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Proceedings Paper

Atmospheric effects on the temperature emissivity separation algorithm
Author(s): Cesar Coll; Thomas J. Schmugge; Simon J. Hook
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Paper 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.

Paper Details

Date Published: 11 December 1998
PDF: 11 pages
Proc. SPIE 3499, Remote Sensing for Agriculture, Ecosystems, and Hydrology, (11 December 1998); doi: 10.1117/12.332776
Show Author Affiliations
Cesar Coll, Univ. de Valencia (Spain)
Thomas J. Schmugge, USDA Agricultural Research Service (United States)
Simon J. Hook, Jet Propulsion Lab. (United States)

Published in SPIE Proceedings Vol. 3499:
Remote Sensing for Agriculture, Ecosystems, and Hydrology
Edwin T. Engman, Editor(s)

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