Articles | Volume 16, issue 23
https://doi.org/10.5194/amt-16-5771-2023
© 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-16-5771-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Dec 2023
Research article |
| 01 Dec 2023
| 01 Dec 2023
Methane point source quantification using MethaneAIR: a new airborne imaging spectrometer
Apisada Chulakadabba
CORRESPONDING AUTHOR
Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Maryann Sargent
Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Thomas Lauvaux
Molecular and Atmospheric Spectrometry Group (GSMA) – UMR 7331, University of Reims Champagne-Ardenne, Reims, France
Joshua S. Benmergui
Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Environmental Defense Fund, New York, NY, USA
MethaneSAT, LLC, Austin, TX, USA
Jonathan E. Franklin
Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Christopher Chan Miller
Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA, USA
Jonas S. Wilzewski
Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA, USA
Sébastien Roche
Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA, USA
Eamon Conway
Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA, USA
Kostas Research Institute for Homeland Security, Northeastern University, Burlington, MA, USA
Amir H. Souri
Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA, USA
Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY, USA
Research and Education in Energy, Environment and Water Institute, University at Buffalo, Buffalo, NY, USA
Bingkun Luo
Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA, USA
Jacob Hawthrone
Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA, USA
Jenna Samra
Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA, USA
Bruce C. Daube
Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Xiong Liu
Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA, USA
Kelly Chance
Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA, USA
Department of Environmental Science, Baylor University, Waco, TX, USA
Ritesh Gautam
Environmental Defense Fund, New York, NY, USA
MethaneSAT, LLC, Austin, TX, USA
Mark Omara
Environmental Defense Fund, New York, NY, USA
MethaneSAT, LLC, Austin, TX, USA
Jeff S. Rutherford
Department of Energy Science & Engineering, Stanford University, Stanford, CA, USA
Evan D. Sherwin
Department of Energy Science & Engineering, Stanford University, Stanford, CA, USA
Adam Brandt
Department of Energy Science & Engineering, Stanford University, Stanford, CA, USA
Steven C. Wofsy
Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
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Cited
25 citations as recorded by crossref.
- CH4Vision: Machine Learning Estimation of Methane Flux with GaoFen-5 Hyperspectral Imagery K. Li et al. https://doi.org/10.34133/remotesensing.1013
- SSRMF: A sparse spectral reconstruction enhanced matched filter for improving point-source methane emission detection in complex terrain K. Li et al. https://doi.org/10.1016/j.isprsjprs.2025.04.034
- FI-SCAPE: A Divergence Theorem Based Emission Quantification Model for Air/Spaceborne Imaging Spectrometer Derived XCH4 Observations Y. Huang et al. https://doi.org/10.1109/JSTARS.2024.3490896
- Methane retrieval from MethaneAIR using the CO2 proxy approach: a demonstration for the upcoming MethaneSAT mission C. Chan Miller et al. https://doi.org/10.5194/amt-17-5429-2024
- High-Resolution Modeling of Methane Plumes: Validation and Sensitivity Experiments to Explore Emission Quantification Approaches R. Yuvaraj et al. https://doi.org/10.1021/acs.est.5c15941
- Technological Maturity of Aircraft-Based Methane Sensing for Greenhouse Gas Mitigation S. El Abbadi et al. https://doi.org/10.1021/acs.est.4c02439
- Sectoral contributions of high-emitting methane point sources from major US onshore oil and gas producing basins using airborne measurements from MethaneAIR J. Warren et al. https://doi.org/10.5194/acp-25-10661-2025
- 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
- Detection and quantification of methane plumes with the MethaneAIR airborne spectrometer L. Guanter et al. https://doi.org/10.5194/amt-18-3857-2025
- Identification and quantification of CH4 emissions from Madrid landfills using airborne imaging spectrometry and greenhouse gas lidar S. Krautwurst et al. https://doi.org/10.5194/acp-25-14669-2025
- Assessment of methane emissions from US onshore oil and gas production using MethaneAIR measurements K. MacKay et al. https://doi.org/10.5194/acp-26-1179-2026
- Constructing a measurement-based spatially explicit inventory of US oil and gas methane emissions (2021) M. Omara et al. https://doi.org/10.5194/essd-16-3973-2024
- Impact of atmospheric turbulence on the accuracy of point source emission estimates using satellite imagery M. Gałkowski et al. https://doi.org/10.5194/acp-25-13831-2025
- A helicopter-based mass balance approach for quantifying methane emissions from industrial activities, applied for coal mine ventilation shafts in Poland E. Förster et al. https://doi.org/10.5194/amt-18-7153-2025
- 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
- High-resolution multi-pollutant mapping in Denver, Colorado . Priyanka deSouza et al. https://doi.org/10.1016/j.aeaoa.2025.100364
- 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
- Deep Learning for Clouds and Cloud Shadow Segmentation in Methane Satellite and Airborne Imaging Spectroscopy M. Pérez-Carrasco et al. https://doi.org/10.1109/TGRS.2026.3672371
- Seasonal Landfill Methane Emissions Driven by Temperature and Pressure A. Hallward-Driemeier et al. https://doi.org/10.1021/acs.est.5c16757
- Rapid Methane Flux Estimation Combining MethaneSAT and Sentinel‐5P Observations: A Case Study of Turkmenistan Y. Huang et al. https://doi.org/10.1029/2025GL119369
- Insights into Elevated Methane Emissions from an Australian Open-Cut Coal Mine Using Two Independent Airborne Techniques J. Borchardt et al. https://doi.org/10.1021/acs.estlett.4c01063
- Newly Launched Satellite Seeks to Keep Methane Emitters Honest S. O’Neill https://doi.org/10.1016/j.eng.2024.08.012
- Small emission sources in aggregate disproportionately account for a large majority of total methane emissions from the US oil and gas sector J. Williams et al. https://doi.org/10.5194/acp-25-1513-2025
- Assessing Methane Emission Patterns and Sensitivities at High-Emission Point Sources in China via Gaussian Plume Modeling H. Li et al. https://doi.org/10.3390/environments13010062
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
25 citations as recorded by crossref.
