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LAI (in situ, simulated, Landsat-derived, and MODIS): A comparison within an Oak-Hickory Forest Complex in southwestern Virginia, USA.
Citation:
Iiames, J. AND E. Cooter. LAI (in situ, simulated, Landsat-derived, and MODIS): A comparison within an Oak-Hickory Forest Complex in southwestern Virginia, USA. Presented at PECORA 19- Sustaining Land Imaging UAS to Satellites, Denver, CO, November 18, 2014.
Impact/Purpose:
This is a poster presentation
Description:
The United States Environmental Protection Agency’s Environmental Sciences and Atmospheric Modeling Analysis Divisions are investigating the viability of simulated (i.e., ‘modeled’) leaf area index (LAI) inputs into various regional and local scale air quality models. Satellite LAI retrievals were generated from in situ collected LAI then regressed against Landsat ETM+ normalized difference vegetation index (NDVI) values at a 30 m spatial scale. This LAI surface map was then aggregated to 990 m to compare this product to the National Aeronautics Space Administration (NASA) 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 8-day composite LAI product. Next, LAI results from the biogeochemical Environmental Policy Integrated Climate (EPIC) model developed by the U.S. Department of Agriculture (USDA) were explored. The EPIC model was designed primarily for managed agricultural field crop ecosystems, but also includes managed woody species that span both xeric and mesic sites (e.g., mesquite, pine, oak, etc.). EPIC site-specific results were calibrated using observations from one oak-hickory site in southwestern Virginia, USA for the year 2002. These results will allow for the comparison to satellite-derived LAI retrievals for 2002 (i.e., MODIS) to detect seasonal patterns of LAI on this site, eventually being tested over a wide range of soil types and forest species. Models such as EPIC are needed for current and future climate applications for which satellite LAI retrievals cannot be performed. One regional climate application, the Community Multiscale Air Quality Model (CMAQ), relies on output from the weather research model (WRF) where LAI is based on deep soil temperature, expected maximum LAI and a fixed seasonal profile. A more detailed temporal and spatial EPIC-and/or MODIS-derived LAI input is likely to improve model estimates of meteorological condition and biogenic emission estimates due to improved spatial and temporal LAI estimates.