Methane emissions from coal mines, especially surface operations, are spatially diffuse, presenting signif-icant challenges for accurate quantification. Satellites such as TROPOMI, GHGSat, PRISMA, Gaofen-5, and GOSAT have been extensively used for detecting methane emissions at various scales, from individual point sources to regional and global assessments. Despite various advancements, methane quantification via sat-ellite observations remains subject to several challenges. Various quantification methods for the same ob-servation can produce variable results. Also, meteorological conditions, terrain complexity, and surface heterogeneity introduce uncertainties in emission estimates. The selection of wind speed and direction, along with retrieval algorithm limitations, can lead to significant discrepancies in reported emissions. Ad-ditionally, satellite-based observations capture emissions only at specific overpass times, which may in-troduce temporal uncertainties compared to inventories derived from continuous emission estimations. This study provides a comprehensive review of satellite-based coal mine methane monitoring, evaluating current methodologies, their limitations, and recent technological advancements. We discussed the potential of emerging machine learning techniques, improved atmospheric modelling, and integrated observational approaches to enhance methane emission quantification. By refining satellite-based monitoring techniques and addressing existing challenges, this research will support the development of more accurate emission inventories and effective mitigation strategies for the coal mining sector.
Chauhan et al. (Wed,) studied this question.