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NARSTO Workshop on Innovative Methods for Emission Inventory Development and Evaluation University of Texas, Austin October 14-17, 2003 |
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Improving Biogenic Emission Estimates with Satellite Imagery
Thomas E. Pierce*
Atmospheric Sciences Modeling Division/ARL
National Oceanic and Atmospheric Administration, Research Triangle Park, North Carolina
This presentation will review how existing and future applications of satellite imagery can improve the accuracy of biogenic emission estimates. Existing applications of satellite imagery to biogenic emission estimates have focused on characterizing land cover. Vegetation data in the current version of the Biogenic Emissions Inventory System (BEIS) are largely based on the USGS National Land Cover Characteristics (NLCC) dataset, which is derived from AVHRR imagery. The NLCC data have been further augmented with a forest fraction database available at 1 km resolution from the U.S. Forest Service and based on analysis of AVHRR, LANDSAT, and ground-truth measurements. Xu et al. (“Estimates of biogenic emissions using satellite observation”, Atmospheric Environment, vol. 36, 2002) demonstrate the utility of using monthly AVHRR data to more directly drive biogenic emission calculations. Beyond characterizing vegetation, satellite imagery is being used quite promisingly to perform inverse analysis of biogenic emissions. Researchers at Harvard University are using data from the GOME platform to derive formaldehyde patterns across the United States as a check against estimated isoprene emission distributions. The GOME data suggest that the distribution of isoprene is correctly represented in a model like BEIS and that the BEIS2 estimates may be underestimated. Satellite imagery can provide meteorological data fields that are vital to biogenic emission algorithms. For example, work supported by the TNRRCC has used GOES data to more accurately depict photosynthetically available radiation (PAR) for input to the GloBEIS program. In addition to current applications of satellite imagery, this presentation will review how emerging satellite imagery datasets may improve future modeling tools. Areas of possible improvements include refined temporal estimates in leaf biomass, quantitative measures of drought stress on vegetation, and better discrimination of vegetation species types.
*On assignment to the National Exposure Research Laboratory, U.S. Environmental Protection Agency.