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
36 citations as recorded by crossref.
- Atmospheric remote sensing for anthropogenic methane emissions: Applications and research opportunities S. Zhang et al. https://doi.org/10.1016/j.scitotenv.2023.164701
- First Investigation of Long-Term Methane Emissions from Wastewater Treatment Using Satellite Remote Sensing S. Mehrdad et al. https://doi.org/10.3390/rs16234422
- Improved monitoring of methane emissions for the oil and gas sector with Sentinel-2 satellite observations B. Zambrano-Luna et al. https://doi.org/10.1016/j.atmosenv.2025.121594
- Assessing uncertainties of Integrated Mass Enhancement (IME) method for estimating landfill methane emissions F. Arkian et al. https://doi.org/10.1080/10962247.2025.2557323
- Single-blind test of nine methane-sensing satellite systems from three continents E. Sherwin et al. https://doi.org/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. https://doi.org/10.3390/rs16061023
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
- Automated detection and monitoring of methane super-emitters using satellite data B. Schuit et al. https://doi.org/10.5194/acp-23-9071-2023
- Detection of changes in the heat emissions signature of buildings related to indoor activity using publicly available satellite data M. Suaza-Medina et al. https://doi.org/10.1007/s12145-025-01926-6
- Surveying methane point-source super-emissions across oil and gas basins with MethaneSAT L. Guanter et al. https://doi.org/10.5194/acp-26-2941-2026
- Quantifying CH4 point source emissions with airborne remote sensing: first results from AVIRIS-4 S. Meier et al. https://doi.org/10.5194/amt-19-333-2026
- S2MetNet: A novel dataset and deep learning benchmark for methane point source quantification using Sentinel-2 satellite imagery A. Radman et al. https://doi.org/10.1016/j.rse.2023.113708
- CH4Net: a deep learning model for monitoring methane super-emitters with Sentinel-2 imagery A. Vaughan et al. https://doi.org/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. https://doi.org/10.1021/acs.est.3c04746
- Methane Retrieval Algorithms Based on Satellite: A Review Y. Jiang et al. https://doi.org/10.3390/atmos15040449
- Assessing the Detection of Methane Plumes in Offshore Areas Using High-Resolution Imaging Spectrometers J. Roger et al. https://doi.org/10.5194/amt-18-5545-2025
- 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. https://doi.org/10.1016/j.rse.2023.113716
- Beyond localized methane plume detection: a dual-path deep learning framework for sensor-agnostic global hyperspectral methane plume monitoring S. Yang et al. https://doi.org/10.1038/s41612-026-01387-8
- Multisatellite Data Depicts a Record-Breaking Methane Leak from a Well Blowout L. Guanter et al. https://doi.org/10.1021/acs.estlett.4c00399
- Assessing the Potential of the MTG-FCI Geostationary Mission for the Detection of Methane Plumes S. Zhou et al. https://doi.org/10.1021/acs.est.5c07974
- A data-efficient deep transfer learning framework for methane super-emitter detection in oil and gas fields using the Sentinel-2 satellite S. Zhao et al. https://doi.org/10.5194/acp-25-4035-2025
- Detection and quantification of methane plumes with the MethaneAIR airborne spectrometer L. Guanter et al. https://doi.org/10.5194/amt-18-3857-2025
- 碧空一号卫星大气甲烷羽流探测及点源排放量遥感分析（特邀） 何. He Zhuo et al. https://doi.org/10.3788/AOSOL250483
- Geostationary satellite observations of extreme and transient methane emissions from oil and gas infrastructure M. Watine-Guiu et al. https://doi.org/10.1073/pnas.2310797120
- Performance and sensitivity of column-wise and pixel-wise methane retrievals for imaging spectrometers A. Ayasse et al. https://doi.org/10.5194/amt-16-6065-2023
- 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. https://doi.org/10.1525/elementa.2023.00110
- Advancements in satellite-based methane point source monitoring: A systematic review F. Mohammadimanesh et al. https://doi.org/10.1016/j.isprsjprs.2025.03.020
- Improved Quantification of Methane Point-Source Emissions from Hyperspectral Imagery Using a Spectrally Corrected Levenberg–Marquardt Matched Filter Z. He et al. https://doi.org/10.3390/rs18081195
- Separating and quantifying facility-level methane emissions with overlapping plumes for spaceborne methane monitoring Y. Pang et al. https://doi.org/10.5194/amt-18-455-2025
- Satellite-Based Methane Emission Monitoring: A Review Across Industries S. Mehrdad & K. Du https://doi.org/10.3390/rs17223674
- Current Status of Satellite Remote Sensing-Based Methane Emission Monitoring Technologies M. Kim et al. https://doi.org/10.9719/EEG.2024.57.5.513
- Satellite Insights into methane Super-Emitters: Regional emissions and yearly growth on Turkmenistan’s west coast Z. He et al. https://doi.org/10.1016/j.jag.2025.104975
- A radiometrically and spatially consistent super-resolution framework for Sentinel-2 C. Aybar et al. https://doi.org/10.1016/j.rse.2025.115222
- Multi-task deep learning for quantifying methane emissions from 2-D plume imagery with Low Signal-to-Noise Ratio Q. Xu et al. https://doi.org/10.1080/01431161.2024.2421946
- Machine Learning in Forecasting Methane Concentration from Satellite Data K. Nowak et al. https://doi.org/10.1088/1742-6596/3107/1/012020
- Comparative Review of Global Methane Budget Estimation: Top-Down, Bottom-Up, and Integrated Approaches B. Alem et al. https://doi.org/10.3390/rs18091336
36 citations as recorded by crossref.
