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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...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2021
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Materias: | |
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 |
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author | 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 |
author_facet | 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 |
author_sort | Hengl, Tomislav |
collection | PubMed |
description | 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, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples ([Formula: see text] ) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmental programs, or targeting of nutrition interventions. |
format | Online Article Text |
id | pubmed-7969779 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-79697792021-03-19 African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning 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 Sci Rep Article 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, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples ([Formula: see text] ) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmental programs, or targeting of nutrition interventions. Nature Publishing Group UK 2021-03-17 /pmc/articles/PMC7969779/ /pubmed/33731749 http://dx.doi.org/10.1038/s41598-021-85639-y Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article 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 African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning |
title | African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning |
title_full | African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning |
title_fullStr | African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning |
title_full_unstemmed | African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning |
title_short | African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning |
title_sort | african soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning |
topic | Article |
url | 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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