EOWetMet: EO‐Driven Insights for Advancing Arctic Wetland and Lake Methane Emissions Monitoring

Arctic wetlands and lakes are integral components of the Arctic region’s methane emissions, primarily due to their association with permafrost and the unique environmental conditions they provide for methane production. Wetlands, encompassing various types, such as bogs, fens, and marshes, are particularly abundant in the Arctic landscape (Albuhaisi et al. 2023). These wetland ecosystems thrive in the presence of waterlogged, oxygen‐deprived conditions — ideal settings for methanogenic micro‐organisms to flourish. As organic matter decomposes in these anaerobic environments, methane is produced as a metabolic byproduct, contributing to the overall methane emissions from wetlands (Cunha‐Santino and Bianchini Júnior 2023). Given the extensive coverage of wetlands in the Arctic, their contribution to methane emissions on a global scale is significant.

In addition to wetlands, Arctic lakes also play a substantial role in methane emissions. These lakes serve as reservoirs for methane produced within their sediments. As organic matter settles at the bottom of the lake, it undergoes decomposition under anaerobic conditions, leading to methane production. Methane bubbles can then accumulate and be released into the atmosphere through various pathways.

This project has the following objectives:

• Improve spatial and temporal resolution of wetland and lake extents using EO data to enhance methane emission estimation, in the context of the methane budget in the Arctic;
• Develop innovative methods and models for wetland and lake dynamics characterization and methane emission assessment by integrating EO data with advanced modelling techniques;
• Address the challenge of double counting of methane emissions in the Arctic region by separating out the individual contributions of lakes and wetlands through improved spatial resolution estimation and data integration;
• Enhance atmospheric inversion modelling based on improved bottom‐up inventories and corrected atmospheric observations;
• Build on the success of WETCHIMP (Wetland and Wetland CH4 Intercomparison of Models Project), establish a new intercomparison of methods and models for wetland/lake characterization and methane emission assessment to improve spatial resolution and constraints from EO data.

These will be addressed through:

1. the development of AI techniques for high‐resolution classification of Arctic wetlands to accurately distinguish them from lakes;
2. the integration of multi‐mission EO data into C‐CORE object‐based cloud native framework for understanding wetland and lakes dynamics characterization and hydrological modelling;
3. the enhancement of bottom‐up budgets for Arctic wetlands and lakes to refine methane emission estimations by incorporating high‐resolution EO data and advanced XAI models;
4. the enhancement of top‐down budgets by improving the accuracy and spatial resolution of the inverse modelling of atmospheric methane based on the IMI approach and Physics‐Informed Neural Networks (PINN);
5. esthe enhancement of top‐down budgets by improving the accuracy and spatial resolution of the inverse modelling of atmospheric methane based on the IMI approach and Physics‐Informed Neural Networks (PINN);