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Spatial prediction of photosynthesis requires an understanding of how foliage nitrogen (N) and phosphorus (P) regulate this process and the relationship between these elements and scalable spectral proxies. Hyperspectral imagery has been used to predict important photosynthetic variables such as the maximum rate of carboxylation (Vcmax) and electron transport (Jmax). However, our understanding of how generally applicable these relationships are for plants that are limited by N and P, characterised by respective mass based ratios of N/P ≤ 10 and N/P > 10, is still incomplete as most studies assume N and P co-limit photosynthesis. Hyperspectral imagery and measurements of photosynthesis were obtained from one-year old Pinus radiata D. Don, grown under a factorial combination of N and P treatments. Using these data, the objectives of this study were to (i) identify whether trees were co-limited or independently limited by N and P, and then use hyperspectral imagery to (ii) partition N and P limited trees, (iii) build models of N and P from a range of hyperspectral indices and (iv) explore links between key plant traits and both Vcmax and Jmax. Compared to the use of all data, which assumes co-limitation, markedly stronger relationships between N and P and photosynthetic capacity were obtained through splitting data at N/P = 10 (independent limitation) for both Vcmax (R2 = 0.40 vs. 0.59) and Jmax (R2 = 0.38 vs. 0.64). A random forest model was used to accurately partition N from P limited trees and the two main variables used within this model were Photochemical Reflectance Index (PRI) and Solar-Induced Chlorophyll Fluorescence (SIF). Using data from the P limiting phase, the most precise models of P were created using PRI (R2 = 0.75) and SIF (R2 = 0.52). Indices that were proxies for chlorophyll were the most precise predictors of N within the N limiting phase but strong positive relationships were also evident between N and both PRI (R2 = 0.83) and SIF (R2 = 0.57). Through their correlations with N and P, there were strong positive relationships between both SIF, PRI and Vcmax (R2 = 0.78 and 0.83, respectively) and Jmax (R2 = 0.80 and 0.83, respectively) that were generalisable across both N and P limiting ranges. These results suggest that quantified SIF and PRI from hyperspectral images may have greater precision and generality for predicting both foliage nutrition and biochemical limitations to photosynthesis than other widely used hyperspectral indices., The project was partly funded through the Resilient Forests programme, which is funded through Scion SSIF as well as the Forest Grower’s Levy Trust. Funding was also received from the National Institute for Forest Products Innovation (Project Number NIF073-1819), which comprised contributions from the Australian Government, Australasian Forestry Companies and South Australian and Tasmanian State Governments.

The prediction of carbon uptake by forests across fertility gradients requires accurate characterisation of how biochemical limitations to photosynthesis respond to variation in key elements such as nitrogen (N) and phosphorus (P). Over the last decade, proxies for chlorophyll and photosynthetic activity have been extracted from hyperspectral imagery and used to predict important photosynthetic variables such as the maximal rate of carboxylation (Vcmax) and electron transport (Jmax). However, little research has investigated the generality of these relationships within the nitrogen (N) and phosphorus (P) limiting phases, which are characterised by mass based foliage ratios of N:P ≤ 10 for N limitations and N:P > 10 for P limitations. Using measurements obtained from one year old Pinus radiata D. Don grown under a factorial range of N and P treatments this research examined relationships between photosynthetic capacity (Vcmax, Jmax) and measured N, P and chlorophyll (Chla+b). Using functional traits quantified from hyperspectral imagery we then examined the strength and generality of relationships between photosynthetic variables and Photochemical Reflectance Index (PRI), Sun-Induced Chlorophyll Fluorescence (SIF) and chlorophyll a + b derived by radiative transfer model inversion. There were significant (P < .001) and strong relationships between photosynthetic variables and both N (R2 = 0.82 for Vcmax; R2 = 0.87 for Jmax) and Chla+b (R2 = 0.85 for Vcmax; R2 = 0.86 for Jmax) within the N limiting phase that were weak (R2 < 0.02) and insignificant within the P limiting phase. Similarly, there were significant (P < .05) positive relationships between P and photosynthetic variables (R2 = 0.50 for Vcmax; R2 = 0.58 for Jmax) within the P limiting phase that were insignificant and weak (R2 < 0.33) within the N limiting phase. Predictions of photosynthetic variables using Chla+b estimated by model inversion were significant (P < .001), positive and strong (R2 = 0.64 for Vcmax; R2 = 0.63 for Jmax) within the N limiting phase but insignificant and weak (R2 < 0.05) within the P limiting phase. In contrast, both SIF and PRI exhibited moderate to strong positive correlations with photosynthetic variables within both the N and P limiting phases. These results suggest that quantified SIF and PRI from hyperspectral images may have greater generality in predicting biochemical limitations to photosynthesis than proxies for N and chlorophyll a + b, particularly under high foliage N content, when P is limiting., The project was partly funded through the Resilient Forests programme, which is funded through Scion SSIF as well as the Forest Grower's Levy Trust. Funding was also received from the National Institute for Forest Products Innovation (Project Number NIF073-1819), which comprised contributions from the Australian Government, Australasian Forestry Companies and South Australian and Tasmanian State Governments.