## 3 Result
### Explainability  
For each predictor used in the RF model, SHAP values were calculated, offering a way to attribute the Sentinel-2 band's contribution to the final prediction of chlorophyll fluorescence. Figure 7 shows the SHAP summary plot, which ranks the features in order of their overall importance (top-to-bottom) and shows the distribution of SHAP values for each predictor, providing a global view of their contributions to the model. On the other hand, Figure 8 shows SHAP partial dependence plots, which show in a more detailed, \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Model** & **Predictors** & **R\({}^{2}\)** & **R\({}^{2}\) sd** & **RMSE** & **RMSE sd** & **RMSE** & **RMSE sd** & **MAE** & **MAE sd** & **MBE** & **MBE sd** \\ \hline \multirow{2}{*}{RF} & S2 Bs & 0.752 & 0.107 & 0.078 & 0.021 & 11.488 & 3.039 & 0.054 & 0.014 & \(-\)0.0002 & 0.081 \\ \cline{2-11}  & VIs & 0.357 & 0.205 & 0.128 & 0.024 & 18.696 & 3.466 & 0.100 & 0.016 & \(-\)0.001 & 0.129 \\ \hline \multirow{2}{*}{SVR} & S2 Bs & 0.724 & 0.101 & 0.083 & 0.018 & 12.226 & 2.583 & 0.059 & 0.011 & 0.004 & 0.085 \\ \cline{2-11}  & VIs & 0.514 & 0.168 & 0.113 & 0.020 & 16.565 & 2.882 & 0.089 & 0.012 & 0.003 & 0.115 \\ \hline \multirow{2}{*}{EN} & S2 Bs & 0.529 & 0.178 & 0.111 & 0.024 & 16.202 & 3.463 & 0.089 & 0.021 & 0.001 & 0.114 \\ \cline{2-11}  & VIs & 0.447 & 0.153 & 0.120 & 0.020 & 17.558 & 3.001 & 0.094 & 0.016 & \(-\)0.001 & 0.121 \\ \hline \multirow{2}{*}{KNN} & S2 Bs & 0.676 & 0.181 & 0.088 & 0.019 & 12.831 & 2.777 & 0.063 & 0.014 & \(-\)0.001 & 0.090 \\ \cline{2-11}  & VIs & 0.269 & 0.131 & 0.134 & 0.028 & 19.664 & 4.059 & 0.104 & 0.020 & 0.003 & 0.137 \\ \hline \multirow{2}{*}{GBM} & S2 Bs & 0.725 & 0.090 & 0.083 & 0.020 & 12.112 & 2.992 & 0.058 & 0.016 & \(-\)0.001 & 0.085 \\ \cline{2-11}  & VIs & 0.338 & 0.205 & 0.133 & 0.024 & 19.497 & 3.572 & 0.103 & 0.016 & 0.005 & 0.135 \\ \hline \multirow{2}{*}{DT} & S2 Bs & 0.567 & 0.141 & 0.107 & 0.023 & 15.684 & 3.316 & 0.074 & 0.014 & 0.001 & 0.110 \\ \cline{2-11}  & VIs & 0.161 & 0.121 & 0.146 & 0.029 & 21.452 & 4.252 & 0.114 & 0.019 & 0.004 & 0.148 \\ \hline \multirow{2}{*}{LM} & S2 Bs & 0.527 & 0.179 & 0.111 & 0.024 & 16.232 & 3.526 & 0.089 & 0.021 & 0.001 & 0.114 \\ \cline{2-11}  & VIs & 0.462 & 0.184 & 0.119 & 0.016 & 17.421 & 2.293 & 0.096 & 0.013 & \(-\)0.001 & 0.120 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics of the machine learning algorithms, using the Sentinel-2 spectral bands (S2 Bs) and vegetation indices (VIs) as variables to predict olive tree Fv\({}^{\prime}\)/Fm\({}^{\prime}\). sd = standard deviation. Figure 6: (**a**) Scatterplot of the overall cross-validated Random forest Prediction of olive tree Fv\({}^{\prime}\)/Fm\({}^{\prime}\) (\(n=144\)); (**b**) scatterplot of the cross-validated Random Forest prediction of olive tree Fv\({}^{\prime}\)/Fm\({}^{\prime}\) considering the two fields (\(n\) field A = 72; \(n\) field B = 72). The dashed line is the bisector while the blue and red ones are the regression lines. feature-specific way the relationship between reflectance values and the corresponding SHAP contributions over the whole range of each predictor. The most influential features were the bands b8, b8a, and b6. High reflectance values for the b8 band contributed mainly positively to the model output, thus increasing the predicted \(\mathrm{F}\mathrm{v}^{\prime}/\mathrm{F}\mathrm{m}^{\prime}\). In contrast, the lower values of b8 band reflectance tended to reduce the predicted \(\mathrm{F}\mathrm{v}^{\prime}/\mathrm{F}\mathrm{m}^{\prime}\). In particular, for the b8 band, significant changes in contributions are observed around the reflectance values of 0.34. The b8a, b6, and b7 bands exhibit a similar trend, with high reflectance values positively influencing the model and medium-to-low reflectance values showing either neutral or slightly negative effects. For the b8a band, significant changes in contributions are observed around reflectance values of 0.36.