## 5 Conclusions  
ChIF and photoproduction-related indices demonstrated improved capacity to track foliar nutrient contents over greenness-based indices (Figure 7) beyond the widely researched nitrogen. The relationships between foliar nutrients and leaf-level spectral measurements were largely mediated by the variation in foliar Cab and leaf morphology. Over the course of the experiment, two groups of nutrient variations emerged. Group 1 nutrients (N, P, K, and Mg) decreased over time and were positively related to Cab. Group 2 nutrients (Cu, Mn, Zn, and S) increased over time and were negatively related to Cab. As a result, relationships between spectral indices and foliar nutrients were of opposite sign between Group 1 and Group 2 nutrients. To summarize, at the leaf scale, although many nutrients were related to chlorophyll and spectral measurements, there was a heterogeneity of responses across the micro and macronutrients. At the canopy scale, spectral indices were also influenced by canopy structure and so their capacity to detect foliar nutrient contents depended on the spatiotemporal covariation between foliar nutrient contents, Cab and canopy structure (e.g., FVC, Figure 7) within the observation. Accounting for this extra factor is critical to scaling leaf-level processes to remote sensing platforms.  
Our findings demonstrate that the measurement scale is of critical importance when attempting to use optical data to estimate nutrients. As high resolution hyperspectral and SIF imaging systems become more widespread, the capacity to resolve foliar nutrient contents separately from canopy development should increase the potential of photoproduction-related and SIF indices for plant nutrient detection. Novel imaging and modelling methods that operate between the common scales of leaves and canopies can therefore play a key role in unravelling the complexities found in our study. In particular, the combination of multi-angle high spatial resolution observations [90; 91] across a wider range of species and treatments coupled to physically based 3D radiative transfer and machine learning methods for concurrent estimation of LAI, Cab and Car will be critical to develop quantitative methods (e.g., [PERSON] et al. [25]) for the estimation of foliar nutrient contents.  
J.O.: data analysis, writing, methodology, data collection; J.A.: data collection, writing, editing, guidance; S.X.: data collection, data analysis, methodology; A.R.: experiment design, data collection, reviewing; C.Z.: data collection, editing, reviewing; T.H.: data collection, reviewing; E.H.: editing, reviewing; A.P.-C.: experiment design, data collection, writing, guidance. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the University of Helsinki (starting package funds to Porcar-Castell) and the Horizon 2020 project number 820852 (\"E-SHAPE\" (EuroGEOSS Showcases: Applications Powered by Europe)). Open access funding provided by University of Helsinki.  
Inquiries regarding the source data can be directed to the corresponding author and will be provided upon reasonable request.  
**Acknowledgments:** The authors want to thank [PERSON] for sharing his knowledge on nutrient dynamics and [PERSON] for processing the UAV datasets. Additionally, we want to thank [PERSON] for his contribution to the FVC paragraph and his valuable comments on the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.