Estimation of net ecosystem carbon exchange for the conterminous United States by combining MODIS and AmeriFlux data

Jingfeng Xiao, Qianlai Zhuang, Dennis D. Baldocchi, Beverly E. Law, Andrew D. Richardson, Jiquan Chen, Ram Oren, Gregory Starr, Asko Noormets, Siyan Ma, Shashi B. Verma, Sonia Wharton, Steven C. Wofsy, Paul V. Bolstad, Sean P. Burns, David R. Cook, Peter S. Curtis, Bert G. Drake, Matthias Falk, Marc L. FischerDavid R. Foster, Lianhong Gu, Julian L. Hadley, David Y. Hollinger, Gabriel G. Katul, Marcy Litvak, Timothy A. Martin, Roser Matamala, Steve McNulty, Tilden P. Meyers, Russell K. Monson, J. William Munger, Walter C. Oechel, Kyaw Tha Paw U, Hans Peter Schmid, Russell L. Scott, Ge Sun, Andrew E. Suyker, Margaret S. Torn

Research output: Contribution to journalArticlepeer-review

219 Scopus citations


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 × 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.

Original languageEnglish (US)
Pages (from-to)1827-1847
Number of pages21
JournalAgricultural and Forest Meteorology
Issue number11
StatePublished - Oct 2008

Bibliographical note

Funding Information:
The work of J. Xiao and Q. Zhuang was partly funded by the National Science Foundation (NSF) Carbon and Water Program (EAR-0630319). We thank the U.S. Department of Energy Biological and Environmental Research, Terrestrial Carbon Program (Award #DE-FG02-04ER63917) for funding Dr. B.E. Law to develop the AmeriFlux network protocols and database design for the network, and the Office of Science, U.S. Department of Energy, through the Midwestern Regional Center of the National Institute for Global Environmental Change under Cooperative Agreements No. DE-FC03-90ER610100 for funding P.S. Curtis. We thank the principal investigators of the MODIS data products including A.R. Huete, Z. Wan, R.B. Myneni, and E.F. Vermote and other contributors as well as the Oak Ridge National Laboratory (ORNL), Distributed Active Archive Center (DAAC), and the Earth Observing System (EOS) Data Gateway for making these products available. We also thank T.A. Boden at the Carbon Dioxide Information Analysis Center, ORNL, S.K.S. Vannan at the ORNL DACC, M. Zhao at the University of Montana, and Z. Wan at the University of California, Santa Barbara, for helpful discussion about AmeriFlux data, MODIS ASCII subsets, MODIS Quality Assurance (QA) flags, and MODIS LST, respectively. The PRISM climate database was provided by the PRISM Group, Oregon State University ( ). Computing support was provided by the Rosen Center for Advanced Computing, Purdue University.


  • AmeriFlux
  • Eddy covariance
  • NEE
  • Net ecosystem carbon exchange
  • Regression tree


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