Super-resolution techniques for Sentinel-5P products

Air pollution is considered a very critical environmental risk to human health. The World Health Organization reports that it is responsible for almost 7 million deaths. As so, motivation is enough to decrease population exposure. However, several unsolved issues that require additional research remain. In particular, despite global monitoring development, coverage is insufficient to accurately describe the spatial variability for specific pollutants within different areas. The TROPOshperic Monitoring Instrument mounted on Sentinel-5P is one of the satellite instruments that retrieve atmospheric pollutants' concentration with a comparatively high spatial resolution, around 5 km. However, the spatial detail of the available products is often unsuitable for the purpose at hand. Also, physical constraints prevent enhancing the sensor's nominal spatial resolution further. So, there is no alternative way to collect high-resolution information than through processing algorithms. In this research, we investigated the problem of super-resolving Sentinel-5P products by employing traditional and deep learning-based approaches. While the former do not require a training phase because they rely on simple physical models, the latter can attain higher performance by reproducing highly complicated models. However, the lack of high-resolution reference data makes the needed training phase of network parameters extremely challenging. In this paper, we studied different approaches tailored to the imagery at hand and evaluated their accuracy with Sentinel-5P data. This study provides insights into the techniques and how they should be employed to monitor air quality accurately. The results of this work give significant information for the development of suitable super-resolution algorithms.