Modeling of LWIR Hyperspectral System Performance for Surface Object and Effluent Detection Applications

In support of hyperspectral sensor system design and parameter tradeoff investigations, an analytical end-to-end remote sensing system performance forecasting model has been extended to the longwave infrared (LWIR). The model uses statistical descriptions of surface emissivities and temperature variations in a scene and propagates them through the effects of the atmosphere, the sensor, and processing transformations. A resultant system performance metric is then calculated based on these propagated statistics. This paper presents the theory and operation of extensions made to the model to cover the LWIR. Theory is presented on combining both surface spectral emissivity variation with surface temperature variation on the upwelling radiance measured by a downward-looking LWIR hyperspectral sensor. Comparisons of the model predictions with measurements from an airborne LWIR hyperspectral sensor at the DoE ARM site are presented. Also discussed is the implementation of a plume model and radiative transfer equations used to incorporate a thin man-made effluent plume in the upwelling radiance. Example parameter trades are included to show the utility of the model for sensor design and operation applications.