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

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 projects...

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Detalles Bibliográficos
Autores principales: Hengl, Tomislav, Miller, Matthew A. E., Križan, Josip, Shepherd, Keith D., Sila, Andrew, Kilibarda, Milan, Antonijević, Ognjen, Glušica, Luka, Dobermann, Achim, Haefele, Stephan M., McGrath, Steve P., Acquah, Gifty E., Collinson, Jamie, Parente, Leandro, Sheykhmousa, Mohammadreza, Saito, Kazuki, Johnson, Jean-Martial, Chamberlin, Jordan, Silatsa, Francis B. T., Yemefack, Martin, Wendt, John, MacMillan, Robert A., Wheeler, Ichsani, Crouch, Jonathan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969779/
https://www.ncbi.nlm.nih.gov/pubmed/33731749
http://dx.doi.org/10.1038/s41598-021-85639-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969779/
https://www.ncbi.nlm.nih.gov/pubmed/33731749
http://dx.doi.org/10.1038/s41598-021-85639-y

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