3.4.9 Soil Ca prediction
Similar to the results of Na prediction, the MARS model had the highest prediction accuracy for Ca using the calibration dataset (R2c=0.81; MBEc=-0.09; RMSEc=1.60; RPDc=1.78). It was then followed by SVR (R2c=0.77; MBEc=-0.06; RMSEc=1.49; RPDc=1.91) which was noticeable here that the rank for calibration using SVR (1.25) was better than MARS (1.75) and their corresponding predictions were considered as excellent. The PLSR provided the highest accuracy using the validation dataset (Figure 4i) with a prediction accuracy of R2p=0.43; MBEp=-0.11; RMSEp=2.27; RPDp=1.20 and rank=2.63. But, this validation prediction was non-reliable. Malmir et al.(2019) also reported excellent prediction using PLSR model of reflectance data (400-1000 nm) with R2cv=0.81 and RMSEcv=260.79 (for grounded sample) and R2cv=0.81 and RMSEcv=260.97 (for sieved sample). With the use of the airborne hyperspectral imaging (350-2400 nm) and VIS-NIR spectroscopy (400-2500nm), the soil Ca concentration could be predicted with acceptable R2=0.69-0.80 (DemattĂȘ et al. , 2016; Hively et al. , 2011). Janik et al. (1998) and Cozzolino & Moron (2003) recorded a very good Ca prediction accuracy (R2=0.89 and 0.90, respectively) using PLSR modeling of VIS-NIR and MIR spectral data, respectively.