Paper
29 October 2007 Roughness effects on thermal-infrared emissivities estimated from remotely sensed images
Amit Mushkin, Iryna Danilina, Alan R. Gillespie, Lee K. Balick, Matthew F. McCabe
Author Affiliations +
Abstract
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.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Amit Mushkin, Iryna Danilina, Alan R. Gillespie, Lee K. Balick, and Matthew F. McCabe "Roughness effects on thermal-infrared emissivities estimated from remotely sensed images", Proc. SPIE 6749, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VII, 67492V (29 October 2007); https://doi.org/10.1117/12.738125
Lens.org Logo
CITATIONS
Cited by 3 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Surface roughness

Calibration

Data acquisition

Multiple scattering

Natural surfaces

Data modeling

Reflection

Back to Top