Articles | Volume 17, issue 9
https://doi.org/10.5194/amt-17-2625-2024
© 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-17-2625-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 May 2024
Research article |
| 06 May 2024
| 06 May 2024
U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers
Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138, USA
GHGSat Inc., Montreal, QC H2W 1Y5, Canada
Dylan Jervis
GHGSat Inc., Montreal, QC H2W 1Y5, Canada
Daniel J. Varon
Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138, USA
Daniel J. Jacob
Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138, USA
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Cited
32 citations as recorded by crossref.
- 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
- Physics-informed regression-residual reconstruction and group fusion dual-attention transformer for methane plume detection L. Wu et al. https://doi.org/10.1016/j.isprsjprs.2026.08.017
- 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
- Towards operational automated greenhouse gas plume detection and delineation B. Bue et al. https://doi.org/10.1016/j.rse.2026.115506
- Detection and quantification of methane plumes with the MethaneAIR airborne spectrometer L. Guanter et al. https://doi.org/10.5194/amt-18-3857-2025
- SAM4CH4: Zero-Shot Methane Plume Mapping With Segment Anything and Vision-Language Models M. Mahdianpari et al. https://doi.org/10.1109/JSTARS.2025.3642040
- Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al. https://doi.org/10.5194/gmd-18-8777-2025
- Satellite On-Orbit Chip-Level Deep Learning Model for Real-Time Dust Storm Monitoring R. Peng et al. https://doi.org/10.1021/acs.est.5c14697
- Airborne imaging spectrometer measurements of methane releases under turbulent conditions M. Queißer et al. https://doi.org/10.1080/01431161.2026.2710354
- Surface-observation-constrained high-frequency coal mine methane emissions in Shanxi, China, reveal more emissions than inventories, consistent with satellite inversion F. Lu et al. https://doi.org/10.5194/acp-25-5837-2025
- Three-Dimensional Analytical Modeling of Carbon Monoxide Dispersion over the Gulf of Guinea with 4D-Var Data Assimilation V. Hounkpe et al. https://doi.org/10.4236/ojap.2026.151001
- kMetha-Mamba: K-means clustering mamba for methane plumes segmentation Y. Liu et al. https://doi.org/10.1016/j.jag.2025.104664
- 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
- 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
- FSSTNet: Frequency Enhanced Spectral-Spatial Transformer for Methane Plumes Segmentation J. Zhang et al. https://doi.org/10.1109/JSTARS.2026.3707824
- 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
- Improvements of AI-driven emission estimation for point sources applied to high resolution 2-D methane-plume imagery T. Plewa et al. https://doi.org/10.1016/j.rse.2025.115002
- FUMESNet: Exploring Frequency-Based Transformer and Improving Skip Connection for Hyperspectral Methane Plume Segmentation A. Dixit & P. Gupta https://doi.org/10.1109/TIM.2026.3667330
- 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 Methods for Inferring Industrial CO2 Hotspots from Co-Emitted NO2 Plumes E. Sun et al. https://doi.org/10.3390/rs17071167
- Sensitivity of Airborne Methane Retrieval Algorithms (MF, ACRWL1MF, and DOAS) to Surface Albedo and Types: Hyperspectral Simulation Assessment J. Chen et al. https://doi.org/10.3390/atmos16111224
- Satellite-Based Methane Emission Monitoring: A Review Across Industries S. Mehrdad & K. Du https://doi.org/10.3390/rs17223674
- MTU-Former: A Multitask Unified Transformer Framework for Global Methane Monitoring With Million-Scale Multispectral Imagery W. Cao et al. https://doi.org/10.1109/TGRS.2026.3722885
- Optimal Methane Plume Extraction of Hyperspectral Imagery Using Fractional Order Matched Filter R. Krzysiak et al. https://doi.org/10.1016/j.ifacol.2026.01.020
- 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
- CH4Vision: Machine Learning Estimation of Methane Flux with GaoFen-5 Hyperspectral Imagery K. Li et al. https://doi.org/10.34133/remotesensing.1013
- Improving Methane Point Sources Detection Over Heterogeneous Land Surface for Satellite Hyperspectral Imagery E. Sun et al. https://doi.org/10.1109/JSTARS.2024.3482278
- Leveraging wide snapshot XCO2 pre-training to estimate urban fossil fuel CO2 emissions from space Z. Wang et al. https://doi.org/10.1016/j.rse.2026.115260
- 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
- Frequency and Spatial Domain Injection Network for Methane Plumes Semantic Segmentation Y. Liu et al. https://doi.org/10.1109/TGRS.2024.3523022
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
- 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
32 citations as recorded by crossref.
