The Experts below are selected from a list of 3 Experts worldwide ranked by ideXlab platform
Albert Rango - One of the best experts on this subject based on the ideXlab platform.
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Estimation of surface emissivity for arid lands
IAHS-AISH publication, 2020Co-Authors: Thomas J. Schmugge, Andrew N. French, Jerry C. Ritchie, Albert RangoAbstract:Knowledge of surface emissivity is important for determining the radiation balance at the land surface. For heavily vegetated surfaces there is little problem since the emissivity is relatively uniform and close to one. For arid lands with sparse vegetation the problem is more difficult because the emissivity of the exposed soils and rocks is highly variable. With multispectral thermal infrared (TIR) observations it is possible to estimate the spectral emissivity variation for these surfaces. The data presented is from the TIMS (Thermal Infrared Multispectral Scanner) instrument which has six Channels in the 8 to 12 urn region. TIMS is a prototype of the TIR portion of the ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) instrument on NASA's EOS-TERRA satellite. The approach is to use the Temperature Emissivity Separation (TES) algorithm developed for use with ASTER data to extract the temperature and six emissivities from the six Channels of TLMS data. The algorithm makes use of the empirical relation between the range of observed emissivities and their minimum value. This approach was applied to the TIMS data acquired over the USDA/ARS Jornada Experimental Range in New Mexico. The Jornada site is typical of a desert grassland where the main vegetation components are grass (black grama) and shrubs (primarily mesquite) in the degraded grassland. The data are from an altitude of 800 m yielding a pixel resolution of approximately 2 m. The result ing spectral emissivities are in qualitative agreement with laboratory measurements of the emissivity (s) for the quartz rich soils of the site with s < 0.8 for the 8-9.5 um Channels. For the Longest Wavelength Channel little spatial variation of s was observed with values of 0.96 ± 0.005 over large areas.
Thomas J. Schmugge - One of the best experts on this subject based on the ideXlab platform.
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Estimation of surface emissivity for arid lands
IAHS-AISH publication, 2020Co-Authors: Thomas J. Schmugge, Andrew N. French, Jerry C. Ritchie, Albert RangoAbstract:Knowledge of surface emissivity is important for determining the radiation balance at the land surface. For heavily vegetated surfaces there is little problem since the emissivity is relatively uniform and close to one. For arid lands with sparse vegetation the problem is more difficult because the emissivity of the exposed soils and rocks is highly variable. With multispectral thermal infrared (TIR) observations it is possible to estimate the spectral emissivity variation for these surfaces. The data presented is from the TIMS (Thermal Infrared Multispectral Scanner) instrument which has six Channels in the 8 to 12 urn region. TIMS is a prototype of the TIR portion of the ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) instrument on NASA's EOS-TERRA satellite. The approach is to use the Temperature Emissivity Separation (TES) algorithm developed for use with ASTER data to extract the temperature and six emissivities from the six Channels of TLMS data. The algorithm makes use of the empirical relation between the range of observed emissivities and their minimum value. This approach was applied to the TIMS data acquired over the USDA/ARS Jornada Experimental Range in New Mexico. The Jornada site is typical of a desert grassland where the main vegetation components are grass (black grama) and shrubs (primarily mesquite) in the degraded grassland. The data are from an altitude of 800 m yielding a pixel resolution of approximately 2 m. The result ing spectral emissivities are in qualitative agreement with laboratory measurements of the emissivity (s) for the quartz rich soils of the site with s < 0.8 for the 8-9.5 um Channels. For the Longest Wavelength Channel little spatial variation of s was observed with values of 0.96 ± 0.005 over large areas.
Andrew N. French - One of the best experts on this subject based on the ideXlab platform.
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Estimation of surface emissivity for arid lands
IAHS-AISH publication, 2020Co-Authors: Thomas J. Schmugge, Andrew N. French, Jerry C. Ritchie, Albert RangoAbstract:Knowledge of surface emissivity is important for determining the radiation balance at the land surface. For heavily vegetated surfaces there is little problem since the emissivity is relatively uniform and close to one. For arid lands with sparse vegetation the problem is more difficult because the emissivity of the exposed soils and rocks is highly variable. With multispectral thermal infrared (TIR) observations it is possible to estimate the spectral emissivity variation for these surfaces. The data presented is from the TIMS (Thermal Infrared Multispectral Scanner) instrument which has six Channels in the 8 to 12 urn region. TIMS is a prototype of the TIR portion of the ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) instrument on NASA's EOS-TERRA satellite. The approach is to use the Temperature Emissivity Separation (TES) algorithm developed for use with ASTER data to extract the temperature and six emissivities from the six Channels of TLMS data. The algorithm makes use of the empirical relation between the range of observed emissivities and their minimum value. This approach was applied to the TIMS data acquired over the USDA/ARS Jornada Experimental Range in New Mexico. The Jornada site is typical of a desert grassland where the main vegetation components are grass (black grama) and shrubs (primarily mesquite) in the degraded grassland. The data are from an altitude of 800 m yielding a pixel resolution of approximately 2 m. The result ing spectral emissivities are in qualitative agreement with laboratory measurements of the emissivity (s) for the quartz rich soils of the site with s < 0.8 for the 8-9.5 um Channels. For the Longest Wavelength Channel little spatial variation of s was observed with values of 0.96 ± 0.005 over large areas.
Jerry C. Ritchie - One of the best experts on this subject based on the ideXlab platform.
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Estimation of surface emissivity for arid lands
IAHS-AISH publication, 2020Co-Authors: Thomas J. Schmugge, Andrew N. French, Jerry C. Ritchie, Albert RangoAbstract:Knowledge of surface emissivity is important for determining the radiation balance at the land surface. For heavily vegetated surfaces there is little problem since the emissivity is relatively uniform and close to one. For arid lands with sparse vegetation the problem is more difficult because the emissivity of the exposed soils and rocks is highly variable. With multispectral thermal infrared (TIR) observations it is possible to estimate the spectral emissivity variation for these surfaces. The data presented is from the TIMS (Thermal Infrared Multispectral Scanner) instrument which has six Channels in the 8 to 12 urn region. TIMS is a prototype of the TIR portion of the ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) instrument on NASA's EOS-TERRA satellite. The approach is to use the Temperature Emissivity Separation (TES) algorithm developed for use with ASTER data to extract the temperature and six emissivities from the six Channels of TLMS data. The algorithm makes use of the empirical relation between the range of observed emissivities and their minimum value. This approach was applied to the TIMS data acquired over the USDA/ARS Jornada Experimental Range in New Mexico. The Jornada site is typical of a desert grassland where the main vegetation components are grass (black grama) and shrubs (primarily mesquite) in the degraded grassland. The data are from an altitude of 800 m yielding a pixel resolution of approximately 2 m. The result ing spectral emissivities are in qualitative agreement with laboratory measurements of the emissivity (s) for the quartz rich soils of the site with s < 0.8 for the 8-9.5 um Channels. For the Longest Wavelength Channel little spatial variation of s was observed with values of 0.96 ± 0.005 over large areas.
