Detection and Quantification of CH <sub>4</sub> Plumes using the WFM-DOAS retrieval on AVIRIS-NG hyperspectral data
Bibliographic record
Abstract
Abstract. Methane is the second most important anthropogenic greenhouse gas in the Earth's atmosphere. Reducing methane emissions is consequently an important element in limiting the global temperature increase below 2 °C compared to preindustrial times. Therefore, a good knowledge of source strengths and source locations is required. Anthropogenic methane emissions often originate from point sources or small areal sources, such as fugitive emissions at oil and gas production sites or landfills. Airborne remote sensing instruments such as the Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) with meter scale imaging capabilities are able to yield information about the locations and magnitudes of methane sources, especially in areas with many potential emission sources. To extract methane column enhancement information from spectra recorded with the AVIRIS-NG instrument, different retrieval algorithms have been used, e.g. the matched filter (MF) or the Iterative Maximum A Posteriori DOAS (IMAP-DOAS) retrieval. The WFM-DOAS algorithm, successfully applied to AVIRIS-NG data in this study, fills a gap between those retrieval approaches by being a fast, non-iterative algorithm based on a first order approximation of the Lambert-Beer law, which calculates the change in gas concentrations from deviations from one background radiative transfer calculation using precalculated weighting functions specific to the state of the atmosphere during the overflight. This allows the fast quantitative processing of large data sets. Although developed for high spectral resolution measurements from satellite instruments such as SCIAMACHY, TROPOMI and the MAMAP airborne sensor, the algorithm can be applied well to lower spectral resolution AVIRIS-NG measurements. The data set examined here was recorded in Canada over different gas and coal extraction sites as part of the larger Arctic Boreal Vulnerability Experiment (ABoVE) Airborne Campaign in 2017. The noise of the retrieved CH4 imagery over bright surfaces (> 1 μW cm−2 nm−1 sr−1 at 2140 nm) was typically ±2.3 % of the background total column of CH4 when fitting strong absorption lines around 2300 nm, but could reach over ±5 % for darker surfaces (
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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".