Spatiotemporal Distribution and Uncertainty of Soil C:N Ratios in Global 0-30cm and 30-100cm Soil Layers

Published: 21 January 2026| Version 1 | DOI: 10.17632/z54f5ncj23.1
Contributor:
嘉宇

Description

We collected 36997 soil C:N data points for the 0-30 cm layer and 31691 data points for the 30-100 cm layer, integrating a total of 24 environmental covariates from five categories closely related to soil C:N, including climate, vegetation, topography, soil properties, and human activities. A zonal direct modeling strategy was employed in conjunction with quantile random forest to develop the predictive models. The 10-fold cross-validation results demonstrated that the model achieved an R² of 0.77 for C:N030 and 0.71 for C:N30100. Based on this model, the spatial distribution and uncertainty of global soil C:N for the 0-30 cm and 30-100 cm layers were mapped at a 1 km resolution. Comparative analysis indicates that the 1 km global soil C:N dataset produced in this study exhibits higher accuracy than existing products.

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Machine Learning, Soil, Climate Change, Vertical Integration, Digital Soil Mapping

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