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<p>Accurate global carbon dioxide (CO<sub>2</sub>) distribution with high spatial and temporal resolution is essential for understanding its dynamics and impacts on climate change. This study tackles the challenge of data gaps in satellite observations of greenhouse gases, caused by orbital and observational limitations. We reconstructed a comprehensive dataset of Column-averaged CO2 (XCO<sub>2</sub>) concentrations by integrating re-analyzed data from the Copernicus Atmosphere Monitoring Service (CAMS) with observations from GOSAT and OCO-3 satellites. Using two advanced data reconstruction methods—Data Interpolating Empirical Orthogonal Functions (DINEOF) and Convolutional Auto-Encoder (DINCAE)—we imputed missing data, preserving spatial and temporal consistency. The combined approach achieved high accuracy, with Pearson correlation values between 0.94 and 0.95 against TCCON measurements, and we also reported root mean square error (RMSE) to assess model performance further. Our results indicate that these techniques generate a daily, high-resolution, gap-free XCO<sub>2</sub> dataset, enabling improved CO<sub>2</sub> monitoring, climate modeling, and policy development.</p>

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Global Daily Column Average CO2 at 0.1° × 0.1° Spatial Resolution Integrating OCO-3, GOSAT, CAMS with EOF and Deep Learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月14日

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  • Franz Pablo Antezana Lopez,
  • Guanhua Zhou,
  • Guifei Jing,
  • Kai Zhang,
  • Liangfu Chen,
  • Lin Chen,
  • Yumin Tan

摘要

Accurate global carbon dioxide (CO2) distribution with high spatial and temporal resolution is essential for understanding its dynamics and impacts on climate change. This study tackles the challenge of data gaps in satellite observations of greenhouse gases, caused by orbital and observational limitations. We reconstructed a comprehensive dataset of Column-averaged CO2 (XCO2) concentrations by integrating re-analyzed data from the Copernicus Atmosphere Monitoring Service (CAMS) with observations from GOSAT and OCO-3 satellites. Using two advanced data reconstruction methods—Data Interpolating Empirical Orthogonal Functions (DINEOF) and Convolutional Auto-Encoder (DINCAE)—we imputed missing data, preserving spatial and temporal consistency. The combined approach achieved high accuracy, with Pearson correlation values between 0.94 and 0.95 against TCCON measurements, and we also reported root mean square error (RMSE) to assess model performance further. Our results indicate that these techniques generate a daily, high-resolution, gap-free XCO2 dataset, enabling improved CO2 monitoring, climate modeling, and policy development.