Numerous studies have investigated reflectance-based estimations of physico-chemical leaf traits such as the contents of light absorbing pigments, leaf mass per area, or carbon and nitrogen contents. Only few studies, however, attempted to estimate leaf traits that are more directly linked to photosynthesis. We tested the feasibility of estimating two important photosynthesis traits, the maximum carboxylation capacity (V-cmax, 25) and the maximum electron transport rate (J(max), 25), from in-situ leaf reflectance spectra using approaches that are applicable also on larger spatial scales. We conducted measurements of reflectance, CO2 response curves, leaf mass per area (LMA), and nitrogen content per area (Na) for 37 temperate deciduous tree species and a total of 242 leaves from widely differing light environments. Partial least squares (PLS) regression was used to estimate V-cmax, 25, J(max), 25, LMA, and Na from reflectance spectra. The results showed that both V-cmax, 25 and J(max), 25 can be estimated from leaf reflectance measurements with good accuracy (R-2= 0.64 for V-cmax, 25, R-2= 0.70 for J(max), 25) even for a large number of tree species and varying light environments. Detailed analysis of reflectance-based PLS and linear regression models with regard to prediction performances and regression coefficients led to the conclusion that the correlation to Na was the dominating mechanism on which the V-cmax, 25 and J(max), 25 PLS models were based. The PLS regression coefficients of Na, V-cmax, 25 and J(max), 25 showed clear signatures of nitrogen-related absorption features. The finding that V-cmax, 25 and J(max), 25 estimations from leaf reflectance are predominantly based on their relationships to Na has important implications for large scale estimations of these photosynthesis parameters. We suggest that future studies should focus more on large scale estimation of Na from remote sensing and estimate V-cmax,V- 25 and J(max), 25 in a separate step using their respective relationships to Na. (C) 2017 Elsevier Inc. All rights reserved.