Articles | Volume 12, issue 12
https://doi.org/10.5194/amt-12-6667-2019
© Author(s) 2019. 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-12-6667-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Dec 2019
Research article |
| 17 Dec 2019
| 17 Dec 2019
Towards accurate methane point-source quantification from high-resolution 2-D plume imagery
Siraput Jongaramrungruang
CORRESPONDING AUTHOR
Division of Geological and Planetary Sciences, California Institute of
Technology, Pasadena, CA 91125, USA
Christian Frankenberg
CORRESPONDING AUTHOR
Division of Geological and Planetary Sciences, California Institute of
Technology, Pasadena, CA 91125, USA
NASA Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Georgios Matheou
Department of Mechanical Engineering, University of Connecticut,
Storrs, CT 06269, USA
Andrew K. Thorpe
NASA Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
David R. Thompson
NASA Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Joint Institute for Regional Earth System Science and University of
California, Los Angeles, CA 90095, USA
Riley M. Duren
NASA Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
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Cited
34 citations as recorded by crossref.
- MethaNet – An AI-driven approach to quantifying methane point-source emission from high-resolution 2-D plume imagery S. Jongaramrungruang et al. 10.1016/j.rse.2021.112809
- S2MetNet: A novel dataset and deep learning benchmark for methane point source quantification using Sentinel-2 satellite imagery A. Radman et al. 10.1016/j.rse.2023.113708
- Quantifying Time-Averaged Methane Emissions from Individual Coal Mine Vents with GHGSat-D Satellite Observations D. Varon et al. 10.1021/acs.est.0c01213
- 煤炭行业甲烷排放卫星遥感研究进展与展望 秦. Qin Kai et al. 10.3788/AOS231293
- The GHGSat-D imaging spectrometer D. Jervis et al. 10.5194/amt-14-2127-2021
- Sensitivity and Uncertainty in Matched-Filter-Based Gas Detection With Imaging Spectroscopy J. Fahlen et al. 10.1109/TGRS.2024.3440174
- Methane point source quantification using MethaneAIR: a new airborne imaging spectrometer A. Chulakadabba et al. 10.5194/amt-16-5771-2023
- U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers J. Bruno et al. 10.5194/amt-17-2625-2024
- Detecting Methane Emissions from Space Over India: Analysis Using EMIT and Sentinel-5P TROPOMI Datasets A. Siddiqui et al. 10.1007/s12524-024-01925-y
- Organization Development in Precipitating Shallow Cumulus Convection: Evolution of Turbulence Characteristics O. Lamaakel & G. Matheou 10.1175/JAS-D-21-0334.1
- Using a deep neural network to detect methane point sources and quantify emissions from PRISMA hyperspectral satellite images P. Joyce et al. 10.5194/amt-16-2627-2023
- Computational Domain Size Effects on Large-Eddy Simulations of Precipitating Shallow Cumulus Convection O. Lamaakel et al. 10.3390/atmos14071186
- First validation of high-resolution satellite-derived methane emissions from an active gas leak in the UK E. Dowd et al. 10.5194/amt-17-1599-2024
- Mapping methane plumes at very high spatial resolution with the WorldView-3 satellite E. Sánchez-García et al. 10.5194/amt-15-1657-2022
- High-frequency monitoring of anomalous methane point sources with multispectral Sentinel-2 satellite observations D. Varon et al. 10.5194/amt-14-2771-2021
- An evaluation of airborne SWIR imaging spectrometers for CH4 mapping: Implications of band positioning, spectral sampling and noise R. Scafutto et al. 10.1016/j.jag.2020.102233
- Determination of the emission rates of CO<sub>2</sub> point sources with airborne lidar S. Wolff et al. 10.5194/amt-14-2717-2021
- In-flight performance of the Multi-band Uncooled Radiometer Instrument (MURI) thermal sensor A. Gerace et al. 10.1016/j.rse.2022.113086
