Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis–NIR spectroscopy

Abstract

The successful determination of soil properties by visible and near-infrared (Vis–NIR) reflectance spectroscopy (350–2500nm) depends on the selection of an appropriate multivariate calibration technique. In this study, four multivariate techniques (principal components regression, PCR; partial least squares regression, PLSR; back-propagation neural network, BPNN; and support vector machine regression, SVMR) were compared with the aim of rapidly and accurately predicting soil properties, including soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), and total potassium (TK). A total of 148 intact soil cores (8.4cm internal diameter and 40cm long) collected from paddy fields in Yujiang, China were used as the dataset for the calibration-validation procedure. The Vis–NIR spectra were measured on flat, horizontal surfaces of soil core sections at four depths (i.e., 5, 10, 15, and 20cm) in the laboratory. The coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation (RPD) were used to evaluate the accuracy of the calibration models. Both the cross-validation and independent validation data sets showed that the SVMR models outperformed the BPNN, PCR, and PLSR models for SOM, TN, and TP predictions, whereas BPNN outperformed the other models for TK. Furthermore, BPNN and SVMR provided better performance than PCR and PLSR. The best predictions were obtained by the SVMR model for SOM (R2P=0.88; RMSEP=4.87; RPDP=2.84) and TN (R2P=0.86; RMSEP=0.31; RPDP=2.69), which were classified as good model predictions. The predictions of TP (R2P=0.76; RMSEP=0.080; RPDP=2.03) by SVMR were approximately quantitative predictions, whereas the TK (R2P=0.65; RMSEP=3.54; RPDP=1.65) prediction with BPNN was unsuccessful. Vis–NIR spectroscopy combined with SVMR has great potential to accurately determine the selected soil properties of intact soil cores of paddy fields.