Irish Soil Information System National Soils Map. Published by Environmental Protection Agency. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).SIS SOIL:The new Irish Soil Information System concludes a 5 year programme, supported by the Irish Environmental Protection Agency (STRIVE Research Programme 2007-2013) and Teagasc, to develop a new 1:250,000 scale national soil map (http://soils.teagasc.ie). The Irish Soil Information System adopted a unique methodology combining digital soil mapping techniques with traditional soil survey application. Developing earlier work conducted by An Foras Talúntais, the project generated soil-landscape models for previously surveyed counties. These soil-landscape (‘soilscape’) models formed the basis for training statistical ‘inference engines’ for predicting soil mapping units, checked during field survey. 213 soil series are identified, each with differing characteristics, having contrasting environmental and agronomic responses. Properties were recorded in a database able to satisfy national and EU policy requirements. The Irish soil map and related soil property data will also serve public interest, providing the means to learn online about Irish soil resources. Use the Symbology layer file 'SOIL_SISNationalSoil.lyr' based on Value Field 'Association_Unit'. SIS SOIL DRAINAGE:In Ireland, soil drainage category is considered to have a predominant influence on soil processes (Schulte et al., 2012). The maritime climate of Ireland drives wet soil conditions, such that excess soil moisture in combination with heavy textured soils is considered a key constraint in relation to achieving productivity and environmental targets. Both soil moisture content and the rate at which water drains from the soil are critical indicators of soil physical quality and the overall functional capacity of soil. Therefore, a natural extension to the Irish Soil Information System included the development of an indicative soil drainage map for Ireland. The soil subgroup map was used to develop the indicative drainage map, based on diagnostic criteria relating to the subgroup categorization. Use the Symbology layer file 'SOIL_SISSoilDrainage.lyr' based on Value Field 'Drainage'. SIS SOIL DEPTH: Soil depth is a measure of the thickness of the soil cover and reflects the relationship between parent material and length of soil forming processes. Soil depth determines the potential rooting depth of plants and any restrictions within the soil that may hinder rooting depth. Plants derive nearly 80 per cent of their water needs from the upper part of the soil solum, i.e. where the root system is denser. The rooting depths depend on plant physiology, type of soil and water availability. Generally, vegetables (beans, tomatoes, potatoes, parsnip, carrots, leek, broccoli, etc.) are shallow rooted, about 50–60 cm; fruit trees and some other plants have medium rooting depths, 70–120 cm and other crops such as barley, wheat, oats, and maize may have deeper roots. Furthermore, rooting depths vary according to the age of the plants. The exact soil depth is difficult to define accurately due to its high variability across the landscape. The effective soil depth can be reduced by the presence of bedrock or impermeable layers. Use the Symbology layer file 'SOIL_SISSoilDepth.lyr' based on Valued Field 'Depth'. SIS SOIL TEXTURE:Soil texture is an important soil characteristic that influences processes such as water infiltration rates, rootability, gas exchanges, leaching, chemical activity, susceptibility to erosion and water holding capacity. The soil textural class is determined by the percentage of sand, silt, and clay. Soil texture also influences how much water is available to the plant; clay soils have a greater water holding capacity than sandy soils. Use the Symbology layer file 'SOIL_SISSoilTexture.lyr' based on Value Field 'Texture'. SIS SOIL SOC:In the previous national soil survey conducted by An Foras Taluntais, 14 counties were described in detail with soil profile descriptions provided for the representative soil series found within a county. Soil samples were taken at each soil horizon to a depth of 1 meter and analyses performed for a range of measurements, including soil organic carbon, texture, cation exchange capacity, pH; however in most cases no bulk density measurements were taken. This meant that while soil organic carbon concentrations were available this could not be related to a stock for a given soil series. In 2012/2013, 246 profile pits were sampled and analysed as part of the Irish Soil Information System project to fill in gaps in the description of representative profile data for Ireland. Use the Symbology layer file 'SOIL_SISSoilSOC.lyr' based on Value Field 'SOC'....

