3.4.8 Soil Na prediction
The MARS model gave the highest prediction accuracy
(R2c=0.87; MBEc=-0.98;
RMSEc=10.65; RPDc=1.79) during
calibration to predict the Na and the predictions were acceptable. The
second-best model was SVR with a prediction accuracy of
R2c=0.74; MBEc=-0.66;
RMSEc=15.67; RPDc=1.21. The Na
prediction using validation dataset had non-reliable prediction accuracy
(R2p=0.55; MBEp=4.81;
RMSEp=20.63; RPDp=0.82) with the SVR
model. The prediction accuracy was the highest with SVR compared to
other models. The best overall rank of 2.38 was recorded by SVR (Figure
4h). Malmir et al. (2019) reported acceptable Na prediction using
PLSR and soil spectral data from 400 to 1000 nm. A very good prediction
of Na extracted from soil saturation (n=402, R2=0.88,
RMSE=2.45, RPD=2.89, excellent) using laboratory-measured soil spectral
reflectance (350-2500 nm) was reported by Das et al. (2015).
Islam et al. (2003) reported poor prediction for exchangeable Na
using PCR modeling of spectral data in the range of 250-2500 nm.
Similarly, Chang et al. (2001) reported inaccuracy in predicting
Mehlich-3 extractable exchangeable Na using NIR-PCR model
(R2<0.50) for a spectral range of 400-2498
nm. Poor prediction (R2=0.33) of exchangeable Na using
the MIR spectroscopy was observed by Janik et al. (1998). Better
performance of the non-linear model could be due to the fact that the
relationship between spectral data and soil characteristic is rarely
linear in nature. Variability in prediction accuracy of Na using
reflectance spectroscopy could be due to variations in modeling,
extraction or spectral data collection methods (field, processed or
dried, intact core soil samples).