HR: 0800h
AN: A41C-0055    [Abstracts]
TI: Modeling Large-Scale Biosphere NEE by Integrating Satellite Images and Climate Data - Vegetation Photosynthesis and Respiration Model (VPRM)
AU: Mahadevan, P
EM: devan@deas.harvard.edu
AF: Harvard University, Department of Earth and Planetary Science and Division of Engineering and Applied Science, 20 Hoffman lab, Cambridge, MA 02138 United States
AU: * Matross, D M
EM: matross@fas.harvard.edu
AF: Harvard University, Department of Earth and Planetary Science and Division of Engineering and Applied Science, 20 Hoffman lab, Cambridge, MA 02138 United States
AU: Wofsy, S C
EM: wofsy@fas.harvard.edu
AF: Harvard University, Department of Earth and Planetary Science and Division of Engineering and Applied Science, 20 Hoffman lab, Cambridge, MA 02138 United States
AU: Xiao, X
EM: xiangming.xiao@unh.edu
AF: University of New Hampshire, Complex Systems Research Center Institute for the Study of Earth, Oceans and Space, 39 College Road, Durham, NH 03824 United States
AU: Lin, J C
EM: jcl@dragon.atmos.colostate.edu
AF: Colorado State University, Department of Atmospheric Science, Fort Collins, CO 80523 United States
AU: Gerbig, C
EM: cgerbig@bgc-jena.mpg.de
AF: Max-Planck-Institute for Biogeochemie, Hans-Knoell-Str. 10, JENA, D-07745 Germany
AU: Chow, V Y
EM: vyc@io.harvard.edu
AF: Harvard University, Department of Earth and Planetary Science and Division of Engineering and Applied Science, 20 Hoffman lab, Cambridge, MA 02138 United States
AU: Gottlieb, E
EM: gottlieb@deas.harvard.edu
AF: Harvard University, Department of Earth and Planetary Science and Division of Engineering and Applied Science, 20 Hoffman lab, Cambridge, MA 02138 United States
AU: Munger, J
EM: jwm@io.harvard.edu
AF: Harvard University, Department of Earth and Planetary Science and Division of Engineering and Applied Science, 20 Hoffman lab, Cambridge, MA 02138 United States
AB: Scaling-up fluxes from site-specific measurements to regional levels is receiving considerable attention, and methods of remote sensing and mathematical modeling are being used in this effort [Running et al., 1999; Xiao et al., 2004]. We developed a satellite-based Vegetation Photosynthesis and Respiration Model (VPRM) to estimate the diurnal, seasonal, and interannual variation of Net Ecosystem Exchange (NEE) of different biomes over North America. The VPRM adds light saturating characteristics and a respiration component to the Vegetation Photosynthesis Model of Xiao et al. [2004,] utilizing improved vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS). Gross Primary Production (GPP) within a vegetation class is represented using 1) the Enhanced Vegetation Index (EVI), which specifies the phenological properties of the fraction of photosynthetically active radiation absorbed by the leaf chlorophyll (FAPARchl), and 2) the Land Surface Water Index (LSWI), which reflects changes in both leaf water content and soil moisture and accounts for the effect of leaf age on photosynthesis at canopy level [Pathmathevan et al., 2005; Xiao et al., 2004]. Since EVI may be regarded as a general index for plant activity, the VPRM utilizes EVI and temperature for the parameterization of Respiration (R). The VPRM, plus data on temperature and sunlight, provides remarkably accurate descriptions of Gross and Net Ecosystem Exchange and Respiration when applied at more than a dozen sites in the Ameriflux network of eddy-covariance towers, for time scales from hours to years, using only 3 parameters per vegetation type. VPRM estimates are much better for ecosystem atmospheric fluxes than estimates by other Production Efficiency Models that are solely based on the Normalized Difference Vegetation Index. The results demonstrate the potential of the satellite-based VPRM for understanding the causes and controls of seasonal and interannual variabilities in NEE in terrestrial ecosystems, which is one of the primary challenges facing the carbon science community.
DE: 3315 Data assimilation
DE: 3346 Planetary meteorology (5445, 5739)
DE: 3355 Regional modeling
DE: 3360 Remote sensing
SC: Atmospheric Sciences [A]
MN: Fall Meeting 2005