Preprints
https://doi.org/10.5194/essd-2018-103
https://doi.org/10.5194/essd-2018-103
13 Sep 2018
 | 13 Sep 2018
Status: this preprint was under review for the journal ESSD but the revision was not accepted.

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

Fapeng Yan, Wei Shangguan, Jing Zhang, and Bifeng Hu

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: https://doi.org/10.6084/m9.figshare.7011524.v1. And they are also available for download at http://globalchange.bnu.edu.cn/ .

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Fapeng Yan, Wei Shangguan, Jing Zhang, and Bifeng Hu
 
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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Fapeng Yan, Wei Shangguan, Jing Zhang, and Bifeng Hu

Data sets

Depth-to-Bedrock Map of China Y. Fapeng, S. Wei, Z. Jing, H. Bifeng https://doi.org/10.6084/m9.figshare.7011524.v2

Model code and software

DTB100China Y. Fapeng, S. Wei https://doi.org/10.6084/m9.figshare.7077797.v1

Fapeng Yan, Wei Shangguan, Jing Zhang, and Bifeng Hu

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Latest update: 23 Jun 2024
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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.
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