Predicting cadmium concentration in soils using laboratory and field reflectance spectroscopy

摘要

Visible and near-infrared spectroscopy (VNIRS, 350-2500 nm) is a promising alternative to rapidly investigate soil contamination by heavy metals. To explore the possibility of predicting heavy metal concentration in soils using laboratory and field reflectance spectroscopy and examine transferability of the prediction method, 46 soil samples from a mining area, 42 soil samples from an agricultural land, and the corresponding two sets of field soil spectra were collected. Cadmium(Cd) was taken as an example in this study. The collected soil samples were air-dried, ground, sieved, and then used for laboratory spectral measurement and chemical analysis. Soil reflectance spectroscopy associated with organic matter was extracted from the VNIRS and used to predict Cd concentration based on strong sorption and retention of Cd on soil organic matter. Genetic algorithm (GA) was adopted for band selection, and the selected bands were used to calibrate the prediction model with partial least squares regression (PLSR). Compared with the prediction using entire VNIR region, the ratio of prediction to deviation (RPD) and the coefficient of determination (R-2) were improved from 1.473 and 0.508 to 2.997 and 0.881 for laboratory spectra and 1.437 and 0.484 to 1.992 and 0.731 for field spectra by using spectral bands associated with organic matter in the mining area. The RPD and R-2 values were improved from 1.919 and 0.707 to 3.727 and 0.923 for laboratory spectra and 1.057 and 0.036 to 1.747 and 0.646 for field spectra by the prediction method in the agricultural land. The improvement was further revealed by prediction of Cd concentration with a selected subset of soil samples from the mining area. The results suggest that predicting Cd concentration in soils with GA-PLSR using reflectance spectroscopy associated with organic matter is feasible and the prediction method could have the potential to be applied to field conditions. (c) 2018 Elsevier B.V. All rights reserved.