Super-Resolution of Sentinel-5P Data Using Deep Learning

Saad Ahmed Jamal

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Abstract: Satellite-based air quality monitoring plays a crucial role in evaluating and managing human-induced emissions. Sentinel-5P, carrying the TROPOspheric Monitoring Instrument (TROPOMI), provides the state of art global data of N O2 total column concentrations. However, its spatial resolution is a limitation in detecting fine-scale emission sources, particularly in densely populated urban regions and maritime corridors. This study critically reviews and highlights the relatively underexplored class of super-resolution frameworks that employ deep learning techniques to enhance the spatial resolution of Sentinel-5P radiance data. In future, large scale super-resolved dataset would be useful for ship level analysis.

Serve As Reviewer: ~Robert_Jansen1

Submission Number: 32