Super resolution for satellite imagery possesses specific challenges due to its unique feature geometry compared to classic computer vision. Specifically, satellite images contain a high amount of relatively small, distributed high-frequency features that are hard to preserve when sampling up to a higher resolution. General adversarial networks (GANs) are suitable for super resolution tasks but need special attention concerning the geometric and radiometric accuracy of the results. We propose a network for versatile satellite imagery super resolution (VSISR) that focuses on high-frequency detail preservation and radiometric consistency. As a novelty, it is able to utilize a reference high resolution image during inference to lower hallucinations without altering the source image’s radiometric characteristics.<br />A GAN that is already handling well high frequencies in standard computer vision cases is adapted for satellite imagery and supplemented with a mixed pixel approach for data augmentation. Training on a diverse RGB dataset from four satellite missions results in a versatile super resolution model that is optimized to preserve radiometric features and minimize hallucinations. Compared to other neural networks for satellite image super resolution in their respective datasets, VSISR performs in the middle regarding mean PSNR (25.30 dB) and SSIM (0.8098). Nevertheless, it is the first time that, using mixed pixel training on a comparably small dataset, this versatility concerning the satellite data source is achieved while maintaining their unique radiometric traits.