## 4 Discussion
### Impact of Canopy Structure on the Capacity of Spectral Indices to Track Foliar Nutrient Contents  
At the canopy scale we also found evidence of a dichotomous split in correlations between optical indices and foliar nutrient contents (Figure 7). The nutrient groupings (Group 1/Group 2) remained consistent with the leaf level correlations, but the correlations displayed either an inverted pattern (FY685 (C) and Fratio), had strongly decreased (Red edge and MTCI), increased (NDVI but especially PRI) or remained similar (CCI). Clearly, these contrasting patterns were influenced by the dynamics in canopy structure that took place between measuring points in response to the fertilization and water stress treatments. It is important to note that we are here comparing canopy-level spectral data to leaf level nutrients, instead of canopy level nutrients. Canopy-level spectral data is often compared to scaled canopy nutrient contents using LAI or total leaf area [44; 84; 85]. Unfortunately, the indirect estimation of total LAI in our potato crop was highly challenging due to highly dynamic variation in leaf angle found in the same experiment [28]. In turn, direct estimation of LAI using destructive methods was not practical in the context of this experiment. Accordingly, we here focus our analysis on the correlations with leaf foliar contents, which remains a key parameter for crop modelling and management [25], avoiding adding an additional source of uncertainty. The implications of this point on the correlations between variables are discussed below. In contrast to the leaf level results, greenness-based indices were largely uncorrelated with Group 1 nutrients, although a weak negative relationship appeared between Group 1 and NDVI. It is important to note however that all these indices, especially the NDVI, were positively related to FVC, suggesting that their correlation with nutrients would have probably improved if they would have been scaled to total canopy nutrient contents using LAI or total leaf area. In previous research, [PERSON] et al. [86] showed that MTCI was able to capture differences in leaf N concentrations, however, as reported earlier by [PERSON] et al. [18], vegetation indices based on the red edge region are susceptible to the impact of bare soil to the signal. This could in our case affect the signal and explain why the high correlation between N concentrations seen at the leaf scale can no longer be detected at the canopy scale. The lack of a stronger correlation between NDVI and N at the canopy scale could possibly be due to the NDVI signal saturating even in moderate LAI conditions. In fact, the negative relationship between NDVI, Group 1 nutrients and Cab observed at this scale (Figure 7) would point to a causal relationship caused by the contrasting patterns observed here between Group 1 and Cab (tendency to decrease over time), and canopy development (tendency to increase over time). When correlations are analyzed separately within each sampling point, where variation in canopy structure remains smaller, the positive correlation between MTCI and Group 1 nutrients (Figure A12) or the negative correlation between red edge reflectance and foliar Cab and Group 1 nutrients (Figure A13) is maintained, as noted also in a previous study for N [87]. Overall, since canopy greenness indices are affected by FVC, their capacity to track foliar nutrient contents seems to be dependent on the covariation between FVC and foliar Cab, which was here very limited. In addition, soil reflectance effects may have also influenced our results as canopy closure tended to increase over the experimental site. The weaker correlations between greenness indices and foliar nutrients during the first measuring day (Figure A12), when FVC tended to be lower, could support such an effect. Likewise, variability in incoming illumination during the first measuring point could have also contributed with some uncertainty (Figure A14).