Generating Global Leaf Area Index from Landsat: Algorithm Formulation and Demonstration

Sangram Ganguly, BAERI/ NASA ARC, sangramganguly@gmail.com (Presenter)
Ramakrishna R. Nemani, NASA ARC, rama.nemani@nasa.gov
Gong Zhang, NASA ARC/ Cal State Univ Monterey, yenite@gmail.com
Hirofumi Hashimoto, CSUMB/NASA ARC, hirofumi.hashimoto@gmail.com
Milesi Cristina, CSUMB/NASA ARC, cristina.milesi@gmail.com
Andrew Michaelis, CSUMB/NASA ARC, andrew.r.michaelis@nasa.gov
Weile Wang, CSUMB/NASA/ARC, weile.wang@gmail.com
Petr Votava, CSUMB/NASA ARC, votavap@gmail.com
Arindam Samanta, Atmospheric and Environmental Research Inc., asamanta@aer.com
Forrest Melton, NASA ARC/CSUMB, forrest.s.melton@nasa.gov
Jennifer Dungan, NASA Ames Research Center, jennifer.l.dungan@nasa.gov
Eric Vermote, University of Maryland, eric@ltdri.org
Yuri Knyazikhin, Boston University, jknjazi@bu.edu
Ranga Babu Myneni, Boston University, rmyneni@bu.edu

This research summarizes the implementation of a physically based algorithm for the retrieval of vegetation green leaf area index (LAI) from Landsat surface reflectance data. The algorithm is based on the canopy spectral invariants theory and provides a computationally efficient way of parameterizing the Bidirectional Reflectance Factor (BRF) as a function of spatial resolution and wavelength. LAI retrievals from the application of this algorithm to aggregated Landsat surface reflectances are consistent with those of MODIS for homogeneous sites represented by different herbaceous and forest cover types. Example results illustrating the physics and performance of the algorithm suggest three key factors that influence the LAI retrieval process: 1) the atmospheric correction procedures to estimate surface reflectances; 2) the proximity of Landsat-observed surface reflectance and corresponding reflectances as characterized by the model simulation; and 3) the quality of the input land cover type in accurately delineating pure vegetated components as opposed to mixed pixels. Accounting for these factors, a pilot implementation of the LAI retrieval algorithm was demonstrated for the state of California utilizing the Global Land Survey (GLS) 2005 Landsat data archive. In a separate exercise, the performance of the LAI algorithm over California was evaluated by using the short-wave infrared band in addition to the red and near-infrared bands. Results show that the algorithm, while ingesting the short-wave infrared band, has the ability to delineate open canopies with understory effects and may provide useful information compared to a more traditional two-band retrieval. Future research will involve implementation of this algorithm at continental scales and a validation exercise will be performed in evaluating the accuracy of the 30-m LAI products at several field sites.

Presentation Type:  Poster

Session:  Global Change Impact & Vulnerability   (Tue 11:30 AM)

Associated Project(s): 

• Related Activity

Poster Location ID: 178