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Calibration of remotely sensed, coarse resolution NDVI to CO2 fluxes in a sagebrush–steppe ecosystem
USGS Staff -- Published Research
  • Bruce K. Wylie, USGS EROS
  • Douglas A. Johnson, Utah State University
  • Emilio Laca, University of California - Davis
  • Nicanor Z. Saliendra, Utah State University
  • Tagir G. Gilmanov, South Dakota State University
  • Bradley C. Reed, USGS EROS Data Center
  • Larry L. Tieszen, U.S. Geological Survey
  • Bruce B. Worstell, USGS EROS Data Center
Date of this Version

Remote Sensing of Environment 85 (2003) 243–255; doi:10.1016/S0034-4257(03)00004-X


The net ecosystem exchange (NEE) of carbon flux can be partitioned into gross primary productivity (GPP) and respiration (R). The contribution of remote sensing and modeling holds the potential to predict these components and map them spatially and temporally. This has obvious utility to quantify carbon sink and source relationships and to identify improved land management strategies for optimizing carbon sequestration. The objective of our study was to evaluate prediction of 14-day average daytime CO2 fluxes ( Fday) and nighttime CO2 fluxes (Rn) using remote sensing and other data. Fday and Rn were measured with a Bowen ratio–energy balance (BREB) technique in a sagebrush (Artemisia spp.)–steppe ecosystem in northeast Idaho, USA, during 1996–1999. Micrometeorological variables aggregated across 14-day periods and time-integrated Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (iNDVI) were determined during four growing seasons (1996–1999) and used to predict Fday and Rn. We found that iNDVI was a strong predictor of Fday (R2= 0.79, n = 66, P < 0.0001). Inclusion of evapotranspiration in the predictive equation led to improved predictions of Fday (R2= 0.82, n = 66, P < 0.0001). Cross-validation indicated that regression tree predictions of Fday were prone to overfitting and that linear regression models were more robust. Multiple regression and regression tree models predicted Rn quite well (R2 = 0.75–0.77, n = 66) with the regression tree model being slightly more robust in cross-validation. Temporal mapping of Fday and Rn is possible with these techniques and would allow the assessment of NEE in sagebrush–steppe ecosystems. Simulations of periodic Fday measurements, as might be provided by a mobile flux tower, indicated that such measurements could be used in combination with iNDVI to accurately predict Fday. These periodic measurements could maximize the utility of expensive flux towers for evaluating various carbon management strategies, carbon certification, and validation and calibration of carbon flux models.

Citation Information
Bruce K. Wylie, Douglas A. Johnson, Emilio Laca, Nicanor Z. Saliendra, et al.. "Calibration of remotely sensed, coarse resolution NDVI to CO2 fluxes in a sagebrush–steppe ecosystem" (2003)
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