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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 cover the visible through longwave infrared portion of the optical spectrum (0.4-14 μm). The model uses statistical descriptions of surface spectral reflectances/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 work presents theory for the analytical transformation of surface statistics to at-sensor spectral radiance statistics for a downward-looking hyperspectral sensor observing both reflected sunlight and thermally emitted radiation. Comparisons of the model predictions with measurements from an airborne hyperspectral sensor are presented. Example parameter trades are included to show the utility of the model for applications in sensor design and operation.