List of Figures
Figure 1. Study area showing part of Uttar Pradesh state of India, covering the districts of Varanasi, Chanduali, Sant Ravidas Nagar and Mirzapur, localized between 82°30′ and 83°30′ East and 24°30′ and 25°30′ North.
Figure 2. Quantile- Quantile plot for (a) electrical conductivity (untransformed) and (b) log10 transformed electrical conductivity
Figure 3. (a) Reflectance Spectra in the visual- near infrared (Vis-NIR) range (350-2500 nm) of soils.
Figure 3. (b) Reflectance Spectra in the middle infra-red (MIR) range of soils.
Figure 4 (a). Correlation between electrical conductivity and reflectance in the visual- near infrared (Vis-NIR) region
Figure 4 (b). Correlation between EC and reflectance in the middle infra-red (MIR) region
Figure 5 . Calibration model developed for EC in the NIR region using (a) Partial Least Square Regression (PLSR) (b) Random Forest Regression (RF) (c) Support Vector Regression (SVR) and (d) Multivariate Adaptive Regression Splines (MARS)
Figure 6 . Validation model developed for EC in the NIR region using (a) Partial Least Square Regression (PLSR) (b) Random Forest Regression (RF) (c) Support Vector Regression (SVR) and (d) Multivariate Adaptive Regression Splines (MARS)
Figure 7 . Calibration model developed for EC in the MIR region using (a) Partial Least Square Regression (PLSR) (b) Random Forest Regression (RF) (c) Support Vector Regression (SVR) and (d) Multivariate Adaptive Regression Splines (MARS)
Figure 8 . Validation model developed for EC in the MIR region using (a) Partial Least Square Regression (PLSR) (b) Random Forest Regression (RF) (c) Support Vector Regression (SVR) and (d) Multivariate Adaptive Regression Splines (MARS)