Retrieval of atmospheric temperature and water vapour content from thermal infrared hyperspectral data in a purpose of atmospheric compensation - art. no. 67451F

Infrared hyperspectral imagery gives new opportunities for night observations for military, or security purposes, and for geological studies as rocks have specific infrared absorption bands. Generally, an optimized utilization of spectral information requires to retrieve spectral emissivity, which involves atmospheric compensation and surface temperature and emissivity separation (TES). This paper presents a new method dedicated to a future airborne hyperspectral sensor that will operate in the 3-5.5 and 8-12 mu m spectral ranges, at 2.2 km height. It combines neural networks in order to characterize the required parameters for atmospheric compensation and a spectral smoothness approach for TES. The network training is performed with radiance spectra simulated with MODTRAN4, and using ASTER emissivities, and the TIGR atmospheric database. A sensitivity study based on experimental design is carried out in order to compare impacts of atmospheric and surface parameters on radiance at several wavelengths. Atmospheric compensation and TES methods are then presented and their accuracy is assessed. Sensitivity of the retrievals to instrumental characteristics such as signal to noise ratio and radiometric calibration, is also studied.