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

• Published in: IEEE transactions on geoscience and remote sensing Vol. 62; p. 1
• Main Authors: Carbone, Alessia, Restaino, Rocco, Vivone, Gemine, Chanussot, Jocelyn
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Air monitoring; Air pollution; Air quality; Algorithms; Artificial Intelligence; Atmospheric measurements; Atmospheric modeling; atmospheric pollution; Computer Science; Contaminants; Degradation; degradation filter; Image acquisition; Image enhancement; Image resolution; Monitoring; Monitoring instruments; Neural networks; Radiance; Remote sensing; Satellites; Sentinel-5P; Spatial data; Spatial resolution; super-resolution; Superresolution
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2024.3387877

Sentinel-5P provides excellent spatial information, but its resolution is insufficient to characterise 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 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 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, most of the work theoretical contribution is the estimation of the degradation model of 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, that 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.