Articles | Volume 6, issue 1
https://doi.org/10.5194/amt-6-151-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-6-151-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
30 Jan 2013
Research article |
| 30 Jan 2013
Quantification of methane emission rates from coal mine ventilation shafts using airborne remote sensing data
T. Krings
University of Bremen, Institute of Environmental Physics, P.O. Box 330440, 28334 Bremen, Germany
K. Gerilowski
University of Bremen, Institute of Environmental Physics, P.O. Box 330440, 28334 Bremen, Germany
M. Buchwitz
University of Bremen, Institute of Environmental Physics, P.O. Box 330440, 28334 Bremen, Germany
J. Hartmann
Alfred Wegener Institute for Polar and Marine Research, Am Handelshafen 12, 27570 Bremerhaven, Germany
Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
J. Erzinger
Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
J. P. Burrows
University of Bremen, Institute of Environmental Physics, P.O. Box 330440, 28334 Bremen, Germany
H. Bovensmann
University of Bremen, Institute of Environmental Physics, P.O. Box 330440, 28334 Bremen, Germany
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- 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
- Advances in methane sensor development: From fundamentals to field deployment and future perspective M. Chen et al. https://doi.org/10.1016/j.cej.2026.177199
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- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
- NeuPlume: generative inversion of atmospheric point-source emissions from sparse observations L. Wang & X. Ma https://doi.org/10.5194/acp-26-14185-2026
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- Quantifying methane emissions from coal mining ventilation shafts using an unmanned aerial vehicle (UAV)-based active AirCore system T. Andersen et al. https://doi.org/10.1016/j.aeaoa.2021.100135
- Evaluating the effects of surface properties on methane retrievals using a synthetic airborne visible/infrared imaging spectrometer next generation (AVIRIS-NG) image A. Ayasse et al. https://doi.org/10.1016/j.rse.2018.06.018
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- Multi-task deep learning for quantifying methane emissions from 2-D plume imagery with Low Signal-to-Noise Ratio Q. Xu et al. https://doi.org/10.1080/01431161.2024.2421946
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- Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system P. Ciais et al. https://doi.org/10.5194/bg-11-3547-2014
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- 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
- Advances in methane sensor development: From fundamentals to field deployment and future perspective M. Chen et al. https://doi.org/10.1016/j.cej.2026.177199
- Towards accurate methane point-source quantification from high-resolution 2-D plume imagery S. Jongaramrungruang et al. https://doi.org/10.5194/amt-12-6667-2019
- CHARM-F—a new airborne integrated-path differential-absorption lidar for carbon dioxide and methane observations: measurement performance and quantification of strong point source emissions A. Amediek et al. https://doi.org/10.1364/AO.56.005182
- Effect of solar zenith angle on satellite cloud retrievals based on O2–O2 absorption band G. Kim et al. https://doi.org/10.1080/01431161.2021.1890267
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
- NeuPlume: generative inversion of atmospheric point-source emissions from sparse observations L. Wang & X. Ma https://doi.org/10.5194/acp-26-14185-2026
- Mapping methane concentrations from a controlled release experiment using the next generation airborne visible/infrared imaging spectrometer (AVIRIS-NG) A. Thorpe et al. https://doi.org/10.1016/j.rse.2016.03.032
- Quantifying particulate matter optical properties and flow rate in industrial stack plumes from the PRISMA hyperspectral imager G. Calassou et al. https://doi.org/10.5194/amt-17-57-2024
- Spaceborne NO2 observations are sensitive to coal mining and processing in the largest coal basin of Russia L. Labzovskii et al. https://doi.org/10.1038/s41598-022-16850-8
- Seasonal Landfill Methane Emissions Driven by Temperature and Pressure A. Hallward-Driemeier et al. https://doi.org/10.1021/acs.est.5c16757
