Current remote sensing based models of vegetation carbon exchange are complex and require a large number of input parameters that can not be determined directly from remote sensing. Models based entirely on remote sensing data would simplify the estimation of vegetation carbon fluxes and potentially reduce errors associated with estimation of ground based parameters. We used hyperspectral image data from the AVIRIS sensor and vegetation carbon exchange data from eddy covariance towers in the Ameriflux network to explore relationships between spectral reflectance indices and either gross carbon flux or the light use efficiency (LUE) of photosynthesis. Indices related to the greenness of the vegetation proved to be the best predictors of both gross flux and LUE. The enhanced vegetation index (EVI), which has greater sensitivity to variation in greenness of dense canopies than does NDVI, was most strongly correlated with both gross flux and LUE. The photochemical reflectance index (PRI), which has shown promise as a predictor of LUE in other studies, did not show a consistent relationship across all the vegetation types. The relationships between spectral indices and either gross flux or LUE were just as good when the flux data were averaged over one week as they were for flux data specific to the time of the spectral data collection. Consequently, these relationships appear to be useful for estimation of 8-day composites, as is done with MODIS. Work is continuing using MODIS satellite data to test these relationships across a wider range of sites.
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