Methane (CH4) is the second most important anthropogenic greenhouse gas, accounting for approximately 30% of current global warming. Since 2007, atmospheric methane concentrations have been increasing at an accelerating rate, reaching a record 1945.85 ppb in November 2025. The emergence of high-resolution satellite constellations has transformed our ability to detect, quantify, and attribute methane emissions from space. This review provides a comprehensive analysis of the current state of high-resolution global methane mapping, examining: (1) the evolution of satellite missions from coarse-resolution sounders like TROPOMI (5.5 × 7 km) to very high-resolution imagers including WorldView-3 (3.7 m), GHGSat (50 m), and the recently launched Tanager-1 (30 m); (2) advances in retrieval algorithms, including the transition from physics-based matched filter methods to deep learning approaches such as U-Net architectures achieving F1-scores of 78.4% on Sentinel-2 imagery; (3) integration of satellite observations with atmospheric inverse models for flux estimation; (4) the impact of satellite-derived data on policy frameworks including the Global Methane Pledge and EPA’s Super-Emitter Program; and (5) remaining challenges including cloud contamination, detection limit trade-offs, and the need for sustained validation networks. We synthesize findings from over 200 peer-reviewed studies and analyze 42 years of NOAA global methane observations to demonstrate how the convergence of improved spatial resolution, machine learning, and international coordination is enabling unprecedented transparency in global methane monitoring. The review concludes with recommendations for future satellite missions and data assimilation strategies needed to meet the Global Methane Pledge target of 30% emission reductions by 2030.
Singh et al. (Tue,) studied this question.