Mapping high-resolution XCO2 concentrations in China from 2015 to 2020 based on spatiotemporal ensemble learning model

Abstract

High-resolution column-averaged dry air mole fraction of CO 2 (XCO 2 ) data is crucial for understanding the spatiotemporal patterns of XCO 2 and for mitigating carbon emissions. Due to the limited scanning range of sensors and strict inversion conditions, satellite-retrieved XCO 2 data are often significantly incomplete. Machine learning models are widely used to fill these gaps in satellite XCO 2 data. However, the limitations of individual machine learning models and the complexity of the spatial distribution of XCO₂ mean that the accuracy of XCO 2 predictions still needs improvement. In this study, a new spatiotemporal stacked ensemble learning model (STEL) was developed by combining random forest (RF), extremely randomized trees (ERT), extreme gradient boosting (XGBoost), optical gradient boosting (LightGBM), and categorical boosting (CatBoost) using the stacking ensemble learning methodology. Considering the spatiotemporal heterogeneity of XCO 2 , a novel spatiotemporal weighting feature was constructed as part of the model's input parameters. Finally, the XCO 2 observed by Orbiting Carbon Observatory 2 (OCO-2) was reconstructed using STEL, and a monthly mean XCO 2 dataset covering China from 2015 to 2020 was generated at a spatial resolution of 0.1°. The results show that STEL exhibits superior performance and generalization capabilities compared to individual machine-learning models. R 2 RMSE and MAPE were 0.9624, 1.0023 ppm, and 0.1583 % on the test set, and 0.8970, 1.4213 ppm, and 0.2475 % for R 2 , RMSE, and MAPE in ground validation, respectively. In 10-fold cross-validation, STEL's RMSE was reduced by 9.52 % compared to the best-performing single model (RF). The spatiotemporal trend of CO 2 in China from 2015 to 2020 was analyzed using STEL XCO 2 data. The results indicate that this dataset accurately reflects the spatiotemporal heterogeneity of XCO 2 distribution at a fine scale. Overall, XCO 2 exhibited a spatiotemporal pattern of “high in the east and low in the west” and “high in spring and low in summer.” Except in summer, high XCO₂ values were mainly distributed in the North China Plain. XCO 2 trends and hotspots showed considerable spatial variation. The Pearl River Delta and Yangtze River Delta urban agglomerations have the fastest XCO 2 growth rates, and the distribution of XCO 2 hotspots is consistent with the distribution of population and economic centers. In the sparsely populated northwest of China, XCO 2 is growing rapidly due to increased thermal power generation and coal mining. XCO 2 hotspots in Northwest China are mainly located in Xinjiang, Ningxia, and Inner Mongolia. The methodology and data presented are useful for further research on carbon emissions, carbon sinks, and climate change. • A stacked spatiotemporal ensemble learning model was developed to fill the gap in satellite XCO 2. • The monthly average XCO 2 dataset covering China from 2015 to 2020 at a spatial resolution of 0.1° was generated. • The spatial pattern of XCO 2 in summer was quite different from that of other seasons. • XCO 2 hotspots in Northwest China are growing rapidly.

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