Articles | Volume 17, issue 9
https://doi.org/10.5194/amt-17-2937-2024
© 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-17-2937-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 May 2024
Research article |
| 15 May 2024
| 15 May 2024
A survey of methane point source emissions from coal mines in Shanxi province of China using AHSI on board Gaofen-5B
Zhonghua He
Zhejiang Climate Centre, Zhejiang Meteorological Bureau, Hangzhou, 310052, China
National Satellite Meteorological Centre, China Meteorological Administration, Beijing, 100081, China
Miao Liang
Meteorological Observation Centre, China Meteorological Administration, Beijing, 100081, China
Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing, 100871, China
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Cited
15 citations as recorded by crossref.
- Instrument Performance Analysis for Methane Point Source Retrieval and Estimation Using Remote Sensing Technique Y. Jiang et al. 10.3390/rs17040634
- SSRMF: A sparse spectral reconstruction enhanced matched filter for improving point-source methane emission detection in complex terrain K. Li et al. 10.1016/j.isprsjprs.2025.04.034
- Assessing uncertainties of Integrated Mass Enhancement (IME) method for estimating landfill methane emissions F. Arkian et al. 10.1080/10962247.2025.2557323
- Improving Methane Point Sources Detection Over Heterogeneous Land Surface for Satellite Hyperspectral Imagery E. Sun et al. 10.1109/JSTARS.2024.3482278
- 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. 10.1016/j.scitotenv.2025.179268
- An Effective Quantification of Methane Point-Source Emissions with the Multi-Level Matched Filter from Hyperspectral Imagery M. Liang et al. 10.3390/rs17050843
- Advancements in satellite-based methane point source monitoring: A systematic review F. Mohammadimanesh et al. 10.1016/j.isprsjprs.2025.03.020
- Spectral calibration method of the field spectrometer using combined solar Fraunhofer lines and atmospheric absorption characteristics: case study at the Baotou RadCalNet site and implications for reflectance measurement Y. Zhao et al. 10.1364/AO.544936
- Global Methane Retrieval, Monitoring, and Quantification in Hotspot Regions Based on AHSI/ZY-1 Satellite T. Lu et al. 10.3390/atmos16050510
- Monitoring fossil fuel CO2 emissions from co-emitted NO2 observed from space: progress, challenges, and future perspectives H. Li et al. 10.1007/s11783-025-1922-x
- Prediction of Vanadium Contamination Distribution Pattern Through Remote Sensing Image Fusion and Machine Learning Z. Zhao et al. 10.3390/rs17071164
- Evaluation of methane emission from MSW landfills in China, India, and the U.S. from space using a two-tier approach S. Zhang et al. 10.1016/j.jenvman.2025.124705
- Prediction of Lithium Mineralization Potential in the Jiulong Area, Western Sichuan (China), Using Spectral Residual Attention Convolutional Neural Network H. Luo et al. 10.1007/s11053-025-10473-2
- High-Resolution Satellite Reveals the Methane Emissions from China’s Coal Mines X. Li et al. 10.3390/rs17020220
- Probability of Detection and Multi-Sensor Persistence of Methane Emissions from Coincident Airborne and Satellite Observations A. Ayasse et al. 10.1021/acs.est.4c06702
14 citations as recorded by crossref.
- Instrument Performance Analysis for Methane Point Source Retrieval and Estimation Using Remote Sensing Technique Y. Jiang et al. 10.3390/rs17040634
- SSRMF: A sparse spectral reconstruction enhanced matched filter for improving point-source methane emission detection in complex terrain K. Li et al. 10.1016/j.isprsjprs.2025.04.034
- Assessing uncertainties of Integrated Mass Enhancement (IME) method for estimating landfill methane emissions F. Arkian et al. 10.1080/10962247.2025.2557323
- Improving Methane Point Sources Detection Over Heterogeneous Land Surface for Satellite Hyperspectral Imagery E. Sun et al. 10.1109/JSTARS.2024.3482278
- 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. 10.1016/j.scitotenv.2025.179268
- An Effective Quantification of Methane Point-Source Emissions with the Multi-Level Matched Filter from Hyperspectral Imagery M. Liang et al. 10.3390/rs17050843
- Advancements in satellite-based methane point source monitoring: A systematic review F. Mohammadimanesh et al. 10.1016/j.isprsjprs.2025.03.020
- Spectral calibration method of the field spectrometer using combined solar Fraunhofer lines and atmospheric absorption characteristics: case study at the Baotou RadCalNet site and implications for reflectance measurement Y. Zhao et al. 10.1364/AO.544936
- Global Methane Retrieval, Monitoring, and Quantification in Hotspot Regions Based on AHSI/ZY-1 Satellite T. Lu et al. 10.3390/atmos16050510
- Monitoring fossil fuel CO2 emissions from co-emitted NO2 observed from space: progress, challenges, and future perspectives H. Li et al. 10.1007/s11783-025-1922-x
- Prediction of Vanadium Contamination Distribution Pattern Through Remote Sensing Image Fusion and Machine Learning Z. Zhao et al. 10.3390/rs17071164
- Evaluation of methane emission from MSW landfills in China, India, and the U.S. from space using a two-tier approach S. Zhang et al. 10.1016/j.jenvman.2025.124705
- Prediction of Lithium Mineralization Potential in the Jiulong Area, Western Sichuan (China), Using Spectral Residual Attention Convolutional Neural Network H. Luo et al. 10.1007/s11053-025-10473-2
- High-Resolution Satellite Reveals the Methane Emissions from China’s Coal Mines X. Li et al. 10.3390/rs17020220
Latest update: 13 Sep 2025
Short summary
Using Gaofen-5B satellite data, this study detected 93 methane plume events from 32 coal mines in Shanxi, China, with emission rates spanning from 761.78 ± 185.00 to 12729.12 ± 4658.13 kg h-1, showing significant variability among sources. This study highlights Gaofen-5B’s capacity for monitoring large methane point sources, offering valuable support in reducing greenhouse gas emissions.
Using Gaofen-5B satellite data, this study detected 93 methane plume events from 32 coal mines...
