Natural Resources, School of



Jingfeng Xiao, Purdue UniversityFollow
Qianlai Zhuang, Purdue UniversityFollow
Beverly E. Law, Oregon State UniversityFollow
Jiquan Chen, University of ToledoFollow
Dennis D. Baldocchi, University of California - BerkeleyFollow
David R. Cook, Argonne National Laboratory
Ram Oren, Duke UniversityFollow
Andrew D. Richardson, Harvard UniversityFollow
Sonia Wharton, University of California - DavisFollow
Siyan Ma, University of California - BerkeleyFollow
Timothy A. Martin, University of FloridaFollow
Shashi Verma, University of Nebraska - LincolnFollow
Andrew E. Suyker, University of Nebraska - LincolnFollow
Russell L. Scott, USDA-ARS Southwest Watershed Research Center
Russell K. Monson, University of Colorado at BoulderFollow
Marcy Litvak, University of New MexicoFollow
David Y. Hollinger, USDA Forest Service
Ge Sun, USDA Forest Service
Kenneth J. Davis, University of New Hampshire
Paul Bolstad, University of MinnesotaFollow
Sean Burns, University of Colorado at BoulderFollow
Peter S. Curtis, Ohio State UniversityFollow
Bert G. Drake, Smithsonian Environmental Research CenterFollow
Matthias Falk, University of California - DavisFollow
Marc L. Fischer, Lawrence Berkeley National LaboratoryFollow
David R. Foster, Harvard UniversityFollow
Lianhong Gu, Oak Ridge National Laboratory Environmental Sciences Division
Julian L. Hadley, Harvard UniversityFollow
Gabriel G. Katul, Duke University
Roser Matamala, Argonne National LaboratoryFollow
Steve McNulty, USDA Forest Service
Tilden P. Meyers, NOAA/ARL, Atmospheric Turbulence and Diffusion Division
J. William Munger, Harvard UniversityFollow
Asko Noormets, North Carolina State University at RaleighFollow
Walter Oechel, San Diego State UniversityFollow
Kyaw Tha Paw U, University of California - DavisFollow
Hans Peter Schmid, Indiana University
Gregory Starr, University of Alabama - Tuscaloosa
Margaret S. Torn, Lawrence Berkeley National LaboratoryFollow
Steven C. Wofsy, Harvard UniversityFollow

Date of this Version



Remote Sensing of Environment 114 (2010) 576–591; doi:10.1016/j.rse.2009.10.013


The quantification of carbon fluxes between the terrestrial biosphere and the atmosphere is of scientific importance and also relevant to climate-policy making. Eddy covariance flux towers provide continuous measurements of ecosystem-level exchange of carbon dioxide spanning diurnal, synoptic, seasonal, and interannual time scales. However, these measurements only represent the fluxes at the scale of the tower footprint. Here we used remotely sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to upscale gross primary productivity (GPP) data from eddy covariance flux towers to the continental scale. We first combined GPP and MODIS data for 42 AmeriFlux towers encompassing a wide range of ecosystem and climate types to develop a predictive GPP model using a regression tree approach. The predictive model was trained using observed GPP over the period 2000–2004, and was validated using observed GPP over the period 2005–2006 and leave-one-out cross-validation. Our model predicted GPP fairly well at the site level. We then used the model to estimate GPP for each 1 km×1 km cell across the U.S. for each 8-day interval over the period from February 2000 to December 2006 using MODIS data. Our GPP estimates provide a spatially and temporally continuous measure of gross primary production for the U.S. that is a highly constrained by eddy covariance flux data. Our study demonstrated that our empirical approach is effective for upscaling eddy flux GPP data to the continental scale and producing continuous GPP estimates across multiple biomes. With these estimates, we then examined the patterns, magnitude, and interannual variability of GPP. We estimated a gross carbon uptake between 6.91 and 7.33 Pg C yr−1 for the conterminous U.S. Drought, fires, and hurricanes reduced annual GPP at regional scales and could have a significant impact on the U.S. net ecosystem carbon exchange. The sources of the interannual variability of U.S. GPP were dominated by these extreme climate events and disturbances.