AI for Sustainable Land Management and Greenhouse Gas Emission Forecasting: Advancing Climate Action

Atmospheric methane, a powerful greenhouse gas (GHG), plays a significant role in accelerating global warming. This study leverages machine learning to analyze methane emissions and forecast their patterns using comprehensive datasets, including TROPOMI Sentinel-5P satellite methane data, MIDAS UK soil temperature records, and the NERC EDS 2021 land cover dataset. By constructing predictive models through a machine learning pipeline, this research identifies key drivers of methane emissions and addresses data gaps caused by incomplete satellite measurements. Experimental results demonstrate the superior performance of the Random Forest model compared to other machine learning models, achieving the highest accuracy with an RMSE of 29.49 and an MAE of 20.84 for methane (CH<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf>) prediction. Shapley analysis is used to enhance model explainability by evaluating how different attributes (e.g., time, land usage, soil temperature) influence methane production. This study highlights a notable increase in methane levels over recent years, and underscores the critical role of sustainable land management, particularly agricultural practices, in strategies to reduce methane emissions and combat global warming. This work is part of a broader initiative to develop data-driven, AI-powered Digital Twins for analyzing the interplay between human activities and natural processes in advancing climate action.