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.
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
15 citations as recorded by crossref.
- A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases N. Balasus et al. 10.5194/amt-16-3787-2023
- Deep learning applied to CO2 power plant emissions quantification using simulated satellite images J. Dumont Le Brazidec et al. 10.5194/gmd-17-1995-2024
- Understanding the potential of Sentinel-2 for monitoring methane point emissions J. Gorroño et al. 10.5194/amt-16-89-2023
- 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. 10.5194/amt-17-2937-2024
- Improving Methane Point Sources Detection Over Heterogeneous Land Surface for Satellite Hyperspectral Imagery E. Sun et al. 10.1109/JSTARS.2024.3482278
- 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
- Detection and quantification of methane plumes with the MethaneAIR airborne spectrometer L. Guanter et al. 10.5194/amt-18-3857-2025
- Simulation evaluation of a single-photon laser methane remote sensor for leakage rate monitoring S. Zhu et al. 10.1364/OE.513894
- Satellite-Based Seasonal Fingerprinting of Methane Emissions from Canadian Dairy Farms Using Sentinel-5P P. Prajesh et al. 10.3390/cli13070135
- Infrared Spectroscopy with Variable Decomposition Level Dual-Tree Complex Wavelet Transform for Quantification of Air Pollutants Y. Qin et al. 10.1021/acs.analchem.5c03806
- Methane Retrieval Algorithms Based on Satellite: A Review Y. Jiang et al. 10.3390/atmos15040449
- Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods J. Dumont Le Brazidec et al. 10.5194/gmd-18-3607-2025
- 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. 10.3390/rs16234503
- Accounting for surface reflectance spectral features in TROPOMI methane retrievals A. Lorente et al. 10.5194/amt-16-1597-2023
- Instrument Performance Analysis for Methane Point Source Retrieval and Estimation Using Remote Sensing Technique Y. Jiang et al. 10.3390/rs17040634
15 citations as recorded by crossref.
- A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases N. Balasus et al. 10.5194/amt-16-3787-2023
- Deep learning applied to CO2 power plant emissions quantification using simulated satellite images J. Dumont Le Brazidec et al. 10.5194/gmd-17-1995-2024
- Understanding the potential of Sentinel-2 for monitoring methane point emissions J. Gorroño et al. 10.5194/amt-16-89-2023
- 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. 10.5194/amt-17-2937-2024
- Improving Methane Point Sources Detection Over Heterogeneous Land Surface for Satellite Hyperspectral Imagery E. Sun et al. 10.1109/JSTARS.2024.3482278
- 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
- Detection and quantification of methane plumes with the MethaneAIR airborne spectrometer L. Guanter et al. 10.5194/amt-18-3857-2025
- Simulation evaluation of a single-photon laser methane remote sensor for leakage rate monitoring S. Zhu et al. 10.1364/OE.513894
- Satellite-Based Seasonal Fingerprinting of Methane Emissions from Canadian Dairy Farms Using Sentinel-5P P. Prajesh et al. 10.3390/cli13070135
- Infrared Spectroscopy with Variable Decomposition Level Dual-Tree Complex Wavelet Transform for Quantification of Air Pollutants Y. Qin et al. 10.1021/acs.analchem.5c03806
- Methane Retrieval Algorithms Based on Satellite: A Review Y. Jiang et al. 10.3390/atmos15040449
- Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods J. Dumont Le Brazidec et al. 10.5194/gmd-18-3607-2025
- 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. 10.3390/rs16234503
- Accounting for surface reflectance spectral features in TROPOMI methane retrievals A. Lorente et al. 10.5194/amt-16-1597-2023
- Instrument Performance Analysis for Methane Point Source Retrieval and Estimation Using Remote Sensing Technique Y. Jiang et al. 10.3390/rs17040634
Latest update: 28 Aug 2025
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...