Combining TROPOMI with high-resolution satellites to detect, attribute, and monitor large methane emission events.
Bibliographic record
Abstract
Anthropogenic methane emissions play an important role in exacerbating climate change, and thus there is a need for accurate and timely monitoring and mitigation of these emissions. With daily global coverage, TROPOMI, onboard Sentinel-5P, maps methane concentrations at 5.5 x 7 km2 resolution and can detect methane super-emitters (>~8 t hr-1) globally [1,2]. Here, we show how we detect, attribute, and quantify methane emissions from super-emitters using TROPOMI in combination with information from high-resolution satellite instruments to support the UNEP IMEO Methane Alert Response System (MARS). To determine optimal targets for high-resolution hyperspectral observations (e.g. PRISMA, EnMAP), we combine longer term TROPOMI data over persistent emitters. When emissions are transient, we combine TROPOMI with data from non-targeted high-resolution band imagers (also known as multispectral sensors) such as Sentinel-2 and Sentinel-3 to trace emissions to facility-level emission sources, in particular oil and gas infrastructure [3]. We illustrate how the combination of satellites with different overpass times and different spatial resolutions gives a comprehensive picture of these emissions. To evaluate the detections, we compare methane enhancements retrieved from band imagers with values from TROPOMI. Even when overpass times do not match, we achieve this by using transient emissions that result in methane plumes with a constant total mass, once detached from the source. Finally, we show how combining information from multiple satellites enables critical evaluation of the winds taken from global reanalysis products that underlie almost all high-resolution emission quantifications based on mass-balance methods. References[1] Maasakkers JD, Varon DJ, Elfarsdóttir A, McKeever J, Jervis D, Mahapatra G, et al. Using satellites to uncover large methane emissions from landfills. Sci Adv 2022;8:eabn9683. https://doi.org/10.1126/sciadv.abn9683.[2] Irakulis-Loitxate I, Guanter L, Maasakkers JD, Zavala-Araiza D, Aben I. Satellites Detect Abatable Super-Emissions in One of the World’s Largest Methane Hotspot Regions. Environ Sci Technol 2022;56:2143–52. https://doi.org/10.1021/acs.est.1c04873.[3] Pandey, Sudhanshu, et al. "Daily detection and quantification of methane leaks using Sentinel-3: a tiered satellite observation approach with Sentinel-2 and Sentinel-5p." Remote Sensing of Environment 296 (2023): 113716. https://doi.org/10.1016/j.rse.2023.1137
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".