SoilGrids250m: Global gridded soil information based on machine learning

T. Hengl*, J.S. Mendes de Jesus, G.B.M. Heuvelink, M. Ruiperez Gonzalez, Milan Kilibarda, Aleksandar Blagotic, Shangguan Wei, Marvin N. Wright, Xiaoyuan Geng, Bernhard Bauer-Marschallinger, Mario Antonio Guevara, Rodrigo Vargas, Robert A. MacMillan, N.H. Batjes, J.G.B. Leenaars, E.D. Carvalho Ribeiro, Ichsani Wheeler, S. Mantel, B. Kempen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1220 Citations (Scopus)

Abstract

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).
Original languageEnglish
Article numbere0169748
Number of pages40
JournalPLoS ONE
Volume12
Issue number2
DOIs
Publication statusPublished - 2017

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