Bulk density of the fine earth fraction*, air dried (kg/dm³). WoSIS_latest is a 'dynamic dataset' that contains the most recent complement of quality-assessed and standardised soil data served from WoSIS (ISRIC World Soil Information Service). The source data were shared by a wide range of data providers (see: https://www.isric.org/explore/wosis/wosis-contributing-institutions-and-experts). Being dynamic, the contents of 'wosis_latest' will change once new point data are acquired, cleansed and standardised, additional soil properties are considered, and/or when possible amendments are required. Static snapshots of 'wosis_latest' are released at irregular intervals for consistent citation purposes and to discuss methodological changes; the last snapshot is available at https://doi.org/10.5194/essd-16-4735-2024. For general information about WoSIS please see the FAQ-page at https://www.isric.org/explore/wosis/faq-wosis.
Volumetric Water Content at 10kPa suction in 10-3 cm3cm-3 (0.1 v% or 1 mm/m) at 6 standard depths. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers. This map is the result of resampling the mean SoilGrids 250 m predictions (Turek et al. 2021) for each 5000 m cell.
Measured in 1:x soil:water solution (mainly determines P in dissolved forms) (mg/kg). WoSIS_latest is a 'dynamic dataset' that contains the most recent complement of quality-assessed and standardised soil data served from WoSIS (ISRIC World Soil Information Service). The source data were shared by a wide range of data providers (see: https://www.isric.org/explore/wosis/wosis-contributing-institutions-and-experts). Being dynamic, the contents of 'wosis_latest' will change once new point data are acquired, cleansed and standardised, additional soil properties are considered, and/or when possible amendments are required. Static snapshots of 'wosis_latest' are released at irregular intervals for consistent citation purposes and to discuss methodological changes; the last snapshot is available at https://doi.org/10.5194/essd-16-4735-2024. For general information about WoSIS please see the FAQ-page at https://www.isric.org/explore/wosis/faq-wosis.
Extractable Zinc (Zn) content of the soil fine earth fraction in mg/100kg (pp100m) as measured according to the soil analytical procedure of Mehlich 3 and spatially predicted for 0-30 cm depth interval at 250 m spatial resolution across sub-Saharan Africa using Machine Learning (ensemble between random forest and gradient boosting) using soil data from the Africa Soil Profiles database (AfSP) compiled by AfSIS and recent soil data newly collected by AfSIS in partnership with EthioSIS (Ethiopia), GhaSIS (Ghana) and NiSIS (Nigeria as made possible by OCP Africa and IITA), combined with soil data as made available by Wageningen University and Research, IFDC, VitalSigns, University of California and the OneAcreFund. [Values M = mean value predicted]. For details see below for peer reviewed paper (T. Hengl, J.G.B. Leenaars, K.D. Shepherd, M.G. Walsh, G.B.M. Heuvelink, Tekalign Mamo, H. Tilahun, E. Berkhout, M. Cooper, E. Fegraus, I. Wheeler, N.A. Kwabena, 2017. Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning. Nutriënt Cycling in Agroecosystems 109(1): 77-102). Maps produced for the Environmental Assessment Agency (PBL), funded by the Netherlands government, in collaboration with the AfSIS and the Vital Signs projects.
Bulk density (fine earth) in cg/cm³ at 6 standard depths. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers. To visualize these layers please use www.soilgrids.org.
This harmonized set of soil parameter estimates for Latin America and the Caribbean was derived from a revised version of the 1:5M Soil and Terrain Database for the region (SOTERLAC, ver. 2.0) and the ISRIC-WISE soil profile database. The land surface of Latin America and the Caribbean has been characterized using 1585 unique SOTER units, corresponding with 5855 polygons. The major soils have been described using 1660 profiles, selected by national soil experts as being representative for these units. The associated soil analytical data have been derived from soil survey reports. These sources seldom hold all the physical and chemical attributes ideally required by SOTER. Gaps in the measured soil profile data have been filled using a step-wise procedure that uses taxotransfer rules, based on about 9600 soil profiles held in the WISE database, complemented with expert-rules. Parameter estimates are presented by soil unit for fixed depth intervals of 0.2 m to 1 m depth for: organic carbon, total nitrogen, pH(H2O), CECsoil, CECclay, base saturation, effective CEC, aluminium saturation, CaCO3 content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity of saturated paste (ECe), bulk density, content of sand, silt and clay, content of coarse fragments (less than 2 mm), and available water capacity (-33 to -1500 kPa). These attributes have been identified as being useful for agro-ecological zoning, land evaluation, crop growth simulation, modelling of soil carbon stocks and change, and analyses of global environmental change. The current parameter estimates should be seen as best estimates based on the current selection of soil profiles and data clustering procedure. Taxotransfer rules have been flagged to provide an indication of the possible confidence in the derived data. Results are presented as summary files and can be linked to the 1:5M scale SOTERLAC map in a GIS, through the unique SOTER-unit code. The secondary data set is considered appropriate for studies at the continental scale (greater than 1:5M). Correlation of soil analytical data should be done more rigorously when more detailed scientific work is considered.
Nutrient clusters based on fuzzy k-means of the soil fine earth fraction and spatially predicted at 250 m spatial resolution across sub-Saharan Africa using Machine Learning (ensemble between random forest and gradient boosting) using soil data from the Africa Soil Profiles database (AfSP) compiled by AfSIS and recent soil data newly collected by AfSIS in partnership with EthioSIS (Ethiopia), GhaSIS (Ghana) and NiSIS (Nigeria as made possible by OCP Africa and IITA), combined with soil data as made available by Wageningen University and Research, IFDC, VitalSigns, University of California and the OneAcreFund. [Values M = mean value predicted]. For details see below for peer reviewed paper (T. Hengl, J.G.B. Leenaars, K.D. Shepherd, M.G. Walsh, G.B.M. Heuvelink, Tekalign Mamo, H. Tilahun, E. Berkhout, M. Cooper, E. Fegraus, I. Wheeler, N.A. Kwabena, 2017. Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning. Nutriënt Cycling in Agroecosystems 109(1): 77-102). Maps produced for the Environmental Assessment Agency (PBL), funded by the Netherlands government, in collaboration with the AfSIS and the Vital Signs projects.
Sand content (50/63-2000 micro meter) mass fraction in ‰ at 6 standard depths. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers. This map is the result of resampling the mean SoilGrids 250 m predictions (Poggio et al. 2021) for each 1000 m cell.
Soil organic carbon stock in t/ha for 0-30 cm depth interval. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers. To visualize these layers please use www.soilgrids.org.
This dataset includes global soil salinity layers for the years 1986, 1992, 2000, 2002, 2005, 2009 and 2016. The maps were generated with a random forest classifier that was trained using seven soil properties maps, thermal infrared imagery and the ECe point data from the WoSIS database. The validation accuracy of the resulting maps was in the range of 67–70%. The total area of salt affected lands by our assessment is around 1 billion hectares, with a clear increasing trend. Further details are provided in a peer-reviewed journal article (https://doi.org/10.1016/j.rse.2019.111260). The code and data used to produce the global soil salinity maps can be accessed by registered Google Earth Engine users at https://code.earthengine.google.com/d43e5a92ae1deed32a0929f57b572756.
