Detection of local atmospheric methane enhancements by analyzing Sentinel-5 Precursor satellite data

Methane CH₄ is an important anthropogenic greenhouse gas and its rising concentration in the atmosphere contributes significantly to global warming. Satellite measurements of the column-averaged dry-air mole fraction of atmospheric methane, denoted as XCH₄, can be used to provide information about the location of methane sources and on their emissions, which can help to improve emission inventories and review policies to mitigate climate change.   

The Sentinel-5 Precursor (S5P) satellite with the TROPOspheric Monitoring Instrument (TROPOMI) onboard was launched in October 2017 into a sun-synchronous orbit with an equator crossing time of 13:30. TROPOMI measures reflected solar radiation in different wavelength bands to generate various data products and combines daily global coverage with high spatial resolution. TROPOMI's observations in the shortwave infrared (SWIR) spectral range yield methane with a horizontal resolution of typically 5.5 x 7 km². 

We used a monthly XCH₄ data set (2018-2021) generated with the WFM-DOAS retrieval algorithm, developed at the University of Bremen, to detect regions with temporally persistent, locally enhanced XCH₄. At first, we applied a spatial high-pass filter to the XCH₄ data set to filter out the large-scale methane fluctuations. The resulting anomaly ΔXCH₄ maps show the difference of the local XCH₄ values compared to its surroundings. We then analyzed the monthly anomaly maps to identify potential source regions with persistent XCH₄ enhancements by utilizing different filter criteria, such as the number of months in which the local methane anomalies ΔXCH₄ must exceed certain threshold values. As a next step, we used a simple mass balance method to estimate the monthly emissions and the corresponding uncertainties of the detected potential source regions from the monthly averaged XCH₄ maps. In the last step, we interpreted the emissions of the potential source regions in terms of the source type, by comparing the detected potential source regions with emission databases based on a spatial analysis. 

In this presentation, the algorithm and initial results concerning the detection of regions with temporally persistent, local XCH₄ enhancements, originating from localized potential methane sources (e.g., wetlands, coal mining areas, oil and gas fields) are presented.