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Proceedings Paper

Model and measurements of linear mixing in thermal IR ground leaving radiance spectra
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Paper Abstract

Hyperspectral thermal IR remote sensing is an effective tool for the detection and identification of gas plumes and solid materials. Virtually all remotely sensed thermal IR pixels are mixtures of different materials and temperatures. As sensors improve and hyperspectral thermal IR remote sensing becomes more quantitative, the concept of homogeneous pixels becomes inadequate. The contributions of the constituents to the pixel spectral ground leaving radiance are weighted by their spectral emissivities and their temperature, or more correctly, temperature distributions, because real pixels are rarely thermally homogeneous. Planck's Law defines a relationship between temperature and radiance that is strongly wavelength dependent, even for blackbodies. Spectral ground leaving radiance (GLR) from mixed pixels is temperature and wavelength dependent and the relationship between observed radiance spectra from mixed pixels and library emissivity spectra of mixtures of 'pure' materials is indirect. A simple model of linear mixing of subpixel radiance as a function of material type, the temperature distribution of each material and the abundance of the material within a pixel is presented. The model indicates that, qualitatively and given normal environmental temperature variability, spectral features remain observable in mixtures as long as the material occupies more than roughly 10% of the pixel. Field measurements of known targets made on the ground and by an airborne sensor are presented here and serve as a reality check on the model. Target spectral GLR from mixtures as a function of temperature distribution and abundance within the pixel at day and night are presented and compare well qualitatively with model output.

Paper Details

Date Published: 29 October 2007
PDF: 12 pages
Proc. SPIE 6749, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VII, 674914 (29 October 2007); doi: 10.1117/12.738102
Show Author Affiliations
Lee Balick, Los Alamos National Lab. (United States)
William Clodius, Los Alamos National Lab. (United States)
Christopher Jeffery, Los Alamos National Lab. (United States)
James Theiler, Los Alamos National Lab. (United States)
Matthew McCabe, Los Alamos National Lab. (United States)
Alan Gillespie, Univ. of Washington (United States)
Amit Mushkin, Univ. of Washington (United States)
Iryna Danilina, Univ. of Washington (United States)


Published in SPIE Proceedings Vol. 6749:
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VII
Manfred Ehlers; Ulrich Michel, Editor(s)

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