Articles | Volume 15, issue 23
https://doi.org/10.5194/amt-15-7155-2022
© Author(s) 2022. 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-15-7155-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Dec 2022
Research article |
| 13 Dec 2022
| 13 Dec 2022
Detecting and quantifying methane emissions from oil and gas production: algorithm development with ground-truth calibration based on Sentinel-2 satellite imagery
Zhan Zhang
Department of Energy Resources Engineering, Stanford University,
Stanford, California 94305, United States
Evan D. Sherwin
Department of Energy Resources Engineering, Stanford University,
Stanford, California 94305, United States
Daniel J. Varon
School of Engineering and Applied Science, Harvard University,
Cambridge, Massachusetts 02138, United States
GHGSat, Inc., Montréal, H2W 1Y5, Canada
Adam R. Brandt
CORRESPONDING AUTHOR
Department of Energy Resources Engineering, Stanford University,
Stanford, California 94305, United States
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Cited
16 citations as recorded by crossref.
- Spatiotemporal analysis of atmospheric methane over Kazakhstan using Sentinel-5P satellite observations between 2019–2024 D. Chepashev et al. https://doi.org/10.3389/fenvs.2026.1939106
- CELNet: A comprehensive efficient learning network for atmospheric plume identification from remotely sensed methane concentration images F. Chen et al. https://doi.org/10.1016/j.rse.2025.114828
- Adaptive background suppression-based plume identification, emission estimation, and ground validation of coal mine methane in Changzhi, Shanxi, China X. Wu et al. https://doi.org/10.1016/j.jag.2026.105587
- 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
- Atmospheric Impacts of Oil Well Blowouts: A Synergistic Assessment Using Satellite Remote Sensing and WRF-Chem Modeling J. Biswas et al. https://doi.org/10.1007/s12524-025-02341-6
- The integration of vision transformers and SAM for automated methane super-emitter detection using TROPOMI data M. Marjani et al. https://doi.org/10.1016/j.jenvman.2025.127034
- 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
- 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
- Разработка статистических моделей оценки концентрации метана в приземном слое атмосферы по материалам космической съемки в зимний период Н. Попов & В. Малинников https://doi.org/10.33764/2411-1759-2025-30-3-77-85
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
- Detecting Methane Emissions from Space Over India: Analysis Using EMIT and Sentinel-5P TROPOMI Datasets A. Siddiqui et al. https://doi.org/10.1007/s12524-024-01925-y
- 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
- High Spatial and Temporal Resolution Simulations of Methane Column Loadings Due to Routine Emissions and Emission Events in Oil and Gas Regions L. Huang et al. https://doi.org/10.1021/acsestair.4c00021
- Deep Learning for Clouds and Cloud Shadow Segmentation in Methane Satellite and Airborne Imaging Spectroscopy M. Perez-Carrasco et al. https://doi.org/10.1109/TGRS.2026.3672371
- A Sentinel-2-Based Framework for Methane Point-Source Detection and Quantification Using Low-Reflectance Artifact Detection K. Cai et al. https://doi.org/10.3390/rs18132251
- N-BPMSNet: An NDMI-Guided Bitemporal Network for Methane Plume Detection and Segmentation From Sentinel-2 Multispectral Observations D. Xu et al. https://doi.org/10.1109/TGRS.2026.3689118
16 citations as recorded by crossref.
- Spatiotemporal analysis of atmospheric methane over Kazakhstan using Sentinel-5P satellite observations between 2019–2024 D. Chepashev et al. https://doi.org/10.3389/fenvs.2026.1939106
- CELNet: A comprehensive efficient learning network for atmospheric plume identification from remotely sensed methane concentration images F. Chen et al. https://doi.org/10.1016/j.rse.2025.114828
- Adaptive background suppression-based plume identification, emission estimation, and ground validation of coal mine methane in Changzhi, Shanxi, China X. Wu et al. https://doi.org/10.1016/j.jag.2026.105587
- 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
- Atmospheric Impacts of Oil Well Blowouts: A Synergistic Assessment Using Satellite Remote Sensing and WRF-Chem Modeling J. Biswas et al. https://doi.org/10.1007/s12524-025-02341-6
- The integration of vision transformers and SAM for automated methane super-emitter detection using TROPOMI data M. Marjani et al. https://doi.org/10.1016/j.jenvman.2025.127034
- 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
- 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
- Разработка статистических моделей оценки концентрации метана в приземном слое атмосферы по материалам космической съемки в зимний период Н. Попов & В. Малинников https://doi.org/10.33764/2411-1759-2025-30-3-77-85
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
- Detecting Methane Emissions from Space Over India: Analysis Using EMIT and Sentinel-5P TROPOMI Datasets A. Siddiqui et al. https://doi.org/10.1007/s12524-024-01925-y
- 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
- High Spatial and Temporal Resolution Simulations of Methane Column Loadings Due to Routine Emissions and Emission Events in Oil and Gas Regions L. Huang et al. https://doi.org/10.1021/acsestair.4c00021
- Deep Learning for Clouds and Cloud Shadow Segmentation in Methane Satellite and Airborne Imaging Spectroscopy M. Perez-Carrasco et al. https://doi.org/10.1109/TGRS.2026.3672371
- A Sentinel-2-Based Framework for Methane Point-Source Detection and Quantification Using Low-Reflectance Artifact Detection K. Cai et al. https://doi.org/10.3390/rs18132251
- N-BPMSNet: An NDMI-Guided Bitemporal Network for Methane Plume Detection and Segmentation From Sentinel-2 Multispectral Observations D. Xu et al. https://doi.org/10.1109/TGRS.2026.3689118
Saved (final revised paper)
Latest update: 28 Sep 2026
Short summary
This work developed a multi-band–multi-pass–multi-comparison-date Sentinel-2 methane retrieval algorithm, and the method was calibrated by data from a controlled release test. To our knowledge, this is the first study that validates the performance of a Sentinel-2 methane detection algorithm by calibration with a ground-truth testing. It illustrates the potential for additional validation with systematic future experiments wherein algorithms can be tuned to meet different detection expectations.
This work developed a multi-band–multi-pass–multi-comparison-date Sentinel-2 methane retrieval...
