Articles | Volume 17, issue 2
https://doi.org/10.5194/amt-17-765-2024
© Author(s) 2024. 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-17-765-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Jan 2024
Research article |
| 31 Jan 2024
| 31 Jan 2024
Single-blind test of nine methane-sensing satellite systems from three continents
Department of Energy Science & Engineering, Stanford University, Stanford, California 94305, United States
present address: Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
Sahar H. El Abbadi
Department of Energy Science & Engineering, Stanford University, Stanford, California 94305, United States
present address: Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
Philippine M. Burdeau
Department of Energy Science & Engineering, Stanford University, Stanford, California 94305, United States
Zhan Zhang
Department of Energy Science & Engineering, Stanford University, Stanford, California 94305, United States
Zhenlin Chen
Department of Energy Science & Engineering, Stanford University, Stanford, California 94305, United States
Jeffrey S. Rutherford
Department of Energy Science & Engineering, Stanford University, Stanford, California 94305, United States
present address: Highwood Emissions Management, Calgary, Alberta T2P 2V1, Canada
Yuanlei Chen
Department of Energy Science & Engineering, Stanford University, Stanford, California 94305, United States
Adam R. Brandt
Department of Energy Science & Engineering, Stanford University, Stanford, California 94305, United States
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Cited
45 citations as recorded by crossref.
- Multisatellite Data Depicts a Record-Breaking Methane Leak from a Well Blowout L. Guanter et al. https://doi.org/10.1021/acs.estlett.4c00399
- 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
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
- Global satellite survey reveals uncertainty in landfill methane emissions M. Dogniaux et al. https://doi.org/10.1038/s41586-025-09683-8
- 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
- Coordinated satellite, aircraft, and ground-based observations of a large transient methane release T. He et al. https://doi.org/10.1073/pnas.2603595123
- Comparative Performance of Gaussian Plume and Backward Lagrangian Stochastic Models for Near-Field Methane Emission Estimation Using a Single Controlled Release Experiment A. Upreti et al. https://doi.org/10.3390/atmos17040417
- Fugitive Methane Monitoring: A Systems Review of Physics, Technology, Economics, and Regulation P. Sobron https://doi.org/10.3390/rs18142433
- US oil and gas system emissions from nearly one million aerial site measurements E. Sherwin et al. https://doi.org/10.1038/s41586-024-07117-5
- A new aerial approach for quantifying and attributing methane emissions: implementation and validation J. Dooley et al. https://doi.org/10.5194/amt-17-5091-2024
- Global Methane Retrieval, Monitoring, and Quantification in Hotspot Regions Based on AHSI/ZY-1 Satellite T. Lu et al. https://doi.org/10.3390/atmos16050510
- Controlled release testing of commercially available methane emission measurement technologies at the TADI facility A. McManemin et al. https://doi.org/10.5194/amt-19-923-2026
- 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
- Measurement of fugitive methane emissions from an abandoned coal exploration hole in Queensland, Australia using a Quantum Gas LiDAR S. Hoerning & P. Hayes https://doi.org/10.1016/j.scitotenv.2025.179925
- Global Identification of Solid Waste Methane Super Emitters Using Hyperspectral Satellites X. Zhang et al. https://doi.org/10.1021/acs.est.4c14196
- Vast and hidden urban methane emissions from the Russia–Ukraine war Z. Feng et al. https://doi.org/10.1038/s44284-025-00309-8
- Estimating Methane Emission Durations Using Continuous Monitoring Systems W. Daniels et al. https://doi.org/10.1021/acs.estlett.4c00687
- 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
- Comparing Continuous Methane Monitoring Technologies for High-Volume Emissions: A Single-Blind Controlled Release Study Z. Chen et al. https://doi.org/10.1021/acsestair.4c00015
- Multiscale Measurement and Modeling of Methane Emissions in US Oil and Gas Production Regions D. Allen et al. https://doi.org/10.1146/annurev-chembioeng-100724-074807
- Optimizing methane continuous monitoring networks in oil and gas fields: A multi-objective framework based on information theory Z. Xie et al. https://doi.org/10.1016/j.jclepro.2026.148010
- 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
- Advancing oil and gas emissions assessment through large language model data extraction Z. Chen et al. https://doi.org/10.1016/j.egyai.2025.100481
- Monitoring Persistent Methane Emissions from the Secunda CTL Synthetic Fuel Plant Using Satellite Observations H. Virta et al. https://doi.org/10.1021/acs.estlett.5c01140
- HyperGas 1.0: a python package for analyzing hyperspectral data for greenhouse gases from retrieval to emission rate quantification X. Zhang et al. https://doi.org/10.5194/gmd-19-5979-2026
- Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer B. Rouet-Leduc & C. Hulbert https://doi.org/10.1038/s41467-024-47754-y
- Comparing the performance of different hyperspectral satellite imaging spectroscopy in mapping methane point-source emissions F. Li et al. https://doi.org/10.1016/j.rse.2025.115224
- Correction of near-surface methane concentrations using a CNN-RF hybrid model based on multi-scale feature extraction L. Fan et al. https://doi.org/10.1016/j.apr.2026.103078
- Detection and quantification of agricultural methane plumes using MethaneAIR through targeted scene selection, wavelet denoising, and divergence-integral analysis P. Smale et al. https://doi.org/10.5194/acp-26-10661-2026
- Drill tool recognition and detection with SERep-CCNet: A lightweight model approach X. Wu et al. https://doi.org/10.1016/j.geoen.2025.213844
- Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT V. Batchu et al. https://doi.org/10.1073/pnas.2612145123
- Conditions for Valid Offshore Methane Quantification S. Riddick https://doi.org/10.3390/eng7080419
- Developing a ‘fit for purpose’ approach to measuring methane emissions I. Joynes et al. https://doi.org/10.1071/EP23067
- Identification of Sources of Methane in Ho Chi Minh City, Vietnam C. Woolley Maisch et al. https://doi.org/10.1021/acsestair.5c00034
- Technological Maturity of Aircraft-Based Methane Sensing for Greenhouse Gas Mitigation S. El Abbadi et al. https://doi.org/10.1021/acs.est.4c02439
- Quantification Error Model for Aerial LiDAR Methane Emission Rate Estimates C. Dudiak et al. https://doi.org/10.1021/acsestair.5c00458
- Tightening up methane plume source rate estimation in EnMAP and PRISMA images E. Ouerghi et al. https://doi.org/10.5194/amt-18-4611-2025
- A controlled release experiment for investigating methane measurement performance at landfills R. Hossain et al. https://doi.org/10.1525/elementa.2025.00048
- A Multi-Sensor Framework for Methane Detection and Flux Estimation with Scale-Aware Plume Segmentation and Uncertainty Propagation from High-Resolution Spaceborne Imaging Spectrometers A. Ferrari et al. https://doi.org/10.3390/methane5010010
- Wind-Robust Methane Source-Rate Inversion from Remote-Sensing Plume Imagery: Soft Physics Guidance Versus Hard IME Coupling Q. Dong et al. https://doi.org/10.3390/rs18121992
- 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
- Automated detection of regions with persistently enhanced methane concentrations using Sentinel-5 Precursor satellite data S. Vanselow et al. https://doi.org/10.5194/acp-24-10441-2024
- An Effective Quantification of Methane Point-Source Emissions with the Multi-Level Matched Filter from Hyperspectral Imagery M. Liang et al. https://doi.org/10.3390/rs17050843
- Intercomparison of Three Continuous Monitoring Systems on Operating Oil and Gas Sites W. Daniels et al. https://doi.org/10.1021/acsestair.4c00298
- CH4Vision: Machine Learning Estimation of Methane Flux with GaoFen-5 Hyperspectral Imagery K. Li et al. https://doi.org/10.34133/remotesensing.1013
45 citations as recorded by crossref.
- Multisatellite Data Depicts a Record-Breaking Methane Leak from a Well Blowout L. Guanter et al. https://doi.org/10.1021/acs.estlett.4c00399
- 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
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
- Global satellite survey reveals uncertainty in landfill methane emissions M. Dogniaux et al. https://doi.org/10.1038/s41586-025-09683-8
- 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
- Coordinated satellite, aircraft, and ground-based observations of a large transient methane release T. He et al. https://doi.org/10.1073/pnas.2603595123
- Comparative Performance of Gaussian Plume and Backward Lagrangian Stochastic Models for Near-Field Methane Emission Estimation Using a Single Controlled Release Experiment A. Upreti et al. https://doi.org/10.3390/atmos17040417
- Fugitive Methane Monitoring: A Systems Review of Physics, Technology, Economics, and Regulation P. Sobron https://doi.org/10.3390/rs18142433
- US oil and gas system emissions from nearly one million aerial site measurements E. Sherwin et al. https://doi.org/10.1038/s41586-024-07117-5
- A new aerial approach for quantifying and attributing methane emissions: implementation and validation J. Dooley et al. https://doi.org/10.5194/amt-17-5091-2024
- Global Methane Retrieval, Monitoring, and Quantification in Hotspot Regions Based on AHSI/ZY-1 Satellite T. Lu et al. https://doi.org/10.3390/atmos16050510
- Controlled release testing of commercially available methane emission measurement technologies at the TADI facility A. McManemin et al. https://doi.org/10.5194/amt-19-923-2026
- 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
- Measurement of fugitive methane emissions from an abandoned coal exploration hole in Queensland, Australia using a Quantum Gas LiDAR S. Hoerning & P. Hayes https://doi.org/10.1016/j.scitotenv.2025.179925
- Global Identification of Solid Waste Methane Super Emitters Using Hyperspectral Satellites X. Zhang et al. https://doi.org/10.1021/acs.est.4c14196
