Estimating Gross Primary Production of forest using MODIS products.
Hirofumi
Hashimoto, CSUMB/NASA ARC, hirofumi.hashimoto@gmail.com
(Presenting)
Weile
Wang, CSUMB/NASA ARC, weile.wang@gmail.com
Cristina
Milesi, CSUMB/NASA ARC, cristina.milesi@gmail.com
Sangram
Gungly, BAERI/NASA ARC, sangramganguly@gmail.com
Ranga
B.
Myneni, Boston University, rmyneni@bu.edu
Ramakrishna
R.
Nemani, NASA ARC, rama.nemani@nasa.gov
Accurate estimation of GPP is imperative for future projection of carbon cycle and climate change, but global estimation of GPP still includes a lot of uncertainty and needs more investigations. It was reported that the Enhanced Vegetation Index (EVI) from MODIS, which is an optimized vegetation index developed from NDVI, showed strong seasonal correlation with GPP. However, there still remains one question whether the spatial extension of the relationship between GPP and EVI is also superior to any other satellite metrics (NDVI, EVI, and FPAR), though the seasonal correlations of GPP with EVI were well examined at each site. We then examined how well seasonal and annual GPP can be estimated by 4 MODIS products (NDVI, EVI, LAI and FPAR).Although EVI was the best satellite-derived metrics for tracking seasonal variations in GPP, LAI was the best estimator of the spatial pattern of annual GPP. We found high correlation between annual mean LAI and flux tower annual GPP (Annual GPP = 580 x annual mean LAI).Similarly to LAI, modeled leaf phenology by means of the Growing Season Index (GSI) can capture well annual variations in GPP.
Presentation Type: Poster
Poster Session: Ecosystems Science
NASA TE Funded Awards Represented:
NONE: Related Activity or Previously Funded TE Award