African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning
Samo za registrovane korisnike
2021
Autori
Hengl, TomislavMiller, 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
Članak u časopisu (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
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≈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.
Izvor:
Scientific Reports, 2021, 11, 6130 (2021)-URI
https://www.nature.com/articles/s41598-021-85639-yhttps://grafar.grf.bg.ac.rs/handle/123456789/2362
Kolekcije
Institucija/grupa
GraFarTY - JOUR AU - Hengl, Tomislav AU - Miller, Matthew A. E. AU - Križan, Josip AU - Shepherd, Keith D. AU - Sila, Andrew AU - Kilibarda, Milan AU - Antonijević, Ognjen AU - Glušica, Luka AU - Dobermann, Achim AU - Haefele, Stephan M. AU - McGrath, Steve P. AU - Acquah, Gifty E. AU - Collinson, Jamie AU - Parente, Leandro AU - Sheykhmousa, Mohammadreza AU - Saito, Kazuki AU - Johnson, Jean‑Martial AU - Chamberlin, Jordan AU - Silatsa, Francis B. T. AU - Yemefack, Martin AU - Wendt, John AU - MacMillan, Robert A. AU - Wheeler, Ichsani AU - Crouch, Jonathan PY - 2021 UR - https://www.nature.com/articles/s41598-021-85639-y UR - https://grafar.grf.bg.ac.rs/handle/123456789/2362 AB - 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≈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. T2 - Scientific Reports T1 - African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning IS - 11 SP - 6130 (2021) DO - https://doi.org/10.1038/s41598-021-85639-y ER -
@article{ author = "Hengl, Tomislav and Miller, Matthew A. E. and Križan, Josip and Shepherd, Keith D. and Sila, Andrew and Kilibarda, Milan and Antonijević, Ognjen and Glušica, Luka and Dobermann, Achim and Haefele, Stephan M. and McGrath, Steve P. and Acquah, Gifty E. and Collinson, Jamie and Parente, Leandro and Sheykhmousa, Mohammadreza and Saito, Kazuki and Johnson, Jean‑Martial and Chamberlin, Jordan and Silatsa, Francis B. T. and Yemefack, Martin and Wendt, John and MacMillan, Robert A. and Wheeler, Ichsani and Crouch, Jonathan", year = "2021", 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≈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.", journal = "Scientific Reports", title = "African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning", number = "11", pages = "6130 (2021)", doi = "https://doi.org/10.1038/s41598-021-85639-y" }
Hengl, T., Miller, M. A. E., Križan, J., Shepherd, K. D., Sila, A., Kilibarda, M., Antonijević, O., Glušica, L., Dobermann, A., Haefele, S. M., McGrath, S. P., Acquah, G. E., Collinson, J., Parente, L., Sheykhmousa, M., Saito, K., Johnson, J., Chamberlin, J., Silatsa, F. B. T., Yemefack, M., Wendt, J., MacMillan, R. A., Wheeler, I.,& Crouch, J.. (2021). African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning. in Scientific Reports(11), 6130 (2021). https://doi.org/https://doi.org/10.1038/s41598-021-85639-y
Hengl T, Miller MAE, Križan J, Shepherd KD, Sila A, Kilibarda M, Antonijević O, Glušica L, Dobermann A, Haefele SM, McGrath SP, Acquah GE, Collinson J, Parente L, Sheykhmousa M, Saito K, Johnson J, Chamberlin J, Silatsa FBT, Yemefack M, Wendt J, MacMillan RA, Wheeler I, Crouch J. African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning. in Scientific Reports. 2021;(11):6130 (2021). doi:https://doi.org/10.1038/s41598-021-85639-y .
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" in Scientific Reports, no. 11 (2021):6130 (2021), https://doi.org/https://doi.org/10.1038/s41598-021-85639-y . .