TY - JOUR
T1 - SoilGrids250m: Global gridded soil information based on machine learning
AU - Hengl, T.
AU - Mendes de Jesus, J.S.
AU - Heuvelink, G.B.M.
AU - Ruiperez Gonzalez, M.
AU - Kilibarda, Milan
AU - Blagotic, Aleksandar
AU - Wei, Shangguan
AU - Wright, Marvin N.
AU - Geng, Xiaoyuan
AU - Bauer-Marschallinger, Bernhard
AU - Guevara, Mario Antonio
AU - Vargas, Rodrigo
AU - MacMillan, Robert A.
AU - Batjes, N.H.
AU - Leenaars, J.G.B.
AU - Carvalho Ribeiro, E.D.
AU - Wheeler, Ichsani
AU - Mantel, S.
AU - Kempen, B.
PY - 2017
Y1 - 2017
N2 - 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).
AB - 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).
U2 - 10.1371/journal.pone.0169748
DO - 10.1371/journal.pone.0169748
M3 - Article
SN - 1932-6203
VL - 12
JO - PLoS ONE
JF - PLoS ONE
IS - 2
M1 - e0169748
ER -