64, respectively). In conclusion, NSS could provide an effective way to clarify the spectral response feature bands from hyperspectral data. And when combined with MC-UVE, it could serve as a promising approach to inverse the concentration of regional soil heavy metal Pb.HIGHLIGHTSNear standard soil samples were used to extract Pb spectral response feature band.Spectral response feature bands of NSS were effective for the modeling of NCS.Spectral response feature bands of soil Pb are 570-760nm, 1710-2100 nm et al.MC-UVE was a relatively optimal method to extract spectral response feature bands. Near standard soil samples were used to extract Pb spectral response feature band. Spectral response feature bands of NSS were effective for the modeling of NCS. Spectral response feature bands of soil Pb are 570-760nm, 1710-2100 nm et al. MC-UVE was a relatively optimal method to extract spectral response feature bands.

Spectral response feature bands extracted from near standard soil samples for estimating soil Pb in a mining area