A machine learning model for mapping methane concentration on the paddy rice fields using meteorological data in South Korea

Jiah Jang; Yangwon Lee

doi:10.1117/12.3031444

20 November 2024 A machine learning model for mapping methane concentration on the paddy rice fields using meteorological data in South Korea

Jiah Jang, Yangwon Lee

Author Affiliations +

Proceedings Volume 13191, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVI; 1319109 (2024) https://doi.org/10.1117/12.3031444
Event: Remote Sensing, 2024, Edinburgh, United Kingdom

Abstract

This work was carried out with the support of the "Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ0162342024)" by the Rural Development Administration, Republic of Korea. The recent surge in greenhouse gas emissions has significantly accelerated global warming, making climate change more serious. In particular, Long-lived greenhouse gases such as methane (CH4) have a warming effect about 28 times stronger than carbon dioxide (CO2), making it important to calculate the amount of methane emissions generated in Korea. This study analyzed LDAPS meteorological data and FluxNet ground observations of the Cheorwon rice paddy region based on the GBM model, and generated a methane concentration map of methane emissions from rice paddies in Korea. The 1.5-kilometer spatial resolution of the data was used to capture more detailed regional variations, and daily maps were created to capture temporal details. This is expected to reveal patterns of methane generation. This helps to accurately predict methane emissions and is expected to reveal patterns of methane generation in response to changing weather conditions.

Conference Presentation

Citation Download Citation

Jiah Jang and Yangwon Lee "A machine learning model for mapping methane concentration on the paddy rice fields using meteorological data in South Korea", Proc. SPIE 13191, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVI, 1319109 (20 November 2024); https://doi.org/10.1117/12.3031444

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