DocumentCode
1027533
Title
LAI inversion using a back-propagation neural network trained with a multiple scattering model
Author
Smith, James A.
Author_Institution
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Volume
31
Issue
5
fYear
1993
fDate
9/1/1993 12:00:00 AM
Firstpage
1102
Lastpage
1106
Abstract
Standard regression methods applied to canopies within a single homogeneous soil type yield good results for estimating leaf area index (LAI) but perform unacceptably when applied across soil boundaries. In contrast, the neural network reported generally yielded absolute percentage errors of <30%. The network was applied, without retraining, to a sample of Landsat TM data for an agriculture/forestry study site
Keywords
backscatter; forestry; inverse problems; neural nets; remote sensing; Landsat TM data; agricultural study site; back-propagation neural network; canopies; forestry study site; homogeneous soil type; inversion; leaf area index; multiple scattering model; soil boundaries; Artificial neural networks; Data analysis; Forestry; Neural networks; Reflectivity; Remote sensing; Satellites; Scattering parameters; Soil measurements; Vegetation mapping;
fLanguage
English
Journal_Title
Geoscience and Remote Sensing, IEEE Transactions on
Publisher
ieee
ISSN
0196-2892
Type
jour
DOI
10.1109/36.263783
Filename
263783
Link To Document