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IMPROVING BIOGENIC EMISSION ESTIMATES WITH SATELLITE IMAGERY
Citation:
Pierce Jr., T E. IMPROVING BIOGENIC EMISSION ESTIMATES WITH SATELLITE IMAGERY. Presented at NARSTO Emission Inventory Workshop, Austin, TX, October 14-17, 2003.
Impact/Purpose:
To improve the accuracy of emissions and dry deposition algorithms in the Agency's regulatory air quality and multimedia simulation models. This effort requires developing process-oriented algorithms, assembling geographical data, evaluating algorithms against field data, and designing and collaborating on field experiments to collect the data needed to test these algorithms.
Description:
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.