The productivity of terrestrial ecosystems is a fundamental component in the global carbon cycle, and therefore is an important factor for predicting the potential effects of future global environmental changes. Our ability to accurately quantify vegetation productivity across wide ranges of spatial and temporal scales is essential for understanding relationships among the global carbon cycle, ecological succession and disturbance, and climate change. Satellite remote sensing has emerged as an important tool for providing information about the earth’s surface, and is currently used with some success to map terrestrial primary productivity in near real-time. While satellites are increasingly relied upon to derive information on earth surface characteristics, such as vegetation greenness, that affect biosphere-atmosphere interactions, there remain substantial uncertainties in the estimates of vegetation productivity and related processes derived from these sensors. These uncertainties are partly due to the relatively coarse spatial and temporal resolutions at which satellite observations occur, and partly due to the fact that additional research is necessary to link the spectral variability of plant canopies with the stressors that influence biosphere-atmosphere trace gas exchange. Hence, we propose to conduct ‘near Earth’ remote sensing experiments using an array of above canopy optical sensor measurements that will provide high spectral, spatial and temporal resolution reflectance data from the canopy surrounding the Ameriflux tower at the University of Michigan Biological Station. This approach will be used to examine the ability of the Monteith primary productivity model to predict vegetation productivity at the individual tree to stand scale. Moreover, our approach will allow us to investigate each component of the model, including the spatio-temporal dynamics of absorbed radiation and light use efficiency of the trees and stands. Through this work, we will clarify relationships between individual tree and stand level CO2 exchange by providing automated and quantitative measures of canopy tree structure and function that drive overall stand-level carbon exchange. In addition, by improving the remote sensing driven modeling of vegetation productivity, we will examine how this productivity may shift given future changes in species composition and/or climate.