Spatial and temporal monitoring of soil properties in smelting regions requires collection of a large number of sam- ples 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 （R^2cv） 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 （R2v 〉 0.80, SD/RMSEcv 〉 2.00）, less accurate but acceptable for screening purposes for pH, Cu, Pb, and Cd （0.50 〈 Rcv 〈 0.80, 1.40 〈 SD/RMSEcv 〈 2.00）, and poor accuracy for Zn （R2v 〈 0.50, SD/RMSEcv 〈 1.40）. Because soil properties in conta- minated 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.
