Researchers from a number of disciplines have long sought the ability to estimate the functional attributes of plant canopies, such as photosynthetic capacity, using remotely sensed data. To date, however, this goal has not been fully realized. In this study, fresh-leaf reflectance spectroscopy (λ=450-2500 nm) and a partial least-squares regression (PLSR) analysis were used to estimate key determinants of photosynthetic capacity-namely the maximum rates of RuBP carboxylation (V(cmax)) and regeneration (J(max))-measured with standard gas exchange techniques on leaves of trembling aspen and eastern cottonwood trees. The trees were grown across an array of glasshouse temperature regimes. The PLSR models yielded accurate and precise estimates of V(cmax) and J(max) within and across species and glasshouse temperatures. These predictions were developed using unique contributions from different spectral regions. Most of the wavelengths selected were correlated with known absorption features related to leaf water content, nitrogen concentration, internal structure, and/or photosynthetic enzymes. In a field application of our PLSR models, spectral reflectance data effectively captured the short-term temperature sensitivities of V(cmax) and J(max) in aspen foliage. These findings highlight a promising strategy for developing remote sensing methods to characterize dynamic, environmentally sensitive aspects of canopy photosynthetic metabolism at broad scales.