Share Email Print

Proceedings Paper

Inverting a canopy reflectance model using an artificial neural network
Author(s): Peng Gong; Duane X. Wang; Shunlin Liang
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

An off-nadir canopy reflectance model, the Liang and Strahler algorithm for the coupled atmosphere and canopy (CAC) model, was used to simulate multi-angle reflectances based on various combinations of canopy biophysical parameters. Biophysical parameters such as leaf angle distribution and leaf area index were input to the CAC model along with reflectances of leaf, soil, and aerosol optical depth. The CAC model, however, can only be inverted through numerical iterations and it is extremely difficult to use for retrieval of those biophysical parameters with ordinary inversion methods. In order to retrieve those biophysical parameters, we employed an error back-propagation feed forward neural network program. We constructed a number of neural network models based on the simulated results from the CAC model. Ideally, through network training we would like to have a neural network model that uses the multi-angle reflectances as its inputs and output simultaneously all the biophysical parameters, the component reflectances of leaf and background soil, and the aerosol optical depth of the atmosphere. We have not yet reached this objective due to the requirement of an extremely large amount of calculation. In this paper, we report the results obtained from retrieving any individual parameter from multi-angle reflectances and results obtained from simultaneously retrieving some combinations of two parameters. We tested the use of a different number of multi-angle reflectances as input to the neural networks. This number varies in the range of 1 - 64. The test results show that a relative error between 1-5% or better is achievable for retrieving one parameter at a time or two parameters simultaneously

Paper Details

Date Published: 24 November 1995
PDF: 11 pages
Proc. SPIE 2585, Remote Sensing for Agriculture, Forestry, and Natural Resources, (24 November 1995); doi: 10.1117/12.227194
Show Author Affiliations
Peng Gong, Univ. of California/Berkeley (United States)
Duane X. Wang, Univ. of Calgary (Canada)
Shunlin Liang, Univ. of Maryland/College Park (United States)

Published in SPIE Proceedings Vol. 2585:
Remote Sensing for Agriculture, Forestry, and Natural Resources
Edwin T. Engman; Gerard Guyot; Carlo M. Marino, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?