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  • 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.

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    Grade of a sub-soil being acid e.g. having a pH greater than 5 and low BS predicted using the global compilation of soil ground observations. Accuracy assessement of the maps is availble in Hengl et at. (2017) DOI: 10.1371/journal.pone.0169748. Data provided as GeoTIFFs with internal compression (co='COMPRESS=DEFLATE'). Measurement units: grade.

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    Volumetric coarse fragments content (v%) of the soil whole earth, aggregated over the Effective Root Zone Depth for Maize, mapped at 1km resolution

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    Extractable Calcium (Ca) content of the soil fine earth fraction in mg/kg (ppm) 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.

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    Extractable Boron (B) 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.

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    Cummulative probability of organic soil based on the TAXOUSDA and TAXNWRB predicted using the global compilation of soil ground observations. Accuracy assessement of the maps is availble in Hengl et at. (2017) DOI: 10.1371/journal.pone.0169748. Data provided as GeoTIFFs with internal compression (co='COMPRESS=DEFLATE'). Measurement units: probability.

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    This soil organic carbon dataset contains the following maps: soil organic carbon concentration (%) for the 0-10 cm, 10-20 cm, 20-30 cm and 0-30 cm soil layers, and bulk density (kg/m3) and soil organic carbon stock (kg/m2) for the 0-30 cm layer. These maps were produced with (geostatistical) regression-kriging models that combined soil data from the NAFORMA survey, the Tanzania National Soil Survey and the African Soil Profiles Database Version 1.1 with a suite of environmental GIS data layers including a land cover map, SOTER soil class map, maps of topographic attributes derived from the SRTM-DEM, maps of surface reflectance and vegetation indices derived from satellite imagery. The regression-kriging models were used to predict carbon concentrations, stocks and bulk density at the nodes of a regular grid with 250 meter cell size covering the Tanzania. Prediction uncertainty was quantified and is available with the data as the lower and upper boundary of the 90% prediction interval. Further details about the input data, modelling framework, and cross validation results are provided in a peer-reviewed, scientific journal article. The project was funded by the UN-REDD Programme Output 2.4 “National Maps inform delivery of the REDD+ Framework” and conducted through a letter of Agreement between Food and Agricultural Organization of the United Nations (FAO) and ISRIC-World Soil Information. The maps were produced by ISRIC - World Soil Information in a collaborative effort with the National Soil Survey, Ministry of Natural Resources and Tourism, Tanzania Forest Services, Sokoine University, and the AfSIS project.

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    Exchangeable acidity (H+Al measured in 1M KCl) in cmolc/kg (fine earth) at 6 standard depths predicted using two sets of Africa soil profiles data. For details see published paper here below (Hengl T., G.B.M. Heuvelink, B. Kempen, J.G.B. Leenaars, M.G. Walsh, K.D. Shepherd, A. Sila, R.A. MacMillan, J. Mendes de Jesus, L.T. Desta, J.E. Tondoh, 2015. Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions. PLoS ONE 10(6)

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    Plant available water holding capacity (v%) of the soil fine earth fraction, with field capacity defined at h=200 cm or pF 2.3, aggregated over rootable depth and the top 30 cm, mapped at 1km resolution

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    Volumetric coarse fragments content (v%) of the soil whole earth, aggregated over rootable depth and the top 30 cm, mapped at 1km resolution