Articles | Volume 23, issue 16
https://doi.org/10.5194/acp-23-9071-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-9071-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Sep 2023
Research article |
| 19 Sep 2023
| 19 Sep 2023
Automated detection and monitoring of methane super-emitters using satellite data
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
GHGSat Inc., Montreal, Canada
Joannes D. Maasakkers
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Pieter Bijl
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Gourav Mahapatra
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Anne-Wil van den Berg
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
now at: Department of Meteorology and Air Quality, Wageningen University, Wageningen, the Netherlands
Sudhanshu Pandey
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
now at: Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Alba Lorente
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Tobias Borsdorff
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Sander Houweling
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Department of Earth Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
Daniel J. Varon
GHGSat Inc., Montreal, Canada
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Jason McKeever
GHGSat Inc., Montreal, Canada
Dylan Jervis
GHGSat Inc., Montreal, Canada
Marianne Girard
GHGSat Inc., Montreal, Canada
Itziar Irakulis-Loitxate
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València (UPV), Valencia, Spain
International Methane Emission Observatory, United Nations Environment Program, Paris, France
Javier Gorroño
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València (UPV), Valencia, Spain
Luis Guanter
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València (UPV), Valencia, Spain
Environmental Defense Fund, Amsterdam, the Netherlands
Daniel H. Cusworth
Carbon Mapper, Inc., Pasadena, CA, USA
Arizona Institute for Resilience, University of Arizona, Tucson, AZ, USA
Ilse Aben
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Department of Earth Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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Cited
9 citations as recorded by crossref.
- 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
- Daily detection and quantification of methane leaks using Sentinel-3: a tiered satellite observation approach with Sentinel-2 and Sentinel-5p S. Pandey et al. 10.1016/j.rse.2023.113716
- CH4Net: a deep learning model for monitoring methane super-emitters with Sentinel-2 imagery A. Vaughan et al. 10.5194/amt-17-2583-2024
- Evidence of animal productivity outcomes when fed diets including food waste: A systematic review of global primary data Y. Wang et al. 10.1016/j.resconrec.2024.107411
- 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
- A critical analysis of challenges and opportunities for upcycling food waste to animal feed to reduce climate and resource burdens Z. Dou et al. 10.1016/j.resconrec.2024.107418
- Long-term investigation of methane and carbon dioxide emissions in two Italian landfills L. Brilli et al. 10.1016/j.heliyon.2024.e29356
- Developing unbiased estimation of atmospheric methane via machine learning and multiobjective programming based on TROPOMI and GOSAT data K. Li et al. 10.1016/j.rse.2024.114039
- Assessing the Relative Importance of Satellite-Detected Methane Superemitters in Quantifying Total Emissions for Oil and Gas Production Areas in Algeria S. Naus et al. 10.1021/acs.est.3c04746
8 citations as recorded by crossref.
- 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
- Daily detection and quantification of methane leaks using Sentinel-3: a tiered satellite observation approach with Sentinel-2 and Sentinel-5p S. Pandey et al. 10.1016/j.rse.2023.113716
- CH4Net: a deep learning model for monitoring methane super-emitters with Sentinel-2 imagery A. Vaughan et al. 10.5194/amt-17-2583-2024
- Evidence of animal productivity outcomes when fed diets including food waste: A systematic review of global primary data Y. Wang et al. 10.1016/j.resconrec.2024.107411
- 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
- A critical analysis of challenges and opportunities for upcycling food waste to animal feed to reduce climate and resource burdens Z. Dou et al. 10.1016/j.resconrec.2024.107418
- Long-term investigation of methane and carbon dioxide emissions in two Italian landfills L. Brilli et al. 10.1016/j.heliyon.2024.e29356
- Developing unbiased estimation of atmospheric methane via machine learning and multiobjective programming based on TROPOMI and GOSAT data K. Li et al. 10.1016/j.rse.2024.114039
Discussed (preprint)
Latest update: 07 May 2024
Short summary
Using two machine learning models, which were trained on TROPOMI methane satellite data, we detect 2974 methane plumes, so-called super-emitters, in 2021. We detect methane emissions globally related to urban areas or landfills, coal mining, and oil and gas production. Using our monitoring system, we identify 94 regions with frequent emissions. For 12 locations, we target high-resolution satellite instruments to enlarge and identify the exact infrastructure responsible for the emissions.
Using two machine learning models, which were trained on TROPOMI methane satellite data, we...
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