Full spectrum modeling of at-sensor spectral radiance variability due to surface variability
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 and near infrared through longwave infrared portion of the optical spectrum (0.4 to 14 /spl mu/m). The model takes statistical descriptions of surface spectral reflectances 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. This paper presents the theory for analytically transforming 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's predictions with measurements from an airborne hyperspectral sensor are presented. An example is included to show the model's utility in understanding the magnitude of full spectrum radiance components.