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Generating global Leaf Area Index from Landsat: Algorithm formulation and demonstration

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Ganguly, S. , Nemani, R. R. , Zhang, G. , Hashimoto, H. , Milesi, C. , & Michaelis, A. , et al. (2012). Generating global leaf area index from landsat: algorithm formulation and demonstration. Remote Sensing of Environment, 122(Complete), 185-202.
Literature information
Type Of Reference JOUR
Title Generating global Leaf Area Index from Landsat: Algorithm formulation and demonstration
Authors Ganguly, Sangram| Nemani, Ramakrishna R.| Zhang, Gong| Hashimoto, Hirofumi| Milesi, Cristina| Michaelis, Andrew| Wang, Weile| Votava, Petr| Samanta, Arindam| Melton, Forrest| Dungan, Jennifer L.| Vermote, Eric| Gao, Feng| Knyazikhin, Yuri| Myneni, Ranga B.|
Secondary Title Remote Sensing of Environment
Abstract 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 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.
Date 2012///
Year 2012
DOI 10.1016/j.rse.2011.10.032
Database Provider ScienceDirect
volume 122
Start Page 185
Alternate Title1 Remote Sensing of Environment
Issn 0034-4257
Url http://www.sciencedirect.com/science/article/pii/S0034425712000442
Access Date 2014/12/27/07:45:13
Keywords Canopy spectral invariants| Global Land Survey (GLS)| Landsat| Leaf Area Index (LAI)|
file_attachments2 http://www.sciencedirect.com/science/article/pii/S0034425712000442
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