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Please use this identifier to cite or link to this item: http://hdl.handle.net/1903/4324

Title: Retrieving Leaf Area Index With a Neural Network Method: Simulation and Validation
Authors: Liang, Shunlin
Fang, Hongliang
Type: Article
Keywords: Enhanced Thematic Mapper Plus
ETM+
leaf area index
LAI
neural networks
NNs
radiative transfer
soil reflectance index
SRI
Issue Date: Sep-2003
Publisher: Institute of Electrical and Electronics Engineers
Citation: Fang, H. and S. Liang, (2003), Retrieving Leaf Area Index With a Neural Network Method: Simulation and Validation, IEEE Transactions on Geoscience and Remote Sensing, 41 (9): 2052-2062.
Abstract: Leaf area index () is a crucial biophysical parameter that is indispensable for many biophysical and climatic models. A neural network algorithm in conjunction with extensive canopy and atmospheric radiative transfer simulations is presented in this paper to estimateLAIfromLandsat-7 Enhanced ThematicMapper Plus data. Two schemes were explored; the first was based on surface reflectance, and the second on top-of-atmosphere (TOA) radiance. The implication of the second scheme is that atmospheric corrections are not needed for estimating the surface LAI. A soil reflectance index (SRI) was proposed to account for variable soil background reflectances. Ground-measured LAI data acquired at Beltsville, MD were used to validate both schemes. The results indicate that both methods can be used to estimate LAI accurately. The experiments also showed that the use of SRI is very critical.
Required Publisher Statement: Copyright Institute of Electrical and Electronics Engineers.
URI: http://hdl.handle.net/1903/4324
Appears in Collections:Geography Research Works

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