Articles | Volume 17, issue 4
https://doi.org/10.5194/amt-17-1333-2024
© Author(s) 2024. 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-17-1333-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Feb 2024
Research article |
| 26 Feb 2024
| 26 Feb 2024
Exploiting the entire near-infrared spectral range to improve the detection of methane plumes with high-resolution imaging spectrometers
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València (UPV), 46022, Valencia, Spain
Luis Guanter
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València (UPV), 46022, Valencia, Spain
Environmental Defense Fund, Reguliersgracht 79, 1017 LN Amsterdam, the Netherlands
Javier Gorroño
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València (UPV), 46022, Valencia, Spain
Itziar Irakulis-Loitxate
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València (UPV), 46022, Valencia, Spain
International Methane Emission Observatory (IMEO), United Nations Environment Programme, Paris, France
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Cited
17 citations as recorded by crossref.
- HyperspectralViTs: General Hyperspectral Models for On-Board Remote Sensing V. Růžička & A. Markham https://doi.org/10.1109/JSTARS.2025.3557527
- Evidence of successful methane mitigation in one of Europe's most important oil production region G. Kuhlmann et al. https://doi.org/10.5194/acp-25-5371-2025
- Identification of Sources of Methane in Ho Chi Minh City, Vietnam C. Woolley Maisch et al. https://doi.org/10.1021/acsestair.5c00034
- Satellite-Derived Approaches for Coal Mine Methane Estimation: A Review A. Chauhan & S. Raval https://doi.org/10.3390/rs17213652
- MFDGNet: multi-domain fusion and dynamic gating network for infrared gas leak detection D. Li et al. https://doi.org/10.1088/1361-6501/ae2cb4
- Global Identification of Solid Waste Methane Super Emitters Using Hyperspectral Satellites X. Zhang et al. https://doi.org/10.1021/acs.est.4c14196
- An Effective Quantification of Methane Point-Source Emissions with the Multi-Level Matched Filter from Hyperspectral Imagery M. Liang et al. https://doi.org/10.3390/rs17050843
- Temporal and spatial comparison of coal mine ventilation methane emissions and mitigation quantified using PRISMA satellite data and on-site measurements C. Karacan et al. https://doi.org/10.1016/j.scitotenv.2025.179268
- SSRMF: A sparse spectral reconstruction enhanced matched filter for improving point-source methane emission detection in complex terrain K. Li et al. https://doi.org/10.1016/j.isprsjprs.2025.04.034
- Improved Quantification of Methane Point-Source Emissions from Hyperspectral Imagery Using a Spectrally Corrected Levenberg–Marquardt Matched Filter Z. He et al. https://doi.org/10.3390/rs18081195
- Space Imaging Point Source Detection and Characterization F. Ribeiro et al. https://doi.org/10.1109/ACCESS.2024.3420217
- CH4Vision: Machine Learning Estimation of Methane Flux with GaoFen-5 Hyperspectral Imagery K. Li et al. https://doi.org/10.34133/remotesensing.1013
- Surveying methane point-source super-emissions across oil and gas basins with MethaneSAT L. Guanter et al. https://doi.org/10.5194/acp-26-2941-2026
- Detecting methane emissions from palm oil mills with airborne and spaceborne imaging spectrometers A. Valverde et al. https://doi.org/10.1088/1748-9326/ad8806
- Inferring methane emissions from African livestock by fusing drone, tower, and satellite data A. van Hove et al. https://doi.org/10.5194/bg-22-4163-2025
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
- Improving Methane Point Sources Detection Over Heterogeneous Land Surface for Satellite Hyperspectral Imagery E. Sun et al. https://doi.org/10.1109/JSTARS.2024.3482278
17 citations as recorded by crossref.
- HyperspectralViTs: General Hyperspectral Models for On-Board Remote Sensing V. Růžička & A. Markham https://doi.org/10.1109/JSTARS.2025.3557527
- Evidence of successful methane mitigation in one of Europe's most important oil production region G. Kuhlmann et al. https://doi.org/10.5194/acp-25-5371-2025
- Identification of Sources of Methane in Ho Chi Minh City, Vietnam C. Woolley Maisch et al. https://doi.org/10.1021/acsestair.5c00034
- Satellite-Derived Approaches for Coal Mine Methane Estimation: A Review A. Chauhan & S. Raval https://doi.org/10.3390/rs17213652
- MFDGNet: multi-domain fusion and dynamic gating network for infrared gas leak detection D. Li et al. https://doi.org/10.1088/1361-6501/ae2cb4
- Global Identification of Solid Waste Methane Super Emitters Using Hyperspectral Satellites X. Zhang et al. https://doi.org/10.1021/acs.est.4c14196
- An Effective Quantification of Methane Point-Source Emissions with the Multi-Level Matched Filter from Hyperspectral Imagery M. Liang et al. https://doi.org/10.3390/rs17050843
- Temporal and spatial comparison of coal mine ventilation methane emissions and mitigation quantified using PRISMA satellite data and on-site measurements C. Karacan et al. https://doi.org/10.1016/j.scitotenv.2025.179268
- SSRMF: A sparse spectral reconstruction enhanced matched filter for improving point-source methane emission detection in complex terrain K. Li et al. https://doi.org/10.1016/j.isprsjprs.2025.04.034
- Improved Quantification of Methane Point-Source Emissions from Hyperspectral Imagery Using a Spectrally Corrected Levenberg–Marquardt Matched Filter Z. He et al. https://doi.org/10.3390/rs18081195
- Space Imaging Point Source Detection and Characterization F. Ribeiro et al. https://doi.org/10.1109/ACCESS.2024.3420217
- CH4Vision: Machine Learning Estimation of Methane Flux with GaoFen-5 Hyperspectral Imagery K. Li et al. https://doi.org/10.34133/remotesensing.1013
- Surveying methane point-source super-emissions across oil and gas basins with MethaneSAT L. Guanter et al. https://doi.org/10.5194/acp-26-2941-2026
- Detecting methane emissions from palm oil mills with airborne and spaceborne imaging spectrometers A. Valverde et al. https://doi.org/10.1088/1748-9326/ad8806
- Inferring methane emissions from African livestock by fusing drone, tower, and satellite data A. van Hove et al. https://doi.org/10.5194/bg-22-4163-2025
- Machine Learning for Methane Detection and Quantification From Space: A survey E. Tiemann et al. https://doi.org/10.1109/MGRS.2025.3599559
- Improving Methane Point Sources Detection Over Heterogeneous Land Surface for Satellite Hyperspectral Imagery E. Sun et al. https://doi.org/10.1109/JSTARS.2024.3482278
Saved (final revised paper)
Latest update: 17 Jul 2026
Short summary
Methane emissions can be identified using remote sensing, but surface-related structures disturb detection. In this work, a variation of the matched filter method that exploits a large fraction of the near-infrared range (1000–2500 nm) is applied. In comparison to the raw matched filter, it reduces background noise and strongly attenuates the surface-related artifacts, which leads to a greater detection capability. We propose this variation as a standard methodology for methane detection.
Methane emissions can be identified using remote sensing, but surface-related structures disturb...
