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Discussion papers
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Peer-reviewed comment 13 Sep 2018

Peer-reviewed comment | 13 Sep 2018

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Earth System Science Data (ESSD).

Depth-to-Bedrock Map of China at a Spatial Resolution of 100 Meters

Fapeng Yan1, Wei Shangguan2, Jing Zhang1, and Bifeng Hu3,4,5 Fapeng Yan et al.
  • 1College of Global Change and Earth System Science, Beijing Nor mal University, Beijing, China
  • 2Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China
  • 3Unité de Recherche en Science du Sol, INRA, Orléans 45075, France
  • 4InfoSol, INRA, US 1106, Orléans, 4075, France
  • 5Sciences de la Terre et de l'Univers, Orléans University, 4506 Orleans, France

Abstract. Depth to bedrock serves as the lower boundary of soil, which influences or controls many of the Earth’s physical and chemical processes. It plays important roles in geology, hydrology, land surface processes, civil engineering, and other related fields. This paper describes the materials and methods to produce a high-resolution (100 m) depth-to-bedrock map of China. Observations were interpreted from borehole log data (ca. 6,382 locations) sampled from the Chinese National Important Geological Borehole Database. To fill in large sampling gaps, additional pseudo-observations generated based on expert knowledge were added. Then, we overlaid the training points on a stack of 133 covariates including climatic images, DEM-derived parameters, land-cover and land-use maps, MODIS surface reflectance bands, vegetation index images, and the Harmonized World Soil Database. Spatial prediction models were developed using the random forests and gradient boosting tree, and ensemble prediction results were then obtained by these two independently fitted models. Finally, uncertainty estimation was generated by the quantile regression forest model. The 10-fold cross-validation showed that the ensemble models explain 57 % of the variation in depth to bedrock. Based on comparison with depth-to-bedrock maps of China extracted from previous global predictions, our predictions showed higher accuracy. More observations, especially those in data-sparse areas, should be added to training data, and more covariates with high precision should be used to further improve the accuracy of spatial predictions. The resulting maps of this study are available on Figshare at the following DOI: And they are also available for download at .

Fapeng Yan et al.
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Fapeng Yan et al.
Data sets

Depth-to-Bedrock Map of China Y. Fapeng, S. Wei, Z. Jing, H. Bifeng

Model code and software

DTB100China Y. Fapeng, S. Wei

Fapeng Yan et al.
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Short summary
A depth-to-bedrock map of China was produced at 100-meter resolution based on 6,382 observations and 133 covariates. This map is the most detailed and accurate one at the national scale. The uncertainty map was provided as a map quality reference. The ensemble machine learning model explains 57% of variation in spatial distribution. The four most important covariates for the map production are topographic wetness index, landform, topographic openness index, and slope.
A depth-to-bedrock map of China was produced at 100-meter resolution based on 6,382 observations...