SoilGrids250m: Global gridded soil information based on machine learning

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 Capa...

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Veröffentlicht in:	PLoS ONE 2017, Vol.12 (2), p.e0169748
Hauptverfasser:	Hengl, Tomislav, Mendes de Jesus, Jorge, Heuvelink, Gerard B. M, Ruiperez Gonzalez, Maria, Kilibarda, Milan, Blagotic, Aleksandar, Shangguan, Wei, Wright, Marvin N, Geng, Xiaoyuan, Bauer-Marschallinger, Bernhard, Guevara, Mario Antonio, Vargas, Rodrigo, MacMillan, Robert A, Batjes, Niels H, Leenaars, Johan G. B, Ribeiro, Eloi, Wheeler, Ichsani, Mantel, Stephan, Kempen, Bas
Format:	Report
Sprache:	eng
Schlagworte:	Machine learning Physiological aspects Properties Soils
Online-Zugang:	Volltext
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