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 (Rcv2) 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 (Rcv2 ＞ 0.80,SD/RMSEcv ＞ 2.00),less accurate but acceptable for screening purposes for pH,Cu,Pb,and Cd (050 ＜ Rcv2 ＜ 0.80,1.40 ＜ SD/RMSEcv ＜ 2.00),and poor accuracy for Zn (Rcv2＜ 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.