An Ensemble Kalman Filter (EnKF) is used to assimilate canopy reflectance data into an ecosystem model. We demonstrate the use of an augmented state vector approach to enable a canopy reflectance model to be used as a non-linear observation operator. A key feature of data assimilation (DA) schemes, such as the EnKF, is that they incorporate information on uncertainty in both the model and the observations to provide a best estimate of the true state of a system. In addition, estimates of uncertainty in the model outputs (given the observed data) are calculated, which is crucial in assessing the utility of model predictions. Results are compared against eddy-covariance observations of CO2 fluxes collected over three years at a pine forest site. The assimilation of 500 m spatial resolution MODIS reflectance data significantly improves estimates of Gross Primary Production (GPP) and Net Ecosystem Productivity (NEP) from the model, with clear reduction in the resulting uncertainty of estimated fluxes. However, foliar biomass tends to be over-estimated compared with measurements. Issues regarding this over-estimate, as well as the various assumptions underlying the assimilation of reflectance data are discussed.