Articles | Volume 23, issue 14
https://doi.org/10.5194/acp-23-8039-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-8039-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Jul 2023
Research article |
| 19 Jul 2023
| 19 Jul 2023
East Asian methane emissions inferred from high-resolution inversions of GOSAT and TROPOMI observations: a comparative and evaluative analysis
Ruosi Liang
College of Environmental and Resource Sciences, Zhejiang University,
Hangzhou, Zhejiang 310058, China
Key Laboratory of Coastal Environment and Resources of Zhejiang
Province, School of Engineering, Westlake University, Hangzhou, Zhejiang
310030, China
Institute of Advanced Technology, Westlake Institute for Advanced
Study, Hangzhou, Zhejiang 310024, China
Yuzhong Zhang
CORRESPONDING AUTHOR
Key Laboratory of Coastal Environment and Resources of Zhejiang
Province, School of Engineering, Westlake University, Hangzhou, Zhejiang
310030, China
Institute of Advanced Technology, Westlake Institute for Advanced
Study, Hangzhou, Zhejiang 310024, China
Wei Chen
College of Environmental and Resource Sciences, Zhejiang University,
Hangzhou, Zhejiang 310058, China
Key Laboratory of Coastal Environment and Resources of Zhejiang
Province, School of Engineering, Westlake University, Hangzhou, Zhejiang
310030, China
Institute of Advanced Technology, Westlake Institute for Advanced
Study, Hangzhou, Zhejiang 310024, China
Peixuan Zhang
Key Laboratory of Coastal Environment and Resources of Zhejiang
Province, School of Engineering, Westlake University, Hangzhou, Zhejiang
310030, China
Institute of Advanced Technology, Westlake Institute for Advanced
Study, Hangzhou, Zhejiang 310024, China
Fudan University, Shanghai 200433, China
Jingran Liu
College of Environmental and Resource Sciences, Zhejiang University,
Hangzhou, Zhejiang 310058, China
Key Laboratory of Coastal Environment and Resources of Zhejiang
Province, School of Engineering, Westlake University, Hangzhou, Zhejiang
310030, China
Institute of Advanced Technology, Westlake Institute for Advanced
Study, Hangzhou, Zhejiang 310024, China
Cuihong Chen
Center for Satellite Application on Ecology and Environment, Ministry
of Ecology and Environment of China, Beijing 100094, China
Huiqin Mao
Center for Satellite Application on Ecology and Environment, Ministry
of Ecology and Environment of China, Beijing 100094, China
Guofeng Shen
Laboratory for Earth Surface Processes, College of Urban and
Environmental Sciences, Peking University, Beijing 100871, China
Department of Marine Earth and Atmospheric Sciences, North Carolina
State University, Raleigh, NC 27607, USA
Zichong Chen
School of Engineering and Applied Science, Harvard University,
Cambridge, MA, USA
Minqiang Zhou
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing
100029, China
Pucai Wang
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing
100029, China
University of Chinese Academy of Sciences, Beijing 100049, China
Robert J. Parker
National Centre for Earth Observation, University of Leicester,
Leicester, UK
Earth Observation Science, School of Physics and Astronomy,
University of Leicester, Leicester, UK
Hartmut Boesch
National Centre for Earth Observation, University of Leicester,
Leicester, UK
Earth Observation Science, School of Physics and Astronomy,
University of Leicester, Leicester, UK
Alba Lorente
SRON Netherlands Institute for Space Research, Utrecht, the
Netherlands
Joannes D. Maasakkers
SRON Netherlands Institute for Space Research, Utrecht, the
Netherlands
Ilse Aben
SRON Netherlands Institute for Space Research, Utrecht, the
Netherlands
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- Assessment of the impact of observations at Nainital (India) and Comilla (Bangladesh) on the CH4 flux inversion D. Belikov et al. https://doi.org/10.1186/s40645-024-00634-x
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- Bridging the Gap: A comprehensive review and cross-check analysis for China's Non-CO2 greenhouse gas emission estimates L. Hu & X. Fang https://doi.org/10.1016/j.jenvman.2025.127362
- Environmental drivers constraining the seasonal variability in satellite-observed and modelled methane at northern high latitudes E. Kivimäki et al. https://doi.org/10.5194/bg-22-5193-2025
- Attributing 2019–2024 methane growth using TROPOMI satellite observations M. He et al. https://doi.org/10.1126/sciadv.adz9007
- Inversing NOx emissions based on an optimization model that combines a source-receptor relationship correction matrix and monitoring data: A case study in Linyi, China M. Li et al. https://doi.org/10.1016/j.atmosenv.2024.120848
- North–south inhomogeneous variations of methane emissions observed by TROPOMI over China S. Yu et al. https://doi.org/10.1016/j.jenvman.2026.129542
