Long-term Data Continuity: A Globally Consistent Leaf Area Index Data Record from AVHRR, MODIS, Landsat and VIIRS.
Sangram
Ganguly, NASA Ames Research Center/ BAERI, sangramganguly@gmail.com
(Presenting)
Ramakrishna
R.
Nemani, NASA Ames Research Center, rama.nemani@nasa.gov
Weile
Wang, NASA Ames Research Center, weile.wang@gmail.com
Hirofumi
Hashimoto, NASA Ames Research Center, hirofumi.hashimoto@gmail.com
Peter
Votava, NASA Ames Research Center, petr.votava-1@nasa.gov
Andrew
Michaelis, NASA Ames Research Center, amac@hyperplane.org
Yuri
Knyazikhin, Department of Geography and Environment, Boston University, jknjazi@bu.edu
Arindam
Samanta, Department of Geography and Environment, Boston University, arindam.sam@gmail.com
Cristina
Milesi, NASA Ames Research Center, cristina.milesi@gmail.com
Jennifer
L.
Dungan, NASA Ames Research Center, jennifer.l.dungan@nasa.gov
Forrest
S.
Melton, NASA Ames Research Center, forrest.s.melton@nasa.gov
Ranga
B.
Myneni, Department of Geography and Environment, Boston University, ranga.myneni@gmail.com
The emphasis for NASA’s role in future space missions (e.g. NPOESS) calls for continuous and consistent Earth System Data Records (ESDRs) that are required for quantitatively understanding Earth system processes due to climate and human-induced changes. A key step in assembling long-term data sets is establishing a link between data from earlier sensors (e.g. AVHRR/SeaWiFS) and present/future sensors (e.g. Landsat/MODIS/NPOESS) such that the derived products are independent of sensor characteristics and represent ground reality both in absolute values and variations in time/space.
Monitoring and modeling of the terrestrial biosphere within the larger context of climate variability and change studies requires global multi-decadal time series of key variables characteristic of vegetation structure such as the Leaf Area Index (LAI). A physical approach based on the radiative transfer theory of canopy spectral invariants, is used, which facilitates parameterization of the canopy spectral bidirectional reflectance factor (BRF). The methodology permits decoupling of the structural and radiometric components of any optical sensor signal and requires a set of sensor-specific values of configurable parameters, namely the single scattering albedo and uncertainties in surface reflectances for maintaining consistency in retrieved LAI. Implementation of this algorithm to derive an LAI dataset from AVHRR shows satisfactory agreement with the MODIS LAI and field data. The scalable algorithm is currently being utilized in deriving global LAI from Landsat GLS data and its functionality will be extended with the new suite of VIIRS data, thus providing a basis for generating continuous data streams of globally consistent LAI since 1970s.
Presentation Type: Poster
Poster Session: Data Records and Systems
NASA TE Funded Awards Represented:
Myneni, Ranga
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