Model-Based Super-Resolution for Sentinel-5P Data

Sentinel-5P provides excellent spatial information, but its resolution is insufficient to characterize the complex distribution of air contaminants within limited areas. As physical constraints prevent significant advances beyond its nominal resolution, employing processing techniques like single-image super-resolution (SISR) can notably contribute to both research and air quality monitoring applications. This study presents the very first use of such methodologies on Sentinel-5P data. We demonstrate that superior results may be obtained if the degrading filter used to simulate pairs of low- and high-resolution (HR) images is tailored to the acquisition technology at hand, an issue frequently ignored in the scientific literature on the subject. Because of this, as well as the fact that these data have never been deployed in any previous studies, the primary theoretical contribution of this article is the estimation of the degradation model of TROPOspheric Monitoring Instrument (TROPOMI), the sensor mounted on Sentinel-5P. Leveraging this model—which is essential for applications involving super-resolution—we additionally improve a well-known deconvolution-based strategy and present a brand-new neural network that outperforms both traditional super-resolution techniques and well-established neural networks in the field. The findings of this study, which are supported by experimental tests on real Sentinel-5P radiance images, using both full-scale and reduced-scale protocols, offer a baseline for enhancing algorithms that are driven by the understanding of the imaging model and provide an efficient way of evaluating innovative approaches on all the available images. The code is available at
https://github.com/alcarbone/S5P_SISR_Toolbox
.