the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Mar 2026
Methane Emissions Estimation from China's Leading Coal Production Hub: A Hybrid Hyperspectral Satellite Observations and Emission Inventory Framework
Abstract. Accurate estimation of coal mine methane (CMM) emissions in Shanxi Province, China's leading coal production hub, is essential for mitigating China's anthropogenic methane emissions. Hyperspectral remote sensing is an emerging method for real-time methane monitoring with significant potential for optimizing CMM emission factors. However, limited satellite revisit frequencies can introduce biases in CMM emission estimates. To address these issues, we developed a Hierarchical Bayesian Inversion Algorithm utilizing time-series observations from seven hyperspectral satellites in Shanxi (2019–2023), comprising 215 methane plumes from 26 coal mines, to estimate annual CMM emission rates with limited satellite revisit frequency. Subsequently, we integrated multi-source satellite observations with inventory data to estimate CMM emissions in Shanxi province. Our analysis yields a CMM emission factor of (7.9 ± 1.4)×10-3 Tg/Mt for Shanxi, with CMM emissions reaching 11 ± 2 Tg/yr in 2023. We demonstrate that CMM emissions follow a right-skewed distribution in Shanxi Province, where low-frequency extreme methane emission events (≥10000 kg/h) constitute approximately 25 % of all time-series observations. Additionally, our results reveal that capacity reduction policies initially decreased CMM emissions, but subsequent production recovery led to emission increases, with asymmetric responses to coal price fluctuations. Our findings establish a novel strategy for CMM accounting from hyperspectral satellite observations.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: open (until 28 Apr 2026)
- RC1: 'Comment on egusphere-2025-4610', Anonymous Referee #4, 31 Mar 2026 reply
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General comments
This manuscript presents an approach to estimate coal mine methane emissions in Shanxi by combining multi-satellite hyperspectral observations with a hierarchical Bayesian inversion framework. The topic is timely and relevant, particularly in the context of increasing interest in facility-scale methane monitoring using emerging satellite observations. The use of multiple hyperspectral datasets and the attempt to address sparse temporal sampling through a hierarchical Bayesian framework are both valuable contributions. The integration of satellite observations with inventory data is also well motivated, and the results provide useful insights into emission factors as well as spatial and temporal patterns of methane emissions.
Overall, the manuscript is clearly written and addresses an important problem. The methodology is promising, and the results are of potential interest to both the remote sensing and atmospheric science communities. While a few aspects could benefit from further clarification and discussion, I believe these can be addressed with relatively minor revisions.
Major comment
One point that may deserve a bit more careful discussion concerns the use of satellite observations to characterize the distribution of methane emissions. While the general approach is well motivated and has been explored in previous studies, it is important to consider the detection limits of hyperspectral instruments. For example, the detection threshold for instruments such as EnMAP is on the order of ~500 kg/h under favorable conditions, and this threshold may be higher in regions with complex or heterogeneous surface conditions, such as Shanxi.
In this context, it would be helpful for the authors to elaborate on how such detection limits might influence the inferred emission distribution. In particular, there may be a tendency for satellite observations to preferentially capture stronger emission events, while smaller but more frequent sources remain undetected. This could potentially affect the characterization of the emission distribution and the relative importance of extreme events. In related studies that focus specifically on emission distributions, airborne campaigns are often used, partly because they can achieve substantially lower detection limits and therefore provide a more complete sampling across the full range of emission strengths. A brief comparison or discussion along these lines would help place the current results in context.
Related to this, the choice of Shanxi as the primary study region is understandable given its importance in coal production, but its heterogeneous surface conditions may pose additional challenges for plume detection. A brief discussion on how representative the observed emission distribution is, given these observational constraints, would help strengthen the interpretation of the results.
Minor comments
1. The description of the hierarchical Bayesian framework could be slightly clarified to improve readability, particularly for readers less familiar with this type of method.
2. It would be helpful to briefly clarify the extent to which plume identification relies on manual interpretation. In addition, in Figure 2 (lower right panel, EMIT), part of the detected plume appears visually correlated with underlying terrain features, which may suggest a potential false positive. A brief clarification on how such cases are handled would improve confidence in the plume identification procedure.
3. The discussion on detection limits could be expanded slightly to note potential implications for smaller emission sources.