Visible and Near-Infrared Diffuse Reflectance Spectroscopy for Prediction of Soil Properties near a Copper Smelter

Abstract

Spatial and temporal monitoring of soil properties in smelting regions requires collection of a large number of samples followed by laboratory cumbersome and time-consuming measurements. Visible and near-infrared diffuse reflectance spectroscopy (VNIR-DRS) provides a rapid and inexpensive tool to predict various soil properties simultaneously. This study evaluated the suitability of VNIR-DRS for predicting soil properties, including organic matter (OM), pH, and heavy metals (Cu, Pb, Zn, Cd, and Fe), using a total of 254 samples collected in soil profiles near a large copper smelter in China. Partial least square regression (PLSR) with cross-validation was used to relate soil property data to the reflectance spectral data by applying different preprocessing strategies. The performance of VNIR-DRS calibration models was evaluated using the coefficient of determination in cross-validation (R2cv) and the ratio of standard deviation to the root mean standard error of cross-validation (SD/RMSEcv). The models provided fairly accurate predictions for OM and Fe (R2cv > 0.80, SD/RMSEcv > 2.00), less accurate but acceptable for screening purposes for pH, Cu, Pb, and Cd (0.50 < R2cv < 0.80, 1.40 < SD/RMSEcv < 2.00), and poor accuracy for Zn (R2CV < 0.50, SD/RMSEcv < 1.40). Because soil properties in contaminated areas generally show large variation, a comparative large number of calibrating samples, which are variable enough and uniformly distributed, are necessary to create more accurate and robust VNIR-DRS calibration models. This study indicated that VNIR-DRS technique combined with continuously enriched soil spectral library could be a nondestructive alternative for soil environment monitoring.