Soil organic carbon stock in kg/m2 for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution ...

Soil organic carbon stock in kg/m 2 for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution. To convert to t/ha multiply by 10. Derived using soil organic carbon content (https://doi.org/10.5281/zenodo.1475457), bulk density (https://doi.org/10.5281/zenodo.1475...

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Bibliographic Details
Main Authors: Hengl, Tomislav, Wheeler, Ichsani
Format: Dataset
Language:English
Published: Zenodo 2018
Subjects:
LandGIS
soil carbon
Gonzalez
Antarc*
Antarctica
Online Access:https://dx.doi.org/10.5281/zenodo.2536040
https://zenodo.org/record/2536040
Description
Summary:Soil organic carbon stock in kg/m 2 for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution. To convert to t/ha multiply by 10. Derived using soil organic carbon content (https://doi.org/10.5281/zenodo.1475457), bulk density (https://doi.org/10.5281/zenodo.1475970) and coarse fragments (https://doi.org/10.5281/zenodo.2525681), predicted from point data at 6 standard depths. Depth to bed rock has been ignored, hence total stocks might be about 10–15% lower then reported. Processing steps are described in detail here . Antarctica is not included. To access and visualize maps use: https://openlandmap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option ... : {"references": ["Sanderman, J., Hengl, T., Fiske, G., (2017). The soil carbon debt of 12,000 years of human land use. PNAS, https://dx.doi.org/10.1073/pnas.1706103114", "Hengl, T., de Jesus, J.M., Heuvelink, G.B., Gonzalez, M.R., Kilibarda, M., Blagoti\u0107, A., Shangguan, W., Wright, M.N., Geng, X., Bauer-Marschallinger, B. and Guevara, M.A., (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2), p.e0169748. https://doi.org/10.1371/journal.pone.0169748", "Hengl, T., MacMillan, R.A., (2019). Predictive Soil Mapping with R. OpenGeoHub foundation, Wageningen, the Netherlands, 370 pages, www.soilmapper.org, ISBN: 978-0-359-30635-0."]} ...

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