Document Type
Article
Publication Date
3-1-2026
Abstract
Agriculture is a major global source of methane (CH4), and accurate emission estimates are essential for refining national greenhouse gas inventories and supporting climate-resilient policies. This study develops a high-resolution estimation framework for CH4 emissions from Korean rice paddies by integrating multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5)-Land meteorological variables, and Harmonized World Soil Database (HWSD) soil properties. Using CH4 flux observations from four global rice ecosystems (Italy, Japan, South Korea, and USA), we constructed parallel daily and hourly machine learning models using an automated machine learning (AutoML) framework to compare their performance and process-level interpretability. The daily model demonstrated high predictive accuracy with correlation coefficients (CC) of 0.897 in 5-fold cross-validation and 0.819 in Leave-One-Year-Out (LOYO) cross-validation. Shapley Additive Explanations (SHAP) analysis revealed that while soil temperature is the dominant predictor for daily emissions (explaining ~50% of the variance), variable importance shifts significantly at finer resolutions. The hourly model exhibited a more complex multivariate structure. In this high-resolution context, although Normalized Difference Vegetation Index (NDVI) remains constant diurnally, its importance strengthens as a critical regulator of emission sensitivity, interacting with hourly meteorological fluctuations to capture short-term dynamics. The resulting 500 m daily gridded maps provide a robust foundation for national inventory refinement and spatially targeted mitigation planning. Our findings suggest that while the daily model offers optimal computational efficiency for long-term monitoring, the hourly model is superior for mechanistic understanding and detecting episodic emission events. This multi-resolution framework establishes an empirical basis for selecting appropriate temporal scales in operational greenhouse gas monitoring systems.
Recommended Citation
Jang, J.; Kim, S.H.; Kafatos, M.; Cho, J.; Yoo, G.; Jeong, S.; Lee, Y. Automated Machine Learning for High-Resolution Daily and Hourly Methane Emission Mapping for Rice Paddies over South Korea: Integrating MODIS, ERA5-Land, and Soil Data. Remote Sens. 2026, 18, 753. https://doi.org/10.3390/rs18050753
Copyright
The authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
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Artificial Intelligence and Robotics Commons, Atmospheric Sciences Commons, Climate Commons, Environmental Health and Protection Commons, Environmental Indicators and Impact Assessment Commons, Environmental Monitoring Commons, Fresh Water Studies Commons, Meteorology Commons, Other Oceanography and Atmospheric Sciences and Meteorology Commons, Remote Sensing Commons, Sustainability Commons
Comments
This article was originally published in Remote Sensing, volume 18, issue 5, in 2026. https://doi.org/10.3390/rs18050753