Development of a Satellite-Based Algorithm for Detecting Methane Emission Changes from Rice Paddies 

With the acceleration of climate change, there is an increasing focus on the management of greenhouse gases. Although carbon dioxide is a primary concern, methane and nitrous oxide significantly contribute to the overall greenhouse gas concentration in the atmosphere, necessitating research on their monitoring and quantification. More than 50% of methane emissions originate from sources including natural gas and oil processing, enteric fermentation, and landfills, making those industries the focus of intensive monitoring attempts, encompassing satellite-based observations for extensive and periodic assessment. Further, methane plumes can be detected and emission rates assessed using wind field data for high-concentration sources. In agriculture, rice paddies are a major source of methane emissions. Nonetheless, a low emission rate per unit area frequently produces undetectable plumes, resulting in dependence on inventory-based simulations instead of measurement-based monitoring. Despite the low emission rate, the extensive expanse of rice fields implies that alterations in fertilizer application or agricultural methodologies can result in substantial changes in overall emissions, thereby requiring prompt monitoring. Moreover, rice cultivation is predominantly concentrated in Asia, which could significantly affect emissions if disrupted by climatic and meteorological changes in the region. This research develops an algorithm to identify changes in methane emissions utilizing satellite-derived methane concentration data from TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5p, TANSO-FTS-2 (Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer-2) onboard GOSAT (Greenhous gases Observing SATellite), and AIRS (Atmospheric Infrared Sounder) onboard Aqua. Through the analysis of over three years of aggregated data and its comparison with crop calendars, reference datasets named baseline data specifically designed for the growth and agricultural cycles of rice were developed with the valid value ranges. These were employed to identify increases or decreases in greenhouse gas emissions or alterations in emission timing by contrasting current observations with baseline data. The algorithm was implemented in principal rice cultivation regions of South Korea, effectively detecting substantial methane emissions during the irrigation phase causing anaerobic fermentations to soil under the water. This method illustrates the capability of satellite data to improve the comprehension and regulation of agricultural methane emissions. Additionally, guidelines for sustainable agricultural practices and the management of greenhouse gas emissions in agriculture will be feasible.