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Les auteurs voulaient sélectionner le modèle qui prévoirait le mieux la concentration de trois cations (, et ) dans le sol et le taux d’adsorption du sodium (SAR). L’analyse spectrale est un bon moyen pour prévoir les propriétés du sol rapidement et avec précision. There are intrinsic factors resulting in an unsatisfied model performance.
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Finally, SAR~SNV using SVM was selected as the best model. However, the models for SAR~ R using PLSR ( R 2 = 0.728 for calibration and 0.661 for validation, RPD = 1.43), SAR~LR using SVM ( R 2 = 0.791 for calibration and 0.712 for validation, RPD = 1.81), and SAR~SNV using SVM ( R 2 = 0.878 for calibration and 0.814 for validation, RPD = 2.13) were valid for further prediction. Of these, 27 models for, , and were not applicable for subsequent prediction, because the coefficients of determination ( R 2) were not high (0.224–0.689), and their relative percent deviations (RPD) were all smaller than the 1.4 threshold. Estimation models for four soil properties were developed using three different spectral processing and transformation techniques, i.e., reflectance ( R e), logarithm of reciprocal R e (LR), and standard normal variable of R e (SNV) were used. Three methods were applied, i.e., stepwise multiple linear regression (SMLR), partial least-squares regression (PLSR), and support vector machine (SVM). Our objective is to select the best model for predicting the three soil cation concentrations (,, and ) and sodium adsorption ratio (SAR).
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