## 3 Result
### Models' Performance
#### 3.2.2 Models' Evaluation  
The results of the 10-fold cross-validation indicated that RF yielded the best overall performance for the prediction of olive tree \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\), using both the S2 Bs and the VIs as predictors. However, generally, the use of the S2 Bs as predicting variables proved to be the most performing approach in all the analyses. In Supplementary Materials Figure S2, the boxplots illustrate the distribution of performance metrics for all machine learning models trained using the S2 Bs and the VIs as predictors, aggregated over all iterations of the 10-fold cross-validation.  
As shown in Table 5, when using the S2 Bs, RF achieved the highest R\({}^{2}\) (0.752), and the error metrics were the lowest (RMSE = 0.078; MAE = 0.054; RRMSE = 11.488); the final hyperparameters for the RF trained with S2 Bs were mtry = 4, splitrule = extratrees, min.node.size = 3, and num.trees = 500. Figure 6 shows the scatterplot of the cross-validated predictions considering all the data (Figure 6) and considering the two fields (Figure 6). The best fit for field A is slightly tighter than for field B, possibly due to the different environmental, canopy shape, and management factors. This suggests that the model performs robustly across different field conditions while still capturing potential field-specific differences in the relationship between spectral data and \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\) values. The dispersion of points varies between the two fields, with field A exhibiting a more compact distribution around the regression line, indicating higher consistency in predictions, while field B shows greater variability, particularly at mid-range \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\) values, reflecting potential environmental or management differences influencing the spectral response. Other models, i.e., SVR and GBM, had a close performance with an R\({}^{2}\) of 0.724 and 0.725, respectively, and a slightly higher RRMSE than RF: 12.26 for SVR and 12.11 for GBM. KNN and EN showed lower performance (R\({}^{2}\): 0.676 and 0.529, respectively). DT and LM yielded the lowest results of the analysis with S2 Bs, with a similar R\({}^{2}\) of 0.567 and 0.527, respectively.  
Figure 5: Panels (**a**,**b**) present the results of Recursive Feature Elimination (RFE) applied to Sentinel-2 spectral bands (S2 Bs) and vegetation indices (VIs), respectively. In each panel, the upper plot shows the Root Mean Squared Error (RMSE) computed via a bootstrap-based Bayesian Ridge Regression, plotted against the number of maintained variables. The red marker identifies the subset size yielding the lowest RMSE, indicating the optimal number of predictors. The lower bar chart displays all candidate variables in descending order of their contribution to RMSE reduction (%). Variables included in the optimal subset are shown in blue, while those excluded are in gray.  
The results worsened when the VIs were used as predictors in the analysis. Even the RF model, while still the top-performing model, also showed significantly lower outcomes when trained with VIs (R\({}^{2}\) = 0.357, RMSE = 0.128, MAE = 0.10, RRMSE = 3.466, MBE = \(-\)0.001). Also, SVR and GBM showed reductions in performance metrics. KNN and DT had a significant drop in performance, with an R\({}^{2}\) of 0.269 and 0.161, respectively. EN and LM trained with VIs yielded close results of the same models trained with S2 Bs, although with lower outcomes.