Paper
11 December 1998 Atmospheric effects on the temperature emissivity separation algorithm
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Abstract
Recovery of land surface temperature (LST) from remotely sensed data requires correction for atmospheric effects and decoupling surface temperature and emissivity. In this study, we have applied the Temperature Emissivity Separation (TES) method to several flight lines of the Thermal Infrared Multispectral Scanner (TIMS) acquired as part of the HAPEX- Sahel experiment. Atmospheric correction of at-sensor radiances is done by means of nearly coincident radiosondes and the MODTRAN radiative transfer code. The sensitivity of the method to the atmospheric corrections has been checked by using different radiosonde data. Even for low altitude flights, ignorance of atmospheric correction can lead to large errors in the retrieved emissivities and temperatures. Errors depend on the surface type, but in all cases channel 1 and 6 of TIMS are the most affected. The TES method is based on an empirical relationship relating the maximum-minimum emissivity difference (or contrast) with the minimum value for the 6 TIMS channels. Residual atmospheric effects dictate the max-min difference, especially for flat targets (e.g. vegetation). Since channels 1 and 6 have shown a greater sensitivity to atmospheric effects, a modified version using only the 4 central channels has been proposed and applied to the TIMS scenes. Preliminary results suggest that this modified version yields better values for vegetation targets, with emissivities around 0.98 and very little spectral variation.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Cesar Coll, Thomas J. Schmugge, and Simon J. Hook "Atmospheric effects on the temperature emissivity separation algorithm", Proc. SPIE 3499, Remote Sensing for Agriculture, Ecosystems, and Hydrology, (11 December 1998); https://doi.org/10.1117/12.332776
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Cited by 5 scholarly publications.
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KEYWORDS
Atmospheric corrections

Atmospheric sensing

Vegetation

Atmospheric modeling

Sensors

Detection and tracking algorithms

Soil science

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