Understanding the potential of Sentinel-2 for monitoring methane point emissions

Gorroño, Javier; Varon, Daniel J.; Irakulis-Loitxate, Itziar; Guanter, Luis

doi:10.5194/amt-2022-261

Cited by 5 publications

(6 citation statements)

References 30 publications

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“…aggregated signal of the entire band, therefore only relatively large quantities of methane can be retrieved and the detection limit worsens considerably over non-homogeneous terrain. The detection limit is estimated at ∼1-2 t h -1 for homogeneous scenes (Gorroño et al, 2022), in agreement with Sherwin et al (2022) where a ∼1800 kg/h emission was detected and quantified. We…”

Section: Sentinel-2supporting

confidence: 90%

“…The plume mask of plume Turkmenistan-c (Figure 9, Table B2) was curated in order to exclude an artifact which was caused by a nearby road. Retrieval artefacts in high-resolution methane retrievals from hyper-spectral instruments resulting from surface features such as roads is a known issue (Sánchez-García et al, 2022;Gorroño et al, 2022). PRISMA and Sentinel-2 are more prone to such issues than GHGSat-Cx.…”

Section: Overview Of the Confirmed Detections In 2021mentioning

confidence: 99%

“…apply the methane retrieval and IME quantification approach fromGorroño et al (2022) that similarly toVaron et al (2021) uses a "reference day" without a plume to isolate the difference caused by methane concentration enhancement. We again use 370 GEOS-FP 10m wind data(Molod et al, 2012).https://doi.org/10.5194/acp-2022-862 Preprint.…”

mentioning

confidence: 99%

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Automated detection and monitoring of methane super-emitters using satellite data

Schuit

Maasakkers

Bijl

et al. 2023

Preprint

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Abstract. A reduction in anthropogenic methane emissions is vital to limit near-term global warming. A small number of so-called super-emitters is responsible for a disproportionally large fraction of total methane emissions. Since late 2017, the TROPOspheric Monitoring Instrument (TROPOMI) has been in orbit providing daily global coverage of methane mixing ratios at a resolution of up to 7 × 5.5 km2, enabling the detection of these super-emitters. However, TROPOMI produces millions of observations each day, which together with the complexity of the methane data, makes manual inspection infeasible. We have therefore designed a two-step machine learning approach using a Convolutional Neural Network to detect plume-like structures in the methane data and subsequently apply a Support Vector Classifier to distinguish emission plumes from retrieval artefacts. The models are trained on pre-2021 data, and subsequently applied to all 2021 observations. We detect 2974 plumes in 2021 with a mean estimated source rate of 44 t h−1 and 5–95th percentile range of 8–122 t h−1. These emissions originate from 94 persistent emission clusters and hundreds of transient sources. Based on bottom-up emission inventories, we find that most detected plumes are related to urban areas/landfills (35 %), followed by plumes from gas infrastructure (24 %), oil infrastructure (21 %) and coal mines (20 %). For twelve (clusters of) TROPOMI detections, we "tip-and-cue" targeted observations and analysis of high-resolution satellite instruments to identify the exact sources responsible for these plumes. Using high-resolution observations from GHGSat, PRISMA and Sentinel-2, we detect and analyze both persistent and transient facility-level emissions underlying the TROPOMI detections. We find emissions from landfills and fossil fuel exploitation facilities, for the latter we find up to ten facilities contributing to one TROPOMI detection. Our automated TROPOMI-based monitoring system in combination with high-resolution satellite data allows for the detection, precise identification and monitoring of these methane super-emitters, which is essential for mitigating their emissions.

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Section: Sentinel-2supporting

confidence: 90%

Section: Overview Of the Confirmed Detections In 2021mentioning

confidence: 99%

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Automated detection and monitoring of methane super-emitters using satellite data

Schuit

Maasakkers

Bijl

et al. 2023

Preprint

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“…Note that the imbalance of detection in the United States compared to the Algeria in the proposed data set is mostly due to more difficult sensing conditions. Indeed, it was shown by Gorroño et al through simulations that the detection limit of Sentinel-2 can be expected to be in the 8–12 /h range in the Permian basin in the United States, while it should be in the 1.5–2.5 /h range in Turkmenistan. We empirically verified these expected detection limits through our detections with Sentinel-2 .…”

Section: Resultsmentioning

confidence: 99%

Global Tracking and Quantification of Oil and Gas Methane Emissions from Recurrent Sentinel-2 Imagery

Ehret

Truchis

Mazzolini

et al. 2022

Environ. Sci. Technol.

