SSRMF: A sparse spectral reconstruction enhanced matched filter for improving point-source methane emission detection in complex terrain

Quantification of anthropogenic methane emissions from coal mines and oil & gas facilities is essential to global methane accounting and management. Matched Filter (MF), a classic method for point-source methane emission detection, was proven less effective over heterogeneous land covers given substantial false positives of methane enhancement retrievals due to large uncertainties in background spectrum estimates. To address this challenge, we developed an enhanced MF method, i.e., Sparse Spectral Reconstruction enhanced Matched Filter (SSRMF), to empower accurate methane emission detection in complex terrain from satellite-based hyperspectral images. Specifically, a spectral matrix decomposition and low-rank reconstruction approach was developed to accurately predict background spectrum for each pixel, rather than applying the regional average spectrum that is commonly used in traditional MF method and its alike. Meanwhile, a spatial continuity regularization term was incorporated in the methane enhancement estimation cost function to ensure spatial coherence of methane plumes. Illustration results with synthetic GaoFen-5 hyperspectral images demonstrate the superior advantages of SSRMF over other MF methods, reducing methane enhancement retrieval errors by 80 % and 2.8 times in scenarios with emission flux below 1,000 kg/h. Also, SSRMF enabled to reduce false positives of methane enhancement by 70 % in complex terrain, effectively suppressing artifacts over land covers resembling the methane spectral absorption curve in shortwave infrared bands. Additionally, SSRMF operates 20 % faster than other MF methods by avoiding an iterative optimization of the background spectrum. The error analysis results demonstrate the high robustness of SSRMF against drastic variations in land albedo, aerosol loading, and water vapor, making it more flexible to heterogeneous terrains. By applying SSRMF to 97 clear-sky GaoFen-5A/B hyperspectral images observed during 2019–2022, 126 methane plumes were successfully detected in Shanxi Province, China. Yangquan, Changzhi, and Jincheng were three major cities with notable methane emissions, by a mean flux rate of 6,076 kg/h, 5,470 kg/h, and 4,797 kg/h, respectively. Overall, our proposed SSRMF method provides a more robust solution for methane emission detection in complex terrain from satellite-based hyperspectral images, and can be easily adapted to other trace gases.