Toward UAV-based methane emission mapping of Arctic terrestrial ecosystems

Abstract Methane is an important greenhouse gas, and emissions are expected to rise in Arctic wetland ecosystems when temperatures increase due to climate change. However, current emission estimates are associated with large uncertainties because methane shows high spatial variability. A central problem is that existing methods are often spatially restricted due to limitations in access, cost, power availability, and in need of high maintenance levels. Our study explores how a setup consisting of an unmanned aerial vehicle and a high-precision trace gas analyzer can complement well-established methods, like mobile flux chambers and eddy covariance towers, by providing independent maps of spatial variability in emissions at the landscape scale. In Zackenberg Valley, Northeast Greenland, we mapped concentration measurements from a high-precision trace gas analyzer with a reported precision of 0.6 parts per billion in a high-Arctic tundra fen ecosystem. We connected the analyzer via a long tube to a consumer-grade quadcopter, finding that the combined setup could differentiate near-surface methane concentrations of less than 5 parts per billion within a few meters under favorable weather conditions. Five of ten campaigns showed that relative methane concentration hot spots and cold spots significantly correlated with areas showing relatively high and low emissions (ranging from 1.40 to 7.4 mg m⁻² h⁻¹) during study campaigns in previous years. Concurrent measurements in a stationary automated chamber setup showed comparatively low methane emissions (~0.1 to 3.9 mg m⁻² h⁻¹) compared to previous years, indicating that a further improved UAV-analyzer setup could demonstrate clear differences in an ecosystem where methane emissions are generally higher. Calm conditions with some degree of air mixing near the surface were best suited for the mapping. Windy and wet conditions should be avoided, both for the reliability of the mapping and for safely navigating the unmanned aerial vehicle