## 3 Result  
### Field Data  
Weather Conditions and Measured \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\)  
During the investigation dates, July was the hottest month, with an average temperature of 28.1 \({}^{\circ}\)C and 30.2 \({}^{\circ}\)C around the area of fields A and B, respectively (Figure 2). The total amount of rainfall in field A was 97.9 mm and 123.7 mm in field B, with June as the driest month for both fields (8.7 mm for field A; 13.9 mm for field B). Instead, in September, a higher amount of rainfall occurred (53.9 mm for field A and 47.5 mm for field B) (Figure 2). The weather trend appears strictly linked to the distribution of the measured \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\) (Figure 3). At the beginning of field measurements, in June, field A showed statistically significantly higher \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\) values than field B (0.72 \(\pm\) 0.02 for field A and 0.65 \(\pm\) 0.04 for field B, \(p\) \(<\) 0.001). A similar trend was observed in July when the \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\) values were lower than in June. In addition to the drought period, the increased temperatures between \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Machine Learning Model** & **Hyperparameter Optimized** & **R Package** & **Refs.** \\ \hline EN & alpha, lambda & caret, glmnet & [54; 70] \\ GBM & n.trees, interaction.depth, shrinkage, & caret, gbm & [54; 71] \\ KNN & n.minobisnode & caret & [54] \\ RF & mtry, splitrule, min.node.size, & caret, randomForest, ranger & [54; 72; 73] \\ LM & - & caret & [54] \\ SVR & sigma, C & caret, kernlab & [54; 74] \\ DT & cp & caret, rpart & [54; 75] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: R package and optimized hyperparameters for each machine learning model. July and August led \(\rm{Fv^{\prime}/Fm^{\prime}}\) to drop significantly; in August, the differences between the two olive groves were more evident with a wider distribution of values, indicating that, in field B, the olive groves were under severe stress. In September, with a renewal of rainfall, \(\rm{Fv^{\prime}/Fm^{\prime}}\) rose, restoring a better physiological situation, especially in field A, where in addition to rainfall, irrigation was performed. Figure 3: Boxplot showing the distribution of the measured \(\rm{Fv^{\prime}/Fm^{\prime}}\) between field A and field B for the dates of sampling. Symbols indicate statistical differences according to the Wilcoxon test (\({}^{*}p<0.05;\)**\(p<0.01;\)***\(p<0.0001\)). Figure 2: Weather conditions around the area of the fields investigated (field A, (**a**); field B, (**b**)) during the irrigation season (Civil Protection Agency of Apulia). In Supplementary Materials, Figure S1 shows the correlation matrix between \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\) and the predictors (S2 Bs and VIs). The bands b2, b3, b4, b5, b11, and b12 had low correlations of 0.08, 0.10, 0.07, 0.14, 0.09, and 0.09, respectively. Meanwhile, near-infrared and vegetation red-edge bands, b7, b8, and b8a, showed slightly higher correlations of 0.40, 0.51, and 0.41, respectively. A representation of changes in reflectance values of S2 Bs and VIs across the investigation dates is shown in Figure 4. For S2 Bs, the b2 shows higher reflectance values in the dates of June and July; thus, it decreased in September. A similar trend was observed for b3, b4, and b5. However, the reflectance values for these bands were slightly higher compared to those of b2. For the bands involving the red-edge and nir regions (b6 to b8a), a decrease in reflectance values is reported. Bands b8 and b8a, probably due to drought stress in August, show lower values, with a narrower distribution than in June and July (Figure 4) since these spectral regions are related to stress conditions. The SWIR bands (b11 and b12) show values between 0.3 and 0.4, with a slight increase from June to September (Figure 4). This trend may be related to the sensitivity of SWIR bands to changes in leaf water status [81].