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
24 citations as recorded by crossref.
- Satellite Insights into methane Super-Emitters: Regional emissions and yearly growth on Turkmenistan’s west coast Z. He et al.
- Assessing uncertainties of Integrated Mass Enhancement (IME) method for estimating landfill methane emissions F. Arkian et al.
- Improving Methane Point Sources Detection Over Heterogeneous Land Surface for Satellite Hyperspectral Imagery E. Sun et al.
- An Operational Ground-Based Vicarious Radiometric Calibration Method for Thermal Infrared Sensors: A Case Study of GF-5A WTI J. Bai et al.
- An Effective Quantification of Methane Point-Source Emissions with the Multi-Level Matched Filter from Hyperspectral Imagery M. Liang et al.
- Satellite-Derived Approaches for Coal Mine Methane Estimation: A Review A. Chauhan & S. Raval
- Advancements in satellite-based methane point source monitoring: A systematic review F. Mohammadimanesh et al.
- 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.
- Global Methane Retrieval, Monitoring, and Quantification in Hotspot Regions Based on AHSI/ZY-1 Satellite T. Lu et al.
- Monitoring fossil fuel CO2 emissions from co-emitted NO2 observed from space: progress, challenges, and future perspectives H. Li et al.
- An Intercomparison of Underground Coal Mine Methane Emission Estimation in Shanxi, China: S5P/TROPOMI vs. GF-5B/AHSI Z. Yang et al.
- 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.
- Instrument Performance Analysis for Methane Point Source Retrieval and Estimation Using Remote Sensing Technique Y. Jiang et al.
- Assessing Methane Emission Patterns and Sensitivities at High-Emission Point Sources in China via Gaussian Plume Modeling H. Li et al.
- SSRMF: A sparse spectral reconstruction enhanced matched filter for improving point-source methane emission detection in complex terrain K. Li et al.
- 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.
- CH4Vision: Machine Learning Estimation of Methane Flux with GaoFen-5 Hyperspectral Imagery K. Li et al.
- Evaluation of satellite-derived methane emissions from coal mines using the Gaussian plume model in a topographically complex area Y. Gao et al.
- GHGPSE-Net: a method towards spaceborne automated extraction of greenhouse-gas point sources using point-object-detection deep neural network Y. Pang et al.
- Prediction of Vanadium Contamination Distribution Pattern Through Remote Sensing Image Fusion and Machine Learning Z. Zhao et al.
- Prediction of Lithium Mineralization Potential in the Jiulong Area, Western Sichuan (China), Using Spectral Residual Attention Convolutional Neural Network H. Luo et al.
- SAM4CH4: Zero-Shot Methane Plume Mapping With Segment Anything and Vision-Language Models M. Mahdianpari et al.
- High-Resolution Satellite Reveals the Methane Emissions from China’s Coal Mines X. Li et al.
- Estimation of Fossil Fuel Carbon Emissions in Mainland China by Incorporating Multiple Spatiotemporal Data and Machine Learning Algorithms L. Zheng et al.
24 citations as recorded by crossref.
- Satellite Insights into methane Super-Emitters: Regional emissions and yearly growth on Turkmenistan’s west coast Z. He et al.
- Assessing uncertainties of Integrated Mass Enhancement (IME) method for estimating landfill methane emissions F. Arkian et al.
- Improving Methane Point Sources Detection Over Heterogeneous Land Surface for Satellite Hyperspectral Imagery E. Sun et al.
- An Operational Ground-Based Vicarious Radiometric Calibration Method for Thermal Infrared Sensors: A Case Study of GF-5A WTI J. Bai et al.
- An Effective Quantification of Methane Point-Source Emissions with the Multi-Level Matched Filter from Hyperspectral Imagery M. Liang et al.
- Satellite-Derived Approaches for Coal Mine Methane Estimation: A Review A. Chauhan & S. Raval
- Advancements in satellite-based methane point source monitoring: A systematic review F. Mohammadimanesh et al.
- 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.
- Global Methane Retrieval, Monitoring, and Quantification in Hotspot Regions Based on AHSI/ZY-1 Satellite T. Lu et al.
- Monitoring fossil fuel CO2 emissions from co-emitted NO2 observed from space: progress, challenges, and future perspectives H. Li et al.
- An Intercomparison of Underground Coal Mine Methane Emission Estimation in Shanxi, China: S5P/TROPOMI vs. GF-5B/AHSI Z. Yang et al.
- 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.
- Instrument Performance Analysis for Methane Point Source Retrieval and Estimation Using Remote Sensing Technique Y. Jiang et al.
- Assessing Methane Emission Patterns and Sensitivities at High-Emission Point Sources in China via Gaussian Plume Modeling H. Li et al.
- SSRMF: A sparse spectral reconstruction enhanced matched filter for improving point-source methane emission detection in complex terrain K. Li et al.
- 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.
- CH4Vision: Machine Learning Estimation of Methane Flux with GaoFen-5 Hyperspectral Imagery K. Li et al.
- Evaluation of satellite-derived methane emissions from coal mines using the Gaussian plume model in a topographically complex area Y. Gao et al.
- GHGPSE-Net: a method towards spaceborne automated extraction of greenhouse-gas point sources using point-object-detection deep neural network Y. Pang et al.
- Prediction of Vanadium Contamination Distribution Pattern Through Remote Sensing Image Fusion and Machine Learning Z. Zhao et al.
- Prediction of Lithium Mineralization Potential in the Jiulong Area, Western Sichuan (China), Using Spectral Residual Attention Convolutional Neural Network H. Luo et al.
- SAM4CH4: Zero-Shot Methane Plume Mapping With Segment Anything and Vision-Language Models M. Mahdianpari et al.
- High-Resolution Satellite Reveals the Methane Emissions from China’s Coal Mines X. Li et al.
- Estimation of Fossil Fuel Carbon Emissions in Mainland China by Incorporating Multiple Spatiotemporal Data and Machine Learning Algorithms L. Zheng et al.
Saved (final revised paper)
Latest update: 02 May 2026
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...