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Methane (CH4) emission estimates from top-down studies over oil and gas basins have revealed systematic underestimation of CH4 emissions in current national inventories. Sparse but extremely large amounts of CH4 from oil and gas production activities have been detected across the globe, resulting in a significant increase of the overall oil and gas contribution. However, attribution to specific facilities remains a major challenge unless high-spatial-resolution images provide sufficient granularity within the oil and gas basin. In this paper, we monitor known oil and gas infrastructures across the globe using recurrent Sentinel-2 imagery to detect and quantify more than 1200 CH4 emissions. In combination with emission estimates from airborne and Sentinel-5P measurements, we demonstrate the robustness of the fit to a power law from 0.1 /h to 600 /h. We conclude here that the prevalence of ultraemitters (>25 /h) detected globally by Sentinel-5P directly relates to emission occurrences below its detection threshold in the range >2 /h, which correspond to large emitters covered by Sentinel-2. We also verified that this relation is also valid at a more local scale for two specific countries, namely, Algeria and Turkmenistan, and the Permian basin in the United States.

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“…A recent trend is monitoring methane point sources using orbital instruments with fine spatial resolution, as smaller pixels are generally more sensitive to dry column concentration of point sources with relatively low emissions rates (Jervis et al, 2021). Remarkable processes have been achieved by these systems, including a dedicated GHG point source monitoring constellation named GHGSat (Varon et al, 2018(Varon et al, , 2019Jervis et al, 2021), hyperspectral satellites such as PRISMA (Guanter et al, 2021) and EnMAP (Green et al, 2020), as well as multispectral satellites such as Sentinel-2 (Zhang et al, 2022;Gorroño et al, 2022;Ehret et al, 2022) and WorldView-3 (Sánchez-García et al, 2022).…”

mentioning

confidence: 99%

Separating and Quantifying Facility-Level Methane Emissions with Overlapping Plumes for Spaceborne Methane Monitoring

Pang,

Tian,

et al. 2023

Preprint

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Abstract. Quantifying facility-level methane emission rates using satellites with fine spatial resolution has recently gained significant attention. However, the existing quantification algorithms usually require the methane column plume from a single point source as input. Such approaches are challenged with overlapping plumes from multiple point sources. To address these challenges, a multi-objective heuristic optimization algorithm is introduced to perform parameter estimations for the 2D multisource Gaussian plume model, which serves as the basis for the separation method. In addition, to improve the separation performance on relatively weaker sources, we proposed a metric called local binary pattern metric (LBPM), which is only sensitive to the sign of the gradient as a minimization objective. To verify the proposed separation method, observation system simulation experiments (OSSE) of various scenarios are performed, where the integrated mass enhancement (IME) is selected as a representative single-source quantization method. The result shows that plume overlapping will increase the quantifying error of IME as overlapping pixels may not be attributed correctly; compared to unseparated overlapping plumes, the proposed separation method decreases the quantification MAPE from 1.46 to 0.45 on synthetic observation over real targets. Our separation method can separate observation of overlapping plumes from multiple sources into several observations each with a plume from a single source, thereby extending single point source quantifying algorithms, such as IME, to be applicable within scenarios of multiple point sources.

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Understanding the potential of Sentinel-2 for monitoring methane point emissions

Cited by 5 publications

References 30 publications

Automated detection and monitoring of methane super-emitters using satellite data

Automated detection and monitoring of methane super-emitters using satellite data

Global Tracking and Quantification of Oil and Gas Methane Emissions from Recurrent Sentinel-2 Imagery

Separating and Quantifying Facility-Level Methane Emissions with Overlapping Plumes for Spaceborne Methane Monitoring

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