The Soil and Terrain database for Tunisia primary data (version 1.0), at scale 1:1 million (SOTER_Tunisia), was compiled of enhanced soil information within the framework of the FAO's program of Land Degradation Assessment in Drylands (LADA). A SOTER database was compiled based on the digital soil map of Tunisia. The primary soil and terrain data for Tunisia has been selected from available survey reports and publications. A SRTM-DEM was used to adjust some unit boundaries in the GIS file. SOTER forms a part of the ongoing activities of ISRIC, FAO and UNEP to update the world's baseline information on natural resources.The project involved collaboration with national soil institutes from the countries in the region as well as individual experts.
This harmonized set of soil parameter estimates for the Upper Tana river catchment, Kenya. The data set was derived from the 1:250 000 scale Soil and Terrain Database for the Upper Tana (SOTER_UT, ver. 1.1; Dijkshoorn et al. 2011) and the ISRIC-WISE soil profile database, using standardized taxonomy-based pedotransfer (taxotransfer) procedures. The land surface of the Upper Tana, Kenya, covering some 18,900 km2, has been mapped in SOTER using 191 unique SOTER units. Each map unit may comprise of up to three different soil components. In so far as possible, each soil component has been characterized by a regionally representative profile, selected and classified by national soil experts. Conversely, in the absence of any measured legacy data, soil components were characterized using synthetic profiles for which only the FAO-Unesco (1988) classification is known. Soil components in SOTER_UT have been characterized using 146 profiles consisting of 109 real and 37 so-called synthetic profiles. The latter were used to represent some 18% per cent of the study area. Comprehensive sets of measured attribute data are seldom available for most profiles (109) collated in SOTER_UT, as these were not considered in the source materials. Consequently, to permit modelling, gaps in the soil analytical data have been filled using consistent taxotransfer procedures. Modal soil property estimates necessary to populate the taxotransfer procedure were derived from statistical analyses of soil profiles held in the ISRIC-WISE database. The current taxotransfer procedure only considers profiles in WISE that: (a) have FAO soil unit names (43) identical to those mapped for the Upper Tana in SOTER, and (b) originate from regions having similar Köppen climate zones (n= 5745). Property estimates are presented for 18 soil variables by soil unit for fixed depth intervals of 0.2 m to 1 m depth: organic carbon, total nitrogen, pH(H2O), CECsoil, CECclay, base saturation, effective CEC, aluminium saturation, CaCO3 content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity (ECe), bulk density, content of sand, silt and clay, content of coarse fragments (less than 2 mm), and volumetric water content (-33 kPa to -1.5 MPa). 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 studies of global environmental change. The soil property estimates can be linked to the spatial data (map), using GIS, through the unique SOTER-unit code; database applications should consider the full map unit composition and depth range.
ABSTRACT: The World Soil Information Service (WoSIS) provides quality-assessed and standardized soil profile data to support digital soil mapping and environmental applications at broad scale levels. Since the release of the ‘WoSIS snapshot 2019’ many new soil data were shared with us, registered in the ISRIC data repository, and subsequently standardized in accordance with the licenses specified by the data providers. The source data were contributed by a wide range of data providers, therefore special attention was paid to the standardization of soil property definitions, soil analytical procedures and soil property values (and units of measurement). We presently consider the following soil chemical properties (organic carbon, total carbon, total carbonate equivalent, total Nitrogen, Phosphorus (extractable-P, total-P, and P-retention), soil pH, cation exchange capacity, and electrical conductivity) and physical properties (soil texture (sand, silt, and clay), bulk density, coarse fragments, and water retention), grouped according to analytical procedures (aggregates) that are operationally comparable. For each profile we provide the original soil classification (FAO, WRB, USDA, and version) and horizon designations as far as these have been specified in the source databases. Three measures for 'fitness-for-intended-use' are provided: positional uncertainty (for site locations), time of sampling/description, and a first approximation for the