Sensitivity of land-type variations across Canada using S-5p products
Bibliographic record
Abstract
Methane (CH 4 ), a potent greenhouse gas, traps heat in the atmosphere and significantly contributes to global warming. It is unclear whether CH 4 emissions from various land-types and other natural sources have increased substantially in the last decade linked, for example, to global warming and uncertainties remain regarding sources and their spatial extent causing discrepancies between emission estimates from inventories/models and estimates inferred by an ensemble of atmospheric inversions. Here we compared remotely sensed CH 4 total column data, along with surface albedo from the Sentinel-5 Precursor (S-5p) satellite against six main temperate zone land types (marsh, swamp, forest, grassland, cropland, and barren-land across Canada over a four-year period (2019–2022). The study developed a machine learning based algorithm that can be used to classify between such different land types using S-5p products. From 2019 to 2022, the average producer’s accuracy (PA) across all land types ranged from 50.8 % to 98.4 %, while the average user’s accuracy (UA) ranged from 69.9 % to 95.4 %. Although the methodology presented does not directly differentiate the methane fluxes from different land types, it does provide a foundation that with better ground truth monitoring and higher resolution imagery, could lead to a being able to differentiate methane emissions between land types with increased confidence, as well as determining whether significant changes are occurring over time. This would yield valuable insights for climate scientists and policy makers at both national and international levels. • Sentinel-5 Precursor shows unique methane sensitivities, enhancing monitoring. • Usage of machine learning to better detect land types linked to methane emissions. • Methodology aids future methane flux differentiation, promising better estimates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".