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<p>Accurate gap-filling of daily soil CO₂ flux is essential for understanding forest carbon cycling and supporting the achievement of carbon neutrality goals. Using observations from the Ailaoshan forest site (2010–2013), we evaluated five machine-learning models with inputs including environmental variables, eddy covariance products such as ecosystem respiration and gross primary productivity, and a seasonal phase indicator. Seasonal patterns remained stable during the 4-year period, and no long-term trend was detected after quality control. After preprocessing, multiple machine learning models were compared and evaluated through rolling-window cross-validation. Model robustness and interpretability were further examined using complexity curves, SHAP analysis, and ablation experiments. The empirical Q10 × SWC model served as a baseline, and independent data from the Xishuangbanna site (2003–2008) were used for external validation. CatBoost achieved the best balance between accuracy and complexity and remained stable across rolling windows. Incorporating GPP, RE, and cos_doy improved test performance, raising <i>R</i><sup>2</sup> from ~ 0.85 to ~ 0.90 and reducing RMSE from ~ 0.66 to ~ 0.57. Compared with Q10 × SWC, CatBoost achieved an average Skill score of 0.18, corresponding to a 3–32% reduction in RMSE. External validation at Xishuangbanna further confirmed model robustness (Skill ≈ 0.81). The proposed framework provides an effective tool for generating continuous and reliable daily soil CO₂ flux datasets, offering methodological support for forest carbon cycle research and the realization of “dual carbon” goals.</p>

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A machine learning gap-filling approach for daily forest soil CO2 flux based on environmental factors and eddy covariance variables

  • 期刊论文

  • 发表日期：

    2026年1月31日

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  • Meihe Li,
  • Naisi Liang,
  • Tingting Duan,
  • Jin Li

摘要

Accurate gap-filling of daily soil CO₂ flux is essential for understanding forest carbon cycling and supporting the achievement of carbon neutrality goals. Using observations from the Ailaoshan forest site (2010–2013), we evaluated five machine-learning models with inputs including environmental variables, eddy covariance products such as ecosystem respiration and gross primary productivity, and a seasonal phase indicator. Seasonal patterns remained stable during the 4-year period, and no long-term trend was detected after quality control. After preprocessing, multiple machine learning models were compared and evaluated through rolling-window cross-validation. Model robustness and interpretability were further examined using complexity curves, SHAP analysis, and ablation experiments. The empirical Q10 × SWC model served as a baseline, and independent data from the Xishuangbanna site (2003–2008) were used for external validation. CatBoost achieved the best balance between accuracy and complexity and remained stable across rolling windows. Incorporating GPP, RE, and cos_doy improved test performance, raising R2 from ~ 0.85 to ~ 0.90 and reducing RMSE from ~ 0.66 to ~ 0.57. Compared with Q10 × SWC, CatBoost achieved an average Skill score of 0.18, corresponding to a 3–32% reduction in RMSE. External validation at Xishuangbanna further confirmed model robustness (Skill ≈ 0.81). The proposed framework provides an effective tool for generating continuous and reliable daily soil CO₂ flux datasets, offering methodological support for forest carbon cycle research and the realization of “dual carbon” goals.