Estimating landscape net ecosystem exchange at high spatial-temporal resolution based on Landsat data, an improved upscaling model framework, and eddy covariance flux measurements

Dongjie Fu, Baozhang Chen, Huifang Zhang, Juan Wang, T. Andy Black, Brian Amiro, Gil Bohrer, Paul Bolstad, Richard Coulter, Faiz Rahman, Allison Dunn, McCaughey Harry, Tilden Meyers, Shashi Verma

Research output: Contribution to journalArticlepeer-review

53 Scopus citations


More accurate estimation of the carbon dioxide flux depends on the improved scientific understanding of the terrestrial carbon cycle. Remote-sensing-based approaches to continental-scale estimation of net ecosystem exchange (NEE) have been developed but coarse spatial resolution is a source of errors. Here we demonstrate a satellite-based method of estimating NEE using Landsat TM/ETM+data and an upscaling framework. The upscaling framework contains flux-footprint climatology modeling, modified regression tree (MRT) analysis and image fusion. By scaling NEE measured at flux towers to landscape and regional scales, this satellite-based method can improve NEE estimation at high spatial-temporal resolution at the landscape scale relative to methods based on MODIS data with coarser spatial-temporal resolution. This method was applied to sixteen flux sites from the Canadian Carbon Program and AmeriFlux networks located in North America, covering forest, grass, and cropland biomes. Compared to a similar method using MODIS data, our estimation is more effective for diagnosing landscape NEE with the same temporal resolution and higher spatial resolution (30m versus 1km) (r2=0.7548 vs. 0.5868, RMSE=1.3979 vs. 1.7497gCm-2day-1, average error=0.8950 vs. 1.0178gCm-2day-1, relative error=0.47 vs. 0.54 for fused Landsat and MODIS imagery, respectively). We also compared the regional NEE estimations using Carbon Tracker, our method and eddy-covariance observations. This study demonstrates that the data-driven satellite-based NEE diagnosed model can be used to upscale eddy-flux observations to landscape scales with high spatial-temporal resolutions.

Original languageEnglish (US)
Pages (from-to)90-104
Number of pages15
JournalRemote Sensing of Environment
StatePublished - Feb 5 2014

Bibliographical note

Funding Information:
This research is supported by a research grant ( 2010CB950704 ) under the Global Change Program of the Chinese Ministry of Science and Technology , a research grant ( 2012ZD010 ) of Key Project for the Strategic Science Plan in IGSNRR , CAS , the research grants ( 41071059 & 41271116 ) funded by the National Science Foundation of China , a Research Plan of LREIS ( O88RA900KA ), CAS, and “One Hundred Talents” program funded by the Chinese Academy of Sciences . Flux data collection in the sites was funded by: US Department of Energy grants: DE-SC0006708 ; US National Science Foundation grants: DEB-0911461 ; Canadian Foundation for Climate and Atmospheric Sciences (CFCAS) and Natural Science and Engineering Council of Canada (NSERC) though grants supporting Fluxnet Canada and the Canadian Carbon Program. We thank the USGS EROS data center for providing free Landsat data and the LP-DAAC and MODIS science team for providing free MODIS products. Additional contributions from the many researchers involved in data collection, as in the Fluxnet-Canada and AmeriFlux research network, and in-kind support from many government and private agencies for each study site are also gratefully acknowledged.


  • Eddy-covariance
  • Footprint climatology
  • Image fusion
  • Net ecosystem exchange
  • Regression tree
  • Upscaling


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