Advancing Point Source Methane Emissions Quantification With UAV Sampling and Deep Learning: A Large‐Eddy Simulation Study

Abstract Quantifying point‐source methane emissions from atmospheric plume observations is essential for climate change mitigation but remains challenged by turbulence‐induced noise. Here, we introduce a deep learning framework that integrates unmanned aerial vehicle (UAV) plume sampling with turbulence‐invariant analysis to overcome this limitation. Using large‐eddy simulations (LESs) spanning a wide range of atmospheric stability conditions, we show that conventional inversion methods (including the inverse Gaussian and mass balance methods) yield mean absolute percentage errors (MAPEs) of 27%–46%, primarily caused by turbulence‐induced plume meandering. Furthermore, we identified the ratio of UAV speed to plume centroid speed as the dominant factor controlling this error, with lower ratios yielding lower MAPE. A denser sampling spacing and multiple consecutive flights can reduce the multi‐condition mean MAPE to 25%–27%, but at the cost of approximately doubling the required time and resources. Our approach employs a U‐Net model to map sparse UAV observations to time‐averaged plume cross‐sections, effectively reconstructing plume morphology and mitigating turbulence‐induced bias. When integrated into an inverse Gaussian framework, our U‐Net model reduces the MAPE from 30% to 22% in LES data, and from 38% to 29% in controlled‐release experiments. This work establishes a robust, physics‐informed strategy for accurate and scalable methane monitoring, highlighting the potential of deep learning to enhance greenhouse gas accountability.