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    Retrieving Leaf Area Index With a Neural Network Method: Simulation and Validation

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    IEEE.LAI.2003.pdf (1.446Mb)
    No. of downloads: 2073

    Date
    2003-09
    Author
    Liang, Shunlin
    Fang, Hongliang
    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.
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    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.
    URI
    http://hdl.handle.net/1903/4324
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    • Geography Research Works
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    Copyright Institute of Electrical and Electronics Engineers.

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