TY - GEN
T1 - Model and measurements of linear mixing in thermal IR ground leaving radiance spectra
AU - Balick, Lee
AU - Clodius, William
AU - Jeffery, Christopher
AU - Theiler, James
AU - McCabe, Matthew
AU - Gillespie, Alan
AU - Mushkin, Amit
AU - Danilina, Iryna
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
KW - Thermal infrared emissivity spectra remote sensing
UR - http://www.scopus.com/inward/record.url?scp=40749158256&partnerID=8YFLogxK
U2 - 10.1117/12.738102
DO - 10.1117/12.738102
M3 - Conference contribution
AN - SCOPUS:40749158256
SN - 9780819469076
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VII
T2 - Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VII
Y2 - 17 September 2007 through 20 September 2007
ER -