Gravimetric content of organic carbon in the fine earth fraction* (g/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.
Soil organic carbon content (fine earth fraction) in dg/kg 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.
The INFOSOLO legacy database is the first effort to develop a soil information system in Portugal, suitable to compile soil data produced in the country, and to support stakeholders and land managers in decision-making. The current version includes soil data from a set of 9934 horizons/layers studied in 3461 soil profiles across the country between 1966 and 2014. Data was extracted from scattered soil surveys, research projects, and academic studies carried out by public Portuguese and other European institutions. As the level of detail of soil information varied between datasets, a series of validation tests and harmonization procedures were implemented in order to access and improve the quality of the data
The aim of the World Soil Information Service (WoSIS) is to serve quality-assessed, geo-referenced soil data (point, polygon, and grid) to the international community upon their standardisation and harmonisation. So far, the focus has been on developing procedures for legacy point data with special attention to the selection of soil analytical and physical properties considered in the GlobalSoilMap specifications (e.g. organic carbon, soil pH, soil texture (sand, silt, and clay), coarse fragments ( greater than 2 mm), cation exchange capacity, electrical conductivity, bulk density, and water holding capacity). Profile data managed in WoSIS were contributed by a wide range of soil data providers; the data have been described, sampled, and analysed according to methods and standards in use in the originating countries. Hence, special attention was paid to measures for soil data quality and the standardisation of soil property definitions, soil property values, and soil analytical method descriptions. At the time of writing, the full WoSIS database contained some 118 400 unique shared soil profiles, of which some 96 000 are geo-referenced within defined limits. In total, this corresponds with over 31 million soil records, of which some 20 % have so far been quality-assessed and standardised using the sequential procedure discussed in this paper. The number of measured data for each property varies between profiles and with depth, generally depending on the purpose of the initial studies. Overall, the data lineage strongly determined which data could be standardised with acceptable confidence in accord with WoSIS procedures, corresponding to over 4 million records for 94 441 profiles. The downloadable ZIP file has the data in TSV (tab separated values). It contains the following files: - ReadmeFirst_WoSIS_2016.pdf (148.1 KB) - wosis_201607_attributes.txt (4.1 KB) - wosis_201607_layers.txt (679.1 MB) - wosis_201607_profiles.txt (8.8 MB) Citation: Batjes NH, Ribeiro E, van Oostrum A, Leenaars J, and Mendes de Jesus J 2016. Standardised soil profile data for the world (WoSIS, July 2016 snapshot), doi:10.17027/isric-wdcsoils.20160003. The dataset accompanies the following data paper: Batjes NH, Ribeiro E, van Oostrum A, Leenaars J, Hengl T, and Mendes de Jesus J 2017. WoSIS: Providing standardised soil profile data for the world, Earth System Science Data 9, 1-14, doi:10.5194/essd-9-1-2017.
Soil organic carbon density in g/dm³ 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, gridded global data set of soil parameter estimates includes files listing: (1) soil parameter estimates for the component soil units of each terrestrial grid cell, in un-binned format, and (2) soil parameter estimates aggregated or binned into a number of predefined classes. The spatial data, with a resolution of ½ by ½ degree, was derived from the ISRIC-WISE soil database. The land surface between longitudes -180o W and +180o E and latitudes +90o N and -90o S has been characterized using 45948 unique map units; each of these can comprise from one to ten soil units, characterized according to the original legend of the 1:5 million scale Soil Map of the World (FAO-Unesco 1974). Soil parameter estimates for each of these units were derived from analyses of some 9600 profiles held in a working copy of WISE (ver. 2.0). Twenty-two soil variables, identified as being useful for agro-ecological zoning, land evaluation, crop growth simulation, modelling of soil gaseous emissions and analyses of global environmental change, were considered. Parameter estimates for the topsoil (0-30 cm) and the subsoil (30-100 cm) are presented for the following variables: content of organic carbon, total nitrogen, the C/N ratio, pH(H2O), CECsoil, CECclay, base saturation, total exchangeable bases, aluminum saturation, exchangeable sodium percentage (ESP), electrical conductivity of saturated paste (ECe), calcium carbonate content, gypsum content, content of sand, silt and clay, content of fragments less than 2 mm, bulk density, total porosity. For soil drainage class, effective soil depth, and available water capacity (-10 to -1500 kPa), however, parameter estimates are presented on a profile basis. The parameter estimates - median values - presented here should be seen as best estimates; possible types and sources of uncertainty are discussed in the report. The data are considered appropriate for exploratory studies at global scale (greater than 1:5 000 000). 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
Soil organic carbon density in hg/m³ 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 uniform soil data set for the development of pedotransfer functions was developed at the request of the Global Soil Data Task (GSDT) of the Data and Information System (DIS) of the International Geosphere Biosphere Programme (IGBP). The necessary chemical and physical soil data have been derived from ISRIC's Soil Information System (ISIS) and the soil CD-ROM of the Natural Resources Conservation Service (USDA-NRCS). Analytical data were clustered into functional groups based on soil textural class (FAO) and calculated activity of the clay size minerals. Samples from organic and allophanic soils were flagged as separate categories. The file contain analytical data for 131,472 soil samples, originating from 20,920 profiles. Being based on available data, there are several gaps in the measured data
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.
