利用可见光-近红外漫反射光谱技术预测铜冶炼厂周边区域土壤属性

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 2 cv) and the ratio of standard deviation to the root mean standard error of cross-validation (SD/RMSE cv).The models provided fairly accurate predictions for OM and Fe (R 2 cv > 0.80,SD/RMSE cv > 2.00),less accurate but acceptable for screening purposes for pH,Cu,Pb,and Cd (0.50 < R 2 cv < 0.80,1.40 < SD/RMSE cv < 2.00),and poor accuracy for Zn (R 2 cv < 0.50,SD/RMSE cv < 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.

Visible and near-infrared diffuse reflectance spectroscopy for prediction of soil properties near a copper smelter

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 a 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.