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<p>Accurate monitoring of forest soil organic carbon (FSOC) is essential for climate change mitigation and biodiversity conservation. Although digital soil mapping has proven effective in capturing the spatial variability of FSOC, its application remains challenging in ecologically heterogeneous regions such as China. This study developed a national 90 m gridded FSOC content product (four depth intervals) with quantified uncertainty at 90 m resolution, covering four depth intervals from 0 to 100 cm (0–20, 20–40, 40–60, and 60–100 cm). By integrating 8,709 soil profiles with 41 environmental covariates within a quantile regression forest (QRF) framework enhanced by forward recursive feature selection (FRFS), we simultaneously predicted FSOC content and quantified associated uncertainties. The model demonstrated robust and unbiased performance, with 10-fold cross-validation mean explained variance (MEC) ranging from 0.69 to 0.80 across soil depths. This high-resolution FSOC content dataset provides a critical spatial baseline for forest carbon management under China’s “Dual Carbon” strategy, with direct applications in carbon sink verification and soil carbon sequestration potential assessment.</p>

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A High-Resolution Forest Soil Organic Carbon Dataset for China Derived from an Enhanced Quantile Regression Forest Model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月30日

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  • Jizhen Chen,
  • Yuxing Ou,
  • Zihao Fan,
  • Xin Zhang,
  • Wenfa Xiao,
  • Qiwu Sun,
  • Xiangyang Sun,
  • Zhilin Huang

摘要

Accurate monitoring of forest soil organic carbon (FSOC) is essential for climate change mitigation and biodiversity conservation. Although digital soil mapping has proven effective in capturing the spatial variability of FSOC, its application remains challenging in ecologically heterogeneous regions such as China. This study developed a national 90 m gridded FSOC content product (four depth intervals) with quantified uncertainty at 90 m resolution, covering four depth intervals from 0 to 100 cm (0–20, 20–40, 40–60, and 60–100 cm). By integrating 8,709 soil profiles with 41 environmental covariates within a quantile regression forest (QRF) framework enhanced by forward recursive feature selection (FRFS), we simultaneously predicted FSOC content and quantified associated uncertainties. The model demonstrated robust and unbiased performance, with 10-fold cross-validation mean explained variance (MEC) ranging from 0.69 to 0.80 across soil depths. This high-resolution FSOC content dataset provides a critical spatial baseline for forest carbon management under China’s “Dual Carbon” strategy, with direct applications in carbon sink verification and soil carbon sequestration potential assessment.