- 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
- Physics-informed regression-residual reconstruction and group fusion dual-attention transformer for methane plume detection L. Wu et al. https://doi.org/10.1016/j.isprsjprs.2026.08.017
- 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
- Towards operational automated greenhouse gas plume detection and delineation B. Bue et al. https://doi.org/10.1016/j.rse.2026.115506
- Detection and quantification of methane plumes with the MethaneAIR airborne spectrometer L. Guanter et al. https://doi.org/10.5194/amt-18-3857-2025
- SAM4CH4: Zero-Shot Methane Plume Mapping With Segment Anything and Vision-Language Models M. Mahdianpari et al. https://doi.org/10.1109/JSTARS.2025.3642040
- Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al. https://doi.org/10.5194/gmd-18-8777-2025
- Satellite On-Orbit Chip-Level Deep Learning Model for Real-Time Dust Storm Monitoring R. Peng et al. https://doi.org/10.1021/acs.est.5c14697
- Airborne imaging spectrometer measurements of methane releases under turbulent conditions M. Queißer et al. https://doi.org/10.1080/01431161.2026.2710354
- Surface-observation-constrained high-frequency coal mine methane emissions in Shanxi, China, reveal more emissions than inventories, consistent with satellite inversion F. Lu et al. https://doi.org/10.5194/acp-25-5837-2025
- Three-Dimensional Analytical Modeling of Carbon Monoxide Dispersion over the Gulf of Guinea with 4D-Var Data Assimilation V. Hounkpe et al. https://doi.org/10.4236/ojap.2026.151001
- kMetha-Mamba: K-means clustering mamba for methane plumes segmentation Y. Liu et al. https://doi.org/10.1016/j.jag.2025.104664
- 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
- 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
- FSSTNet: Frequency Enhanced Spectral-Spatial Transformer for Methane Plumes Segmentation J. Zhang et al. https://doi.org/10.1109/JSTARS.2026.3707824
- 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
- Improvements of AI-driven emission estimation for point sources applied to high resolution 2-D methane-plume imagery T. Plewa et al. https://doi.org/10.1016/j.rse.2025.115002
- FUMESNet: Exploring Frequency-Based Transformer and Improving Skip Connection for Hyperspectral Methane Plume Segmentation A. Dixit & P. Gupta https://doi.org/10.1109/TIM.2026.3667330
- 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 Methods for Inferring Industrial CO2 Hotspots from Co-Emitted NO2 Plumes E. Sun et al. https://doi.org/10.3390/rs17071167
- Sensitivity of Airborne Methane Retrieval Algorithms (MF, ACRWL1MF, and DOAS) to Surface Albedo and Types: Hyperspectral Simulation Assessment J. Chen et al. https://doi.org/10.3390/atmos16111224
- Satellite-Based Methane Emission Monitoring: A Review Across Industries S. Mehrdad & K. Du https://doi.org/10.3390/rs17223674
- MTU-Former: A Multitask Unified Transformer Framework for Global Methane Monitoring With Million-Scale Multispectral Imagery W. Cao et al. https://doi.org/10.1109/TGRS.2026.3722885
- Optimal Methane Plume Extraction of Hyperspectral Imagery Using Fractional Order Matched Filter R. Krzysiak et al. https://doi.org/10.1016/j.ifacol.2026.01.020
- 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
- CH4Vision: Machine Learning Estimation of Methane Flux with GaoFen-5 Hyperspectral Imagery K. Li et al. https://doi.org/10.34133/remotesensing.1013
- Improving Methane Point Sources Detection Over Heterogeneous Land Surface for Satellite Hyperspectral Imagery E. Sun et al. https://doi.org/10.1109/JSTARS.2024.3482278
- Leveraging wide snapshot XCO2 pre-training to estimate urban fossil fuel CO2 emissions from space Z. Wang et al. https://doi.org/10.1016/j.rse.2026.115260
- 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
- Frequency and Spatial Domain Injection Network for Methane Plumes Semantic Segmentation Y. Liu et al. https://doi.org/10.1109/TGRS.2024.3523022
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
- 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
Saved (final revised paper)
Latest update: 09 Sep 2026
Short summary
Methane is a potent greenhouse gas and a current high-priority target for short- to mid-term climate change mitigation. Detection of individual methane emitters from space has become possible in recent years, and the volume of data for this task has been rapidly growing, outpacing processing capabilities. We introduce an automated approach, U-Plume, which can detect and quantify emissions from individual methane sources in high-spatial-resolution satellite data.
Methane is a potent greenhouse gas and a current high-priority target for short- to mid-term...
