Articles | Volume 16, issue 1
https://doi.org/10.5194/amt-16-89-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-89-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
10 Jan 2023
Research article | Highlight paper |
| 10 Jan 2023
| 10 Jan 2023
Understanding the potential of Sentinel-2 for monitoring methane point emissions
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València, València, Spain
Daniel J. Varon
School of Engineering and Applied Science, Harvard University, Cambridge, 02138, USA
Itziar Irakulis-Loitxate
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València, València, Spain
United Nations Environment Programme, Paris, France
Luis Guanter
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València, València, Spain
Environmental Defense Fund, Reguliersgracht 79, 1017 LN Amsterdam, the Netherlands
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Cited
17 citations as recorded by crossref.
- Atmospheric remote sensing for anthropogenic methane emissions: Applications and research opportunities S. Zhang et al. 10.1016/j.scitotenv.2023.164701
- Single-blind test of nine methane-sensing satellite systems from three continents E. Sherwin et al. 10.5194/amt-17-765-2024
- Exploiting the Matched Filter to Improve the Detection of Methane Plumes with Sentinel-2 Data H. Wang et al. 10.3390/rs16061023
- Geostationary satellite observations of extreme and transient methane emissions from oil and gas infrastructure M. Watine-Guiu et al. 10.1073/pnas.2310797120
- Performance and sensitivity of column-wise and pixel-wise methane retrievals for imaging spectrometers A. Ayasse et al. 10.5194/amt-16-6065-2023
- Automated detection and monitoring of methane super-emitters using satellite data B. Schuit et al. 10.5194/acp-23-9071-2023
- Detection, localization, and quantification of single-source methane emissions on oil and gas production sites using point-in-space continuous monitoring systems W. Daniels et al. 10.1525/elementa.2023.00110
- S2MetNet: A novel dataset and deep learning benchmark for methane point source quantification using Sentinel-2 satellite imagery A. Radman et al. 10.1016/j.rse.2023.113708
- CH4Net: a deep learning model for monitoring methane super-emitters with Sentinel-2 imagery A. Vaughan et al. 10.5194/amt-17-2583-2024
- Assessing the Relative Importance of Satellite-Detected Methane Superemitters in Quantifying Total Emissions for Oil and Gas Production Areas in Algeria S. Naus et al. 10.1021/acs.est.3c04746
- Current Status of Satellite Remote Sensing-Based Methane Emission Monitoring Technologies M. Kim et al. 10.9719/EEG.2024.57.5.513
- Methane Retrieval Algorithms Based on Satellite: A Review Y. Jiang et al. 10.3390/atmos15040449
- Daily detection and quantification of methane leaks using Sentinel-3: a tiered satellite observation approach with Sentinel-2 and Sentinel-5p S. Pandey et al. 10.1016/j.rse.2023.113716
- Multisatellite Data Depicts a Record-Breaking Methane Leak from a Well Blowout L. Guanter et al. 10.1021/acs.estlett.4c00399
- Multi-task deep learning for quantifying methane emissions from 2-D plume imagery with Low Signal-to-Noise Ratio Q. Xu et al. 10.1080/01431161.2024.2421946
- Global Tracking and Quantification of Oil and Gas Methane Emissions from Recurrent Sentinel-2 Imagery T. Ehret et al. 10.1021/acs.est.1c08575
- Semantic segmentation of methane plumes with hyperspectral machine learning models V. Růžička et al. 10.1038/s41598-023-44918-6
15 citations as recorded by crossref.
- Atmospheric remote sensing for anthropogenic methane emissions: Applications and research opportunities S. Zhang et al. 10.1016/j.scitotenv.2023.164701
- Single-blind test of nine methane-sensing satellite systems from three continents E. Sherwin et al. 10.5194/amt-17-765-2024
- Exploiting the Matched Filter to Improve the Detection of Methane Plumes with Sentinel-2 Data H. Wang et al. 10.3390/rs16061023
- Geostationary satellite observations of extreme and transient methane emissions from oil and gas infrastructure M. Watine-Guiu et al. 10.1073/pnas.2310797120
- Performance and sensitivity of column-wise and pixel-wise methane retrievals for imaging spectrometers A. Ayasse et al. 10.5194/amt-16-6065-2023
- Automated detection and monitoring of methane super-emitters using satellite data B. Schuit et al. 10.5194/acp-23-9071-2023
- Detection, localization, and quantification of single-source methane emissions on oil and gas production sites using point-in-space continuous monitoring systems W. Daniels et al. 10.1525/elementa.2023.00110
- S2MetNet: A novel dataset and deep learning benchmark for methane point source quantification using Sentinel-2 satellite imagery A. Radman et al. 10.1016/j.rse.2023.113708
- CH4Net: a deep learning model for monitoring methane super-emitters with Sentinel-2 imagery A. Vaughan et al. 10.5194/amt-17-2583-2024
- Assessing the Relative Importance of Satellite-Detected Methane Superemitters in Quantifying Total Emissions for Oil and Gas Production Areas in Algeria S. Naus et al. 10.1021/acs.est.3c04746
- Current Status of Satellite Remote Sensing-Based Methane Emission Monitoring Technologies M. Kim et al. 10.9719/EEG.2024.57.5.513
- Methane Retrieval Algorithms Based on Satellite: A Review Y. Jiang et al. 10.3390/atmos15040449
- Daily detection and quantification of methane leaks using Sentinel-3: a tiered satellite observation approach with Sentinel-2 and Sentinel-5p S. Pandey et al. 10.1016/j.rse.2023.113716
- Multisatellite Data Depicts a Record-Breaking Methane Leak from a Well Blowout L. Guanter et al. 10.1021/acs.estlett.4c00399
- Multi-task deep learning for quantifying methane emissions from 2-D plume imagery with Low Signal-to-Noise Ratio Q. Xu et al. 10.1080/01431161.2024.2421946
2 citations as recorded by crossref.
Latest update: 20 Nov 2024
Executive editor
Accurate detection and quantification of methane emissions are urgently needed for climate change mitigation. Multiple observations and measurement approaches can contribute to this challenge. This study shows how Sentinel-2 can provide useful coverage and spatial resolution for methane plumes, despite limited spectral sensitivity for methane absorption.
Accurate detection and quantification of methane emissions are urgently needed for climate...
Short summary
We present a methane flux rate retrieval methodology using the Sentinel-2 mission, validating the algorithm for different scenes and plumes. The detection limit is 1000–2000 kg h−1 for homogeneous scenes and temporally invariant surfaces and above 5000 kg h−1 for heterogeneous ones. Dominant quantification errors are wind-related or plume mask-related. For heterogeneous scenes, the surface structure underlying the methane plume can become a dominant source of uncertainty.
We present a methane flux rate retrieval methodology using the Sentinel-2 mission, validating...
