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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 or 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 emissivity as well as 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. This paper presents results from a
simple model of linear mixing of pixel spectral GLR. A pixel consists of one or more materials each with a temperature
distribution and an emissivity spectrum. Temperature distributions consistent with high resolution thermal images are
used as inputs to the model. The impact of spatial-temporal fluctuation of skin temperature on skin temperature
variability will be discussed. The results show the strong sensitivity of spectral GLR at shorter wavelengths to
temperature and significant variation of radiance mixture proportions with wavelength in the mid-infrared (3-5μm).
Spectral GLR of mixtures in the 8-12μm domain are more modestly impacted but the impact of subpixel mixing and
variability is still significant. A demonstration of the effects of linear mixing on linear un-mixing is also presented.
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