Detection of Methane Emissions Using Pattern Recognition

Abstract

Reducing methane emissions is essential to tackle climate change. Here, we address the problem of detecting large methane leaks by using hyperspectral data from the satellite Sentinel-5P. By sampling Sentinel-5P spectral data at fine scale, we detect methane absorption features in the shortwave infrared wavelength range (SWIR). Our method involves two separate steps: i) background subtraction and ii) detection of local maxima in the negative logarithmic spectrum of each pixel. In the first step, we remove the impact of the albedo using albedo maps and the impact of the atmosphere by using a principal component analysis (PCA) over a time series of past observations. In the second step, we count for each pixel the number of local maxima that correspond to a subset of local maxima in the methane absorption spectrum. This counting method allows us to set up a statistical a contrario test that controls the false alarm rate of our detections.