Soil depth refers to the depth of unconsolidated soil layers and is restricted in vertical extent by the occurrence of solid rock, solidified or hardened layers, or a predominant coarse fraction. The Agricultural Soil Map of Austria distinguishes a total of six classes, three primary and three secondary classes. The primary classes comprise "shallow" (<30cm), "medium" (30cm - 70cm), and "deep" (>70cm), while the secondary classes include the transition classes "shallow to medium," "medium to deep," and "highly variable." Soil depth as distinguished in this map refers to the extent to which a manually-operated soil drill (“Pürckhauer”) can be driven into the soil. This should not be confused with the soil depth to which root growth is possible known as the "effective depth" or "physiological depth". Typically, physiological depth extends beyond soil depth, since plant roots can partially penetrate dense layers or layers with a high proportion of coarse material. Nevertheless, the soil depth offers a suitable estimate of the soil volume accessible for root growth, as well as the soil's capacity to retain water and nutrients.

A standardized global data set of soil horizon thicknesses and textures (particle size distributions) was compiled by Webb et al. This data set will be used for the improved ground hydrology parameterization design for the Goddard Institute for Space Studies General Circulation Model (GISS GCM) Model III. The data set specifies the top and bottom depths and the percent abundance of sand, silt, and clay of individual soil horizons in each of the 106 soil types cataloged for nine continental divisions. When combined with the World Soil Data File (Zobler, 1986), the result is a global data set of variations in physical properties throughout the soil profile. These properties are important in the determination of water storage in individual soil horizons and exchange of water with the lower atmosphere. The incorporation of this data set into the GISS GCM should improve model performance by including more realistic variability in land-surface properties. All data are global at a 1 degree resolution and are provided in ASCII format. The primary data consist of 2 files. One file contains the depth and particle size (percent sand, silt, and clay) information for each major continent, soil type, and soil horizon. The other file contains ocean/continental coding (corresponding to FAO/UNESCO Soil Map of the World) (FAO/UNESCO, 1971-1981) and Zobler soil type classifications (Zobler, 1986). A fortran code for reading these data files is provided. In addition to the primary data files, there are also 5 derived data sets available for download: 1) soil profile thickness, 2) potential storage of water in the soil profile, 3) potential storage of water in the root zone, 4) potential storage of water derived from soil texture, 5) data set used to prescribe water-holding capacity in the GISS GCM (Model II). Data Citation The data set should be cited as follows: Webb, Robert W., Cynthia E. Rosenzweig, and Elissa R. Levine. 2000. Global Soil Texture and Derived Water-Holding Capacities (Webb et al.). Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.

This is Version 2 of the Australian Soil Depth product of the Soil and Landscape Grid of Australia. It supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/546F540FE10AA The map gives a modelled estimate of the spatial distribution of soil depth in soils across Australia. The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm and 100-200 cm. These depths are consistent with the specifications of the GlobalSoilMap.net project - GlobalSoilMaps. The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels). Detailed information about the Soil and Landscape Grid of Australia can be found at - SLGA Attribute Definition: Depth of soil profile (A & B horizons); Units: metres; Period (temporal coverage; approximately): 1950-2021; Spatial resolution: 3 arc seconds (approx 90 m); Total number of gridded maps for this attribute: 18; Number of pixels with coverage per layer: 2007M (49200 * 40800); Data license : Creative Commons Attribution 4.0 (CC BY); Target data standard: GlobalSoilMap specifications; Format: Cloud Optimised GeoTIFF;

This layer shows soil depth, bsaed on the result of a classification established from Kalimantan RePPProT data on 'Depthmnr1' field (1990, 1:250,000 scale). This data was provided and processed by Daemeter Consulting and was prepared by the World Resources Institute for use in the Suitability Mapper (2012). Data separated into categories: very shallow (0-10 cm); moderately shallow (26-50 cm); moderately deep (51-75 cm); deep (76-100 cm); very deep (101-150 cm); extremely deep (>150 cm).

