Constraining Annual Coal Mine Methane Emissions with Multitemporal Hyperspectral Satellite Observations

Abstract Methane emissions from coal mining are difficult to constrain because of temporal variability and sparse observations. We used multitemporal hyperspectral observations from GF5A, GF5B, ZY1E, and ZY1F to quantify facility-scale emissions and evaluate sampling requirements for annualized estimates. Of 301 scenes acquired from January 2024 to 15 April 2025, 117 cloud-free scenes yielded 237 confirmed plume events from 16 sources at 10 underground coal mines. Event-scale emission rates varied substantially and reached 22.1 ± 4.7 t h–1. The highest-emitting source had an annualized mean rate of 10.2 ± 2.3 t h–1. Source-specific bootstrap analysis showed decreasing estimation error with increasing sampling, although convergence varied among sources; approximately 20 usable plume-level observations per year provided an empirical guideline for the studied source population. We then simulated potential clear-sky observation opportunities for the world’s 100 largest active coal mines by production using six satellite missions, orbital and illumination constraints, and cloud cover. These mines received an average of approximately 18 opportunities per year, and 40% received more than 20. Coordinated multisatellite observations can strengthen methane measurement, reporting, and verification, provided that plume detectability, source attribution, and quantification requirements are met.

Authors

Institutions

Publication Details

Journal
Environmental Science & Technology
Published
2026-09-25
DOI
https://doi.org/10.1021/acs.est.6c09705
Primary Topic
Atmospheric and Environmental Gas Dynamics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Constraining Annual Coal Mine Methane Emissions with Multitemporal Hyperspectral Satellite Observations

Haotian Luo, Zhipeng Pei, Weida Xu, Mengnan Li et al.
Environmental Science & Technology
Atmospheric and Environmental Gas Dynamics
article

Constraining Annual Coal Mine Methane Emissions with Multitemporal Hyperspectral Satellite Observations

Haotian Luo, Zhipeng Pei, Weida Xu, Mengnan Li, Wenjun Yin, Ge Han, Yiyang Huang, Kai Qin, Huayi Wang, Wei Gong, Huiqin Mao
article en

Abstract

Abstract Methane emissions from coal mining are difficult to constrain because of temporal variability and sparse observations. We used multitemporal hyperspectral observations from GF5A, GF5B, ZY1E, and ZY1F to quantify facility-scale emissions and evaluate sampling requirements for annualized estimates. Of 301 scenes acquired from January 2024 to 15 April 2025, 117 cloud-free scenes yielded 237 confirmed plume events from 16 sources at 10 underground coal mines. Event-scale emission rates varied substantially and reached 22.1 ± 4.7 t h–1. The highest-emitting source had an annualized mean rate of 10.2 ± 2.3 t h–1. Source-specific bootstrap analysis showed decreasing estimation error with increasing sampling, although convergence varied among sources; approximately 20 usable plume-level observations per year provided an empirical guideline for the studied source population. We then simulated potential clear-sky observation opportunities for the world’s 100 largest active coal mines by production using six satellite missions, orbital and illumination constraints, and cloud cover. These mines received an average of approximately 18 opportunities per year, and 40% received more than 20. Coordinated multisatellite observations can strengthen methane measurement, reporting, and verification, provided that plume detectability, source attribution, and quantification requirements are met.

Environmental Science & Technology
China University of Mining and Technology (CN), Wuhan University (CN), Clinical Insights (US), Quantum Technology Sciences (United States) (US), Satellite Application Center for Ecology and Environment (CN), Wuhan Institute of Quantum Technology (CN)
Openalex Percentile: Top 15%
Atmospheric and Environmental Gas Dynamics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.