- Rapid summer methane emission decline in high-latitude plains linked to 2021 drought M. Zhao et al. https://doi.org/10.1038/s43247-026-03433-y
- Recent advances in TROPOMI-based methane source detection: a systematic review R. Liu et al. https://doi.org/10.1080/15481603.2026.2650822
- Locating and quantifying CH4 sources within a wastewater treatment plant based on mobile measurements J. Yang et al. https://doi.org/10.5194/acp-25-4571-2025
- The added value of new ground-based observations in improving China's methane emission quantification H. Zhong et al. https://doi.org/10.5194/amt-19-4759-2026
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- Spatiotemporal variations of atmospheric XCH4 in China based on multiple spatially continuous satellite-derived products Y. Liu et al. https://doi.org/10.1016/j.jenvman.2025.126309
- Reconciling Bottom–Up and Top–Down Approaches to Quantify Sub-Regional Methane Emissions with Improved Inventory and Three-Year High-Resolution Satellite Measurements C. He et al. https://doi.org/10.1021/acsestair.5c00446
- Long-term changes in city-level CH4 emissions from rice cultivation in China: Patterns, drivers, projections, and sustainable pathways S. Liang et al. https://doi.org/10.1016/j.resconrec.2025.108738
- Satellite quantification of methane emissions from South American countries: a high-resolution inversion of TROPOMI and GOSAT observations S. Hancock et al. https://doi.org/10.5194/acp-25-797-2025
- Towards verifying and improving estimations of China's CO2 and CH4 budgets using atmospheric inversions Y. Wang et al. https://doi.org/10.1093/nsr/nwaf090
- High-Resolution Inversion of GOSAT-2 Retrievals for Sectoral Methane Emission Estimates During 2019–2022: A Consistency Analysis with GOSAT Inversion R. Janardanan et al. https://doi.org/10.3390/rs17172932
- Five years of GOSAT-2 retrievals with RemoTeC: XCO2 and XCH4 data products with quality filtering by machine learning A. Barr et al. https://doi.org/10.5194/amt-18-6093-2025
- Strong monsoon influence on South Asian methane emissions in 2020 revealed by a Bayesian inversion constrained by satellite observations R. Subramanian et al. https://doi.org/10.5194/acp-26-9757-2026
- High-resolution regional inversion reveals overestimation of anthropogenic methane emissions in China S. Feng et al. https://doi.org/10.5194/acp-25-15121-2025
- Rapid Methane Flux Estimation Combining MethaneSAT and Sentinel‐5P Observations: A Case Study of Turkmenistan Y. Huang et al. https://doi.org/10.1029/2025GL119369
- Two decades of methane budgets at the sub-national scale in China P. Zhao et al. https://doi.org/10.1016/j.scib.2026.06.019
- Assessment of the differences in European CH4 emission estimates from three TROPOMI products A. Sicsik-Paré et al. https://doi.org/10.5194/acp-26-10423-2026
- Leveraging TROPOMI observations and WRF-GHG modeling towards improving methane emission assessments in India T. Mathew et al. https://doi.org/10.5194/acp-26-4453-2026
- Spatiotemporal investigation of near-surface CH4 and factors influencing CH4 over South, East, and Southeast Asia M. Khaliq et al. https://doi.org/10.1016/j.scitotenv.2024.171311
- State of the Art in Monitoring Methane Emissions from Arctic–boreal Wetlands and Lakes M. Mahdianpari et al. https://doi.org/10.3390/rs18060926
- Emerging Trends, Sectoral, and Regional Patterns in China’s Methane Emissions: A Satellite-Constrained Perspective D. Chen et al. https://doi.org/10.1021/acsestair.5c00405
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- How can we trust TROPOMI based methane emissions estimation: calculating emissions over unidentified source regions B. Zheng et al. https://doi.org/10.5194/acp-26-1931-2026
- Evaluation of model-simulated atmospheric methane over South Korea using ground-based and aircraft observations S. Do et al. https://doi.org/10.1007/s10661-026-15220-5
- Spatiotemporal variations in atmospheric CH4 concentrations and enhancements in northern China based on a comprehensive dataset: ground-based observations, TROPOMI data, inventory data, and inversions P. Han et al. https://doi.org/10.5194/acp-25-4965-2025
- Monthly methane emissions in Chinese mainland provinces from 2013–2022 D. Cui et al. https://doi.org/10.1038/s41597-025-05107-4
Saved (final revised paper)
Latest update: 13 Aug 2026
Short summary
We compare and evaluate East Asian methane emissions inferred from different satellite observations (GOSAT and TROPOMI). The results show discrepancies over northern India and eastern China. Independent ground-based observations are more consistent with TROPOMI-derived emissions in northern India and GOSAT-derived emissions in eastern China.
We compare and evaluate East Asian methane emissions inferred from different satellite...
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