This data set provides soil maps for the United States (US) (including Alaska), Canada, Mexico, and a part of Guatemala. The map information content includes maximum soil depth and eight soil attributes including sand, silt, and clay content, gravel content, organic carbon content, pH, cation exchange capacity, and bulk density for the topsoil layer (0-30 cm) and the subsoil layer (30-100 cm). The spatial resolution is 0.25 degree. The Unified North American Soil Map (UNASM) combined information from the state-of-the-art US General Soil Map (STATSGO2) and Soil Landscape of Canada (SLCs) databases. The area not covered by these data sets was filled by using the Harmonized World Soil Database version 1.21 (HWSD1.21). The Northern Circumpolar Soil Carbon (NCSCD) database was used to provide more accurate and up-to-date soil organic carbon information for the high-latitude permafrost region and was combined with soil organic carbon content derived from the UNASM (Liu et al., 2013). The UNASM data were utilized in the North American Carbon Program (NACP) Multi-Scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as model input driver data (Huntzinger et al., 2013). The driver data were used by 22 terrestrial biosphere models to run baseline and sensitivity simulations. The compilation of these data was facilitated by the NACP Modeling and Synthesis Thematic Data Center (MAST-DC). MAST-DC was a component of the NACP (www.nacarbon.org) designed to support NACP by providing data products and data management services needed for modeling and synthesis activities.

The England Peat Map is a map of England's peaty soils. It models the extent, depth, and condition of our peat including vegetation and upland peat erosion & drainage features (grips, gullies, bare peat and peat hagging). The map and, where possible, the associated data, are available openly and free to use for any purpose.This map is funded by the Nature for Climate Fund and the Natural Capital and Ecosystem Assessment (NCEA), both part of the Department for the Environment, Food and Rural Affairs (DEFRA).The map layers were created using machine learning and deep learning modelling techniques, trained with pre-existing survey data collated from Defra organisations and other stakeholders, as well as new survey data collected by contractors and quality-assured by an in-house team. Predictor data used in the modelling process included national-scale satellite imagery, topographic LiDAR, geological and historic land-use data. Data collated from multiple sources and collated by the England Peat Map project. See NERR149 England Peat Map Final Report Annex 5 for more information.Confidence in the England Peat Map depth prediction on a 10m by 10m square basis. Confidence is measured by root mean square deviation. See NERR149 England Peat Map Final Report, Natural England, 2025.

Soil depth is defined according to a traditional agronomic concept of soil depth (Webb & Lilburne 2011). Note that while the layer defining soil depth may cause some restriction in root penetration, it does not necessarily define the base of the potential rooting depth. This layer is a "dissolved" representation of the soil depth attribute for S-map soils, where neighbouring S-map polygons have been combined if they have the same value of the attribute. Refer to document Smap Data Dictionary Dissolved Layers.pdf at https://lris.scinfo.org.nz/document/22129-smap-data-dictionary-dissolved-layers/

