Calibration of remotely sensed, coarse resolution NDVI to CO2 fluxes in a sagebrush–steppe ecosystem

Authors

Bruce K. Wylie, USGS EROSFollow
Douglas A. Johnson, Utah State University
Emilio Laca, University of California - DavisFollow
Nicanor Z. Saliendra, Utah State University
Tagir G. Gilmanov, South Dakota State UniversityFollow
Bradley C. Reed, USGS EROS Data CenterFollow
Larry L. Tieszen, U.S. Geological SurveyFollow
Bruce B. Worstell, USGS EROS Data CenterFollow

Document Type

Article

Date of this Version

2003

Citation

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

Abstract

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 ( F_day) and nighttime CO₂ fluxes (R_n) using remote sensing and other data. F_day and R_n 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 F_day and R_n. We found that iNDVI was a strong predictor of F_day (R²= 0.79, n = 66, P < 0.0001). Inclusion of evapotranspiration in the predictive equation led to improved predictions of F_day (R²= 0.82, n = 66, P < 0.0001). Cross-validation indicated that regression tree predictions of F_day were prone to overfitting and that linear regression models were more robust. Multiple regression and regression tree models predicted R_n quite well (R² = 0.75–0.77, n = 66) with the regression tree model being slightly more robust in cross-validation. Temporal mapping of F_day and R_n is possible with these techniques and would allow the assessment of NEE in sagebrush–steppe ecosystems. Simulations of periodic F_day measurements, as might be provided by a mobile flux tower, indicated that such measurements could be used in combination with iNDVI to accurately predict F_day. 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.