TY - JOUR
T1 - Evaluation of MODIS Gross Primary Productivity (GPP) in tropical monsoon regions
AU - Gebremichael, Mekonnen
AU - Barros, Ana P.
N1 - Funding Information:
We thank Dr. Maosheng Zhao for his generous help with MODIS data sets. The first author was a postdoctoral associate at Duke University when this work was conducted. The research was funded in part by NASA Grant NNGO04GP02G with the second author and by the Pratt School of Engineering at Duke University. The Sonora tower data were gracefully shared by Julio Cesar Rodriguez and Christopher Watts. The Land EcoHydrology Model is available from Ana Barros upon request.
Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2006/1/30
Y1 - 2006/1/30
N2 - Near real-time vegetation indices derived from MODIS (MODerate resolution Imaging Spectroradiometer) observations (http://modis.gsfc.nasa.gov) provide a first opportunity to monitor ecohydrological systems globally at a spatial resolution consistent with biophysical processes at the field scale. Here, we present work toward the quantitative estimation of the uncertainty associated with MODIS Gross Primary Productivity (GPP), an end-product that depends on several MODIS derived vegetation indices. GPP products, available at 8-day and 1-km resolutions, were evaluated in two representative tropical ecosystems: a mixed forest site in the humid tropics (the Marsyandi river basin in the Nepalese Himalayas), and an open shrubland site in a semi-arid region (the Sonora river basin in northern Mexico). The MODIS-GPP products were compared against simulations made with a process-based biochemical-hydrology model driven by flux tower meteorological observations. Whereas the temporal march of vegetation indices and GPP products is consistent between the model and the algorithm, our study indicates that that there is a positive bias in the case of the mixed forest biome in the Marsyandi basin, and a negative bias in the case of open shrublands in the Sonora basin. We examined the error contribution from the DAO meteorological data used in the standard MODIS GPP products. The bias between the GPP estimates using DAO and tower meteorology is - 2.77 gC/m 2/day (i.e., - 77% of the mean of the tower-based GPP) in the Marsyandi, and 0.33 gC/m2/day (i.e., 18% of the mean of the tower-based GPP) in Sonora. Analysis of the temporal evolution of the discrepancies between the model and the MODIS algorithm points to the need for examining the light use efficiency parameterization, especially with regard to the representation of nonlinear functional dependencies on vapor pressure deficit (VPD), photosynthetically available radiation (PAR), and seasonal evolution of the productive capacity of vegetation as influenced by water stress.
AB - Near real-time vegetation indices derived from MODIS (MODerate resolution Imaging Spectroradiometer) observations (http://modis.gsfc.nasa.gov) provide a first opportunity to monitor ecohydrological systems globally at a spatial resolution consistent with biophysical processes at the field scale. Here, we present work toward the quantitative estimation of the uncertainty associated with MODIS Gross Primary Productivity (GPP), an end-product that depends on several MODIS derived vegetation indices. GPP products, available at 8-day and 1-km resolutions, were evaluated in two representative tropical ecosystems: a mixed forest site in the humid tropics (the Marsyandi river basin in the Nepalese Himalayas), and an open shrubland site in a semi-arid region (the Sonora river basin in northern Mexico). The MODIS-GPP products were compared against simulations made with a process-based biochemical-hydrology model driven by flux tower meteorological observations. Whereas the temporal march of vegetation indices and GPP products is consistent between the model and the algorithm, our study indicates that that there is a positive bias in the case of the mixed forest biome in the Marsyandi basin, and a negative bias in the case of open shrublands in the Sonora basin. We examined the error contribution from the DAO meteorological data used in the standard MODIS GPP products. The bias between the GPP estimates using DAO and tower meteorology is - 2.77 gC/m 2/day (i.e., - 77% of the mean of the tower-based GPP) in the Marsyandi, and 0.33 gC/m2/day (i.e., 18% of the mean of the tower-based GPP) in Sonora. Analysis of the temporal evolution of the discrepancies between the model and the MODIS algorithm points to the need for examining the light use efficiency parameterization, especially with regard to the representation of nonlinear functional dependencies on vapor pressure deficit (VPD), photosynthetically available radiation (PAR), and seasonal evolution of the productive capacity of vegetation as influenced by water stress.
KW - Ecohydrology
KW - GPP
KW - MODIS
KW - Tropics
KW - Validation
KW - Water stress
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U2 - 10.1016/j.rse.2005.10.009
DO - 10.1016/j.rse.2005.10.009
M3 - Article
AN - SCOPUS:30544447430
SN - 0034-4257
VL - 100
SP - 150
EP - 166
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
IS - 2
ER -