Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Spatial predictions of soil macro and micro-nutrient content across Sub-Saharan Africa at 250 m spatial resolution and for 0–30 cm depth interval are presented. Predictions were produced for 15 target nutrients: organic carbon (C) and total (organic) nitrogen (N), total phosphorus (P), and extrac...

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Bibliographic Details

Main Authors:	Hengl, T., Leenaars, Johan G.B., Shepherd, Keith D., Walsh, Markus G., Heuvelink, Gerard B.M., Mamo, T., Tilahun, H., Berkhout, Ezra D., Cooper, M., Fegraus, E., Wheeler, I., Kwabena, N. A.
Format:	Journal Article
Language:	Inglés
Published:	Springer 2017
Subjects:	macro-nutrients micro-nutrients random forest machine learning soil nutrient map spatial prediction
Online Access:	https://hdl.handle.net/10568/83171