- CH4Vision: Machine Learning Estimation of Methane Flux with GaoFen-5 Hyperspectral Imagery K. Li et al. https://doi.org/10.34133/remotesensing.1013
- SSRMF: A sparse spectral reconstruction enhanced matched filter for improving point-source methane emission detection in complex terrain K. Li et al. https://doi.org/10.1016/j.isprsjprs.2025.04.034
- FI-SCAPE: A Divergence Theorem Based Emission Quantification Model for Air/Spaceborne Imaging Spectrometer Derived XCH4 Observations Y. Huang et al. https://doi.org/10.1109/JSTARS.2024.3490896
- Methane retrieval from MethaneAIR using the CO2 proxy approach: a demonstration for the upcoming MethaneSAT mission C. Chan Miller et al. https://doi.org/10.5194/amt-17-5429-2024
- High-Resolution Modeling of Methane Plumes: Validation and Sensitivity Experiments to Explore Emission Quantification Approaches R. Yuvaraj et al. https://doi.org/10.1021/acs.est.5c15941
- Technological Maturity of Aircraft-Based Methane Sensing for Greenhouse Gas Mitigation S. El Abbadi et al. https://doi.org/10.1021/acs.est.4c02439
- Sectoral contributions of high-emitting methane point sources from major US onshore oil and gas producing basins using airborne measurements from MethaneAIR J. Warren et al. https://doi.org/10.5194/acp-25-10661-2025
- 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
- Detection and quantification of methane plumes with the MethaneAIR airborne spectrometer L. Guanter et al. https://doi.org/10.5194/amt-18-3857-2025
- Identification and quantification of CH4 emissions from Madrid landfills using airborne imaging spectrometry and greenhouse gas lidar S. Krautwurst et al. https://doi.org/10.5194/acp-25-14669-2025
- Assessment of methane emissions from US onshore oil and gas production using MethaneAIR measurements K. MacKay et al. https://doi.org/10.5194/acp-26-1179-2026
- Constructing a measurement-based spatially explicit inventory of US oil and gas methane emissions (2021) M. Omara et al. https://doi.org/10.5194/essd-16-3973-2024
- Impact of atmospheric turbulence on the accuracy of point source emission estimates using satellite imagery M. Gałkowski et al. https://doi.org/10.5194/acp-25-13831-2025
- A helicopter-based mass balance approach for quantifying methane emissions from industrial activities, applied for coal mine ventilation shafts in Poland E. Förster et al. https://doi.org/10.5194/amt-18-7153-2025
- 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
- High-resolution multi-pollutant mapping in Denver, Colorado . Priyanka deSouza et al. https://doi.org/10.1016/j.aeaoa.2025.100364
- 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
- Deep Learning for Clouds and Cloud Shadow Segmentation in Methane Satellite and Airborne Imaging Spectroscopy M. Pérez-Carrasco et al. https://doi.org/10.1109/TGRS.2026.3672371
- Seasonal Landfill Methane Emissions Driven by Temperature and Pressure A. Hallward-Driemeier et al. https://doi.org/10.1021/acs.est.5c16757
- Rapid Methane Flux Estimation Combining MethaneSAT and Sentinel‐5P Observations: A Case Study of Turkmenistan Y. Huang et al. https://doi.org/10.1029/2025GL119369
- Insights into Elevated Methane Emissions from an Australian Open-Cut Coal Mine Using Two Independent Airborne Techniques J. Borchardt et al. https://doi.org/10.1021/acs.estlett.4c01063
- Newly Launched Satellite Seeks to Keep Methane Emitters Honest S. O’Neill https://doi.org/10.1016/j.eng.2024.08.012
- Small emission sources in aggregate disproportionately account for a large majority of total methane emissions from the US oil and gas sector J. Williams et al. https://doi.org/10.5194/acp-25-1513-2025
- Assessing Methane Emission Patterns and Sensitivities at High-Emission Point Sources in China via Gaussian Plume Modeling H. Li et al. https://doi.org/10.3390/environments13010062
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
Latest update: 25 Jun 2026
Short summary
We show that MethaneAIR, a precursor to the MethaneSAT satellite, demonstrates accurate point source quantification during controlled release experiments and regional observations in 2021 and 2022. Results from our two independent quantification methods suggest the accuracy of our sensor and algorithms is better than 25 % for sources emitting 200 kg h−1 or more. Insights from these measurements help establish the capabilities of MethaneSAT and MethaneAIR.
We show that MethaneAIR, a precursor to the MethaneSAT satellite, demonstrates accurate point...
