Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data
Net ecosystem exchange (NEE) of CO2 between the atmosphere and forest ecosystems is determined by gross primary production (GPP) of vegetation and ecosystem respiration. CO2 flux measurements at individual CO2 eddy flux sites provide valuable information on the seasonal dynamics of GPP. In this paper, we developed and validated the satellite-based Vegetation Photosynthesis Model (VPM), using site-specific CO2 flux and climate data from a temperate deciduous broadleaf forest at Harvard Forest, Massachusetts, USA. The VPM model is built upon the conceptual partitioning of photosynthetically active vegetation and non-photosynthetic vegetation (NPV) within the leaf and canopy. It estimates GPP, using satellite-derived Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), air temperature and photosynthetically active radiation (PAR). Multi-year (1998–2001) data analyses have shown that EVI had a stronger linear relationship with GPP than did the Normalized Difference Vegetation Index (NDVI). Two simulations of the VPM model were conducted, using vegetation indices from the VEGETATION (VGT) sensor onboard the SPOT-4 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite. The predicted GPP values agreed reasonably well with observed GPP of the deciduous broadleaf forest at Harvard Forest, Massachusetts. This study highlighted the biophysical performance of improved vegetation indices in relation to GPP and demonstrated the potential of the VPM model for scaling-up of GPP of deciduous broadleaf forests.
Earth Sciences, Earth Systems Research Center
Remote Sensing of Environment
Digital Object Identifier (DOI)
Xiangming Xiao, Qingyuan Zhang, Bobby Braswell, Shawn Urbanski, Stephen Boles, Steven Wofsy, Berrien Moore III, Dennis Ojima, Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data, Remote Sensing of Environment, Volume 91, Issue 2, 30 May 2004, Pages 256-270, ISSN 0034-4257, http://dx.doi.org/10.1016/j.rse.2004.03.010.
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