As the second most predominant greenhouse gas, methane-targeted emission mitigation holds the potential to decelerate the pace of global warming. Satellite remote sensing is an important monitoring tool, and we review developments in the satellite detection of methane. This paper provides an overview of the various types of satellites, including the various instrument parameters, and describes the different types of satellite retrieval algorithms. In addition, the currently popular methane point source quantification method is presented. Based on existing research, we delineate the classification of methane remote sensing satellites into two overarching categories: area flux mappers and point source imagers. Area flux mappers primarily concentrate on the assessment of global or large-scale methane concentrations, with a further subclassification into active remote sensing satellites (e.g., MERLIN) and passive remote sensing satellites (e.g., TROPOMI, GOSAT), contingent upon the remote sensing methodology employed. Such satellites are mainly based on physical models and the carbon dioxide proxy method for the retrieval of methane. Point source imagers, in contrast, can detect methane point source plumes using their ultra-high spatial resolution. Subcategories within this classification include multispectral imagers (e.g., Sentinel-2, Landsat-8) and hyperspectral imagers (e.g., PRISMA, GF-5), contingent upon their spectral resolution disparities. Area flux mappers are mostly distinguished by their use of physical algorithms, while point source imagers are dominated by data-driven methods. Furthermore, methane plume emissions can be accurately quantified through the utilization of an integrated mass enhancement model. Finally, a prediction of the future trajectory of methane remote sensing satellites is presented, in consideration of the current landscape. This paper aims to provide basic theoretical support for subsequent scientific research.

Spaceborne measurements by the Atmospheric Infrared Sounder (AIRS) on the EOS/Aqua satellite provide a global view of methane (CH₄) distribution in the mid-upper troposphere (MUT-CH₄). The focus of this study is to analyze the spatiotemporal variations in MUT-CH₄ over China from 2003 to 2008. Validation of AIRS CH4 products versus Fourier transform infrared profiles demonstrates that its RMS error is mostly less than 1.5%. A typical atmospheric methane profile is found that shows how concentrations decrease as height increases because of surface emissions. We found that an important feature in the seasonal variation in CH4 is the two peaks that exist in summer and winter in most parts of China, which is also observed in in-situ measurements at Mt. Waliguan, Qinghai Province, China (36.2879°N 100.8964°E, 3810 m). Also, in the summer, only one peak existed in western and southern China since there are no more significant anthropogenic sources in winter than at any other time of the year. Further analysis of the deseasonalized time-series of AIRS CH4 in three fixed pressure layers of AIRS from 2003 to 2008 indicates that CH4 in the Northern Hemisphere has increased abruptly since 2007, with no significant increase occurring before 2007. The increase in China is generally more significant than in other areas around the world, which again correlates with in-situ measurements at Mt. Waliguan.

This study focuses on analyzing the changes to aerosol properties caused by the dust storm called “China’s Great Wall of Dust” that originated from the Taklimakan Desert in April 2014. IDDI (Infrared Difference Dust Index) images from FY-2E and true color composite images from FY-3C MERSI (Medium Resolution Spectral Imager) show the breakout and transport path of the dust storm. Three-hourly ground-based measurements from MICAPS (Meteorological Information Comprehensive Analysis and Process System) suggest that anticyclonic circulation occupying the Southern Xinjiang basin and cyclonic circulation in Mongolia form a dipole pressure system that leads to strong northwesterly winds (13.7–20 m/s), which favored the breakout of the dust storm. IDDI results indicate that the dust storm breakout occurred at ~2:00 UTC on 23 April in the Taklimakan Desert. Four-day forward air mass trajectories with the HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model gives the simulation results of the dust transport paths and dust vertical distributions, which are consistent with the corresponding aerosol vertical distributions derived from CALIPSO. The Aerosol Index (AI) data of TOU (Total Ozone Unit) aboard FY-3B are first used to study the areas affected by the dust storm. From the AI results, the dust-affected areas agree well with the synoptic meteorological condition analysis, which supports that the synoptic meteorological conditions are the main reason for the breakout and transport of the dust storm. Anomalies of the average MODIS (Moderate Resolution Imaging Spectroradiometer) AOD (Aerosol Optical Depth) distributions over northeast Asia during the dust storm to the average of the values in April between 2010 and 2014 are calculated as a percent. The results indicate high aerosol loading with a spatially-averaged anomaly of 121% for dusty days between 23 April and 25 April. Aerosol Robotic Network (AERONET) retrievals of VSD (Volume Size Distribution) and SSA (Single Scattering Albedo) show that while the aerosol properties in Dalanzadgad, which is closer to the dust source, were influenced primarily by coarse dust particles, the aerosol properties in Beijing were mostly contributed by fine dust particles that transported over longer distances and at high atmospheric levels.