Articles | Volume 14, issue 12
https://doi.org/10.5194/amt-14-7999-2021
© Author(s) 2021. 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-14-7999-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Dec 2021
Research article |
| 22 Dec 2021
| 22 Dec 2021
Remote sensing of methane plumes: instrument tradeoff analysis for detecting and quantifying local sources at global scale
Siraput Jongaramrungruang
CORRESPONDING AUTHOR
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, 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
Zhao-Cheng Zeng
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
Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
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Cited
25 citations as recorded by crossref.
- Deep learning applied to CO2 power plant emissions quantification using simulated satellite images J. Dumont Le Brazidec et al.
- A survey of methane point source emissions from coal mines in Shanxi province of China using AHSI on board Gaofen-5B Z. He et al.
- Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane D. Jacob et al.
- A U.S. scientific community review of carbon cycle science gaps and opportunities to better support earth system science and carbon management N. Parazoo et al.
- GHGPSE-Net: a method towards spaceborne automated extraction of greenhouse-gas point sources using point-object-detection deep neural network Y. Pang et al.
- Methane Retrieval Algorithms Based on Satellite: A Review Y. Jiang et al.
- Satellite Insights into methane Super-Emitters: Regional emissions and yearly growth on Turkmenistan’s west coast Z. He et al.
- Improved Quantification of Methane Point-Source Emissions from Hyperspectral Imagery Using a Spectrally Corrected Levenberg–Marquardt Matched Filter Z. He et al.
- Accounting for surface reflectance spectral features in TROPOMI methane retrievals A. Lorente et al.
- Instrument Performance Analysis for Methane Point Source Retrieval and Estimation Using Remote Sensing Technique Y. Jiang et al.
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al.
- A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases N. Balasus et al.
- Understanding the potential of Sentinel-2 for monitoring methane point emissions J. Gorroño et al.
- Improving Methane Point Sources Detection Over Heterogeneous Land Surface for Satellite Hyperspectral Imagery E. Sun et al.
- Surface reflectance biases in XCH4 retrievals from the 2.3 µm band are enhanced in the presence of aerosols P. Somkuti et al.
- Detection and quantification of methane plumes with the MethaneAIR airborne spectrometer L. Guanter et al.
- Simulation evaluation of a single-photon laser methane remote sensor for leakage rate monitoring S. Zhu et al.
- Assessing the Potential of the MTG-FCI Geostationary Mission for the Detection of Methane Plumes S. Zhou et al.
- Satellite-Based Seasonal Fingerprinting of Methane Emissions from Canadian Dairy Farms Using Sentinel-5P P. Prajesh et al.
- High NA multi-wavelength achromatic metalens design based on Latin square matrix partitioning strategy and hybrid optimization algorithm Y. Miao et al.
- Infrared Spectroscopy with Variable Decomposition Level Dual-Tree Complex Wavelet Transform for Quantification of Air Pollutants Y. Qin et al.
- Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods J. Dumont Le Brazidec et al.
- Considering the observation and illumination angular configuration for an improved detection and quantification of methane emissions J. Gorroño et al.
- Towards Supporting Satellite Design Through the Top-Down Approach: A General Model for Assessing the Ability of Future Satellite Missions to Quantify Point Source Emissions L. Yao et al.
- Comparing the performance of different hyperspectral satellite imaging spectroscopy in mapping methane point-source emissions F. Li et al.
25 citations as recorded by crossref.
- Deep learning applied to CO2 power plant emissions quantification using simulated satellite images J. Dumont Le Brazidec et al.
- A survey of methane point source emissions from coal mines in Shanxi province of China using AHSI on board Gaofen-5B Z. He et al.
- Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane D. Jacob et al.
- A U.S. scientific community review of carbon cycle science gaps and opportunities to better support earth system science and carbon management N. Parazoo et al.
- GHGPSE-Net: a method towards spaceborne automated extraction of greenhouse-gas point sources using point-object-detection deep neural network Y. Pang et al.
- Methane Retrieval Algorithms Based on Satellite: A Review Y. Jiang et al.
- Satellite Insights into methane Super-Emitters: Regional emissions and yearly growth on Turkmenistan’s west coast Z. He et al.
- Improved Quantification of Methane Point-Source Emissions from Hyperspectral Imagery Using a Spectrally Corrected Levenberg–Marquardt Matched Filter Z. He et al.
- Accounting for surface reflectance spectral features in TROPOMI methane retrievals A. Lorente et al.
- Instrument Performance Analysis for Methane Point Source Retrieval and Estimation Using Remote Sensing Technique Y. Jiang et al.
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al.
- A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases N. Balasus et al.
- Understanding the potential of Sentinel-2 for monitoring methane point emissions J. Gorroño et al.
- Improving Methane Point Sources Detection Over Heterogeneous Land Surface for Satellite Hyperspectral Imagery E. Sun et al.
- Surface reflectance biases in XCH4 retrievals from the 2.3 µm band are enhanced in the presence of aerosols P. Somkuti et al.
- Detection and quantification of methane plumes with the MethaneAIR airborne spectrometer L. Guanter et al.
- Simulation evaluation of a single-photon laser methane remote sensor for leakage rate monitoring S. Zhu et al.
- Assessing the Potential of the MTG-FCI Geostationary Mission for the Detection of Methane Plumes S. Zhou et al.
- Satellite-Based Seasonal Fingerprinting of Methane Emissions from Canadian Dairy Farms Using Sentinel-5P P. Prajesh et al.
- High NA multi-wavelength achromatic metalens design based on Latin square matrix partitioning strategy and hybrid optimization algorithm Y. Miao et al.
- Infrared Spectroscopy with Variable Decomposition Level Dual-Tree Complex Wavelet Transform for Quantification of Air Pollutants Y. Qin et al.
- Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods J. Dumont Le Brazidec et al.
- Considering the observation and illumination angular configuration for an improved detection and quantification of methane emissions J. Gorroño et al.
- Towards Supporting Satellite Design Through the Top-Down Approach: A General Model for Assessing the Ability of Future Satellite Missions to Quantify Point Source Emissions L. Yao et al.
- Comparing the performance of different hyperspectral satellite imaging spectroscopy in mapping methane point-source emissions F. Li et al.
Saved (final revised paper)
Latest update: 04 May 2026
Short summary
This study shows how precision error and bias in column methane retrieval change with different instrument specifications and the impact of spectrally complex surface albedos on retrievals. We show how surface interferences can be mitigated with an optimal spectral resolution and a higher polynomial degree in a retrieval process. The findings can inform future satellite instrument designs to have robust observations capable of separating real CH4 plume enhancements from surface interferences.
This study shows how precision error and bias in column methane retrieval change with different...