- Joint Use of in-Scene Background Radiance Estimation and Optimal Estimation Methods for Quantifying Methane Emissions Using PRISMA Hyperspectral Satellite Data: Application to the Korpezhe Industrial Site N. Nesme et al. 10.3390/rs13244992
- Galilean invariance of shallow cumulus convection large-eddy simulations O. Lamaakel & G. Matheou 10.1016/j.jcp.2020.110012
- Current Status of Satellite Remote Sensing-Based Methane Emission Monitoring Technologies M. Kim et al. 10.9719/EEG.2024.57.5.513
- Single photon Lidar gas imagers for practical and widespread continuous methane monitoring J. Titchener et al. 10.1016/j.apenergy.2021.118086
- Detection and quantification of CH<sub>4</sub> plumes using the WFM-DOAS retrieval on AVIRIS-NG hyperspectral data J. Borchardt et al. 10.5194/amt-14-1267-2021
- Towards spaceborne monitoring of localized CO<sub>2</sub> emissions: an instrument concept and first performance assessment J. Strandgren et al. 10.5194/amt-13-2887-2020
- Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane D. Jacob et al. 10.5194/acp-22-9617-2022
- Multi-task deep learning for quantifying methane emissions from 2-D plume imagery with Low Signal-to-Noise Ratio Q. Xu et al. 10.1080/01431161.2024.2421946
- Quantitative imaging of carbon dioxide plumes using a ground-based shortwave infrared spectral camera M. Knapp et al. 10.5194/amt-17-2257-2024
- Monitoring and regression analysis of landfill surface temperatures using remote sensing and image processing techniques K. Sharma et al. 10.1080/01431161.2024.2372081
- Using satellites to uncover large methane emissions from landfills J. Maasakkers et al. 10.1126/sciadv.abn9683
- Deep Learning-Based quantifications of methane emissions with field applications I. Jahan et al. 10.1016/j.jag.2024.104018
- Fast and Accurate Retrieval of Methane Concentration From Imaging Spectrometer Data Using Sparsity Prior M. Foote et al. 10.1109/TGRS.2020.2976888
- Remote sensing of methane plumes: instrument tradeoff analysis for detecting and quantifying local sources at global scale S. Jongaramrungruang et al. 10.5194/amt-14-7999-2021
- Comparing airborne algorithms for greenhouse gas flux measurements over the Alberta oil sands B. Erland et al. 10.5194/amt-15-5841-2022
- Satellite Discovery of Anomalously Large Methane Point Sources From Oil/Gas Production D. Varon et al. 10.1029/2019GL083798
33 citations as recorded by crossref.
- MethaNet – An AI-driven approach to quantifying methane point-source emission from high-resolution 2-D plume imagery S. Jongaramrungruang et al. 10.1016/j.rse.2021.112809
- S2MetNet: A novel dataset and deep learning benchmark for methane point source quantification using Sentinel-2 satellite imagery A. Radman et al. 10.1016/j.rse.2023.113708
- Quantifying Time-Averaged Methane Emissions from Individual Coal Mine Vents with GHGSat-D Satellite Observations D. Varon et al. 10.1021/acs.est.0c01213
- 煤炭行业甲烷排放卫星遥感研究进展与展望 秦. Qin Kai et al. 10.3788/AOS231293
- The GHGSat-D imaging spectrometer D. Jervis et al. 10.5194/amt-14-2127-2021
- Sensitivity and Uncertainty in Matched-Filter-Based Gas Detection With Imaging Spectroscopy J. Fahlen et al. 10.1109/TGRS.2024.3440174
- Methane point source quantification using MethaneAIR: a new airborne imaging spectrometer A. Chulakadabba et al. 10.5194/amt-16-5771-2023
- U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers J. Bruno et al. 10.5194/amt-17-2625-2024
- Detecting Methane Emissions from Space Over India: Analysis Using EMIT and Sentinel-5P TROPOMI Datasets A. Siddiqui et al. 10.1007/s12524-024-01925-y
- Organization Development in Precipitating Shallow Cumulus Convection: Evolution of Turbulence Characteristics O. Lamaakel & G. Matheou 10.1175/JAS-D-21-0334.1
- Using a deep neural network to detect methane point sources and quantify emissions from PRISMA hyperspectral satellite images P. Joyce et al. 10.5194/amt-16-2627-2023
