Natural Resources, School of



Jingfeng Xiao, Purdue University, West Lafayette, IN
Qianlai Zhuang, Purdue University, West Lafayette, IN
Dennis D. Baldocchi, University of California, BerkeleyFollow
Beverly E. Law, Oregon State UniversityFollow
Andrew D. Richardson, University of New Hampshire, DurhamFollow
Jiquan Chen, University of ToledoFollow
Ram Oren, Duke University
Gregory Starr, University of Alabama
Asko Noormets, North Carolina State University
Siyan Ma, University of California, Berkeley
Shashi Verma, University of Nebraska-LincolnFollow
Sonia Wharton, University of California, Davis
Steven C. Wofsy, Harvard University
Paul V. Bolstad, University of Minnesota, St. Paul
Sean P. Burns, University of Colorado, Boulder
David R. Cook, Argonne National Laboratory, Environmental Science Division, Argonne, IL
Peter S. Curtis, Ohio State UniversityFollow
Bert G. Drake, Smithsonian Environmental Research Center, Edgewater, MD
Matthias Falk, University of California, Davis
Marc L. Fischer, Lawrence Berkeley National LaboratoryFollow
David R. Foster, Harvard Forest and Department of Organismic and Evolutionary Biology, Harvard University, Petersham, MA
Lianhong Gu, Oak Ridge National Laboratory Environmental Sciences Division, Oak Ridge, TN
Julian L. Hadley, Harvard Forest, Harvard University, Petersham, MA
David Y. Hollinger, vUSDA Forest Service, Northeastern Research Station, Durham, NH
Gabriel G. Katul, Nicholas School of the Environment, Duke University, Durham, NC
Marcy Litvak, University of New MexicoFollow
Timothy A. Martin, University of FloridaFollow
Roser Matamala, Argonne National Laboratory, Biosciences Division, Argonne, IL
Steve McNulty, USDA Forest Service, Southern Research Station, Raleigh, NC
Tilden P. Meyers, ANOAA/ARL, Atmospheric Turbulence and Diffusion Division, Oak Ridge, TN
Russell K. Monson, Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO
J. William Munger, Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA
Walter C. Oechel, Department of Biology, San Diego State University, San Diego, CA
Kyaw Tha Paw U, University of California, Davis, CA
Hans Peter Schmid, Indiana University, Bloomington, IN
Russell L. Scott, USDA-ARS Southwest Watershed Research Center, Tucson, AZ
Ge Sun, USDA Forest Service, Southern Research Station, Raleigh, NC
Andrew E. Suyker, University of Nebraska - LincolnFollow
Margaret S. Torn, Nicholas School of the Environment, Duke University, Durham, NC

Date of this Version



Published in Agricultural and Forest Meteorlogy 148 (2008) 1827 – 1847.


Eddy covariance flux towers provide continuous measurements of net ecosystem carbon exchange (NEE) for a wide range of climate and biome types. However, these measurements only represent the carbon fluxes at the scale of the tower footprint. To quantify the net exchange of carbon dioxide between the terrestrial biosphere and the atmosphere for regions or continents, flux tower measurements need to be extrapolated to these large areas. Here we used remotely sensed data from the Moderate Resolution Imaging Spectrometer (MODIS) instrument on board the National Aeronautics and Space Administration’s (NASA) Terra satellite to scale up AmeriFlux NEE measurements to the continental scale.We first combined MODIS and AmeriFlux data for representative U.S. ecosystems to develop a predictive NEE model using a modified regression tree approach. The predictive model was trained and validated using eddy flux NEE data over the periods 2000–2004 and 2005–2006, respectively. We found that the model predicted NEE well (r = 0.73, p < 0.001). We then applied the model to the continental scale and estimated NEE for each 1 km x 1 km cell across the conterminous U.S. for each 8-day interval in 2005 using spatially explicit MODIS data. The model generally captured the expected spatial and seasonal patterns of NEE as determined from measurements and the literature. Our study demonstrated that our empirical approach is effective for scaling up eddy flux NEE measurements to the continental scale and producing wall-to-wall NEE estimates across multiple biomes. Our estimates may provide an independent dataset from simulations with biogeochemical models and inverse modeling approaches for examining the spatiotemporal patterns of NEE and constraining terrestrial carbon budgets over large areas.