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...
Guardado en:
| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/99f484c0771a491480592db97c08eade |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:99f484c0771a491480592db97c08eade |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:99f484c0771a491480592db97c08eade2021-12-02T13:17:56ZAfrican soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning10.1038/s41598-021-85639-y2045-2322https://doaj.org/article/99f484c0771a491480592db97c08eade2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85639-yhttps://doaj.org/toc/2045-2322Abstract 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 ( $$N \approx 150,000$$ N ≈ 150 , 000 ) 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.Tomislav HenglMatthew A. E. MillerJosip KrižanKeith D. ShepherdAndrew SilaMilan KilibardaOgnjen AntonijevićLuka GlušicaAchim DobermannStephan M. HaefeleSteve P. McGrathGifty E. AcquahJamie CollinsonLeandro ParenteMohammadreza SheykhmousaKazuki SaitoJean-Martial JohnsonJordan ChamberlinFrancis B. T. SilatsaMartin YemefackJohn WendtRobert A. MacMillanIchsani WheelerJonathan CrouchNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Medicine R Science Q |
| spellingShingle |
Medicine R Science Q 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 African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning |
| description |
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 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 ( $$N \approx 150,000$$ N ≈ 150 , 000 ) 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 |
article |
| author |
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 |
| author_facet |
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 |
| author_sort |
Tomislav Hengl |
| title |
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_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_sort |
african soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/99f484c0771a491480592db97c08eade |
| work_keys_str_mv |
AT tomislavhengl africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT matthewaemiller africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT josipkrizan africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT keithdshepherd africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT andrewsila africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT milankilibarda africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT ognjenantonijevic africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT lukaglusica africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT achimdobermann africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT stephanmhaefele africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT stevepmcgrath africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT giftyeacquah africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT jamiecollinson africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT leandroparente africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT mohammadrezasheykhmousa africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT kazukisaito africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT jeanmartialjohnson africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT jordanchamberlin africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT francisbtsilatsa africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT martinyemefack africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT johnwendt africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT robertamacmillan africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT ichsaniwheeler africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning AT jonathancrouch africansoilpropertiesandnutrientsmappedat30mspatialresolutionusingtwoscaleensemblemachinelearning |
| _version_ |
1718393346330722304 |