Capabilities of optical and radar Earth observation data for up-scaling methane emissions linked to subsidence and permafrost degradation in sub-Arctic peatlands

. Permafrost thaw in Arctic regions is increasing methane (CH4) emissions to 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 both 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 – that landscape CH4 emissions result from a complex local-scale mixture of micro-topographies and vegetation types that support widely differing CH4 emissions and the difficulty in detecting 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, together with Sentinel-1 and -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 Sentinel-1 and Sentinel-2 can be used. 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 matched closely that generated using the UAV data. 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. We estimate that a transition of an area currently experiencing subsidence to fen type vegetation are estimated to increase emissions from 116 kg CH4 season−1 from the 50 ha study area, to emissions as high as 6500 to 13000 kg CH4 season−1. The key outcome from this study is that a fusion of EO data types provides the ability to estimate CH4 emissions from large geographies covered by a fine mixture of vegetation types and vulnerable to transitioning to CH4 emitters in the near future. This points to an opportunity to measure and monitor CH4 from the Arctic over space and time with confidence.