## 4 Discussion
### Evaluation of the Predictors  
To monitor the changes in plant vigor and photosynthetic activities, the use of VIs is considered a valid approach [95]. In our study, \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\) showed no strong correlation with VIs; for instance, the NDVI, a widely used VI in plant monitoring [96], had a correlation coefficient of 0.19. This result is also in line with the findings of [PERSON] et al. [97]. In their experiments conducted on olive, peach, orange (water stress trials), and olive orchard (variety trial), the NDVI of the crown had no relationship with crown fluorescence. [PERSON] et al. [98] indicated that the NDVI tends to better reflect plant vigor than the photosynthesis processes. On the other hand, several studies showed the high sensitivity of the near-infrared and red-edge spectral bands to the canopy photosynthesis activities [99; 100; 101]. Thus, the choice of the predictors appears to be another important aspect of this research.  
While many studies traditionally use VIs as predictors for machine learning analysis [102], we found that model performance increases when the reflectance data are used instead of the VIs. This is probably because the calculation of VIs typically involves the combination of specific spectral bands, which can lead to aggregation and/or potential loss of detailed spectral information due to their tendency to saturate [103]. Whereas with the use of bands, more spectral information from each observation would be preserved, allowing the models to capture variations that might be overlooked when trained with VIs. In addition, the use of raw reflectance data reduces the risk of bias introduced by selecting some indices over others, keeping the full dynamic range.  
Random Forest Explainability  
The explainability analysis indicated the spectral bands b8, b8a, and b6 as the most important in the prediction of \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\). The role of these spectral regions has been studied in previous research since they were linked to plant physiological traits. These results align with the known strong link between canopy water content, cell structure, and NIR/red-edge reflectance in stressed olive trees [99; 100; 101; 102; 103; 104; 105; 106]. While when considered individually, these bands did not have a high correlation with the target (0.40, 0.51, 0.41 for b7, b8, and b8a, respectively--see Figure S1), their high significance resulting from the SHAP analysis may further highlight the ability of RF to detect nonlinear or complex patterns [107] and extract useful information for prediction in remote sensing contexts. Through the explainability analysis, it has been possible to better understand the contribution of each variable to the predictive model. This approach not only increases reliability in the results of the model but also offers agronomists and researchers a tool to interpret the complex relationships between satellite data and the physiological state of plants. In an agricultural context that is increasingly dependent on advanced technologies, explainability is necessary to ensure that decisions based on artificial intelligence models are transparent, thus facilitating a wider and more informed adoption of such technologies in the agricultural sector [53].