Advancing airborne remote sensing of CO2 and CH4 emissions from point sources
Bibliographic record
Abstract
The global mean surface temperature (GMST) on Earth in the period 2011 − 2020 has increased ∼ 1.1 °C above preindustrial temperatures. This temperature change results mainly from the increased radiative forcing due to increased levels of anthropogenic greenhouse gases in the atmosphere, especially CO2 and CH4. For an efficient reduction of CO2 and CH4 emissions, their locations and emission strengths have to be known. Additionally, emission reductions must be monitored, and new satellite sensors must be validated. Airborne remote sensing instruments allow observing dedicated regions for source detection, emission monitoring, and satellite validation. However, new instruments and methods to infer gas concentrations from the acquired data are needed to advance the airborne remote sensing of greenhouse gases. In this thesis, three research questions were studied: Is it possible to apply the weighting function modified differential optical absorption spectroscopy (WFM-DOAS) retrieval method to hyperspectral data to infer greenhouse gas emissions? Is the observation and quantification of emissions improved with a new imaging instrument specifically designed for that task? And can the retrieval of greenhouse gases from airborne remote sensing measurements be improved by taking scattering in the atmosphere into account? The first question was studied by applying the WFM-DOAS retrieval to AVIRIS-NG hyperspectral data (spectral resolution ∼ 5.5 nm) acquired during the ABoVE measurement campaign in Canada and a data set containing the observation of a coal mine ventilation shaft plume. In the data set, multiple methane emission plumes could be detected, and the emissions were estimated for five of them. Additionally, the influence of different surface types on the retrieval results was studied. For some surface types, the biases reached ±5 − 10 %, while the retrieval precision was 2 − 5% total column increase. The second question was examined by developing, building, and deploying the MAMAP2DLight instrument successfully. It is an imaging airborne remote sensing spectrometer with ∼ 1.1nm spectral resolution covering the absorption bands of CO2 and CH4 between 1560 and 1690 nm. It observes 28 ground scenes with a spatial resolution of 22 × 6m2, creating an image of the ground while flying over it. The total column precision was 0.28% after binning to 100×100m2 ground scenes, and 0.7% unbinned. During the first measurement flight over the coal-fired power plant Jänschwalde in Eastern Germany in June 2021, the CO2 emission plume was mapped successfully, and emissions of 10.3 Mt CO2 yr−1 were estimated, which were close to the emission estimate based on activity data of 11.6 Mt CO2 yr−1. For the third question, a forward model adapted to the airborne geometry was implemented in the optimal estimation-based Fast atmOspheric traCe gAs retrievaL (FOCAL AIR). It included a parametrized treatment of scattering. Nevertheless, the Jacobian of the forward model could be calculated analytically for all atmospheric parameters, reducing the computational resources needed for the retrieval. In synthetic measurements, treating scattering parametrized reduced errors compared to an absorption-only forward model by up to 50 %. Additionally, applying the FOCAL AIR retrieval and the WFM-DOAS on the MAMAP data set acquired over the power plant Jänschwalde in May 2018, different FOCAL AIR retrieval configurations were tested. With the best retrieval configuration, an emission of 18.6 Mt CO2 yr−1 was estimated for the power plant Jänschwalde, close to the emission estimate from WFM-DOAS retrieval results of 19.4 Mt CO2 yr−1.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".