%0 Journal Article %K Ameriflux %K Eddy covariance %K Modis %K Nee %K Net ecosystem carbon exchange %K Regression tree %A Jingfeng Xiao %A Qianlai Zhuang %A Dennis D Baldocchi %A Beverly E Law %A Andrew D Richardson %A Jiquan Chen %A Ram Oren %A Gregory Starr %A Asko Noormets %A Siyan Ma %A Shashi B Verma %A Sonia Wharton %A Steven C Wofsy %A Paul V Bolstad %A Sean P Burns %A David R Cook %A Peter S Curtis %A Bert G Drake %A Matthias Falk %A Marc L Fischer %A David R Foster %A Lianhong Gu %A Julian L Hadley %A David Y Hollinger %A Gabriel G Katul %A Marcy Litvak %A Timothy A Martin %A Roser Matamala %A Steve McNulty %A Tilden P Meyers %A Russell K Monson %A William J Munger %A Walter C Oechel %A Kyaw Tha Paw U %A Hans Peter Schmid %A Russell L Scott %A Ge Sun %A Andrew E Suyker %A Margaret S Torn %B Agricultural and Forest Meterology %D 2008 %P 1827-1847 %T Estimation of net ecosystem carbon exchange for the conterminous United States by combining MODIS and AmeriFlux data %V 148 %X

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.