A global depth to bedrock (DTB) dataset was developedfor use in Earth System Models and other applications as well (Shangguan et al. 2017). It provides three variables, the absolute DTB in cm, the censored DTB in cm within 0¨C200 cm (here values equal to 200 cm indicate ¡°deep as or deeper than¡±), and the occurrence of R horizon (bedrock) within 0¨C200 cm expressed as 0¨C1 probability values. This product is developed under an automated soil mapping framework as part of the SoilGrids system (Hengl, T. et al., 2017). This dataset is based on observations extracted from a global compilation of soil profile data (ca. 1,30,000 locations) and borehole data (ca. 1.6 million locations). Additional pseudo-observations generated by expert knowledge were added to fill in large sampling gaps. The model training points were then overlaid on a stack of 155 covariates including DEM-based hydrological and morphological derivatives, lithologic units, MODIS surface reflectance bands and vegetation indices derived from the MODIS land products. Global spatial prediction models were developed using random forest and Gradient Boosting Tree algorithms. The final predictions were generated at the spatial resolution of 250 m as an ensemble prediction of the two independently fitted models. The dataset can be also aggregate to a lower resolution (1km and 10km).
The documentation of the dataset can be downloaded here, including readme file and the data citation paper.
Further references:
https://soil.copernicus.org/articles/7/217/2021/soil-7-217-2021.pdf
Shangguan, W., T. Hengl, J. Mendes de Jesus, H. Yuan, and Y. Dai (2017), Mapping the global depth to bedrock for land surface modeling, J. Adv. Model. Earth Syst., 9, 65–88, doi: 10.1002/2016MS000686. Hengl, T. et al., 2017. SoilGrids250m: global gridded soil information based on Machine Learning. PLOS One, Accepted.
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