- Quantifying methane emissions from coal mining ventilation shafts using an unmanned aerial vehicle (UAV)-based active AirCore system T. Andersen et al. https://doi.org/10.1016/j.aeaoa.2021.100135
- Evaluating the effects of surface properties on methane retrievals using a synthetic airborne visible/infrared imaging spectrometer next generation (AVIRIS-NG) image A. Ayasse et al. https://doi.org/10.1016/j.rse.2018.06.018
- Simulation evaluation of a single-photon laser methane remote sensor for leakage rate monitoring S. Zhu et al. https://doi.org/10.1364/OE.513894
- Probability of detection for airborne sensing of methane plumes using long wavelength infrared hyperspectral imaging K. Daun et al. https://doi.org/10.1016/j.rse.2026.115556
- Monitoring the effectiveness of the coal mine degassing system-the basis for safe work of miners V. Malashkina https://doi.org/10.25018/0236-1493-2020-61-0-38-45
- Impact of stray light on greenhouse gas concentration retrievals and emission estimates as observed with the passive airborne remote sensing imager MAMAP2D-Light O. Huhs et al. https://doi.org/10.5194/amt-19-871-2026
- 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. https://doi.org/10.3390/rs13244992
- Quantifying NOxpoint sources with Landsat and Sentinel-2 satellite observations of NO2plumes D. Varon et al. https://doi.org/10.1073/pnas.2317077121
- Three-dimensional radiative transfer effects on airborne and ground-based trace gas remote sensing M. Schwaerzel et al. https://doi.org/10.5194/amt-13-4277-2020
- Deep Learning-Based quantifications of methane emissions with field applications I. Jahan et al. https://doi.org/10.1016/j.jag.2024.104018
- Analysis of a tiered top-down approach using satellite and aircraft platforms to monitor oil and gas facilities in the Permian basin Á. Esparza et al. https://doi.org/10.1016/j.rser.2023.113265
- Multi-task deep learning for quantifying methane emissions from 2-D plume imagery with Low Signal-to-Noise Ratio Q. Xu et al. https://doi.org/10.1080/01431161.2024.2421946
- Quantifying Time-Averaged Methane Emissions from Individual Coal Mine Vents with GHGSat-D Satellite Observations D. Varon et al. https://doi.org/10.1021/acs.est.0c01213
- Detection and quantification of CH4 plumes using the WFM-DOAS retrieval on AVIRIS-NG hyperspectral data J. Borchardt et al. https://doi.org/10.5194/amt-14-1267-2021
- Reduced Methane Emissions from Santa Barbara Marine Seeps T. Krings et al. https://doi.org/10.3390/rs9111162
- Monitoring oil and gas field CH4 leaks by Sentinel-5P and Sentinel-2 B. He et al. https://doi.org/10.1016/j.fuel.2024.133889
- Machine learning-based emission rate estimates of global methane super-emissions C. Roberts et al. https://doi.org/10.5194/amt-19-5659-2026
- Monitoring greenhouse gases (GHGs) in China: status and perspective Y. Sun et al. https://doi.org/10.5194/amt-15-4819-2022
- Quantifying Urban Nitrogen Dioxide Emission From Space Based on Cross-Sectional Flux Method and Satellite Data X. Ye et al. https://doi.org/10.1109/TGRS.2025.3544815
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- Quantifying CO2 emissions of a city with the Copernicus Anthropogenic CO2 Monitoring satellite mission G. Kuhlmann et al. https://doi.org/10.5194/amt-13-6733-2020
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- 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
- Potential of Spaceborne Lidar Measurements of Carbon Dioxide and Methane Emissions from Strong Point Sources C. Kiemle et al. https://doi.org/10.3390/rs9111137
- MethaNet – An AI-driven approach to quantifying methane point-source emission from high-resolution 2-D plume imagery S. Jongaramrungruang et al. https://doi.org/10.1016/j.rse.2021.112809
- Space‐based remote imaging spectroscopy of the Aliso Canyon CH4 superemitter D. Thompson et al. https://doi.org/10.1002/2016GL069079
- Detectability of CO2 emission plumes of cities and power plants with the Copernicus Anthropogenic CO2 Monitoring (CO2M) mission G. Kuhlmann et al. https://doi.org/10.5194/amt-12-6695-2019
- In situ observations of greenhouse gases over Europe during the CoMet 1.0 campaign aboard the HALO aircraft M. Gałkowski et al. https://doi.org/10.5194/amt-14-1525-2021