This rating presents the taxonomic classification based on Soil Taxonomy. The system of soil classification used by the National Cooperative Soil Survey has six categories (Soil Survey Staff, 1999 and 2003). Beginning with the broadest, these categories are the order, suborder, great group, subgroup, family, and series. Classification is based on soil properties observed in the field or inferred from those observations or from laboratory measurements. This table shows the classification of the soils in the survey area. The categories are defined in the following paragraphs. ORDER. Twelve soil orders are recognized. The differences among orders reflect the dominant soil-forming processes and the degree of soil formation. Each order is identified by a word ending in sol. An example is Alfisols. SUBORDER. Each order is divided into suborders primarily on the basis of properties that influence soil genesis and are important to plant growth or properties that reflect the most important variables within the orders. The last syllable in the name of a suborder indicates the order. An example is Udalfs (Ud, meaning humid, plus alfs, from Alfisols). GREAT GROUP. Each suborder is divided into great groups on the basis of close similarities in kind, arrangement, and degree of development of pedogenic horizons; soil moisture and temperature regimes; type of saturation; and base status. Each great group is identified by the name of a suborder and by a prefix that indicates a property of the soil. An example is Hapludalfs (Hapl, meaning minimal horizonation, plus udalfs, the suborder of the Alfisols that has a udic moisture regime). SUBGROUP. Each great group has a typic subgroup. Other subgroups are intergrades or extragrades. The typic subgroup is the central concept of the great group; it is not necessarily the most extensive. Intergrades are transitions to other orders, suborders, or great groups. Extragrades have some properties that are not representative of the great group but do not indicate transitions to any other taxonomic class. Each subgroup is identified by one or more adjectives preceding the name of the great group. The adjective Typic identifies the subgroup that typifies the great group. An example is Typic Hapludalfs. FAMILY. Families are established within a subgroup on the basis of physical and chemical properties and other characteristics that affect management. Generally, the properties are those of horizons below plow depth where there is much biological activity. Among the properties and characteristics considered are particle-size class, mineralogy class, cation-exchange activity class, soil temperature regime, soil depth, and reaction class. A family name consists of the name of a subgroup preceded by terms that indicate soil properties. An example is fine-loamy, mixed, active, mesic Typic Hapludalfs. SERIES. The series consists of soils within a family that have horizons similar in color, texture, structure, reaction, consistence, mineral and chemical composition, and arrangement in the profile. References: Soil Survey Staff. 1999. Soil taxonomy: A basic system of soil classification for making and interpreting soil surveys. 2nd edition. Natural Resources Conservation Service. U.S. Department of Agriculture Handbook 436. Soil Survey Staff. 2006. Keys to soil taxonomy. 10th edition. U.S. Department of Agriculture, Natural Resources Conservation Service. (The soils in a given survey area may have been classified according to earlier editions of this publication.) SSURGO depicts information about the kinds and distribution of soils on the landscape. The soil map and data used in the SSURGO product were prepared by soil scientists as part of the National Cooperative Soil Survey. The most common use of these data is communication of soil conditions to contractors working in the park. Additional uses of these data include analysis by park partners and researchers of the physical and chemical properties of soils, including their effect and influence on the management of natural habitats, ecosystem health, and natural resource inventory. This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a 7.5 minute quadrangle format and include a detailed, field verified inventory of soils and nonsoil areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is required. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the Map Unit Record relational database, which gives the proportionate extent of the component soils and their properties. These data represent a specific interpretation of the SSURGO soils data produced by the NRCS, using the NRCS Soil Data Viewer version 6.0. Building site development interpretations are designed to be used as tools for evaluating soil suitability and identifying soil limitations for various construction purposes. As part of the interpretation process, the rating applies to each soil in its described condition and does not consider present land use. Example interpretations can include corrosion of concrete and steel, shallow excavations, dwellings with and without basements, small commercial buildings, local roads and streets, and lawns and landscaping. This is a hybrid data product produced using NRCS SSURGO soils data. These data should not be considered SSURGO-compliant, as data used in this product is the result of merging data from several separate SSURGO databases. The NRCS does not endorse or support this hybrid product.The corresponding Integration of Resource Management Applications (IRMA) NPS Data Store reference is Great Smoky Mountains National Park Soil Taxonomy.

The United States Department of Agriculture, Natural Resource Conservation Service (USDA, NRCS) has developed the U.S. General Soil Map (STATSGO2) Database (formerly known as the State Soil Geographic (STATSGO) database). Data are available for all 50 states as well as for Puerto Rico. Soil maps for the STATSGO2 database are made by generalizing detailed soil survey data. The mapping scale for STATSGO2 map is 1:250,000 (with the exception of Alaska, which is 1:1,000,000). The level of mapping is designed to be used for broad planning and management uses covering state, regional, and multi-state areas. STATSGO2 data are designed for use in a geographic information system (GIS).

