In support of hyperspectral sensor system design and parameter tradeoff investigations, an analytical end-to-end remote sensing system performance forecasting model is being developed. The model uses statistical descriptions of class reflectances in a scene and propagates them through the effects of the atmosphere, the sensor, and any processing transformations. A resultant system performance metric is then calculated based on these propagated statistics. The model divides a remote sensing system into three main components: the scene, the sensor, and the processing algorithms. Scene effects modeled include the solar illumination, atmospheric transmittance, shade effects, adjacency effects, and overcast clouds. Sensor effects modeled include the following radiometric noise sources: shot noise, thermal noise, detector readout noise, quantization noise, and relative calibration error. The processing component includes atmospheric compensation, various linear transformations, and a spectral matched filter used to obtain detection probabilities. This model has been developed for the HYDICE airborne imaging spectrometer covering the reflective solar spectral region from 0.4 to 2.5 micrometers . The paper presents the theory and operation of the model, as well as provides the results of validation studies comparing the model predictions to results obtained using HYDICE data. An example parameter trade study is also included to show the utility of the model for system design and operation applications.