Methane Retrieval Algorithms Based on Satellite: A Review

• Published in: Atmosphere Vol. 15; no. 4; p. 449
• Main Authors: Jiang, Yuhan, Zhang, Lu, Zhang, Xingying, Cao, Xifeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2024
• Subjects: Active satellites; Algorithms; Atmosphere; Carbon dioxide; Classification; Climate change; Emissions; Fluctuations; Gas industry; Global warming; Greenhouse effect; Greenhouse gases; Landsat; Landsat satellites; Methane; Mitigation; Permafrost; Plumes; point source detection; Radiation; Remote sensing; Retrieval; retrieval algorithm; Satellite imagery; satellite remote sensing; Satellites; Scientific research; Spatial discrimination; Spatial resolution; Spectral resolution; Water pollution; North America
• DOI: 10.3390/atmos15040449
• ISSN: 2073-4433

As the second most predominant greenhouse gas, methane-targeted emission mitigation holds the potential to decelerate the pace of global warming. Satellite remote sensing is an important monitoring tool, and we review developments in the satellite detection of methane. This paper provides an overview of the various types of satellites, including the various instrument parameters, and describes the different types of satellite retrieval algorithms. In addition, the currently popular methane point source quantification method is presented. Based on existing research, we delineate the classification of methane remote sensing satellites into two overarching categories: area flux mappers and point source imagers. Area flux mappers primarily concentrate on the assessment of global or large-scale methane concentrations, with a further subclassification into active remote sensing satellites (e.g., MERLIN) and passive remote sensing satellites (e.g., TROPOMI, GOSAT), contingent upon the remote sensing methodology employed. Such satellites are mainly based on physical models and the carbon dioxide proxy method for the retrieval of methane. Point source imagers, in contrast, can detect methane point source plumes using their ultra-high spatial resolution. Subcategories within this classification include multispectral imagers (e.g., Sentinel-2, Landsat-8) and hyperspectral imagers (e.g., PRISMA, GF-5), contingent upon their spectral resolution disparities. Area flux mappers are mostly distinguished by their use of physical algorithms, while point source imagers are dominated by data-driven methods. Furthermore, methane plume emissions can be accurately quantified through the utilization of an integrated mass enhancement model. Finally, a prediction of the future trajectory of methane remote sensing satellites is presented, in consideration of the current landscape. This paper aims to provide basic theoretical support for subsequent scientific research.