Space-ground integration system of methane emission monitoring and quantification: cases in Dongying, China
Calibrating traditional inventory-based emission estimates with top-down point source inversion results is of significant importance. To address the challenges posed by satellite remote sensing in accurately assessing methane point source emissions and the inefficiency of ground-based mobile measure...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Earth Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2025.1577961/full |
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| Summary: | Calibrating traditional inventory-based emission estimates with top-down point source inversion results is of significant importance. To address the challenges posed by satellite remote sensing in accurately assessing methane point source emissions and the inefficiency of ground-based mobile measurement due to the lack of prior information, this paper proposes a novel space-ground integration system of methane emission monitoring and quantification. The system utilizes a classic matched filter (CMF) algorithm to retrieve greenhouse gas concentration increments from multi-temporal hyperspectral images, thereby identifying continuous point sources, which subsequently guides the development of ground-based emission data collection plans. The EMISSION-PARTITION model is applied to quantify point source emission intensities. In April 2024, our team conducted an experiment based on this system in a petrochemical industrial park in Dongying, China. Satellite observations identified key continuous point sources with an uncertainty of 8.08%. The point source emission intensities quantified from mobile measurement ranged from a minimum of 139.36 kg/hto a maximum of 107.42 kg/h, with uncertainties controlled within 19.1%. This experiment provides valuable insights for similar greenhouse gas emission monitoring and quantification tasks. |
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| ISSN: | 2296-6463 |