In this paper, the U.S. Air Force's Research Laboratory Space-Time Adaptive Processing (RLSTAP) tool is used to demonstrate the impact of large sidelobe discretes on modern Synthetic Aperture Radar (SAR) signal and image processing. Sidelobe discretes ay mask or even completely obscure weak target returns of interest in the immediate vicinity of these strong returns. Adaptive processing offers the potential to mitigate the effects of strong sidelobe discretes on image formation. In this paper, we characterize the severity of the problems caused by these discretes. RLSTAP can simulate high-fidelity airborne, spaceborne, or ground based multi-channel radar data in jamming and clutter environments, develop and evaluate new signal and image processing algorithms, and assess the performance of advanced radar systems. RLSTAP is a time domain simulation, updating object positions for every radar pulse and allowing modeling of realistic effects such as returns 'walking' across range bins and Doppler filters. The site-specific clutter model uses terrain elevation and cover data to derive the line-of-site visibility, grazing angle, and clutter type for each range-angle cell. Spatial and temporal clutter statistics are applied to each cell and the signal strength at the receiver is calculated as a function of the backscatter coefficient, range, atmospheric attenuation, antenna gain, and system gains/losses. The scene generation capability in RLSTAP is unique in that it exploits Defense Terrain Elevation Data (DTED) and Land Use Land Cover Data (LULC) to create realistic clutter scenes (data cubes) for any given geographic location. As such, the application of adaptive multi-channel/multi-pulse processing to radar data that is characteristic of the area being imaged is now possible. Furthermore, the selection of waveform parameters, signal and image processing techniques, and associated radar parameters may be improved upon.