- Computational Domain Size Effects on Large-Eddy Simulations of Precipitating Shallow Cumulus Convection O. Lamaakel et al. 10.3390/atmos14071186
- First validation of high-resolution satellite-derived methane emissions from an active gas leak in the UK E. Dowd et al. 10.5194/amt-17-1599-2024
- Mapping methane plumes at very high spatial resolution with the WorldView-3 satellite E. Sánchez-García et al. 10.5194/amt-15-1657-2022
- High-frequency monitoring of anomalous methane point sources with multispectral Sentinel-2 satellite observations D. Varon et al. 10.5194/amt-14-2771-2021
- An evaluation of airborne SWIR imaging spectrometers for CH4 mapping: Implications of band positioning, spectral sampling and noise R. Scafutto et al. 10.1016/j.jag.2020.102233
- Determination of the emission rates of CO<sub>2</sub> point sources with airborne lidar S. Wolff et al. 10.5194/amt-14-2717-2021
- In-flight performance of the Multi-band Uncooled Radiometer Instrument (MURI) thermal sensor A. Gerace et al. 10.1016/j.rse.2022.113086
- Joint Use of in-Scene Background Radiance Estimation and Optimal Estimation Methods for Quantifying Methane Emissions Using PRISMA Hyperspectral Satellite Data: Application to the Korpezhe Industrial Site N. Nesme et al. 10.3390/rs13244992
- Galilean invariance of shallow cumulus convection large-eddy simulations O. Lamaakel & G. Matheou 10.1016/j.jcp.2020.110012
- Current Status of Satellite Remote Sensing-Based Methane Emission Monitoring Technologies M. Kim et al. 10.9719/EEG.2024.57.5.513
- Single photon Lidar gas imagers for practical and widespread continuous methane monitoring J. Titchener et al. 10.1016/j.apenergy.2021.118086
- Detection and quantification of CH<sub>4</sub> plumes using the WFM-DOAS retrieval on AVIRIS-NG hyperspectral data J. Borchardt et al. 10.5194/amt-14-1267-2021
- Towards spaceborne monitoring of localized CO<sub>2</sub> emissions: an instrument concept and first performance assessment J. Strandgren et al. 10.5194/amt-13-2887-2020
- Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane D. Jacob et al. 10.5194/acp-22-9617-2022
- Multi-task deep learning for quantifying methane emissions from 2-D plume imagery with Low Signal-to-Noise Ratio Q. Xu et al. 10.1080/01431161.2024.2421946
- Quantitative imaging of carbon dioxide plumes using a ground-based shortwave infrared spectral camera M. Knapp et al. 10.5194/amt-17-2257-2024
- Monitoring and regression analysis of landfill surface temperatures using remote sensing and image processing techniques K. Sharma et al. 10.1080/01431161.2024.2372081
- Using satellites to uncover large methane emissions from landfills J. Maasakkers et al. 10.1126/sciadv.abn9683
- Deep Learning-Based quantifications of methane emissions with field applications I. Jahan et al. 10.1016/j.jag.2024.104018
- Fast and Accurate Retrieval of Methane Concentration From Imaging Spectrometer Data Using Sparsity Prior M. Foote et al. 10.1109/TGRS.2020.2976888
- Remote sensing of methane plumes: instrument tradeoff analysis for detecting and quantifying local sources at global scale S. Jongaramrungruang et al. 10.5194/amt-14-7999-2021
- Comparing airborne algorithms for greenhouse gas flux measurements over the Alberta oil sands B. Erland et al. 10.5194/amt-15-5841-2022
1 citations as recorded by crossref.
Latest update: 20 Nov 2024
Short summary
This paper demonstrates the use of high-resolution 2-D plume imagery from airborne remote sensing retrievals to quantify methane point-source emissions. It shows significant improvements on the flux estimates without the need for direct wind speed measurements. This paves the way for enhanced flux estimates in future field campaign and space-based observations to better understand the magnitude and distribution of various point sources of methane.
This paper demonstrates the use of high-resolution 2-D plume imagery from airborne remote...
