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'.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

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.

Predicted clay content in the topsoil. FAO/USDA soil texture classes

This is Version 1 of the Australian Soil Clay product of the Soil and Landscape Grid of 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: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).

These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.

Attribute Definition: 2 μm mass fraction of the less than 2 mm soil material determined using the pipette method; Units: %; Period (temporal coverage; approximately): 1950-2013; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 18; Number of pixels with coverage per layer: 2007M (49200 * 40800); Total size before compression: about 8GB; Total size after compression: about 4GB; Data license : Creative Commons Attribution 4.0 (CC BY); Target data standard: GlobalSoilMap specifications; Format: GeoTIFF.

Lineage: The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. Two DSM methods have been utilised across and in various parts of Australia, these being:

1) Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a); 2) Disaggregation of existing polygon soil mapping using DSMART (Odgers et al. 2015a).

Version 1 of the National Digital Soil Property Maps combines mapping from the:

1) Australia-wide three-dimensional Digital Soil Property Maps; 2) Western Australia Polygon Disaggregation Maps; 3) South Australian Agricultural Areas Polygon Disaggregation Maps; 4) Tasmanian State-wide DSM Maps.

These individual mapping products are also available in the Data Access Portal. Please refer to these individual products for more detail on the DSM methods used.

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 clay content (claytotal) as defined by United States soil survey program. Content is calculated on the fine earth fraction (<2mm).

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.

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: claytotal_r_0_cm_2D_QRF_bt.tif

Indicates clay content (claytotal) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest that is has gone through transfomation and backtransformation (_bt) in the modeling process. 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.

Soil is the foundation of life on earth. More living things by weight live in the soil than upon it. It determines what crops we can grow, what structures we can build, what forests can take root.This layer contains the physical soil variable percent clay (clay).Within the subset of soil that is smaller than 2mm in size, also known as the fine earth portion, clay is defined as particles that are smaller than 0.002mm, making them only visible in an electron microscope. Clay soils contain low amounts of air, and water drains through them very slowly.This layer is a general, medium scale global predictive soil layer suitable for global mapping and decision support. In many places samples of soils do not exist so this map represents a prediction of what is most likely in that location. The predictions are made in six depth ranges by soilgrids.org, funded by ISRIC based in Wageningen, Netherlands.Each 250m pixel contains a value predicted for that area by soilgrids.org from best available data worldwide. Data for percent clay are provided at six depth ranges from the surface to 2 meters below the surface. Each variable and depth range may be accessed in the layer's multidimensional properties.Dataset SummaryPhenomenon Mapped: Proportion of clay particles (< 0.002 mm) in the fine earth fraction in g/100g (%)Cell Size: 250 metersPixel Type: 32 bit float, converted from online data that is 16 Bit Unsigned IntegerCoordinate System: Web Mercator Auxiliary Sphere, projected via nearest neighbor from goode's homolosine land (250m)Extent: World land area except AntarcticaVisible Scale: All scales are visibleNumber of Columns and Rows: 160300, 100498Source: Soilgrids.orgPublication Date: May 2020Data from the soilgrids.org mean predictions for clay were used to create this layer. You may access the percent clay in one of six depth ranges. To select one choose the depth variable in the multidimensional selector in your map client.Mean depth (cm)Actual depth range of data-2.50-5cm depth range-105-15cm depth range-22.515-30cm depth range-4530-60cm depth range-8060-100cm depth range-150100-200cm depth rangeWhat can you do with this Layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map: In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "world soils soilgrids" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "world soils soilgrids" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.This layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.More information about soilgrids layersAnswers to many questions may be found at soilgrids.org (ISRIC) frequently asked questions (faq) page about the data.To make this layer, Esri reprojected the expected value of ISRIC soil grids from soilgrids' source projection (goode's land WKID 54052) to web mercator projection, nearest neighbor, to facilitate online mapping. The resolution in web mercator projection is the same as the original projection, 250m. But keep in mind that the original dataset has been reprojected to make this web mercator version.This multidimensional soil collection serves the mean or expected value for each soil variable as calculated by soilgrids.org. For all other distributions of the soil variable, be sure to download the data directly from soilgrids.org. The data are available in VRT format and may be converted to other image formats within ArcGIS Pro.Accessing this layer's companion uncertainty layerBecause data quality varies worldwide, the uncertainty of the predicted value varies worldwide. A companion uncertainty layer exists for this layer which you can use to qualify the values you see in this map for analysis. Choose a variable and depth in the multidimensional settings of your map client to access the companion uncertainty layer.

This map layer contains the selection of all clay-containing soils below non-clay soils in the Soil Map. This selection was made based on the substrate and the variant of the parent material. This is a representation of the situation at the time of the soil mapping.

