Comment on egusphere-2023-1343

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<strong class="journal-contentHeaderColor">Abstract.</strong> Current methods for detecting atmospheric plumes and inferring point source rates from high-resolution satellite imagery are labor intensive and not scalable to the growing satellite dataset available for methane point sources. Here we present a two-step algorithm called U-Plume for automated detection and quantification of point sources from satellite imagery. The first step delivers plume detection and delineation (masking) with a machine learning U-Net architecture for image segmentation. The second step quantifies point source rate from the masked plume using wind speed information and either a convolution neural network (CNN) or a physics-based Integrated Mass Enhancement (IME) method. The algorithm can process 62 128&times;128 images per second on a single core. We train the algorithm with large-eddy simulations of methane plumes superimposed on noisy and variable methane background scenes from the GHGSat-C1 satellite instrument. We introduce the concept of point source observability <em>O_ps</em> = <em>Q</em>/(<em>UW</em>&Delta;<em>B</em>) as a single dimensionless number to predict plume detectability and source rate quantification error from an instrument as a function of source rate <em>Q</em>, wind speed <em>U</em>, instrument pixel size <em>W</em>, and instrument-dependent background noise &Delta;<em>B</em>. We show that <em>O_ps</em> can powerfully diagnose the ability of an imaging instrument to observe point sources of a certain magnitude under given conditions. U-Plume successfully detects and masks plumes from sources as small as 100 kg h^-1 over surfaces with low background noise and succeeds for larger point sources over surfaces with substantial background noise. We find that the IME method for source quantification is unbiased over the full range of source rates while the CNN method is biased toward the mean of its training range. The total error in source rate quantification is dominated by wind speed at low wind speeds and by the masking algorithm at high wind speeds. A wind speed of 2&ndash;4 m s^-1 is optimal for detection and quantification of point sources from satellite data.