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This on going seminal work presents a new methodology for the design and implementation of high performance artificial raw data generation algorithms for remote-sensed imaging applications. Special attention is given in this work to synthetic aperture radar (SAR) system modeling and simulation imaging applications in the geosciences. Of particular importance are processes such as soil moisture content, backscattering from crops, nearshore ocean surface currents, and subsurface imaging in hyperarid regions. Our computing approach is based on the successful use of cross-ambiguity functions, in a Weyl-Heisenberg computational framework, as surface point target response functions for nonlinearly modulated, time-frequency structured, artificially created transmitted signals for our SAR system raw data modeling and simulation efforts. The functions are correlated with prescribed target reflectivity density functions to produce the desired results.