Title :
LAI inversion using a back-propagation neural network trained with a multiple scattering model
Author_Institution :
NASA Goddard Space Flight Center, Greenbelt, MD, USA
fDate :
9/1/1993 12:00:00 AM
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;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
