## 5 Conclusions  
This study proposed an advanced approach to predict olive tree \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\) by integrating explainable machine learning with multispectral Sentinel-2 data. The aim was to develop a model to improve the management of olive groves in a hotspot climate change area. Field measurements were conducted on two olive groves in southeastern Italy, one was irrigated, and the other one was trained under rainfed conditions. Sentinel-2 reflectance bands and vegetation indices were used as predictors and various machine-learning models were tested and compared for their predictive performance. The Random Forest algorithm demonstrated the highest predictive accuracy, particularly when using Sentinel-2 spectral bands as predictors. The near-infrared and red-edge bands contributed significantly to the predictions, as demonstrated by SHAP analyses, which provided insights into the explainability of the Random Forest model.  
The developed model can provide a practical tool to support farmers and agronomists in managing olive groves more effectively under drought conditions. Giving information on crop status could support targeted interventions such as optimizing irrigation. The model could also be integrated into decision support systems to improve real-time, data-driven decision-making; this could serve as a baseline for orchard management under climate uncertainties to assess whether the model could provide benefits not only for agronomic management but also in terms of economic sustainability.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15052746/s1](https://www.mdpi.com/article/10.3390/app15052746/s1)]([https://www.mdpi.com/article/10.3390/app15052746/s1](https://www.mdpi.com/article/10.3390/app15052746/s1)), Figure S1. Heatmap for the correlation among the target (Fv\({}^{\prime}\)/Fm\({}^{\prime}\)) and the predictors (Sentinel-2 spectral bands and vegetation indices) considered in this study; Figure S2. Boxplots showing the distribution of the performance metrics (R\({}^{2}\), RRMSE, RMSE, MAE) of the machine learning models using S2 Bs (a) and VIs (b) as predictors. The black line within the boxes represents the median value.  
Conceptualization, L.C., F.P.S., V.G. and S.P.G.; data curation, L.C., B.L., C.M.M.M. and S.P.G.; formal analysis, C.M.M.M. and S.P.G.; investigation, L.C., F.A. and S.P.G.; methodology, L.C., B.L., F.P., V.G. and S.P.G.; software, L.C. and S.P.G.; supervision, S.P.G.; visualization, L.C. and S.P.G.; writing--original draft, L.C. and S.P.G.; writing--review and editing, B.L., F.P.S., F.P., V.G., F.A. and C.M.M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding authors.  
Author [PERSON] was employed by the company \"Ubotica Technologies\". The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.