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.