DOI: 10.5281/zenodo.2784001
title: Soil water content (volumetric %) for 33kPa and 1500kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution

description: Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Training points are based on a global compilation of soil profiles (USDA NCSS, AfSPDB, ISRIC WISE, EGRPR, SPADE, CanNPDB, UNSODA, SWIG, HYBRAS and HydroS). Data import steps are available <strong>here</strong>. Spatial prediction steps are described in detail <strong>here</strong>. Note: these are actually measured and mapped soil content values; no Pedo-Transfer-Functions have been used (except to fill-in the missing NCSS bulk densities). Available water capacity in mm (derived as a difference between field capacity and wilting point multiplied by layer thickness) per layer is available <strong>here</strong>. Antarctica is not included. To access and visualize some of the maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: sol = theme: soil, watercontent.33kPa = water content (volumetric percent) under field capacity (33 kPa suction), usda.4b1c = determination method: laboratory method code, m = mean value, 250m = spatial resolution / block support: 250 m, b10..10cm = vertical reference: 10 cm depth below surface, 1950..2017 = time reference: period 1950-2017, v0.1 = version number: 0.1,
description: {"references": ["Batjes, N. H. 2009. \"Harmonized soil profile data for applications at global and continental scales: Updates to the WISE database.\" Soil Use and Management 25 (2):124\u201327. https://doi.org/10.1111/j.1475-2743.2009.00202.x.", "Geng, X., Fraser, W., VandenBygaart, B., Smith, S., Waddell, A., Jiao, Y., & Patterson, G. (2010). Toward digital soil mapping in Canada: Existing soil survey data and related expert knowledge. In Digital soil mapping (pp. 325-335). Springer, Dordrecht.", "Hengl, T., MacMillan, R.A., (2019). Predictive Soil Mapping with R. OpenGeoHub foundation, Wageningen, the Netherlands, 370 pages, www.soilmapper.org, ISBN: 978-0-359-30635-0.", "Hollis, J. M., Jones, R. J. A., Marshall, C. J., Holden, A., Van de Veen, J. R., & Montanarella, L. (2006). SPADE-2: The soil profile analytical database for Europe, version 1.0. Luxembourg: Office for official publications of the European Communities. EUR22127EN.", "Leenaars, J. G. B. (2013). Africa Soil Profiles Database, Version 1.3. A compilation of georeferenced and standardised legacy soil profile data for Sub-Saharan Africa (with dataset). Africa Soil Information Service (AfSIS) project (No. 2013/03). ISRIC-World Soil Information.", "Nemes, A. D., Schaap, M. G., Leij, F. J., & W\u00f6sten, J. H. M. (2001). Description of the unsaturated soil hydraulic database UNSODA version 2.0. Journal of Hydrology, 251(3-4), 151-162.", "Ottoni, M. V., Ottoni Filho, T. B., Schaap, M. G., Lopes-Assad, M. L. R., & Rotunno Filho, O. C. (2018). Hydrophysical database for Brazilian soils (HYBRAS) and pedotransfer functions for water retention. Vadose Zone Journal, 17(1).", "Schindler, U. G., & M\u00fcller, L. (2017). Soil hydraulic functions of international soils measured with the Extended Evaporation Method (EEM) and the HYPROP device. Open Data Journal for Agricultural Research, 3.", "USDA Natural Resources Conservation Service (2017). National Cooperative Soil Characterization Database. NRCS. https://data.nal.usda.gov/dataset/national-cooperative-soil-characterization-database", "Weiherm\u00fcller, L., Vanderborght, J., Pachepsky, Y. A., & Mao, L. (2018). Development and analysis of the Soil Water Infiltration Global database. Earth System Science Data, 10.", "\u0421\u0442\u043e\u043b\u0431\u043e\u0432\u043e\u0439, \u0412. \u0421., & \u041c\u043e\u043b\u0447\u0430\u043d\u043e\u0432, \u042d. \u041d. (2015). \u0415\u0434\u0438\u043d\u044b\u0439 \u0433\u043e\u0441\u0443\u0434\u0430\u0440\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u0439 \u0440\u0435\u0435\u0441\u0442\u0440 \u043f\u043e\u0447\u0432\u0435\u043d\u043d\u044b\u0445 \u0440\u0435\u0441\u0443\u0440\u0441\u043e\u0432 \u0420\u043e\u0441\u0441\u0438\u0438 \u043a\u0430\u043a \u043c\u043e\u0434\u0435\u043b\u044c \u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0439 \u043e\u0440\u0433\u0430\u043d\u0438\u0437\u0430\u0446\u0438\u0438 \u043f\u043e\u0447\u0432\u0435\u043d\u043d\u043e\u0433\u043e \u043f\u043e\u043a\u0440\u043e\u0432\u0430. \u0418\u0437\u0432\u0435\u0441\u0442\u0438\u044f \u0420\u043e\u0441\u0441\u0438\u0439\u0441\u043a\u043e\u0439 \u0430\u043a\u0430\u0434\u0435\u043c\u0438\u0438 \u043d\u0430\u0443\u043a. \u0421\u0435\u0440\u0438\u044f \u0433\u0435\u043e\u0433\u0440\u0430\u0444\u0438\u0447\u0435\u0441\u043a\u0430\u044f, (5), 135-143."]}