摘要：Core Ideas Conventional methods for measuring CEC are time consuming and costly. A Vis – NIRS model and pedotransfer function (PTF) were developed for CEC determination. The Vis – NIRS model estimated CEC accurately for different soil types. The Vis – NIRS CEC model performed better than the PTF based on clay and organic C contents. Knowledge of the cation exchange capacity (CEC) for soils or other porous media is very important for civil engineering and agricultural applications. However, the standard laboratory methods to measure CEC are costly and laborious. The aim of this research was to develop a visible–near‐infrared spectroscopy (Vis–NIRS, 400–2500 nm) calibration model to predict CEC based on multivariate analysis and to compare the predictive ability of Vis–NIRS with that of a pedotransfer function (PTF). For this purpose, reference CEC was measured by the ammonium acetate method for 235 soil samples, collected from 21 countries. Diffuse spectral reflectance data were also collected by using a NIRSTM DS2500 spectrometer. The model was constructed on a calibration subset (80%) and evaluated with a validation subset (20%) using partial least squares regression. The Vis–NIRS calibration model was sufficiently robust based on the cross‐validation results [ R 2 = 0.79, RMSE of cross‐validation values of 7.9 cmol c kg −1 and bias = −0.14]. The independent validation of the Vis–NIRS model showed good prediction accuracy, regardless of sample origin (RMSE of prediction value of 5.0 cmol c kg −1 and ratio of performance to interquartile distance value of 4.5). Moreover, the Vis–NIRS prediction performance was superior to that of the PTF, which was influenced by the sample origin (RMSE values of 11.5 cmol c kg −1 ). The better prediction of CEC by the Vis–NIRS calibration model suggests that it is due to the co‐variation of CEC with clay (type and content) and organic C content.
关键词：CEC; cation exchange capacity; OC; organic carbon; OM; organic matter; PLSR; partial least squares regression; PTF; pedotransfer function; RMSEC; root mean square error of calibration; RMSECV; root mean square error of cross-validation; RMSEP; root mean square error of prediction; RPIQ; the ratio of performance to interquartile distance; Vis–NIR; visible–near-infrared; Vis–NIRS; visible–near-infrared spectroscopy
