Analysis of the Performance of the TES Algorithm Over Urban Areas

Abstract

The temperature and emissivity separation (TES) algorithm is used to retrieve the land surface emissivity (LSE) and land surface temperature (LST) values from multispectral thermal infrared sensors. In this paper, we analyze the performance of this methodology over urban areas, which are characterized by a large number of different surface materials, a variability in the lowest layer of the atmospheric profiles, and a 3-D structure. These specificities induce errors in the LSE and LST retrieval, which should be quantified. With this aim, the efficiency of the TES algorithm over urban materials, the atmospheric correction, and the impact of the 3-D architecture of urban scenes are analyzed. The method is based on the use of a 3-D radiative transfer tool, TITAN, for modeling all of the radiative components of the signal registered by a sensor. From the sensor radiance, an atmosphere compensation process is applied, followed by a TES methodology that considers the observed scene to be a flat surface. Finally, the retrieved LSE and LST are compared with the original parameters. Results show the following: First, the TES algorithm used reproduces the LSE (LST) of urban materials within a root-mean-square error (rmse) of 0.017 (0.9 K). Second, 20% of uncertainty in the water vapor content of the total atmosphere introduces an rmse of 0.005 (0.4 K) for the LSE (LST) product. Third, in a standard case, the 3-D structure of an urban canyon leads to an rmse of 0.005 (0.2 K) for the LSE (LST) retrieval of the asphalt at the bottom of the scene.