In the global fight against climate change, a cutting-edge approach is emerging to specifically target methane, which is responsible for roughly 30 percent of global warming since pre-industrial times
Experts from the Netherlands Institute for Space Research (SRON) have revolutionized methane detection by combining the power of Sentinel satellites and machine learning algorithms.
This groundbreaking technology could provide a major boost to efforts aimed at reducing greenhouse gas emissions by identifying super-emitters and other large methane sources.
Methane is a potent greenhouse gas, ranking second only to carbon dioxide in its contribution to global warming.
However, its impact is more intense: a ton of methane can retain 30 times more heat than an equal amount of carbon dioxide over a century.
Importantly, the short lifespan of methane – about 10 years – makes it a prime target for rapid climate action.
Methane emissions are not evenly distributed. Methane super-emitters, which are typically industrial facilities like oil and gas operations or coal mines, disproportionately contribute to the problem.
These sources often have equipment or infrastructure issues that lead to significant leaks. Identifying super-emitters has been a major challenge.
The Tropomi instrument aboard the Copernicus Sentinel-5P satellite creates a global map of methane concentrations every day, using shortwave infrared bands as unique fingerprints for methane detection.
SRON researchers have developed a new algorithm that uses machine learning to automatically identify methane super-emitters based on the satellite data.
“Before, we manually identified the largest emitters, but it remains difficult to search through the millions of Tropomi pixels. A methane plume often only covers a few pixels. We now automatically get a list of detections from the machine learning model every day,” explained Berend Schuit from SRON.
“We check those manually every week to make sure we are confident about the detections. What remains, dozens of methane plumes every week, we publish online. We communicate persistent leaks to other satellites with higher resolution so they can precisely identify the source.”
“This information is used by the United Nations’ International Methane Emissions Observatory to find a solution together with the responsible companies or authorities.”
Study co-author Bram Maasakkers said that the dozens of methane plumes that are detected by Tropomi every week present a golden opportunity in the fight against global warming,
“If it’s visible from space, it is serious. For the first time, we now get a good global picture of these super-emitters. In our publication, we describe the 2974 plumes that we found in 2021; 45% originate from oil and gas facilities but we also see plumes from urban areas (35%) and coal mines (20%),” said Maasakkers.
“We detect human-made emissions with a climate impact that is significantly larger than total greenhouse gas emissions of The Netherlands. In many cases, those leaks are easy to fix.”
The Sentinel-5P, Sentinel-2, and Sentinel-3 satellites are used in a tiered approach to methane detection.
While Sentinel-5P offers daily global coverage but lacks spatial resolution, Sentinel-2 can identify exact locations but doesn’t offer daily coverage. Sentinel-3 sits at the sweet spot, providing both daily global coverage and remarkable resolution for methane detection.
This new approach of using machine learning and a multi-tiered satellite system opens up vast opportunities in the fight against climate change.
Sudhanshu Pandey, lead author and now scientist at NASA Jet Propulsion Laboratory, commented, “Who would have thought we can use three different Sentinel missions in a tiered approach to first spot methane super emitters from space globally with Tropomi on Sentinel-5P, then zooming in with Sentinel-3 and Sentinel-2, we’re able to identify the exact source responsible at facility level. This is the type of information we need to take swift action.”
The study is published in the journal Atmospheric Chemistry and Physics.
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