Digital Repository at the University of Maryland (DRUM) >
College of Behavioral & Social Sciences >
Geography Research Works >
Please use this identifier to cite or link to this item:
|Title: ||Retrieving Leaf Area Index With a Neural Network Method: Simulation and Validation|
|Authors: ||Liang, Shunlin|
|Keywords: ||Enhanced Thematic Mapper Plus|
leaf area index
soil reflectance index
|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.|
|Appears in Collections:||Geography Research Works|
All items in DRUM are protected by copyright, with all rights reserved.