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

This paper 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 Landsatobserved
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.
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