Quantum computing for earth observation: monitoring methane emissions
Problem
Methane is a potent greenhouse gas with a 100-year global warming potential about 30 times greater than CO₂, and is responsible for roughly 30% of today’s global temperature rise. More than 60% of emissions are human-made, yet current monitoring is hindered by incomplete, fragmented, and uncertain data. Reconciling diverse data sources is therefore critical to achieving accurate and actionable methane inventories. Classical Earth observation approaches use low-resolution but frequent Sentinel-5P imagery to flag possible methane plumes, which are then analysed in higher-resolution Sentinel-2 images. Deep learning models, such as U-Nets, segment plumes and enable inversion for leak locations and emission rates. While effective, these methods remain computationally demanding and often struggle with the scale, heterogeneity, and uncertainty of global data.
Solution
This applied research project investigates whether quantum computing can offer advantages for methane monitoring. Two complementary angles are explored: a top-down approach to detect new leaks and a bottom-up approach to update emission inventories.
Benefit
The goal is to accelerate large-scale models, integrate heterogeneous datasets, and improve methane emission estimates at higher resolution.
This work is supported by the Dutch National Growth Fund (NGF) as part of the Quantum Delta NL programme.
