Spatio-temporal distribution of XCO2 concentrations in northeast China based on Downscaling-XGBoost model

Yulei Tang; Jianfang Hu; Die Tang; Zhansheng Chen

doi:10.1117/12.3048860

27 November 2024 Spatio-temporal distribution of XCO₂ concentrations in northeast China based on Downscaling-XGBoost model

Yulei Tang, Jianfang Hu, Die Tang, Zhansheng Chen

Author Affiliations +

Proceedings Volume 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024); 134021G (2024) https://doi.org/10.1117/12.3048860
Event: International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 2024, Zhengzhou, China

Abstract

Carbon dioxide in the atmosphere, the most abundant greenhouse gas, is recognized as one of the main causes of climate change. A large amount of missing data is contained in XCO₂ datasets obtained from carbon observing satellites due to unfavorable atmospheric conditions and satellite orbits, making it inadequate to reflect the heterogeneity of XCO₂ in space. Our understanding of carbon dynamics is limited by the sparsity of terrestrial measurements and the lack of fine-scale XCO₂ modeling datasets. A downscaled machine learning model was developed to fill the gaps in the retrieval of data from the Orbiting Carbon Observatory 2 satellite retrievals across Northeast China during 2018–2023 (cross-validated R²=0.88, RMSE=1.49ppm). The results showed that the annual mean value of XCO2 in the northeast region for the last 6 years was (407.83±67.31) ppm, and the XCO₂ content increases annually with 2.68ppm/year. The highest concentrations of XCO₂ were found in the southern region and around large cities. The seasonal distribution pattern of “high in spring and winter, low in summer” was observed to be obvious. This work confirms the necessity for downscaled modeling of XCO₂ from satellites, which can enhance our comprehension of carbon cycles.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yulei Tang, Jianfang Hu, Die Tang, and Zhansheng Chen "Spatio-temporal distribution of XCO₂ concentrations in northeast China based on Downscaling-XGBoost model", Proc. SPIE 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 134021G (27 November 2024); https://doi.org/10.1117/12.3048860

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