U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers

<p>Current methods for detecting atmospheric plumes and inferring point-source rates from high-resolution satellite imagery are labor-intensive and not scalable with regard 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 U-Net machine learning architecture for image segmentation. The second step quantifies the point-source rate from the masked plume using wind speed information and either a convolutional neural network (CNN) or a physics-based integrated mass enhancement (IME) method. The algorithm can process 62 images (each measuring 128 pixels <span class="inline-formula">×</span> 128 pixels) per second on a single 2.6 GHz Intel Core i7-9750H CPU. We train the algorithm using 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, <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M2" display="inline" overflow="scroll" dspmath="mathml"><mrow><msub><mi>O</mi><mtext>ps</mtext></msub><mo>=</mo><mi>Q</mi><mo>/</mo><mo>(</mo><mi>U</mi><mi>W</mi><mi mathvariant="normal">Δ</mi><mi>B</mi><mo>)</mo></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="88pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="4ce9e616e41414ff0022cfedbc0dcbb0"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="amt-17-2625-2024-ie00001.svg" width="88pt" height="16pt" src="amt-17-2625-2024-ie00001.png"/></svg:svg></span></span>, as a single dimensionless number to predict plume detectability and source rate quantification error from an instrument as a function of source rate <span class="inline-formula"><i>Q</i></span>, wind speed <span class="inline-formula"><i>U</i></span>, instrument pixel size <span class="inline-formula"><i>W</i></span>, and instrument-dependent background noise <span class="inline-formula">Δ<i>B</i></span>. We show that <span class="inline-formula"><i>O</i>_ps</span> 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 <span class="inline-formula">kg h⁻¹</span> in GHGSat-C1 images over surfaces with low background noise and successfully handles 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 towards 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 2–4 <span class="inline-formula">m s⁻¹</span> is optimal for detection and quantification of point sources from satellite data.</p>