Digitizing is done by line segment (vector) format in accordance with Natural Resources Conservation Service (NRCS) digitizing standards. The base map used is the U.S. Geological Survey 1:250,000 topographic quadrangles. The number of soil polygons per quadrangle map is between 100 and 400. The minimum area mapped is about 1,544 acres.

STATSGO2 data are collected in 1:250,000 quadrangle units. Map unit delineations match at state boundaries. States have been joined as one complete seamless data base to form statewide coverage. Composition of soil map units was coordinated across state boundaries, so that component identities and relative extents would match.

Each STATSGO2 map is linked to the Soil Interpretations Record (SIR) attribute database. The attribute database gives the proportionate extent of the component soils and their properties for each map unit. The STATSGO map units consist of 1 to 21 components each. The Soil Interpretations Record database includes over 25 physical and chemical soil properties, interpretations, and productivity. Examples of information that can be queried from the database are available water capacity, soil reaction, salinity, flooding, water table, bedrock, and interpretations for engineering uses, cropland, woodland, rangeland, pastureland, wildlife, and recreation development.

STATSGO2 data are available in the USGS Digital Line Graph (DLG-3) Optional distribution format. NRCS soil map symbols are not normally carried within the DLG-3 file; however, these map symbols are made available as a unique ascii file when NRCS soils data are distributed in the DLG-3 format. STATSGO2 data are also available in ArcInfo 7.0 coverage and GRASS 4.13 vector formats. Both DOS/Windows and UNIX compatible file formats are available. Data are available via ftp by state including Puerto Rico. The complete data set is also available on CD-ROM for a fee.

The data here were originally posted to facilitate timely and transparent peer review. The final public data release with formal metadata is now available from at the following location:

Nauman, T.W., and Duniway, M.C., 2020, Predictive soil property maps with prediction uncertainty at 30 meter resolution for the Colorado River Basin above Lake Mead: U.S. Geological Survey data release, https://doi.org/10.5066/P9SK0DO2.

Associated publication:

Nauman, T. W., and Duniway, M. C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 1170-1194. https://doi.org/10.1002/saj2.20080.

Repository includes maps of silt content (siltdtotal) as defined by United States soil survey program. Silt content is estimated by percent weight of the <2mm portion of the soil.

These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.

This data should be used in combination with a soil depth or depth to restriction layer map (both layers that will be released soon as part of this project) to eliminate areas mapped at deeper depths than the soil actually goes. This is a limitation of this data which will hopefully be updated in future updates.

The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.

Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal.

File Name Details:

ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (NRCS field pedons plus NRCS laboratory pedons; file ending _CV_plots.tif) and for just the CV results at laboratory pedons (file ending _CV_SCD_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000).

Elements are separated by underscore (_) in the following sequence:

property_r_depth_cm_geometry_model_additional_elements.extension

Example: silttotal_r_0_cm_2D_QRF.tif

Indicates silt content (silttotal) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions.

The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).

_95PI_h: Indicates the layer is the upper 95% prediction interval value.

_95PI_l: Indicates the layer is the lower 95% prediction interval value.

_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI.

References

Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma

UPDATE: WE FOUND A RENDERING ERROR IN MANY AREAS OF THE 5 CM MAP. WE HAVE RECREATED THE MAP AND INCLUDED IN A NEW VERSION OF THE REPOSITORY. Repository includes maps of organic matter content (% wt) as defined by United States soil survey program. These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data. This data should be used in combination with a soil depth or depth to restriction layer map (both layers that will be released soon as part of this project) to eliminate areas mapped at deeper depths than the soil actually goes. This is a limitation of this data which will hopefully be updated in future updates. The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds. Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal. File Name Details: ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (_CV_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000). Predictions are also evaluated with the U.S. soil survey laboratory database soil organic carbon (SOC) data. The SOC measurements were coverted to OM matter values using the common 1.724 conversion factor. The converted OM values are compared to predicted OM values using an accuracy plot (OM_SOC_plots.tif). Elements are separated by underscore (_) in the following sequence: property_r_depth_cm_geometry_model_additional_elements.extension Example: om_r_0_cm_2D_QRF_bt.tif Indicates soil organic matter content (om) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions. The _bt indicates that the map has been back transformed from ln or sqrt transformation used in modeling. The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below). _95PI_h: Indicates the layer is the upper 95% prediction interval value. _95PI_l: Indicates the layer is the lower 95% prediction interval value. _95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI. References Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attribute ...

