The use of satellite and airborne remote sensing data to predict foliar macronutrients and pigments for a boreal mixedwood forest composed of black and white spruce, balsam fir, northern white cedar, white birch, and trembling aspen was investigated. Specifically, imaging spectroscopy (IS) and light detection and ranging (LiDAR) are used to model the foliar N:P ratio, macronutrients (N, P, K, Ca, Mg) and chlorophyll. Measurement of both foliar macronutrients and foliar chlorophyll provide critical information about plant physiological and nutritional status, stress, as well as ecosystem processes such as carbon (C) exchange (photosynthesis and net primary production), decomposition and nutrient cycling. Results show that airborne and spaceborne IS data explained approximately 70% of the variance in the canopy N:P ratio with predictions errors of less than 8% in two consecutive years. LiDAR models explained more than 50% of the variance in the canopy N:P ratio with similar predictions errors. Predictive models using spaceborne Hyperion IS data were developed with adjusted R2 values of 0.73, 0.72, 0.62, 0.25, and 0.67 for N, P, K, Ca and Mg, respectively. The LiDAR model explained 80% of the variance in canopy Ca concentration with an RMSE of less than 10%, suggesting strong correlations between forest height and Ca. Two IS derivative indices emerged as good predictors of chlorophyll across time and space. When the models of these two indices with the same parameters as generated from Hyperion data were applied to other years' data for chlorophyll concentration prediction, they could explain 71, 63 and 6% and 61, 54 and 8 % of the variation in chlorophyll concentration in 2002, 2004 and 2008, respectively with prediction errors ranging from 11.7% to 14.6%. Results demonstrate that the N:P ratio, N, P, K, Mg and chlorophyll can be modeled by spaceborne IS data and Ca can only be predicted by LiDAR data in the canopy of this forest. The ability to model the N:P ratio and macronutrients using spaceborne Hyperion data demonstrates the potential for mapping them at the canopy scale across larger geographic areas and being able to integrate them in future studies of ecosystem processes.