## 3 Results
### Correlations between Leaf and Canopy Spectral Indices and Foliar Nutrient Contents
Finally, we also identified situations where correlations were absent within a measuring day but appeared only when pooling data from the two measuring points, such as the relationship between F685 and Mn (Appendix B, Figure A5), suggesting that indirect interaction mediates the relationship between nutrients and spectral indices. For example, with nutrient contents influencing the leaf morphology instead of or above the stoichiometry of the photosynthetic apparatus. Figure 9: Detecting leaf N contents with 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 indices, 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 measurements are not significantly different from the model that includes data from both measurements. At the canopy scale, we could also identify correlations that remained more consistent between early and late measurements, such as CCI and N (Figure 9), albeit many less than at the leaf level, which was expected due to the impact of a varying canopy structure not present in the leaf level correlations. For example, in the correlation between N, NDVI, and MTCI (Figure 9), the slopes remained similar between early and late measurements, but the intercept was different. In addition, reversed correlation patterns could be found at the canopy scale, such as between Ca and Fratio (Appendix B, Figure A8). Similarly, as at the leaf level, these patterns lead to correlations that have an opposite sign within measuring points relative to the temporally pooled dataset; an example of this can be seen in the correlation between Zn and CCI (Appendix B, Figure A10) and Cu and CCI (Appendix B, Figure A3). Stronger correlations were found in the latter measurements between canopy optical properties and nutrients and pigments relative to the earlier measurements. For example, there were stronger correlations between Cab and canopy red edge reflectance, MTCI, and FY685 (C) in the later measurements, which were not evident in the earlier or pooled measurements (Appendix C, Figure A13), possibly due to the enhanced variability during the second measuring point once nutrient and especially water stress treatments had time to impact leaf and canopy development. There were also some correlations between canopy spectral measurements and foliar nutrient and pigment contents that were weak within measuring point data, but emerged when pooling both early and late measurements together, such as, for example, between P and Fratio (Appendix B, Figure A7), emphasizing the influence of canopy development in mediating the relationships between foliar nutrient contents and spectral indices at this scale, which is discussed further in the next section.