Mapping seamless monthly XCO2 in East Asia: Utilizing OCO-2 data and machine learning

High spatial resolution XCO₂ data is key to investigating the mechanisms of carbon sources and sinks. However, current carbon satellites have a narrow swath and uneven observation points, making it difficult to obtain seamless and full-coverage data. We propose a novel method combining extreme gradient boosting (XGBoost) with particle swarm optimization (PSO) to construct the relationship between OCO-2 XCO₂ data and auxiliary data (i.e., vegetation, meteorological, anthropogenic emissions, and LST data), and to map the seamless monthly XCO₂ concentration in East Asia from 2015 to 2020. Validation results based on TCCON ground station data demonstrate the high accuracy of the model with an average R² of 0.93, Root Mean Square Error (RMSE) of 1.33 and Mean Absolute Percentage Error (MAPE) of 0.24 % in five sites. The results show that the average atmospheric XCO₂ concentration in East Asia shows a continuous increasing trend from 2015 to 2020, with an average annual growth rate of 2.21 ppm/yr. This trend is accompanied by clear seasonal variations, with the highest XCO₂ concentration in winter and the lowest in summer. Additionally, anthropogenic activities contributed significantly to XCO₂ concentrations, which were higher in urban areas. These findings highlight the dynamics of regional XCO₂ concentrations over time and their association with human activities. This study provides a detailed examination of XCO₂ distribution and trends in East Asia, enhancing our comprehension of atmospheric CO₂ dynamics.