- Vast and hidden urban methane emissions from the Russia–Ukraine war Z. Feng et al. https://doi.org/10.1038/s44284-025-00309-8
- Estimating Methane Emission Durations Using Continuous Monitoring Systems W. Daniels et al. https://doi.org/10.1021/acs.estlett.4c00687
- 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
- Comparing Continuous Methane Monitoring Technologies for High-Volume Emissions: A Single-Blind Controlled Release Study Z. Chen et al. https://doi.org/10.1021/acsestair.4c00015
- Multiscale Measurement and Modeling of Methane Emissions in US Oil and Gas Production Regions D. Allen et al. https://doi.org/10.1146/annurev-chembioeng-100724-074807
- Optimizing methane continuous monitoring networks in oil and gas fields: A multi-objective framework based on information theory Z. Xie et al. https://doi.org/10.1016/j.jclepro.2026.148010
- 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
- Advancing oil and gas emissions assessment through large language model data extraction Z. Chen et al. https://doi.org/10.1016/j.egyai.2025.100481
- Monitoring Persistent Methane Emissions from the Secunda CTL Synthetic Fuel Plant Using Satellite Observations H. Virta et al. https://doi.org/10.1021/acs.estlett.5c01140
- HyperGas 1.0: a python package for analyzing hyperspectral data for greenhouse gases from retrieval to emission rate quantification X. Zhang et al. https://doi.org/10.5194/gmd-19-5979-2026
- Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer B. Rouet-Leduc & C. Hulbert https://doi.org/10.1038/s41467-024-47754-y
- Comparing the performance of different hyperspectral satellite imaging spectroscopy in mapping methane point-source emissions F. Li et al. https://doi.org/10.1016/j.rse.2025.115224
- Correction of near-surface methane concentrations using a CNN-RF hybrid model based on multi-scale feature extraction L. Fan et al. https://doi.org/10.1016/j.apr.2026.103078
- Detection and quantification of agricultural methane plumes using MethaneAIR through targeted scene selection, wavelet denoising, and divergence-integral analysis P. Smale et al. https://doi.org/10.5194/acp-26-10661-2026
- Drill tool recognition and detection with SERep-CCNet: A lightweight model approach X. Wu et al. https://doi.org/10.1016/j.geoen.2025.213844
- Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT V. Batchu et al. https://doi.org/10.1073/pnas.2612145123
- Conditions for Valid Offshore Methane Quantification S. Riddick https://doi.org/10.3390/eng7080419
- Developing a ‘fit for purpose’ approach to measuring methane emissions I. Joynes et al. https://doi.org/10.1071/EP23067
- Identification of Sources of Methane in Ho Chi Minh City, Vietnam C. Woolley Maisch et al. https://doi.org/10.1021/acsestair.5c00034
- Technological Maturity of Aircraft-Based Methane Sensing for Greenhouse Gas Mitigation S. El Abbadi et al. https://doi.org/10.1021/acs.est.4c02439
- Quantification Error Model for Aerial LiDAR Methane Emission Rate Estimates C. Dudiak et al. https://doi.org/10.1021/acsestair.5c00458
- Tightening up methane plume source rate estimation in EnMAP and PRISMA images E. Ouerghi et al. https://doi.org/10.5194/amt-18-4611-2025
- A controlled release experiment for investigating methane measurement performance at landfills R. Hossain et al. https://doi.org/10.1525/elementa.2025.00048
- A Multi-Sensor Framework for Methane Detection and Flux Estimation with Scale-Aware Plume Segmentation and Uncertainty Propagation from High-Resolution Spaceborne Imaging Spectrometers A. Ferrari et al. https://doi.org/10.3390/methane5010010
- Wind-Robust Methane Source-Rate Inversion from Remote-Sensing Plume Imagery: Soft Physics Guidance Versus Hard IME Coupling Q. Dong et al. https://doi.org/10.3390/rs18121992
- 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
- Automated detection of regions with persistently enhanced methane concentrations using Sentinel-5 Precursor satellite data S. Vanselow et al. https://doi.org/10.5194/acp-24-10441-2024
- An Effective Quantification of Methane Point-Source Emissions with the Multi-Level Matched Filter from Hyperspectral Imagery M. Liang et al. https://doi.org/10.3390/rs17050843
- Intercomparison of Three Continuous Monitoring Systems on Operating Oil and Gas Sites W. Daniels et al. https://doi.org/10.1021/acsestair.4c00298
- CH4Vision: Machine Learning Estimation of Methane Flux with GaoFen-5 Hyperspectral Imagery K. Li et al. https://doi.org/10.34133/remotesensing.1013
Saved (final revised paper)
Latest update: 06 Sep 2026
Short summary
Countries and companies increasingly rely on a growing fleet of satellites to find large emissions of climate-warming methane, particularly from oil and natural gas systems across the globe. We independently assessed the performance of nine such systems by releasing controlled, undisclosed amounts of methane as satellites passed overhead. The tested systems produced reliable detection and quantification results, including the smallest-ever emission detected from space in such a test.
Countries and companies increasingly rely on a growing fleet of satellites to find large...
