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.
Soil property and class maps for the continent of Africa were so far only available at very genern class="Chemical">alised 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 totalnitrogen (N), totalcarbon, 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.
Authors: Christian Folberth; Rastislav Skalský; Elena Moltchanova; Juraj Balkovič; Ligia B Azevedo; Michael Obersteiner; Marijn van der Velde Journal: Nat Commun Date: 2016-06-21 Impact factor: 14.919
Authors: Tomislav Hengl; Johan G B Leenaars; Keith D Shepherd; Markus G Walsh; Gerard B M Heuvelink; Tekalign Mamo; Helina Tilahun; Ezra Berkhout; Matthew Cooper; Eric Fegraus; Ichsani Wheeler; Nketia A Kwabena Journal: Nutr Cycl Agroecosyst Date: 2017-08-02 Impact factor: 3.270
Authors: José A M Demattê; José Lucas Safanelli; Raul Roberto Poppiel; Rodnei Rizzo; Nélida Elizabet Quiñonez Silvero; Wanderson de Sousa Mendes; Benito Roberto Bonfatti; André Carnieletto Dotto; Diego Fernando Urbina Salazar; Fellipe Alcântara de Oliveira Mello; Ariane Francine da Silveira Paiva; Arnaldo Barros Souza; Natasha Valadares Dos Santos; Cláudia Maria Nascimento; Danilo Cesar de Mello; Henrique Bellinaso; Luiz Gonzaga Neto; Merilyn Taynara Accorsi Amorim; Maria Eduarda Bispo de Resende; Julia da Souza Vieira; Louise Gunter de Queiroz; Bruna Cristina Gallo; Veridiana Maria Sayão; Caroline Jardim da Silva Lisboa Journal: Sci Rep Date: 2020-03-10 Impact factor: 4.379