Multispectral thermal-infrared images from the Mauna Loa caldera in Hawaii, USA are examined to study the effects of
surface roughness on remotely retrieved emissivities. We find up to a 3% decrease in spectral contrast in ASTER
(Advanced Spaceborne Thermal Emission and Reflection Radiometer) 90-m/pixel emissivities due to sub-pixel surface
roughness variations on the caldera floor. A similar decrease in spectral contrast of emissivities extracted from
MASTER (MODIS/ASTER Airborne Simulator) ~12.5-m/pixel data can be described as a function of increasing surface
roughness, which was measured remotely from ASTER 15-m/pixel stereo images. The ratio between ASTER stereo
images provides a measure of sub-pixel surface-roughness variations across the scene. These independent roughness
estimates complement a radiosity model designed to quantify the unresolved effects of multiple scattering and
differential solar heating due to sub-pixel roughness elements and to compensate for both sub-pixel temperature
dispersion and cavity radiation on TIR measurements.
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.
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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