Effect of calibration set size on prediction at local scale of soil carbon by Vis-NIR spectroscopy

Predicting soil properties through visible and near-infrared (Vis–NIR) spectroscopy by a limited number of calibration samples can reduce the cost and time for physic-chemical analyses. This study was aimed to assess the influence of calibration set size on the prediction of total carbon (TC) in the soil by Vis–NIR spectroscopy. In a forested area of 33 ha in southern Italy (Calabria), 216 soil samples were analyzed for TC concentration, and reflectance spectra were measured in the laboratory. The whole data set was randomly split into calibration and validation sets (70% and 30%, respectively). To study the effect of the number of samples on TC prediction, ten calibration subsets of samples between 14 and 144 were selected. Three techniques including principal components regression (PCR), partial least squares regression (PLSR) and support vector machine regression (SVMR) were used to develop 84 calibration models, validated through the same independent data. The models were compared through the coefficient of determination (R2), the root mean square error of prediction (RMSEP) and the ratio of the interquartile distance (RPIQ). Validation results showed that to obtain not significant differences with models based on the full calibration set, 29, 72 and 115 samples were required for PCR, SVMR and PLSR respectively. Although PCR appeared less sensitive than PLSR and SVMR to calibration sample size, SVMR outperformed PLSR and PCR with higher R2 and RPIQ values and lower RMSEP. To obtain RMSEP not significantly different from the best model achieved in this study, the required minimum number of samples was 72 for SVMR and 130 for PLSR. All PCR model were significantly poorest than the best model.