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- Challenges in Methane Column Retrievals from AVIRIS-NG Imagery over Spectrally Cluttered Surfaces: A Sensitivity Analysis M. Zhang et al. https://doi.org/10.3390/rs9080835
- Atmospheric remote sensing constraints on direct sea-air methane flux from the 22/4b North Sea massive blowout bubble plume K. Gerilowski et al. https://doi.org/10.1016/j.marpetgeo.2015.07.011
- Airborne DOAS retrievals of methane, carbon dioxide, and water vapor concentrations at high spatial resolution: application to AVIRIS-NG A. Thorpe et al. https://doi.org/10.5194/amt-10-3833-2017
- CoMet: an airborne mission to simultaneously measure CO2 and CH4 using lidar, passive remote sensing, and in-situ techniques A. Fix et al. https://doi.org/10.1051/epjconf/201817602003
- Detecting the Sources of Methane Emission from Oil Shale Mining and Processing Using Airborne Hyperspectral Data C. Xiao et al. https://doi.org/10.3390/rs12030537
- Simultaneous Retrieval of Trace Gases, Aerosols, and Cirrus Using RemoTAP—The Global Orbit Ensemble Study for the CO2M Mission S. Lu et al. https://doi.org/10.3389/frsen.2022.914378
- Carbon Monitoring Satellite (CarbonSat): assessment of atmospheric CO2 and CH4 retrieval errors by error parameterization M. Buchwitz et al. https://doi.org/10.5194/amt-6-3477-2013
- Quantifying CH4 emissions from hard coal mines using mobile sun-viewing Fourier transform spectrometry A. Luther et al. https://doi.org/10.5194/amt-12-5217-2019
- MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane G. Ehret et al. https://doi.org/10.3390/rs9101052
- Individual coal mine methane emissions constrained by eddy covariance measurements: low bias and missing sources K. Qin et al. https://doi.org/10.5194/acp-24-3009-2024
- Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes D. Varon et al. https://doi.org/10.5194/amt-11-5673-2018
- Selected Meteorological Factors Influencing Gas Emissions from an Abandoned Coal Mine Shaft—Results of In Situ Measurements P. Wrona et al. https://doi.org/10.3390/su17093875
- Retrieval techniques for airborne imaging of methane concentrations using high spatial and moderate spectral resolution: application to AVIRIS A. Thorpe et al. https://doi.org/10.5194/amt-7-491-2014
- GreenHouse gas Observations of the Stratosphere and Troposphere (GHOST): an airborne shortwave-infrared spectrometer for remote sensing of greenhouse gases N. Humpage et al. https://doi.org/10.5194/amt-11-5199-2018
- U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers J. Bruno et al. https://doi.org/10.5194/amt-17-2625-2024
- Evaluation of coal mine methane inventory methods using aircraft-based approaches in the Bowen Basin, Australia S. Harris et al. https://doi.org/10.5194/acp-26-10043-2026
- 基于DOAS技术的电厂及城区二氧化碳排放遥测研究 张. Zhang Huarong et al. https://doi.org/10.3788/AOS230762
- Methane emissions from a Californian landfill, determined from airborne remote sensing and in situ measurements S. Krautwurst et al. https://doi.org/10.5194/amt-10-3429-2017
- Airborne remote sensing and in situ measurements of atmospheric CO2 to quantify point source emissions T. Krings et al. https://doi.org/10.5194/amt-11-721-2018
- Quantification of CH4 coal mining emissions in Upper Silesia by passive airborne remote sensing observations with the Methane Airborne MAPper (MAMAP) instrument during the CO2 and Methane (CoMet) campaign S. Krautwurst et al. https://doi.org/10.5194/acp-21-17345-2021
- 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
- 温室气体通量测量方法及进展 岳. Yue Bin et al. https://doi.org/10.3788/AOS222172
- Simulation Analysis of Unmanned Aerial Vehicle-Based Laser Remote Sensing for Methane Point Source Traceability and Leakage Quantification S. Zhu et al. https://doi.org/10.3390/rs17223670
- Satellite observations of atmospheric methane and their value for quantifying methane emissions D. Jacob et al. https://doi.org/10.5194/acp-16-14371-2016
- The ddeq Python library for point source quantification from remote sensing images (version 1.0) G. Kuhlmann et al. https://doi.org/10.5194/gmd-17-4773-2024
- A wide field-of-view imaging DOAS instrument for two-dimensional trace gas mapping from aircraft A. Schönhardt et al. https://doi.org/10.5194/amt-8-5113-2015
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