Heinsch, F.A., M. Zhao, S.W. Running, J.S. Kimball, R.R. Nemani, K.J. Davis, P.V. Bolstad, B.D. Cook, A.R. Desai, D.M. Ricciuto, B.E. Law, W.C. Oechel, H. Kwon, H. Luo, S.C. Wofsy, A.L. Dunn, J.W. Munger, D.D. Baldocchi, L. Xu, D.Y. Hollinger, A.D. Richardson, P.C. Stoy, M.B.S. Siqeira, R.K. Monson, S. Burns, and L.B. Flanagan, in press. Evaluation of remote sensing based terrestrial producitivity from MODIS using regional tower eddy flux network observations, IEEE Transactions on Geosciences and Remote Sensing.

The Moderate Resolution Spectroradiometer (MODIS) sensor has provided near real-time estimates of gross primary production (GPP) since March 2000. We compare four years (2000-2003) of satellite-based calculations of GPP with tower eddy CO2 flux-based estimates across diverse landcover types and climate regimes. We examine the error contributions from meteorology, LAI/fPAR, and land cover. The relative error of the difference between DAO and tower meteorology based annual GPP results is 27% (±45%), indicating that the global DAO meteorology plays an important role in the accuracy of the GPP algorithm. Approximately 63% of leaf area indices (LAI) were within specifications of the MOD15 algorithm, although remaining values overestimated site values. Land cover presented the fewest errors, with most errors within the forest classes, reducing potential error. Tower-based and MODIS estimates of annual GPP compare favorably for most biomes, although MODIS GPP overestimates tower estimates by 20 - 30%. Seasonally, summer estimates of MODIS GPP are closest to tower estimates, while spring estimates are the worst, most likely the result of the relatively rapid onset of leaf-out. This study indicates that the current MODIS GPP adequately captures GPP spatial patterns and temporal variability across a diverse range of biomes and climate regimes and can be used for estimating global vegetation productivity.