Improving Methane Point Sources Detection Over Heterogeneous Land Surface for Satellite Hyperspectral Imagery - Erchang Sun; Xianhua Wang; Shichao Wu; Hanhan Ye; Hailiang Shi; Yuan An; Chao Li; Yun Jiang (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
Hyperspectral imaging for satellites is currently an important tool for global monitoring of methane point sources, and it can be used to retrieve methane concentration to enable source location and “top-down” emission estimation. The matched filter (MF) is the main method used to retrieve methane enhancement from hyperspectral imaging. However, many false positive retrievals occur over heterogeneous land surfaces because of the confusion between methane absorption and surface reflectance spectra. This hinders the accurate quantification of methane point source emissions from hyperspectral imaging. To overcome this hindrance, we present an improved matched filter that includes background filtering to mitigate the reflectance spectra mismatch between the target and background. By analyzing the land cover shortwave-infrared spectral library, we found that wideband spectral slopes can be used to distinguish between surface types. Based on this, we designed the background sample filtering process and verified its performance using simulation and the advanced hyperspectral imager data. The results show that the improved matched filter can effectively reduce false retrievals over heterogeneous land surfaces and obtain a more realistic methane plume. For example, near an emission source with a $Δ$ XCH $_{4}$ of 1000 ppb, the simulated retrieval bias can be less than 1.3% using a 1% filter threshold. Our method can enhance the ability of satellites to quantify methane concentrations on complex land surfaces.