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Permafrost thaw in Arctic regions is increasing methane (CH₄) emissions into the atmosphere, but quantification of such emissions is difficult given the large and remote areas impacted. Hence, Earth observation (EO) data are critical for assessing permafrost thaw, associated ecosystem change and increased CH₄ emissions. Often extrapolation from field measurements using EO is the approach employed. However, there are key challenges to consider. Landscape CH₄ emissions result from a complex local-scale mixture of micro-topographies and vegetation types that support widely differing CH₄ emissions, and it is difficult to detect the initial stages of permafrost degradation before vegetation transitions have occurred. This study considers the use of a combination of ultra-high-resolution unoccupied aerial vehicle (UAV) data and Sentinel-1 and Sentinel-2 data to extrapolate field measurements of CH₄ emissions from a set of vegetation types which capture the local variation in vegetation on degrading palsa wetlands. We show that the ultra-high-resolution UAV data can map spatial variation in vegetation relevant to variation in CH₄ emissions and extrapolate these across the wider landscape. We further show how this can be integrated with Sentinel-1 and Sentinel-2 data. By way of a soft classification and simple correction of misclassification bias of a hard classification, the output vegetation mapping and subsequent extrapolation of CH₄ emissions closely matched the results generated using the UAV data. Interferometric synthetic-aperture radar (InSAR) assessment of subsidence together with the vegetation classification suggested that high subsidence rates of palsa wetland can be used to quantify areas at risk of increased CH₄ emissions. The transition of a 50 ha area currently experiencing subsidence to fen vegetation is estimated to increase emissions from 116 kg CH₄ per season to emissions as high as 6500 to 13 000 kg CH₄ per season. The key outcome from this study is that a combination of high- and low-resolution EO data of different types provides the ability to estimate CH₄ emissions from large geographies covered by a fine mixture of vegetation types which are vulnerable to transitioning to CH₄ emitters in the near future. This points to an opportunity to measure and monitor CH₄ emissions from the Arctic over space and time with confidence.