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

Abstract. Sentinel-2 satellite imagery has been shown by studies to be capable of detecting and quantifying methane emis- sions from oil and gas production. However, current methods lack performance validation by calibration with ground-truth testing. This study developed a multi-band-multi-pass-multi-comparison 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 days are neither necessary nor computationally efficient. A high percentage threshold when determining the spatial extent of a plume helps enhance the quan- tification 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 3 true plumes which reduced the yes/no detection accuracy. On the contrary, all the true plumes were detected in the highest detection accuracy scenario, but the emission rate quantification had higher errors. We also 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 avoid false positives by applying it to a nearby region with no emissions.