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When designing and operating Earth observation satellites, trade-offs must often be made between different hardware components, operational considerations, etc... For example, considerations include how much of the mass budget should be spent on imaging lenses?, or how long should image exposure times be?. Resulting limitations in image resolution and quality can be partially compensated for using super-resolution (SR) techniques. However, deep SR networks recently applied to satellite imagery are often trained and tested on data that lacks the typical image degrading noise present in real satellite images. In this work, we combine a method for generating realistically degraded satellite images with a deep SR network, in the context of different satellite hardware configurations and geographical types. We use this framework to assess deep SR performance given realistic remote sensing payloads across different terrain types, by evaluating payload- and terrain-dependent SR performance in reconstructing realistically degraded images. The framework allows us to model the effect of alternative satellite hardware configurations on resulting SR image quality, providing insight into optimal satellite operations and payload design in the context of SR-based image quality enhancements. [ABSTRACT FROM AUTHOR]