Share Email Print

Proceedings Paper

Fitting remote sensing data with linear bidirectional reflectance models
Author(s): Jeffrey L. Privette; Eric F. Vermote
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Kernel-driven linear bidirectional reflectance models are gaining increasing attention for their potential use in operational processing of global remote sensing data. Nevertheless, the ability of these models to simulate actual reflectance anisotropy has not been completely explored with remote sensing data. To assess the suitability of linear models for the MODIS atmospheric correction system, we inverted a series of models with AVHRR and MODIS airborne simulator (MAS) data. For comparison, we also fit 2-stream turbid medium models to the respective data sets. Although the more complex models produced more accurate fits, the linear models were acceptably accurate and considerably faster. We conclude that linear models perform with sufficient speed and accuracy for atmospheric correction algorithms.

Paper Details

Date Published: 18 December 1995
PDF: 8 pages
Proc. SPIE 2586, Global Process Monitoring and Remote Sensing of the Ocean and Sea Ice, (18 December 1995); doi: 10.1117/12.228620
Show Author Affiliations
Jeffrey L. Privette, Univ. of Maryland/College Park (United States)
Eric F. Vermote, Univ. of Maryland/College Park (United States)

Published in SPIE Proceedings Vol. 2586:
Global Process Monitoring and Remote Sensing of the Ocean and Sea Ice
Donald W. Deering; Preben Gudmandsen, Editor(s)

© SPIE. Terms of Use
Back to Top