African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning

African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning

Abstract Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded...

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Bibliographic Details
Main Authors: Tomislav Hengl, Matthew A. E. Miller, Josip Križan, Keith D. Shepherd, Andrew Sila, Milan Kilibarda, Ognjen Antonijević, Luka Glušica, Achim Dobermann, Stephan M. Haefele, Steve P. McGrath, Gifty E. Acquah, Jamie Collinson, Leandro Parente, Mohammadreza Sheykhmousa, Kazuki Saito, Jean-Martial Johnson, Jordan Chamberlin, Francis B. T. Silatsa, Martin Yemefack, John Wendt, Robert A. MacMillan, Ichsani Wheeler, Jonathan Crouch
Format: Article
Language:English
Published: Nature Publishing Group 2021-03-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-021-85639-y

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https://doi.org/10.1038/s41598-021-85639-y

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