Evaluating the Impact of Atmospheric CO2 Emissions via Super Resolution of Remote Sensing Data

Understanding how emissions from point sources affect the atmospheric concentrations of Greenhouse Gases (GHGs) locally and on a wider scale is crucial to quantify their impact on climate change. To this end, different ways of performing global monitoring of GHGs concentration using remote sensing data have been explored. The main difficulty remains to find the right balance between high resolution monitoring, which is often incomplete, and global monitoring, but at a coarser resolution. This study proposes the application of Super Resolution (SR), a Deep Learning (DL) technique commonly employed in Computer Vision, to increase the resolution of atmospheric CO2 L3 satellite data. The resulting maps are achieving an approximate resolution of 1 km * 1 km and are then compared with a benchmark of existing methods, before being used for emissions monitoring.