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Estimating forest aboveground carbon stock from remote sensing data with an enhanced XGBoost model
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DOI：10.3389/frsen.2026.1877406.png)
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Abstract
En 中文
The accurate assessment of forest aboveground carbon (AGC) is crucial for improving the efficiency of forest resource management; mitigating climate change; and fostering sustainable development. However; the extensive distribution of forests; their complex ecosystem structures; insufficiently representative assessment data; and methodological inconsistencies generally lead to estimates with low accuracy and high uncertainty. To address these issues systematically; this study introduces a novel framework that integrates remote sensing features with field-measured plot data. This framework leverages an Optuna-optimized eXtreme Gradient Boosting (XGBoost) model to achieve accurate estimation of forest AGC. A key contribution of this study is the application of Optuna to optimize the hyperparameters of the XGBoost model; which improves both its predictive performance and generalization ability. For feature selection; we employed a combination of the Pearson correlation coefficient and the Boruta algorithm; which identified ten core feature variables from the initial set. This process effectively improved the relevance and interpretability of the model inputs. The experimental results demonstrate that feature selection markedly improved model performance: R2 increased by 0.2; RMSE decreased by 2.33 Mg C/ha; and MAE was reduced by 6.21% compared to the model using the unselected feature set. The Optuna-optimized XGBoost model demonstrated excellent performance; achieving an R2 of 0.72; an improvement of 0.12 over the baseline XGBoost model; with an RMSE of 24.48 Mg C/ha and an MAE of 18.56%. These results indicate superior predictive accuracy and stability. In conclusion; the integrated framework developed in this study; which combines multi-source remote sensing data with machine learning; effectively enhances the estimation accuracy of forest AGC at a regional scale. This approach provides a reliable theoretical basis and a practical methodology for the dynamic monitoring and management of forest carbon sinks.
Keywords:
remote sensing
XGBoost
multisource data
above-ground carbon storage in forests
Optuna framework
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