## 3 Results
### Correlations between Leaf and Canopy Spectral Indices and Foliar Nutrient Contents
Interestingly, the correlation trends between FY760 (C) and foliar nutrient contents remained very weak, albeit with a reversed sign, despite the fact that FY760 (C) was positively and strongly correlated with FVC. In fact, the only nutrient that correlated with FY760 (C) was Fe, which turned out to be the nutrient with the strongest relationship to FVC (Figure 7). Similarly, strong correlations emerged also at the canopy level between foliar nutrient contents and PRI, here with positive correlations along with Group 1 nutrients and Cab and negative correlations with Group 2 nutrients and Car/Cab. For CCI, correlations with Group 1 macronutrients remained moderate and negative. Similarly, to FY760 (C), the strongest correlation was found between CCI and Fe. At this scale, CCI was also positively related to FVC and Car/Cab. We also analyzed the correlations between spectral indices and leaf nutrients separately for each of the measuring dates (see Appendices B and C and examples in Figures 8 and 9 for the cases of Cab and N, respectively). By separating the correlations between the early and late measurement we could visualize how the temporal development of leaf and canopy properties, in response to the nutrient and water stress treatments, influenced the relationship between spectral indices and leaf nutrient contents, allowing us to identify correlations that remained consistent over time as well as those that presented contrasting patterns, denoting changes in the mechanisms underlying the relationships. Figure 8: Correlation between foliar Cab contents and spectral indices at the leaf level and the canopy scale. Open circles represent measurements from measurement point 1 (n = 26), and closed circles represent measurements from measurement point 2 (n = 26). The top row of data represents the leaf level spectral measurements, and the bottom row of data represents the canopy measurements. The R\({}^{2}\) values in blue represent the model from the early July measurements, whereas the R\({}^{2}\) values in black represent the model from the late July measurements. A red linear correlation line in the plot (together with a R\({}^{2}\) value), indicates that the models from individual measurement points are not significantly different from the model that includes data from both early and late measurements. In terms of Cab, the correlations in Figure 8 indicate that the relationship between leaf level F685, Fratio, MTCI, the red edge reflectance, and the CCI-index, and foliar Cab remained consistent over time despite potential variations in leaf thickness and morphology (Figure 5) between early and late measurements. Similar results were obtained for N in Figure 9. These results suggest that the leaf-level relationship between these spectral indices and Cab or N remains largely undisturbed by other factors. In fact, the relationship between Cab and N or Mg remained constant across early and late measurements, suggesting that the foliar partitioning of N and Mg into Cab synthesis would have remained constant (Figure A15). Yet, leaf-level correlations between spectral indices and foliar nutrients were not always consistent over time. For example, although the correlation between leaf level red edge and Ca (Appendix B, Figure A2) or between leaf level F685 and Mg (Appendix B, Figure A6) presented a similar slope, their intercepts were different, indicating that at least a third factor had changed between the early and late measurements affecting the relationship. Likewise, we also observed reversed correlation patterns between early and late measurements, such as in leaf level PRI and Cab (Figure 8) or leaf level F760 and K (Appendix B, Figure A8), suggesting that different factors mediate the relationships at different points in time. These patterns lead also to correlations that were of opposite sign within a single measuring point relative to the pooled dataset, such as for Fratio and Cu (Appendix B, Figure A3).