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

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

Abstract read

In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Jizhen ChenKey Laboratory of Forest Ecology and Environment of National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Beijing, 100091, China.

Yuxing OuKey Laboratory of Forest Ecology and Environment of National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Beijing, 100091, China.

Zihao FanKey Laboratory of Forest Ecology and Environment of National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Beijing, 100091, China.

Xin ZhangKey Laboratory of Forest Ecology and Environment of National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Beijing, 100091, China.

Wenfa XiaoKey Laboratory of Forest Ecology and Environment of National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Beijing, 100091, China.

Qiwu SunResearch Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China.

Xiangyang SunCollege of Forestry, Beijing Forestry University, Beijing, 100083, China.

Zhilin HuangKey Laboratory of Forest Ecology and Environment of National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Beijing, 100091, China. hzlin66@caf.ac.cn.

Funding

the Special Investigation Foundation of Basic Resources, Ministry of Science and Technology of the People's Republic of China 2021FY100800

6 · The paper itself

Abstract

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.

Identifiers

PMID41912611

DOI10.1038/s41597-026-07133-2

PMCPMC13195093

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.