uncertainty associated with the operationally defined analytical methods. These measures should be considered during digital soil mapping and subsequent earth system modelling that use the present set of soil data. DATA SET DESCRIPTION: The 'WoSIS 2023 snapshot' comprises data for 228k profiles from 217k geo-referenced sites that originate from 174 countries. The profiles represent over 900k soil layers (or horizons) and over 6 million records. The actual number of measurements for each property varies (greatly) between profiles and with depth, this generally depending on the objectives of the initial soil sampling programmes. The data are provided in TSV (tab separated values) format and as GeoPackage. The zip-file (446 Mb) contains the following files: - Readme_WoSIS_202312_v2.pdf: Provides a short description of the dataset, file structure, column names, units and category values (this file is also available directly under 'online resources'). The pdf includes links to tutorials for downloading the TSV files into R respectively Excel. See also 'HOW TO READ TSV FILES INTO R AND PYTHON' in the next section. - wosis_202312_observations.tsv: This file lists the four to six letter codes for each observation, whether the observation is for a site/profile or layer (horizon), the unit of measurement and the number of profiles respectively layers represented in the snapshot. It also provides an estimate for the inferred accuracy for the laboratory measurements. - wosis_202312_sites.tsv: This file characterizes the site location where profiles were sampled. - wosis_2023112_profiles: Presents the unique profile ID (i.e. primary key), site_id, source of the data, country ISO code and name, positional uncertainty, latitude and longitude (WGS 1984), maximum depth of soil described and sampled, as well as information on the soil classification system and edition. Depending on the soil classification system used, the number of fields will vary . - wosis_202312_layers: This file characterises the layers (or horizons) per profile, and lists their upper and lower depths (cm). - wosis_202312_xxxx.tsv : This type of file presents results for each observation (e.g. “xxxx” = “BDFIOD” ), as defined under “code” in file wosis_202312_observation.tsv. (e.g. wosis_202311_bdfiod.tsv). - wosis_202312.gpkg: Contains the above datafiles in GeoPackage format (which stores the files within an SQLite database). HOW TO READ TSV FILES INTO R AND PYTHON: A) To read the data in R, please uncompress the ZIP file and specify the uncompressed folder. setwd("/YourFolder/WoSIS_2023_December/") ## For example: setwd('D:/WoSIS_2023_December/') Then use read_tsv to read the TSV files, specifying the data types for each column (c = character, i = integer, n = number, d = double, l = logical, f = factor, D = date, T = date time, t = time). observations = readr::read_tsv('wosis_202312_observations.tsv', col_types='cccciid') observations ## show columns and first 10 rows sites = readr::read_tsv('wosis_202312_sites.tsv', col_types='iddcccc') sites profiles = readr::read_tsv('wosis_202312_profiles.tsv', col_types='icciccddcccccciccccicccci') profiles layers = readr::read_tsv('wosis_202312_layers.tsv', col_types='iiciciiilcc') layers Do this for each observation 'XXXX', e.g. file 'Wosis_202312_orgc.tsv': orgc = readr::read_tsv('wosis_202312_orgc.tsv', col_types='iicciilccdccddccccc') orgc Note: One may also use the following R code (example is for file 'observations.tsv'): observations <- read.table("wosis_202312_observations.tsv", sep = "\t", header = TRUE, quote = "", comment.char = "", stringsAsFactors = FALSE ) B) To read the files into python first decompress the files to your selected folder. Then in python: import the required library import pandas as pd Read the observations data observations = pd.read_csv("wosis_202312_observations.tsv", sep="\t") # print the data frame header and some rows observations.head() Read the sites data sites = pd.read_csv("wosis_202312_sites.tsv", sep="\t") Read the profiles data profiles = pd.read_csv("wosis_202312_profiles.tsv", sep="\t") Read the layers data layers = pd.read_csv("wosis_202312_layers.tsv", sep="\t") Read the soil property data, e.g. 'cfvo' (do this for each observation) cfvo = pd.read_csv("wosis_202312_cfvo.tsv", sep="\t") CITATION: Calisto, L., de Sousa, L.M., Batjes, N.H., 2023. Standardised soil profile data for the world (WoSIS snapshot – December 2023), https://doi.org/10.17027/isric-wdcsoils-20231130 Supplement to: Batjes N.H., Calisto, L. and de Sousa L.M., 2023. Providing quality-assessed and standardised soil data to support global mapping and modelling (WoSIS snapshot 2023). Earth System Science Data, https://doi.org/10.5194/essd-16-4735-2024.
