2.5 Model calibration and validation
The full dataset (n=372) was split into two as calibration dataset with 70% (n=260) samples for model development and validation dataset with 30% (n=112) samples to assess the performance of the model independently. The spectral data were calibrated using the laboratory estimated soil properties by five multivariate techniques: (a) linear - PCR and PLSR and (b) non-linear - MARS, RF and support vector regression (SVR). The models were evaluated for prediction accuracy using model evaluation parameters like R2, mean bias error (MBE), RMSE and RPD. The prediction accuracy of different models was categorized based on RPD as excellent (RPD > 2), acceptable (2 ≥ RPD ≥ 1.4) and non-reliable (RPD > 1.40) (Changet al. , 2001). Generally, high R2 and RPD and low RMSE indicate a model with good predictive ability. We, in the present study, calculated ranks for different models considering the R2, MBE, RMSE and RPD. Based on the ranking for calibration and validation, an average rank was calculated to interpret the prediction performance of the model. Lower the value of the rank, better was the prediction performance.