Depth to bedrock (R horizon) up to 200 cm 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: cm.

The England Peat Map is a map of England's peaty soils. It models the extent, depth, and condition of our peat including vegetation and upland peat erosion & drainage features (grips, gullies, bare peat and peat hagging). The map and, where possible, the associated data, are available openly and free to use for any purpose.

This map is funded by the Nature for Climate Fund and the Natural Capital and Ecosystem Assessment (NCEA) programme, both part of the Department for the Environment, Food and Rural Affairs (DEFRA).

The map layers were created using machine learning and deep learning modelling techniques, trained with pre-existing survey data collated from Defra organisations and other stakeholders, as well as new survey data collected by contractors and quality-assured by an in-house team. Predictor data used in the modelling process included national-scale satellite imagery, topographic LiDAR, geological and historic land-use data. Data collated from multiple sources and collated by the England Peat Map project. See NERR149 England Peat Map Final Report Annex 5 for more information.

England Peat Map predicted depth of peaty soils modelled in 2025 and Confidence in the England Peat Map depth prediction on a 10m by 10m square basis. Confidence is measured by root mean square deviation.

These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive layer (resdept) and surface rock size and cover. Accuracy and error estimated using a 10-fold cross validation indicated a range of model performances with coefficient of variation (R2) for models ranging from 0.20 to 0.76 with mean of 0.52 and a standard deviation of 0.12. Models of pH, om and ec had the best accuracy (R2 > 0.6). Most texture fractions, CaCO3, and SAR models had R2 values from 0.5-0.6. Models of kwfact, dbovendry, resdept, rock models, gypsum and awc had R2 values from 0.4-0.5 excepting near surface models which tended to perform better. Very fine sands and 200 cm estimates for other models generally performed poorly (R2 from 0.2-0.4), and sample size for the 200 cm models was too low for reliable model building. More than 90% of the soils data used was sampled since 2000, but some older samples are included. Uncertainty estimates were also developed by creating relative prediction intervals, which allow end users to evaluate uncertainty easily.

Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations. Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals. Data from thegSSURGO databasewas used to create this layer. To download ready-to-use project packages of useful soil data derived from the SSURGO dataset, please visit the USA SSURGO Downloader app. Dataset Summary Phenomenon Mapped:Soils of the United States and associated territoriesGeographic Extent:The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaCoordinate System:Web Mercator Auxiliary SphereVisible Scale:1:144,000 to 1:1,000Source:USDA Natural Resources Conservation Service Update Frequency:AnnualPublication Date:December 2024 What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS Online Add this layer to a map in the map viewer. The layer is limited to scales of approximately 1:144,000 or larger but avector tile layercreated from the same data can be used at smaller scales to produce awebmapthat displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter forFarmland Class= "All areas are prime farmland" to create a map of only prime farmland.Add labels and set their propertiesCustomize the pop-upArcGIS Pro Add this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of theLiving Atlas of the Worldthat provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Data DictionaryAttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them. Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units. Area SymbolSpatial VersionMap Unit Symbol Map Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field. Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability Rating Legend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field. Project Scale Survey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields. Survey Area VersionTabular Version Map Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field. Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - Presence Rating for Manure and Food Processing Waste - Weighted Average Component Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected. Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent Key Component Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r). Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence -

