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Proceedings Paper

MODTRAN4: radiative transfer modeling for remote sensing
Author(s): Gail P. Anderson; Alexander Berk; Prabhat K. Acharya; Michael W. Matthew; Lawrence S. Bernstein; James H. Chetwynd; H. Dothe; Steven M. Adler-Golden; Anthony J. Ratkowski; Gerald W. Felde; James A. Gardner; Michael L. Hoke; Steven C. Richtsmeier; Brian Pukall; Jason B. Mello; Laila S. Jeong
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Paper Abstract

MODTRAN4, the newly released version of the U.S. Air Force atmospheric transmission, radiance and flux model is being developed jointly by the Air Force Research Laboratory / Space Vehicles Directorate (AFRL / VS) and Spectral Sciences, Inc. It is expected to provide the accuracy required for analyzing spectral data for both atmospheric and surface characterization. These two quantities are the subject of satellite and aircraft campaigns currently being developed and pursued by, for instance: NASA (Earth Observing System), NPOESS (National Polar Orbiting Environmental Satellite System), and the European Space Agency (GOME - Global Ozone Monitoring Experiment). Accuracy improvements in MODTRAN relate primarily to two major developments: (1) the multiple scattering algorithms have been made compatible with the spectroscopy by adopting a correlated-^ approach to describe the statistically expected transmittance properties for each spectral bin and atmospheric layer, and (2) radiative transfer calculations can be conducted with a Beer-Lambert formulation that improves the treatment of path inhomogeneities. Other code enhancements include the incorporation of solar azimuth dependence in the DISORT-based multiple scattering model, the introduction of surface BRDF (Bi-directional Radiance Distribution Functions) models and a 15 cm-1 band model for improved computational speed. Finally, recent changes to the HITRAN data base, relevant to the 0.94 and 1.13 um bands of water vapor, have been incorporated into the MODTRAN4 databases.

Paper Details

Date Published: 23 August 2000
PDF: 8 pages
Proc. SPIE 4049, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, (23 August 2000); doi: 10.1117/12.410338
Show Author Affiliations
Gail P. Anderson, Air Force Research Lab. and National Oceanic and Atmospheric Administration (United States)
Alexander Berk, Spectral Sciences, Inc. (United States)
Prabhat K. Acharya, Spectral Sciences, Inc. (United States)
Michael W. Matthew, Spectral Sciences, Inc. (United States)
Lawrence S. Bernstein, Spectral Sciences, Inc. (United States)
James H. Chetwynd, Air Force Research Lab. (United States)
H. Dothe, Spectral Sciences, Inc. (United States)
Steven M. Adler-Golden, Spectral Sciences, Inc. (United States)
Anthony J. Ratkowski, Air Force Research Lab. (United States)
Gerald W. Felde, Air Force Research Lab. (United States)
James A. Gardner, Air Force Research Lab. and Univ. of Arizona (United States)
Michael L. Hoke, Air Force Research Lab. (United States)
Steven C. Richtsmeier, Spectral Sciences, Inc. (United States)
Brian Pukall, Air Force Research Lab. (United States)
Jason B. Mello, Air Force Research Lab. (United States)
Laila S. Jeong, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 4049:
Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI
Sylvia S. Shen; Michael R. Descour, Editor(s)

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