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Abstract. Correct estimation of spatially distributed CO2 flux is of utmost importance for regional and global carbon balance studies. Tower-based instruments provide flux data from a small footprint area and may not be suitable for spatial extrapolation over areas not represented by the towers. In this study we developed a method of combining optical indices from remotely sensed hyperspectral images with flux data from towers covering different vegetation types to make spatially continuous maps of gross CO2 fluxes. Using a simple light-use efficiency model, we tested the ability of spectral indices derived from Airborne Visible Infrared Imaging Spectrometer (AVIRIS) imagery to estimate photosynthetic fluxes of several boreal forest stands. Because CO 2 flux from terrestrial ecosystems is dependent on both vegetation cover and physiological state, we hypothesized that measures of both forest structure and physiology were important for flux estimation. Consequently, the modeled fluxes considered both the normalized difference vegetation index (NDVI) and a scaled value of the photochemical reflectance index (PRI), both

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