4 May 2006 Land surface temperature and emissivity retrieval from thermal infrared hyperspectral imagery
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A new algorithm, optimized land surface temperature and emissivity retrieval (OLSTER), was developed to compensate for atmospheric effects and retrieve land surface temperature (LST) and emissivity from airborne thermal infrared hyperspectral data. The OLSTER algorithm is designed to retrieve both natural and man-made materials. Multi-directional or multi-temporal observations are not required, and the scenes do not have to be dominated by blackbody features. The OLSTER algorithm consists of a preprocessing step, an iterative near-blackbody pixels search, and an iterative constrained optimization loop. The preprocessing step provides initial estimates of LST per pixel and the atmospheric parameters of transmittance and upwelling radiance for the entire image. Pixels that are under- or over-compensated for the atmospheric parameters are classified as near-blackbody and lower emissivity pixels, respectively. A constrained optimization of the atmospheric parameters using generalized reduced gradients on the near-blackbody pixels ensures physical results. The downwelling radiance is estimated from the upwelling radiance by applying a look-up table of coefficients based on a polynomial regression of radiative transfer model runs for the same sensor altitude. The LST and emissivity per pixel are retrieved simultaneously using the well established ISSTES algorithm. The OLSTER algorithm can retrieve LST within about ± 2.0 K, and emissivities within about ± 0.01 based on numerical simulation.
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Marvin Boonmee, Marvin Boonmee, John R. Schott, John R. Schott, David W. Messinger, David W. Messinger, } "Land surface temperature and emissivity retrieval from thermal infrared hyperspectral imagery", Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 62331V (4 May 2006); doi: 10.1117/12.665899; https://doi.org/10.1117/12.665899

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