SoilGrids250m: Global gridded soil information based on machine learning
- Hengl, T.
- Mendes de Jesus, J.S.
- Heuvelink, G.B.M.
- Ruiperez Gonzalez, M.
- Kilibarda, Milan
- Blagotic, Aleksandar
- Wei, Shangguan
- Wright, Marvin N.
- Geng, Xiaoyuan
- Bauer-Marschallinger, Bernhard
- Guevara, Mario Antonio
- Vargas, Rodrigo
- MacMillan, Robert A.
- Batjes, N.H.
- Leenaars, J.G.B.
- Carvalho Ribeiro, E.D.
- Wheeler, Ichsani
- Mantel, S.
- Kempen, B.
Publication date
January 2017
DOI Publisher
Public Library of Science (PLoS) ISSN
1932-6203 Citation count (estimate)
81Abstract
This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total)
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