This harmonized set of soil parameter estimates for Central and Eastern Europe has been derived from a revised version of the 1:2.5M Soil and Terrain (SOTER) Database for Central and Eastern Europe (SOVEUR ver. 1.1) and the ISRIC-WISE soil profile database. The land surface of Central and Eastern Europe, West of the Ural Mountains, has been characterized using 8361 unique maps or SOTER units. The corresponding GIS files include some 9500 mapped polygons, including miscellaneous units. The major soils have been described using 662 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 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 confidence in the derived data. Results are presented as summary files and can be linked to the 1:2.5M scale SOVEUR map in a GIS, through the unique SOTER-unit code. The secondary data are considered appropriate for studies at the continental scale (greater than1:2.5 million); correlation of soil analytical data should be done more rigorously when more detailed scientific work is considered.
The World Soil Information Service (WoSIS) provides quality-assessed and standardised soil profile data to support digital soil mapping and environmental applications at broad scale levels. Since the release of the first ‘WoSIS snapshot’, in July 2016, many new soil data were shared with us, registered in the ISRIC data repository, and subsequently standardised in accordance with the licences specified by the data providers. Soil profile data managed in WoSIS were contributed by a wide range of data providers, therefore special attention was paid to measures for soil data quality and the standardisation of soil property definitions, soil property values (and units of measurement), and soil analytical method descriptions. We presently consider the following soil chemical properties (organic carbon, total carbon, total carbonate equivalent, total Nitrogen, Phosphorus (extractable-P, total-P, and P-retention), soil pH, cation exchange capacity, and electrical conductivity) and physical properties (soil texture (sand, silt, and clay), bulk density, coarse fragments, and water retention), grouped according to analytical procedures (aggregates) that are operationally comparable. Further, for each profile, we provide the original soil classification (FAO, WRB, USDA, and version) and horizon designations insofar as these have been specified in the source databases. Measures for geographical accuracy (i.e. location) of the point data as well as a first approximation for the uncertainty associated with the operationally defined analytical methods are presented, for possible consideration in digital soil mapping and subsequent earth system modelling. The present snapshot, referred to as ‘WoSIS snapshot - September 2019’, comprises 196,498 geo-referenced profiles originating from 173 countries. They represent over 832 thousand soil layers (or horizons), and over 6 million records. The actual number of observations for each property varies (greatly) between profiles and with depth, this generally depending on the objectives of the initial soil sampling programmes. The downloadable ZIP file has the data in TSV (tab separated values) and GeoPackage format. It contains the following files: - ReadmeFirst_WoSIS_2019dec04.pdf (546.7 KB) - wosis_201909.gpkg (2.2 GB, same data as in the tsv) - wosis_201909_attributes.tsv (8.7 KB) - wosis_201909_layers_chemical.tsv (893.5 MB) - wosis_201909_layers_physical.tsv (890.7 MB) - wosis_201909_profiles.tsv (18.8 MB) To read the data in R, please, uncompress the ZIP file and specify the uncompressed folder. Then use read_tsv to read the TSV files, specifying the data types for each column (c = character, i = integer, n = number, d = double, l = logical, f = factor, D = date, T = date time, t = time). setwd("/YourFolder/WoSIS_2019_September/") attributes = readr::read_tsv('wosis_201909_attributes.tsv', col_types='cccciicd') profiles = readr::read_tsv('wosis_201909_profiles.tsv', col_types='icccdddiicccciccccicccc') chemical = readr::read_tsv('wosis_201909_layers_chemical.tsv', col_types='iiddclcdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccc') physical = readr::read_tsv('wosis_201909_layers_physical.tsv', col_types='iiddclcdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccc') For more detailed instructions on how to read the data with R, please visit https://www.isric.org/accessing-wosis-using-r. Citation: Batjes N.H, Ribeiro E, and van Oostrum A.J.M, 2019. Standardised soil profile data for the world (WoSIS snapshot - September 2019), https://doi.org/10.17027/isric-wdcsoils.20190901. The dataset accompanies the following data paper: Batjes N.H., Ribeiro E., and van Oostrum A.J.M., 2019. Standardised soil profile data to support global mapping and modelling (WoSIS snapshot - 2019). Earth System Science Data, https://doi.org/10.5194/essd-12-299-2020.
