High-Resolution Wetland Methane Flux Modeling for the Tibetan Plateau Using Machine Learning

Methane (CH₄) emissions from the Tibetan Plateau, often referred to as the "Third Pole," are critical to understanding global methane dynamics due to the region's extensive wetland ecosystems and unique environmental characteristics. However, quantifying CH₄ fluxes in this region is challenging due to sparse observational data, complex topography, and highly variable climatic and hydrological conditions. This study introduces a high-resolution machine learning framework tailored for the Tibetan Plateau by integrating satellite-based observations, ground measurements, and modeled data. The framework incorporates a diverse set of environmental drivers, including temperature, soil moisture, vegetation indices, and hydrological factors. This approach aims to address spatial and temporal gaps in methane flux estimates while capturing the complex interactions governing CH₄ emissions in high-altitude mountainous ecosystems.