The Global Methane Pledge calls for a reduction of methane emissions by at least 30% by 2030. The reduction of methane emissions in the ener
The Global Methane Pledge calls for a reduction of methane emissions by at least 30% by 2030. The reduction of methane emissions in the energy sector is critical to achieving this target. Remote sensing plays a crucial role in identifying and quantifying methane superemitters. In the forthcoming years, multiple promising missions carrying imaging spectrometers will be sent into orbit to obtain XCH4 observations with extensive coverage and high resolution. Traditional emission quantification models, such as the Gaussian plume model and some based on chemical transport models, are not optimally suited to the characteristics of new data. In this article, we propose a divergence-theorem-based emission quantification model, named flux integration method based on sinusoidal cosine optimization algorithm to inverse the methane point source emissions, which utilizes XCH4 observations derived from airborne imaging spectrometers to achieve rapid and accurate estimation of methane point source emission rates. This approach overcomes limitations of other methods, such as the inability of Gaussian plume models to recover the integrity of regional concentration enhancements, excessive disruption caused by integrated mass enhancement and divergence integral masking operators, and the requirement for effective wind speed fitting. The extraction of plume regions only causes a perturbation of approximately ±5% in the results, and the R value of this method on real datasets exceeds 0.89. It provides technical support for rapid and accurate monitoring of methane point source emissions on a global scale, aiding in the establishment of routine methane emission monitoring systems based on satellite remote sensing.
