Offshore methane detection and quantification from space using sun glint measurements with the GHGSat constellation
Bibliographic record
Abstract
Abstract. The ability to detect and quantify methane emissions from offshore platforms is of considerable interest in providing actionable feedback to industrial operators. While satellites offer a distinctive advantage for remote sensing of offshore platforms which may otherwise be difficult to reach, offshore measurements of methane from satellite instruments in the shortwave infrared are challenging due to the low levels of diffuse sunlight reflected from water surfaces. Here, we use the GHGSat satellite constellation in a sun glint configuration to detect and quantify methane emissions from offshore targets around the world. We present a variety of examples of offshore methane plumes, including the largest single emission at (84 000 ± 24 000) kg h−1 observed by GHGSat from the Nord Stream 2 pipeline leak in 2022 and the smallest offshore emission measured from space at (180 ± 130) kg h−1 in the Gulf of Mexico. In addition, we provide an overview of the constellation's offshore measurement capabilities. We measure a median column precision of 2.1 % of the background methane column density and estimate a detection limit, from analytical modelling and orbital simulations, that varies between 160 and 600 kg h−1 depending on the latitude and season.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".