Detecting and quantifying methane emissions from oil and gas production: algorithm development with ground-truth calibration based on Sentinel-2 satellite imagery

<p>Sentinel-2 satellite imagery has been shown by studies to be capable of detecting and quantifying methane emissions from oil and gas production. However, current methods lack performance calibration with ground-truth testing. This study developed a multi-band–multi-pass–multi-comparison-date methane retrieval algorithm that enhances Sentinel-2 sensitivity to methane plumes. The method was calibrated using data from a large-scale controlled-release test in Ehrenberg, Arizona, in fall 2021, with three algorithm parameters tuned based on the true emission rates. Tuned parameters are the pixel-level concentration upper-bound threshold during extreme value removal, the number of comparison dates, and the pixel-level methane concentration percentage threshold when determining the spatial extent of a plume. We found that a low value of the upper-bound threshold during extreme value removal can result in false negatives. A high number of comparison dates helps enhance the algorithm sensitivity to the plumes in the target date, but values in excess of 12 d are neither necessary nor computationally efficient. A high percentage threshold when determining the spatial extent of a plume helps enhance the quantification accuracy, but it may harm the yes/no detection accuracy. We found that there is a trade-off between quantification accuracy and detection accuracy. In a scenario with the highest quantification accuracy, we achieved the lowest quantification error and had zero false-positive detections; however, the algorithm missed three true plumes, which reduced the yes/no detection accuracy. In contrast, all of the true plumes were detected in the highest detection accuracy scenario, but the emission rate quantification had higher errors. We illustrated a two-step method that updates the emission rate estimates in an interim step, which improves quantification accuracy while keeping high yes/no detection accuracy. We also validated the algorithm's ability to detect true positives and true negatives in two application studies.</p>