Nitrous oxide (N₂O) has experienced an approximate 25% increase in atmospheric concentration since the preindustrial era and is recognized as a potent greenhouse gas with a 100-year global warming potential about 273 times that of carbon dioxide. Marine N₂O is primarily produced through microbial nitrogen cycling processes and accounts for roughly one-third of natural emissions to the atmosphere. Recent advances in satellite remote sensing provide high-quality environmental data with broad spatiotemporal coverage, which can be combined with machine learning techniques to estimate biogeochemical variables. Greenhouse gas concentrations cannot be directly retrieved from satellite observations and are influenced by complex interactions among physical and biogeochemical processes. Machine learning approaches are increasingly used to model these complex relationships by linking in situ observations with satellite-derived environmental variables. While such approaches have been widely applied to carbon dioxide, applications to N₂O remain limited. A support vector machine (SVM) model, trained using in situ observations collected from 15 stations between May 2022 and February 2023, was used to estimate surface N₂O concentrations in the northern East China Sea. Surface N₂O concentrations were then predicted using satellite-derived sea surface temperature, salinity, and chlorophyll-a as input variables. Limited observational coverage still introduces uncertainties in nearshore and estuarine areas, but the model improves the spatial understanding of surface N₂O variability. These results suggest that machine learning approaches using satellite-derived data can serve as an effective tool for assessing N₂O distributions in marine environments.
 

Nitrous oxide (N2O),Machine learning,northern East China Sea,Satellite remote sensing,Air-sea flux