- Atmospheric remote sensing for anthropogenic methane emissions: Applications and research opportunities S. Zhang et al. https://doi.org/10.1016/j.scitotenv.2023.164701
- First Investigation of Long-Term Methane Emissions from Wastewater Treatment Using Satellite Remote Sensing S. Mehrdad et al. https://doi.org/10.3390/rs16234422
- Improved monitoring of methane emissions for the oil and gas sector with Sentinel-2 satellite observations B. Zambrano-Luna et al. https://doi.org/10.1016/j.atmosenv.2025.121594
- Assessing uncertainties of Integrated Mass Enhancement (IME) method for estimating landfill methane emissions F. Arkian et al. https://doi.org/10.1080/10962247.2025.2557323
- Single-blind test of nine methane-sensing satellite systems from three continents E. Sherwin et al. https://doi.org/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. https://doi.org/10.3390/rs16061023
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
- Automated detection and monitoring of methane super-emitters using satellite data B. Schuit et al. https://doi.org/10.5194/acp-23-9071-2023
- Detection of changes in the heat emissions signature of buildings related to indoor activity using publicly available satellite data M. Suaza-Medina et al. https://doi.org/10.1007/s12145-025-01926-6
- Surveying methane point-source super-emissions across oil and gas basins with MethaneSAT L. Guanter et al. https://doi.org/10.5194/acp-26-2941-2026
- Quantifying CH4 point source emissions with airborne remote sensing: first results from AVIRIS-4 S. Meier et al. https://doi.org/10.5194/amt-19-333-2026
- S2MetNet: A novel dataset and deep learning benchmark for methane point source quantification using Sentinel-2 satellite imagery A. Radman et al. https://doi.org/10.1016/j.rse.2023.113708
- CH4Net: a deep learning model for monitoring methane super-emitters with Sentinel-2 imagery A. Vaughan et al. https://doi.org/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. https://doi.org/10.1021/acs.est.3c04746
- Methane Retrieval Algorithms Based on Satellite: A Review Y. Jiang et al. https://doi.org/10.3390/atmos15040449
- Assessing the Detection of Methane Plumes in Offshore Areas Using High-Resolution Imaging Spectrometers J. Roger et al. https://doi.org/10.5194/amt-18-5545-2025
- 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. https://doi.org/10.1016/j.rse.2023.113716
- Beyond localized methane plume detection: a dual-path deep learning framework for sensor-agnostic global hyperspectral methane plume monitoring S. Yang et al. https://doi.org/10.1038/s41612-026-01387-8
- Multisatellite Data Depicts a Record-Breaking Methane Leak from a Well Blowout L. Guanter et al. https://doi.org/10.1021/acs.estlett.4c00399
- Assessing the Potential of the MTG-FCI Geostationary Mission for the Detection of Methane Plumes S. Zhou et al. https://doi.org/10.1021/acs.est.5c07974
- A data-efficient deep transfer learning framework for methane super-emitter detection in oil and gas fields using the Sentinel-2 satellite S. Zhao et al. https://doi.org/10.5194/acp-25-4035-2025
- Detection and quantification of methane plumes with the MethaneAIR airborne spectrometer L. Guanter et al. https://doi.org/10.5194/amt-18-3857-2025
- 碧空一号卫星大气甲烷羽流探测及点源排放量遥感分析（特邀） 何. He Zhuo et al. https://doi.org/10.3788/AOSOL250483
- Geostationary satellite observations of extreme and transient methane emissions from oil and gas infrastructure M. Watine-Guiu et al. https://doi.org/10.1073/pnas.2310797120
- Performance and sensitivity of column-wise and pixel-wise methane retrievals for imaging spectrometers A. Ayasse et al. https://doi.org/10.5194/amt-16-6065-2023
- 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. https://doi.org/10.1525/elementa.2023.00110
- Advancements in satellite-based methane point source monitoring: A systematic review F. Mohammadimanesh et al. https://doi.org/10.1016/j.isprsjprs.2025.03.020
- Improved Quantification of Methane Point-Source Emissions from Hyperspectral Imagery Using a Spectrally Corrected Levenberg–Marquardt Matched Filter Z. He et al. https://doi.org/10.3390/rs18081195
- Separating and quantifying facility-level methane emissions with overlapping plumes for spaceborne methane monitoring Y. Pang et al. https://doi.org/10.5194/amt-18-455-2025
- Satellite-Based Methane Emission Monitoring: A Review Across Industries S. Mehrdad & K. Du https://doi.org/10.3390/rs17223674
- Current Status of Satellite Remote Sensing-Based Methane Emission Monitoring Technologies M. Kim et al. https://doi.org/10.9719/EEG.2024.57.5.513
- Satellite Insights into methane Super-Emitters: Regional emissions and yearly growth on Turkmenistan’s west coast Z. He et al. https://doi.org/10.1016/j.jag.2025.104975
- A radiometrically and spatially consistent super-resolution framework for Sentinel-2 C. Aybar et al. https://doi.org/10.1016/j.rse.2025.115222
- Multi-task deep learning for quantifying methane emissions from 2-D plume imagery with Low Signal-to-Noise Ratio Q. Xu et al. https://doi.org/10.1080/01431161.2024.2421946
- Machine Learning in Forecasting Methane Concentration from Satellite Data K. Nowak et al. https://doi.org/10.1088/1742-6596/3107/1/012020
- Comparative Review of Global Methane Budget Estimation: Top-Down, Bottom-Up, and Integrated Approaches B. Alem et al. https://doi.org/10.3390/rs18091336
Saved (final revised paper)
Latest update: 03 Jun 2026
Editorial statement
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...
