FI-SCAPE: A Divergence Theorem Based Emission Quantification Model for Air/Spaceborne Imaging Spectrometer Derived X<sub>CH4</sub> Observations
The Global Methane Pledge calls for a reduction of methane emissions by at least 30% by 2030. The reduction of methane emissions in the energy sector is critical to achieving this target. Remote sensing plays a crucial role in identifying and quantifying methane superemitters. In the fort...
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2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10742394/ |
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| author | Yiyang Huang Ge Han Tianqi Shi Siwei Li Huiqin Mao Yihuang Nie Wei Gong |
| author_facet | Yiyang Huang Ge Han Tianqi Shi Siwei Li Huiqin Mao Yihuang Nie Wei Gong |
| author_sort | Yiyang Huang |
| collection | DOAJ |
| description | The Global Methane Pledge calls for a reduction of methane emissions by at least 30% by 2030. The reduction of methane emissions in the energy sector is critical to achieving this target. Remote sensing plays a crucial role in identifying and quantifying methane superemitters. In the forthcoming years, multiple promising missions carrying imaging spectrometers will be sent into orbit to obtain XCH4 observations with extensive coverage and high resolution. Traditional emission quantification models, such as the Gaussian plume model and some based on chemical transport models, are not optimally suited to the characteristics of new data. In this article, we propose a divergence-theorem-based emission quantification model, named flux integration method based on sinusoidal cosine optimization algorithm to inverse the methane point source emissions, which utilizes XCH4 observations derived from airborne imaging spectrometers to achieve rapid and accurate estimation of methane point source emission rates. This approach overcomes limitations of other methods, such as the inability of Gaussian plume models to recover the integrity of regional concentration enhancements, excessive disruption caused by integrated mass enhancement and divergence integral masking operators, and the requirement for effective wind speed fitting. The extraction of plume regions only causes a perturbation of approximately ±5% in the results, and the <italic>R</italic> value of this method on real datasets exceeds 0.89. It provides technical support for rapid and accurate monitoring of methane point source emissions on a global scale, aiding in the establishment of routine methane emission monitoring systems based on satellite remote sensing. |
| format | Article |
| id | doaj-art-f13afd5f2f33430fa1e29a2e930f0973 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-f13afd5f2f33430fa1e29a2e930f09732024-11-29T00:00:41ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-011825527210.1109/JSTARS.2024.349089610742394FI-SCAPE: A Divergence Theorem Based Emission Quantification Model for Air/Spaceborne Imaging Spectrometer Derived X<sub>CH4</sub> ObservationsYiyang Huang0https://orcid.org/0009-0008-9362-1725Ge Han1https://orcid.org/0000-0003-2561-3244Tianqi Shi2https://orcid.org/0000-0003-4815-4175Siwei Li3Huiqin Mao4Yihuang Nie5Wei Gong6https://orcid.org/0000-0002-2276-8024Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaHubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaLaboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, FranceHubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaMinistry of Ecology and Environment Center for Satellite Application on Ecology and Environment/State Environmental Protection Key Laboratory of Satellite Remote Sensing, Beijing, ChinaMinistry of Ecology and Environment Center for Satellite Application on Ecology and Environment/State Environmental Protection Key Laboratory of Satellite Remote Sensing, Beijing, ChinaElectronic Information School, Wuhan University, Wuhan, ChinaThe Global Methane Pledge calls for a reduction of methane emissions by at least 30% by 2030. The reduction of methane emissions in the energy sector is critical to achieving this target. Remote sensing plays a crucial role in identifying and quantifying methane superemitters. In the forthcoming years, multiple promising missions carrying imaging spectrometers will be sent into orbit to obtain XCH4 observations with extensive coverage and high resolution. Traditional emission quantification models, such as the Gaussian plume model and some based on chemical transport models, are not optimally suited to the characteristics of new data. In this article, we propose a divergence-theorem-based emission quantification model, named flux integration method based on sinusoidal cosine optimization algorithm to inverse the methane point source emissions, which utilizes XCH4 observations derived from airborne imaging spectrometers to achieve rapid and accurate estimation of methane point source emission rates. This approach overcomes limitations of other methods, such as the inability of Gaussian plume models to recover the integrity of regional concentration enhancements, excessive disruption caused by integrated mass enhancement and divergence integral masking operators, and the requirement for effective wind speed fitting. The extraction of plume regions only causes a perturbation of approximately ±5% in the results, and the <italic>R</italic> value of this method on real datasets exceeds 0.89. It provides technical support for rapid and accurate monitoring of methane point source emissions on a global scale, aiding in the establishment of routine methane emission monitoring systems based on satellite remote sensing.https://ieeexplore.ieee.org/document/10742394/Divergenceflux integration method based on sinusoidal cosine optimization algorithm to inverse the methane point source emissions (FI-SCAPE)methane emissionspoint source |
| spellingShingle | Yiyang Huang Ge Han Tianqi Shi Siwei Li Huiqin Mao Yihuang Nie Wei Gong FI-SCAPE: A Divergence Theorem Based Emission Quantification Model for Air/Spaceborne Imaging Spectrometer Derived X<sub>CH4</sub> Observations IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Divergence flux integration method based on sinusoidal cosine optimization algorithm to inverse the methane point source emissions (FI-SCAPE) methane emissions point source |
| title | FI-SCAPE: A Divergence Theorem Based Emission Quantification Model for Air/Spaceborne Imaging Spectrometer Derived X<sub>CH4</sub> Observations |
| title_full | FI-SCAPE: A Divergence Theorem Based Emission Quantification Model for Air/Spaceborne Imaging Spectrometer Derived X<sub>CH4</sub> Observations |
| title_fullStr | FI-SCAPE: A Divergence Theorem Based Emission Quantification Model for Air/Spaceborne Imaging Spectrometer Derived X<sub>CH4</sub> Observations |
| title_full_unstemmed | FI-SCAPE: A Divergence Theorem Based Emission Quantification Model for Air/Spaceborne Imaging Spectrometer Derived X<sub>CH4</sub> Observations |
| title_short | FI-SCAPE: A Divergence Theorem Based Emission Quantification Model for Air/Spaceborne Imaging Spectrometer Derived X<sub>CH4</sub> Observations |
| title_sort | fi scape a divergence theorem based emission quantification model for air x002f spaceborne imaging spectrometer derived x sub ch4 sub observations |
| topic | Divergence flux integration method based on sinusoidal cosine optimization algorithm to inverse the methane point source emissions (FI-SCAPE) methane emissions point source |
| url | https://ieeexplore.ieee.org/document/10742394/ |
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