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Updated: Apr 1, 2026
Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils
Published on: November 25, 2016
A High-Resolution Forest Soil Organic Carbon Dataset for China Derived from an Enhanced Quantile Regression Forest
Jizhen Chen1,2, Yuxing Ou1,2, Zihao Fan1,2
1Key Laboratory of Forest Ecology and Environment of National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Beijing, 100091, China.
None:
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.
