iSDAsoil dataset soil clay content (USDA system) in % predicted at 30 m resolution for 0-20 and 20-50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB, LandPKS and other national and regional soil datasets). Cite as:
\nHengl, T., Miller, M.A.E., Kri\u017ean, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.
\nTo open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF version.
\nLayer description:
iSDAsoil dataset soil extractable Calcium log-transformed predicted at 30 m resolution for 0u201320 and 20u201350 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB, LandPKS and other national and regional soil datasets). Cite as:
\nHengl, T., Miller, M.A.E., Kri\u017ean, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.
\nTo open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF version.
\nLayer description:
iSDAsoil dataset soil effective Cation Exchange Capacity (eCEC) log-transformed predicted at 30 m resolution for 0-0 and 20-50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB, and other national and regional soil datasets). Cite as:
\nHengl, T., Miller, M.A.E., Kri\u017ean, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.
\nTo open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF version.
\nLayer description:
iSDAsoil dataset soil extractable Potassium (K) log-transformed predicted at 30 m resolution for 0-20 and 20-50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB, and other national and regional soil datasets). Cite as:
\nHengl, T., Miller, M.A.E., Kri\u017ean, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.
\nTo open the maps in QGIS and/or directly compute with them, please use the please use the Cloud-Optimized GeoTIFF version.
\nLayer description:
iSDAsoil dataset soil fine-earth bulk density in 10xkg/m3 predicted at 30 m resolution for 0-20 and 20-50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB, LandPKS and other national and regional soil datasets).
\nCite as: Hengl, T., Miller, M.A.E., Kriu017Ean, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.
\nTo open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF version.
\nLayer description:
iSDAsoil dataset soil total carbon in permilles (g/kg) log-transformed predicted at 30 m resolution for 0-20 and 20-50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB and other national and regional soil datasets). Cite as:
\nHengl, T., Miller, M.A.E., Kri\u017ean, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.
\nTo open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF version.
\nLayer description:
iSDAsoil dataset soil extractable Iron (Fe) log-transformed predicted at 30 m resolution for 0-20 and 20-50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB, and other national and regional soil datasets). Cite as:
\nHengl, T., Miller, M.A.E., Kri\u017ean, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.
\nTo open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF version.
\nLayer description:
iSDAsoil dataset soil extractable Magnesium (Mg) log-transformed predicted at 30 m resolution for 0u201320 and 20u201350 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB, and other national and regional soil datasets). Cite as:
\nHengl, T., Miller, M.A.E., Kri\u017ean, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.
\nTo open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF version.
\nLayer description:
iSDAsoil dataset soil total organic Nitrogen (N) log-transformed predicted at 30 m resolution for 0-20 and 20-50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB, and other national and regional soil datasets). Cite as:
\nHengl, T., Miller, M.A.E., Kri\u017ean, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.
\nTo open the maps in QGIS and/or directly compute with them, please use the b>Cloud-Optimized GeoTIFF version.
\nLayer description:
iSDAsoil dataset soil organic carbon (C) log-transformed predicted at 30 m resolution for 0-20 and 20-50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB, and other national and regional soil datasets). Cite as:
\nHengl, T., Miller, M.A.E., Kri\u017ean, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.
\nTo open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF version.
\nLayer description:
iSDAsoil dataset soil extractable Sulphur (S) log-transformed predicted at 30 m resolution for 0-20 and 20-50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB and other national and regional soil datasets). Cite as:
\nHengl, T., Miller, M.A.E., Kri\u017ean, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.
\nTo open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF version.
\nLayer description:
iSDAsoil dataset soil extractable Zinc (Zn) log-transformed predicted at 30 m resolution for 0-20 and 20-50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB, and other national and regional soil datasets). Cite as:
\nHengl, T., Miller, M.A.E., Kri\u017ean, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.
\nTo open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF version.
\nLayer description:
iSDAsoil dataset soil extractable Aluminium (Al) log-transformed predicted at 30 m resolution for 0-20 and 20-50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB, LandPKS, and other national and regional soil datasets). Cite as:
\nHengl, T., Miller, M.A.E., Kri\u017ean, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.
\nTo open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF version.
\nLayer description:
iSDAsoil dataset soil extractable Phosphorus (P) log-transformed predicted at 30 m resolution for 0-20 and 20-50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB and other national and regional soil datasets). Cite as:
\nHengl, T., Miller, M.A.E., Kri\u017ean, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.
