This research explores the application of generative artificial intelligence, specifically the novel ARISGAN framework, for generating high-resolution synthetic satellite imagery in the challenging arctic environment. The study addresses the crucial need for realistic and high-resolution surface imagery in the Arctic, vital for applications ranging from satellite retrieval systems to the wellbeing and safety of Inuit populations relying on detailed surface observations. The ARISGAN framework combines dense block, multireceptive field, and Pix2Pix architecture, showcasing promising results that surpass existing state-of-the-art models across diverse tasks and metrics. Land-based imagery super-resolution exhibits superior metrics in comparison to sea ice-based imagery across multiple models. This research contributes to the advancement of Earth Observation in polar regions by introducing a framework that combines advanced image processing techniques and a well-designed architecture. The ARISGAN framework’s effectiveness in outperforming existing models underscores its potential for generating perceptually valid high-resolution arctic surface imagery. The study concludes with a discussion on identified limitations and proposes avenues for future research, emphasizing the importance of addressing challenges in temporal synchronicity, multi-spectral image analysis, preprocessing, and quality metrics. The findings encourage further refinement of the ARISGAN framework, ultimately advancing the quality and availability of highresolution satellite imagery in the Arctic.