The Soil and Terrain Database (SOTER) was part of the Soil Degradation and Vulnerability Assessment for Central and Eastern Europe (SOVEUR project) and was implemented within the framework of the Food and Agriculture Organization (FAO) of the United Nations and the Netherlands Government Cooperative Programme. The project was technically coordinated by ISRIC - World Soil Information and assisted by the Land and Water Development Division (AGL) of FAO. The SOVEUR project has been carried out in close collaboration with specialists from soil survey institutes in Belarus, Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Moldova, Poland, Romania, the Russian Federation, Slovak Republic and the Ukraine, who collated the various national soil data using uniform guidelines.
Texture class (USDA system) at 7 standard depths 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')
Version 1.2 of describes a harmonized dataset of derived soil properties for the world. It was created using the soil distribution shown on the 1:5 million scale FAO-Unesco Soil Map of the World (DSMW), rasterised at 5 by 5 arcminutes, and soil property estimates derived from the ISRIC-WISE soil profile database, version 3.1. The dataset considers 19 soil variables that are commonly required for agro-ecological zoning, land evaluation, crop growth simulation, modelling of soil gaseous emissions, and analyses of global environmental change. It presents ‘best’ estimates for: soil drainage class, organic carbon content, total nitrogen, C/N ratio, pH (H2O), CECsoil, CECclay, effective CEC, base saturation, aluminium saturation, calcium carbonate content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity, particle size distribution (i.e. content of sand, silt and clay), content of coarse fragments (less than 2 mm), bulk density, and available water capacity (-33 to -1500 kPa). These estimates are presented by FAO soil unit for fixed depth intervals of 20 cm up to 100 cm depth (or less when appropriate) for so-called virtual profiles. The associated soil property values were derived from analyses of some 10,250 profiles held in WISE using a scheme of taxonomy-based taxotransfer rules complemented with expert-rules. The type of rules used to derive the various soil property values have been flagged in the database to provide an indication of the possible confidence in the derived data. Most map units on the DSMW are complex, comprising up to eight different soil units. Assessments and model applications that use the derived soil properties therefore should consider the full map unit composition and depth range. The soil property values presented here should be seen as best estimates based on the current selection of soil profiles in WISE, the procedure for clustering the measured data, taxotransfer scheme used for deriving soil, properties, and the spatial data of the digital Soil Map of the World. The derived information may be used for exploratory assessments at a broad scale (greater than 1:5 million; 5 by 5 arcminutes and coarser), pending the global update of the information on world soil resources at more detailed scales, upon due consideration of the underlying generalisations and assumptions. Note: A more recent assessment, at a resolution of 30arcsec (WISE30sec), is available at: http://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/dc7b283a-8f19-45e1-aaed-e9bd515119bc
The Soil and Terrain database for Southern Africa (SOTERSAF version 1.0), at scale 1:2 million, include the spatial and soil attribute data for 8 Southern African countries. The SOTERSAF database was compiled using the existing soil information and following the SOTER methodology of the 1:1 million scale with respect to pedon attribute data storage. The data (SOTERSAF) were compiled in the framework of the ongoing activities of ISRIC, FAO and UNEP to update the world's baseline information on natural resources in SOTER.The project involved collaboration with national soil institutes from the countries in the region as well as individual experts.