\nTo open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF version.\nLayer description:
iSDAsoil dataset soil stone content / coarse fragments log-transformed predicted at 30 m resolution for 0-20 and 20-50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB, and other national and regional soil datasets). Cite as:
\nHengl, T., Miller, M.A.E., Kri\u017ean, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.
\nTo open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF version.
\nLayer description:
iSDAsoil dataset soil sand content in % predicted at 30 m resolution for 0-20 and 20-50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB, LandPKS, and other national and regional soil datasets). Cite as:
\nHengl, T., Miller, M.A.E., Kri\u017ean, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.
\nTo open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF version.
\nLayer description:
iSDAsoil dataset soil silt content in % predicted at 30 m resolution for 0-20 and 20-50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB, LandPKS, and other national and regional soil datasets). Cite as:
\nHengl, T., Miller, M.A.E., Kri\u017ean, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.
\nTo open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF version.
\nLayer description:
iSDAsoil dataset soil pH (1:1 Soil-Water Suspension) predicted at 30 m resolution for 0-20 and 20-50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB, and other national and regional soil datasets). Cite as:
\nHengl, T., Miller, M.A.E., Kri\u017ean, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.
\nTo open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF version.
\nLayer description:
iSDAsoil dataset soil texture classes derived from sand, silt and clay fractions at 30 m resolution for 0-20 and 20-50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB, and other national and regional soil datasets). Cite as:
\nHengl, T., Miller, M.A.E., Kri\u017ean, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.
\nTo open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF version.
\nLayer description:
The land surface forms were identified using the method developed by the Missouri Resource Assessment Partnership (MoRAP). The MoRAP method is an automated land surface form classification based on Hammonds (1964a, 1964b) classification. MoRAP made modifications to Hammonds classification, which allowed finer-resolution elevation data to be used as input data and analyses to be made using 1 km2 moving window (True, 2002; True et al., 2000). While Hammonds methodology was based on three variables, slope, local relief, and profile type, MoRAPs methodology uses only slope and local relief (True, 2002). Slope is classified as gently sloping or not gently sloping using a threshold value of 8%. Local relief, the difference between the maximum and minimum elevation in a 1km2 neighborhood for analysis, is classified into five classes (0-15m, 16-30m, 31-90m, 91-150m, and >150m). Slope classes and relief classes were subsequently combined to produce eight land surface form classes (flat plains, smooth plains, irregular plains, escarpments, low hills, hills, breaks/foothills, and low mountains). In the implementation for the contiguous United States, Sayre et al. (2009) further refined the MoRAP methodology to identify a new land surface form class, \"high mountains/deep canyons\", by using an additional local relief class (>400 m). This method was implemented for Africa using a void-filled 90m SRTM elevation dataset which was created from the 30m SRTM elevation data provided by the National Geospatial-Intelligence Agency. In the preliminary output, which had nine land surface form classes (flat plains, smooth plains, irregular plains, escarpments, low hills, hills, breaks/foothills, and low mountains, and high mountains/deep canyons), artifacts were identified over flat desert areas affecting the classification between the two lowest relief classes, \"flat plains\" and \"smooth plains.\" Since this problem was especially pronounced in areas where the input SRTM elevation data originally had data-voids, the problem could have been caused by anomalies or artifacts in the input data, which resulted from the void-filling processes. Instead of further investigating causes of the problem, the two land surface form classes were combined. In addition, the \"low hills\" class which had a very low occurrence was combined with the \"hills\" class. As a result, seven land surface form classes were identified in the final dataset (smooth plains, irregular plains, escarpments, hills, breaks/foothills, low mountains, and high mountains/deep canyons).
\nReferences
\nHammond, E.H., 1964a. Analysis of Properties in Land Form Geography - An Application to Broad-Scale Land Form Mapping. Annals of the Association of American Geographers, v. 54, no. 1, p. 11-19.
\nHammond, E.H. 1964b. Classes of land surface form in the forty-eight states, U.S.A. Annals of the Association of American Geographers. 54(1), map supplement no. 4, 1:5,000,000.
\nSayre, R., P. Comer, H. Warner, and J. Cress. 2009. A new map of standardized terrestrial ecosystems of the conterminous United States, U. S. Geological Survey professional Paper 1768, 17 p.
\nTrue, D. 2002. Landforms of the Lower Mid-West. Missouri Resource Assessment Partnership. MoRAP Map Series MS-2003-001, scale 1:1,500,000.
\nTrue, D., T. Gordon, and D. Diamond. 2000. How the size of a sliding window impacts the generation of landforms. Missouri Resources Assessment Partnership.
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