Improving quantification of methane point source emissions from imaging spectroscopy

Abstract

The matched filter (MF) method is widely used for hyperspectral imaging spectrometers to detect and quantify methane point sources due to its high computational efficiency. However, it would result in an unavoidable underestimation, especially for large concentration enhancements. The lognormal matched filter (LMF) has provided a unique and not limited to weak methane plumes theory through lognormal background radiances modeling, but validations on simulated experiments and applications to real data remain to be explored. Moreover, the covariance contamination caused by the enhanced pixels and the surface heterogeneity have detrimental effects on the detection methods for real data application. In this study, we propose the iterative lognormal matched filter (ILMF) method to address these challenges. We evaluate the performance of the ILMF with two ideal simulations and the end-to-end simulation. The results of random simulated enhancement retrievals show that the retrieved enhancement by the ILMF method agrees well with the true enhancement, with an R2 of 0.984 and a small root-mean-square error (RMSE) of 55.856 ppb. The ILMF method reduce the RMSE of retrieved enhancement by 80% compared with the MF method. The results of end-to-end simulations show the underestimation of MF method at different sites regarding the emission rate, as well as the improvement of the ILMF method. Further, we apply the ILMF method to detect and quantify point sources in some methane hotspot regions. Our study reports the underestimation in traditional MF method and provides an unbiased and robust method for quantifying methane emissions.