X-MethaneWet: A Cross-scale Global Wetland Methane Emission Benchmark Dataset for Advancing Science Discovery with AI

• URL: http://arxiv.org/abs/2505.18355v1
• Date: Fri, 23 May 2025 20:24:09 GMT
• Title: X-MethaneWet: A Cross-scale Global Wetland Methane Emission Benchmark Dataset for Advancing Science Discovery with AI
• Authors: Yiming Sun, Shuo Chen, Shengyu Chen, Chonghao Qiu, Licheng Liu, Youmi Oh, Sparkle L. Malone, Gavin McNicol, Qianlai Zhuang, Chris Smith, Yiqun Xie, Xiaowei Jia,
• Abstract summary: Methane (CH$_4$) is the second most powerful greenhouse gas after carbon dioxide and plays a crucial role in climate change.<n>We introduce the first-of-its-kind cross-scale global wetland methane benchmark dataset (X-MethaneWet)<n>This dataset can offer opportunities for improving global wetland CH$_4$ modeling and science discovery with new AI algorithms.
• Score: 15.813459313530625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Methane (CH$_4$) is the second most powerful greenhouse gas after carbon dioxide and plays a crucial role in climate change due to its high global warming potential. Accurately modeling CH$_4$ fluxes across the globe and at fine temporal scales is essential for understanding its spatial and temporal variability and developing effective mitigation strategies. In this work, we introduce the first-of-its-kind cross-scale global wetland methane benchmark dataset (X-MethaneWet), which synthesizes physics-based model simulation data from TEM-MDM and the real-world observation data from FLUXNET-CH$_4$. This dataset can offer opportunities for improving global wetland CH$_4$ modeling and science discovery with new AI algorithms. To set up AI model baselines for methane flux prediction, we evaluate the performance of various sequential deep learning models on X-MethaneWet. Furthermore, we explore four different transfer learning techniques to leverage simulated data from TEM-MDM to improve the generalization of deep learning models on real-world FLUXNET-CH$_4$ observations. Our extensive experiments demonstrate the effectiveness of these approaches, highlighting their potential for advancing methane emission modeling and contributing to the development of more accurate and scalable AI-driven climate models.

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