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[1] Carbon flux models based on light use efficiency (LUE), such as the MOD17 algorithm, have proved difficult to parameterize because of uncertainties in the LUE term, which is usually estimated from meteorological variables available only at large spatial scales. In search of simpler models based entirely on remote-sensing data, we examined direct relationships between the enhanced vegetation index (EVI) and gross primary productivity (GPP) measured at nine eddy covariance flux tower sites across North America. When data from the winter period of inactive photosynthesis were excluded, the overall relationship between EVI and tower GPP was better than that between MOD17 GPP and tower GPP. However, the EVI/GPP relationships vary between sites. Correlations between EVI and GPP were generally greater for deciduous than for evergreen sites. However, this correlation declined substantially only for sites with the smallest seasonal variation in EVI, suggesting that this relationship can be used for all but the most evergreen sites. Within sites dominated by either evergreen or deciduous species, seasonal variation in EVI was best explained by the severity of summer drought. Our results demonstrate that EVI alone can provide estimates of GPP that are as good as, if not better than, current versions of the MOD17 algorithm for many sites during the active period of photosynthesis. Preliminary data suggest that inclusion of other remote-sensing products in addition to EVI, such as the MODIS land surface temperature (LST), may result in more robust models of carbon balance based entirely on remote-sensing data.

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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