The scientifically sound soil mapping with the issuance of soil maps is the most important basis for soil use, soil protection and soil history research. These are area data of soil mapping for the distribution and properties of the soils. They are the basis for the soil map 1: 25 000. The soil maps of North Rhine-Westphalia 1: 25000 (BK 25) represent the distribution of soils combined into soil units in the leaf area. For each soil unit, the map legend contains information on soil species stratification up to 2 m depth, soil types and initial geological rocks. In a special column, the values of the soil estimate, the suitability of use, the yieldability and workability as well as the water conditions of the soils are given. To the sheets of the ground maps 1: 25000 There are explanatory booklets in which the soils, including their chemical and physical properties, are described in detail. The soil maps form an important document for tasks of agriculture and forestry, land planning, state maintenance, water management and nature conservation, as well as for research, teaching and teaching. On a scale 1: Between 1964 and 1980, 25000 were published by the then Geological State Office of North Rhine-Westphalia. The map was then in favor of the ground map on a scale of 1: 50000 discontinued. The map sheets depicting the soil units (soil types and soil species stratification) and the water conditions (groundwater, slope water, slopes and dams) are provided with a detailed explanatory booklet, including list of writings and maps. In addition to a geological-morphological overview and the description of the soil forming factors, the soil units shown on the map are explained in detail. Tables with soil analyses as well as descriptions of agricultural and forestry land use and plant societies of arable and grassland complete the chapters on soil history. Some hands are attached as an attachment to usability cards or natural yield cards. The following soil maps of North Rhine-Westphalia 1: 25000 (BK 25) are available: Sheet No. Leaf name Year of publication ISBN Special features [old name] Map Allowed. 3516 Lemförde 1967 1970 3-86029-510-1 3517 Rahden 1965 1968 3-86029-511-X U 3617 Lübbecke 1968 1971 3-86029-512-8 GE 3618 3618 Hille [Hartum] 1971 3-86029-512-8 GE 3617 4107 Borken 1973 1973 3-86029-514-4 4116 Rietberg 1977 1980 3-86029-515-2 4117 Verl 1977 1980 3-Ω-516-0 4206 Brünen 1971 3-Ω-517-9 4207 Raesfeld 1973 3-Ω-518-7 4216 Mastholte 1970 1970 3- and 524-1 GE 4603 5003 Linnich 1972 3-Ω-525-X U 4703 Schwalmtal [Waldniel] 1968 3-Ω-524-1 GE 4703 5003 Linnich 1972 3-Ω-525-X U 4703 Schwalmtal [Waldniel] 1968 3-Ω-524-1 GE 4603 5003 Linnich 1972 5004 Jülich 1971 1972 3-Ω-528-4 5104 Düren 1965 1968 3-Ω-529-2 U GE joint explanation with... U card only unfolded (plano) available

We developed a comprehensive, gridded Global Soil Dataset for use in Earth System Models (GSDE) and other applications as well. GSDE provides soil information including soil particle-size distribution, organic carbon, and nutrients, etc. and quality control information in terms of confidence level. GSDE is based on the Soil Map of the World and various regional and national soil databases, including soil attribute data and soil maps. We used a standardized data structure and data processing procedures to harmonize the data collected from various sources. We then used a soil type linkage method (i.e. taxotransfer rules) and the polygon linkage method to derive the spatial distribution of soil properties. To aggregate the attributes of different compositions of a mapping unit, we used three mapping approaches: area-weighting method, the dominant soil type method and the dominant binned soil attribute method. In the released gridded dataset, we used the area-weighting method as it will meet the demands of most applications. The dataset can be also aggregate to a lower resolution. The resolution is 30 arc-seconds (about 1 km at the equator). The vertical variation of soil property was captured by eight layers to the depth of 2.3 m (i.e. 0- 0.045, 0.045- 0.091, 0.091- 0.166, 0.166- 0.289, 0.289- 0.493, 0.493- 0.829, 0.829- 1.383 and 1.383- 2.296 m).

This data set was gridded from vector layers of soil maps that were received from Dr. Darwin Anderson TE-01 who did the original soil mapping in the field during 1994. The vector layers were gridded into raster files that cover approximately 1 square kilometer over each of the tower sites in the SSA.