Methane is a significant and powerful greenhouse gas, with a global warming potential over 80 times than that of carbon dioxide over a 20-year time scale. At the same time, methane decomposes much faster than carbon dioxide, with an average lifespan (namely, the duration an emitted molecule of methane stays in the atmosphere) of about 12 years, compared with hundreds of years for carbon dioxide. This means that compared with a reduction of carbon dioxide, a reduction of methane emissions can offer more control over the greenhouse effect, including controls on global average temperature rise in the short term. In addition, methane is also a precursor of both tropospheric ozone and carbon monoxide. Therefore, a reduction in its emissions will help reduce air pollution and improve air quality.

During coal mining activities, the methane contained in the coal seam is released in a variety of ways, including escape from the coal seam in open pit mines, discharge through ventilation and drainage in underground coal mines, and release from pockets trapped in the coal matrix during mining. Escape continues during post-operation activities and coal processing. Methane in abandoned mine shafts also continues to escape from coal remaining after operations. According to data from the International Energy Agency, in 2022, global coal mine methane emissions were about 40.5 million tons, accounting for more than 10% of total anthropogenic methane emissions. China is the largest coal producer in the world. According to the 2014 National Greenhouse Gas Inventory of the Second Biennial Update Report on Climate Change in China, methane emissions from the country's energy industry accounted for about 46% of the total emissions, mainly attributed to coal emissions from mining. Studies have shown that the increase in global anthropogenic methane emissions from 2000 to 2012 and the increase in China's methane emissions from 2010 to 2015 are both significantly impacted by China's coal mining industry. Accelerating the establishment of a dynamically updateable high-spatial-resolution methane emission inventory for the coal industry is an important starting point for promoting methane emission reductions in the coal industry.

Using the short-wave infrared absorption spectrum of methane at 1.65 μm and 2.3 μm, satellite remote sensing technology has been successfully used in the detection and quantification of methane emissions in the coal industry. Satellite remote sensing detection of methane emissions in the coal industry requires close cooperation between sensors, algorithms, and detection targets. We analyze the research progress from the aspects of remote sensing satellites that can be used for the study of methane emissions in the coal industry, the corresponding methane column concentration, and emission rate inversion technology, and propose a satellite remote sensing method for building a high-spatial-resolution methane emission inventory of China's coal industry research focus.

The spatial scale of the methane emission capacity of the coal industry detected by remote sensing satellites has been previously divided into regional and point source types (Table 1). Regional remote sensing satellites mainly include SCIAMACHY/ENVISAT, Sentinel-5P/TROPOMI, GOSAT, etc. In order to realize the accurate observation of methane concentration, its spectral resolution is higher (within 0.3 nm), and the band is more concentrated in the methane absorption window, so it is mainly aimed at the study of spatially large-scale and temporally long-term methane emission sources. GHGSat-D is the representative remote sensing satellite for point source methane emission targeting the coal industry. It observes an area of about 12 km² at a time, with a spatial resolution of 25 m. Substantial progress has been made in the detection of emissions from many coal mines around the world. In addition, the scientists found that the high spatial resolution (3.7-60 m) imager originally used for Earth observation also can detect methane plumes in its broad absorption (2.3 μm) band.

The methane column concentration inversion of regional satellite sensors such as SCIAMACHY, TROPOMI, and GOSAT estimates the atmospheric methane column concentration ΩCH4 by fitting the observed spectrum to its simulated spectrum. In order to eliminate the influence of surface pressure changes, the column mass is first normalized by the dry air column concentration Ω_air, yielding the dry air column average mixing ratio XCH4. According to the band difference of the specific configuration of the instrument, two types of algorithms, namely the full physical inversion and the CO₂ proxy method, can be used. Remote sensing satellites for coal mine point source emissions can be divided into hyperspectral type and multispectral type. The former includes GHGSat-D with a spectral resolution of 0.3 nm and AHSI, PRISMA, and EnMAP with a spectral resolution of 10 nm; the latter includes Sentinel-2 A/2B. Landsat-8/9, WorldView-3, etc.

According to the spatial scale of methane emissions identified by satellite observation data, there are two main methods for estimating the methane emission rate of the coal industry: regional remote sensing satellites usually use atmospheric chemical models to invert and optimize the two-dimensional distribution of methane emissions on a regional scale, while point source remote sensing satellites estimate the emission rate of a single point source through mass conservation of methane emissions within a plume-model assumption, where a point source is generally a single facility that emits more than 10 kg/h in an area less than 3×30 m².

This work recommends speeding up the construction of a "top-down" emission inventory of China's coal industry from two different scales: coal mine agglomerations and single coal mines. This work further points out three weaknesses that need to be focused on and improved in the future: 1) simplified mass balance methods using TROPOMI observations to constrain and retrieve the methane emissions over 14 large coal bases across the country; 2) detection and quantification of the methane emissions of thousands of coal mines across the country based on hyperspectral remote sensing satellites with 10 nm resolution; 3) more closely examining the internal links between remote sensing satellite observations at different scales and other observations collaboratively and analytically.