Discovery of Large Methane Emissions Using a Complementary Method Based on Multispectral and Hyperspectral Data
As global atmospheric methane concentrations surge at an unprecedented rate, the identification of methane super-emitters with significant mitigation potential has become imperative. In this study, we utilize remote sensing satellite data with varying spatiotemporal coverage and resolutions to detec...
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MDPI AG
2025-04-01
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| author | Xiaoli Cai Yunfei Bao Qiaolin Huang Zhong Li Zhilong Yan Bicen Li |
| author_facet | Xiaoli Cai Yunfei Bao Qiaolin Huang Zhong Li Zhilong Yan Bicen Li |
| author_sort | Xiaoli Cai |
| collection | DOAJ |
| description | As global atmospheric methane concentrations surge at an unprecedented rate, the identification of methane super-emitters with significant mitigation potential has become imperative. In this study, we utilize remote sensing satellite data with varying spatiotemporal coverage and resolutions to detect and quantify methane emissions. We exploit the synergistic potential of Sentinel-2, EnMAP, and GF5-02-AHSI for methane plume detection. Employing a matched filtering algorithm based on EnMAP and AHSI, we detect and extract methane plumes within emission hotspots in China and the United States, and estimate the emission flux rates of individual methane point sources using the IME model. We present methane plumes from industries such as oil and gas (O&G) and coal mining, with emission rates ranging from 1 to 40 tons per h, as observed by EnMAP and GF5-02-AHSI. For selected methane emission hotspots in China and the United States, we conduct long-term monitoring and analysis using Sentinel-2. Our findings reveal that the synergy between Sentinel-2, EnMAP, and GF5-02-AHSI enables the precise identification of methane plumes, as well as the quantification and monitoring of their corresponding sources. This methodology is readily applicable to other satellite instruments with coarse SWIR spectral bands, such as Landsat-7 and Landsat-8. The high-frequency satellite-based detection of anomalous methane point sources can facilitate timely corrective actions, contributing to the reduction in global methane emissions. This study underscores the potential of spaceborne multispectral imaging instruments, combining fine pixel resolution with rapid revisit rates, to advance the global high-frequency monitoring of large methane point sources. |
| format | Article |
| id | doaj-art-2bea33d474b741ee8e52f11a84c47f17 |
| institution | OA Journals |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Atmosphere |
| spelling | doaj-art-2bea33d474b741ee8e52f11a84c47f172025-08-20T01:56:20ZengMDPI AGAtmosphere2073-44332025-04-0116553210.3390/atmos16050532Discovery of Large Methane Emissions Using a Complementary Method Based on Multispectral and Hyperspectral DataXiaoli Cai0Yunfei Bao1Qiaolin Huang2Zhong Li3Zhilong Yan4Bicen Li5Beijing Institute of Space Mechanics and Electricity, China Academy of Space Technology, Beijing 100094, ChinaBeijing Institute of Space Mechanics and Electricity, China Academy of Space Technology, Beijing 100094, ChinaBeijing Institute of Space Mechanics and Electricity, China Academy of Space Technology, Beijing 100094, ChinaKey Laboratory of Advanced Technologies of Materials, Ministry of Education, School of Materials Science and Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaBeijing Institute of Space Mechanics and Electricity, China Academy of Space Technology, Beijing 100094, ChinaBeijing Institute of Space Mechanics and Electricity, China Academy of Space Technology, Beijing 100094, ChinaAs global atmospheric methane concentrations surge at an unprecedented rate, the identification of methane super-emitters with significant mitigation potential has become imperative. In this study, we utilize remote sensing satellite data with varying spatiotemporal coverage and resolutions to detect and quantify methane emissions. We exploit the synergistic potential of Sentinel-2, EnMAP, and GF5-02-AHSI for methane plume detection. Employing a matched filtering algorithm based on EnMAP and AHSI, we detect and extract methane plumes within emission hotspots in China and the United States, and estimate the emission flux rates of individual methane point sources using the IME model. We present methane plumes from industries such as oil and gas (O&G) and coal mining, with emission rates ranging from 1 to 40 tons per h, as observed by EnMAP and GF5-02-AHSI. For selected methane emission hotspots in China and the United States, we conduct long-term monitoring and analysis using Sentinel-2. Our findings reveal that the synergy between Sentinel-2, EnMAP, and GF5-02-AHSI enables the precise identification of methane plumes, as well as the quantification and monitoring of their corresponding sources. This methodology is readily applicable to other satellite instruments with coarse SWIR spectral bands, such as Landsat-7 and Landsat-8. The high-frequency satellite-based detection of anomalous methane point sources can facilitate timely corrective actions, contributing to the reduction in global methane emissions. This study underscores the potential of spaceborne multispectral imaging instruments, combining fine pixel resolution with rapid revisit rates, to advance the global high-frequency monitoring of large methane point sources.https://www.mdpi.com/2073-4433/16/5/532methanematched filterSentinel-2high-frequency monitoring |
| spellingShingle | Xiaoli Cai Yunfei Bao Qiaolin Huang Zhong Li Zhilong Yan Bicen Li Discovery of Large Methane Emissions Using a Complementary Method Based on Multispectral and Hyperspectral Data Atmosphere methane matched filter Sentinel-2 high-frequency monitoring |
| title | Discovery of Large Methane Emissions Using a Complementary Method Based on Multispectral and Hyperspectral Data |
| title_full | Discovery of Large Methane Emissions Using a Complementary Method Based on Multispectral and Hyperspectral Data |
| title_fullStr | Discovery of Large Methane Emissions Using a Complementary Method Based on Multispectral and Hyperspectral Data |
| title_full_unstemmed | Discovery of Large Methane Emissions Using a Complementary Method Based on Multispectral and Hyperspectral Data |
| title_short | Discovery of Large Methane Emissions Using a Complementary Method Based on Multispectral and Hyperspectral Data |
| title_sort | discovery of large methane emissions using a complementary method based on multispectral and hyperspectral data |
| topic | methane matched filter Sentinel-2 high-frequency monitoring |
| url | https://www.mdpi.com/2073-4433/16/5/532 |
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