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Record W4414091412 · doi:10.3390/rs17183143

Estimating Methane Emissions by Integrating Satellite Regional Emissions Mapping and Point-Source Observations: Case Study in the Permian Basin

2025· article· en· W4414091412 on OpenAlexaff
Mozhou Gao, Zhenyu Xing

Bibliographic record

VenueRemote Sensing · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsCoquitlam CollegeUniversity of Calgary
Fundersnot available
KeywordsGreenhouse gasPermianSatelliteStructural basinMethaneDivergence (linguistics)Climate change

Abstract

fetched live from OpenAlex

Methane (CH4) is known as the most potent greenhouse gas in the short term. With the growing urgency of mitigating climate change and monitoring CH4 emissions, many emerging satellite systems have been launched in the past decade to observe CH4 and other greenhouse gases from space. These satellites are either capable of pinpointing and quantifying super emitters or deriving regional emissions with a more frequent revisit time. This study aims to reconcile emissions estimated from point source satellites and those from regional mapping satellites, and to investigate the potential of integrating point-based quantification and regional-based quantification techniques. To do that, we quantified CH4 emissions from the Permian Basin separately by applying the divergence method to the TROPOMI Level-2 data product, as well as an event-based approach using CH4 plumes quantified by Carbon Mapper systems. The resulting annual CH4 emissions estimates from the Permian Basin in 2024 are 1.83 ± 0.96 Tg and 1.26 [0.78, 2.02] Tg for divergence and event-based methods, respectively. The divergence-based emissions estimate shows a more comprehensive spatial distribution of emissions across the Permian Basin, whereas the event-based approach highlights the grid cells with the short-duration super-emitters. The emissions from grids with detectable emissions under both methods show strong agreement (R2 ≈ 0.642). After substituting the overlap cells’ values from divergence-based emissions estimation with those from event-based estimation, the combined emissions estimate is 2.68 [1.88, 3.54] Tg, which is reconciled with Permian Basin emissions estimates from previous studies. We found that CH4 emissions from the Permian Basin gradually reduced over the past five years. Furthermore, this case study indicates the potential for integrating estimations from both methods to generate a more comprehensive regional emissions estimate.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.262
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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