From gridded National Soil Survey Geographic Database (gNATSGO). Used Soil Data Development Toolbox > gSSURGO Mapping Toolset > Create Soil Map Tool, Exported Data Layer to TIFF, and Used Spatial Analyst > Reclass > Lookup Tool to create this data layer and display the HYDROLGRP_. Follow instructions in "How to Create an On-Demand Soil Property or Interpretation Grid from gNATSGO". Shows sSSURGO data for California. A - sand, loamy sand, sandy loam B - loam, silt, loam or silt C - sandy clay loam D - clay loam, silty clay loam, sandy clay, silty clay, or clay The gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase. The state-wide gNATSGO databases contain a 10-meter raster of the soil map units and 70 related tables of soil properties and interpretations. It is designed to work with the SPSD gSSURGO ArcTools. Users can create full coverage thematic maps and grids of soil properties and interpretations for large geographic areas, such as the extent of a State or the conterminous United States. Please note that for the CONUS database, only a 30 meter raster is included. SSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. Click here for the current completion status of SSURGO mapping. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods. https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625) Use the Create A Soil Map ArcTool from the gSSURGO Mapping Toolset in the Soil Data Development Toolbox to make a TIFF data layer (Instructions: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625#grid). Make a Hydrological Soils Group Map, and display it using the Hydrolgrp_ attribute. NotesThe SPSD refreshes all published soil databases annually. gNATSGO will be included in the refresh cycle, which will provide a new up-to-date version of the database each year. gNATSGO is an ESRI file geodatabase. The soil map units are delivered only as a 10-meter raster version and are uniquely identified by the mukey, which is included in the attribute table. No vectorized version of the soil map units is included in gNATSGO. The database has 70 tables that contain soil attributes, and relationship classes are built into the database to define relationships among tables. The raster can be joined to the Mapunit and Muaggatt tables in the MUKEY field. The database contains a feature class called SAPOLYGON. The “source” field in this feature class indicates whether the data was derived from SSURGO, STATSGO2, or an RSS. A gNATSGO database was created for the conterminous United States and for each state or island territory that does not have complete coverage in SSURGO or has a published RSS. If you encounter an ArcMap error when working with a gNATSGO dataset that reads “The number of unique values exceeds the limit” try increasing the maximum number of unique values to render in your Raster ArcMap Options. Specific instructions can be obtained here: https://support.esri.com/en/technical-article/000010117

This resource contains a global map of soil texture classes, derived from SOILGRIDS data (Hengl et al., 2017) using the revised soil texture triangle definitions from Benham et al. (2009). Global maps of sand, silt and clay percentages were downloaded for 7 soil depths (0, 5, 15, 30, 60, 100 and 200 cm) at the native SOILGRIDS resolution of 250 m by 250 m. For each depth, percentages were converted into 1 out of 12 possible soil texture classes. The map in this resource represents the mode soil texture class over the 7 depths at each pixel. In case of a tie on a given pixel (i.e. two or more soil texture classes occur the most often with an equal number of times), the lowest class number is shown. This is a choice of convenience; no physical considerations support this choice.

Soil texture class definitions: 0: no class assigned (source data sand, silt, clay percentages all contain "no data" values) 1: Clay 2: Clay loam 3: Loam 4: Loamy sand 5: Sand 6: Sandy clay 7: Sandy clay loam 8: Sandy loam 9: Silt 10: Silty clay 11: Silty clay loam 12: Silt loam

Source data downloaded on 25-26 April, 2020, from: https://files.isric.org/soilgrids/data/recent/. URL has since changed to: https://files.isric.org/soilgrids/former/2017-03-10/data/. Code used to generate this map can be found in the folder code that is part of this resource.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset is the average clay content of the top 2m of soil over the Sydney Basin. It is derived from the TERN digital soil mapping (http://www.tern.org.au/Soil-and-Landscape-Grid-of-Australia-pg17731.html).

Purpose

This dataset was created to estimate groundwater recharge in the alluvial areas of the Sydney Basin using the empirical method of Wohling et al (2012).

Wohling DL, Leaney FW and Crosbie RS (2012) Deep drainage estimates using multiple linear regression with percent clay content and rainfall. Hydrol. Earth Syst. Sci. 16(2), 563-572. DOI: 10.5194/hess-16-563-2012.

Dataset History

This is a depth-weighted average of the clay content from 6 soil layers in the TERN digital soil mapping (http://data.bioregionalassessments.gov.au/dataset/f8640540-4bb7-42ee-995a-219881e67705).

It has then been re-projected to MGA94 Zone 56 and re-sampled to a 500m raster.

Dataset Citation

Bioregional Assessment Programme (XXXX) SYD Soil - Average Clay Content Top 2m v01. Bioregional Assessment Derived Dataset. Viewed 22 June 2018, http://data.bioregionalassessments.gov.au/dataset/95fc3145-5249-4bd4-bfd2-a812c39d68f9.

Dataset Ancestors

• Derived From Bioregional Assessment areas v02

• Derived From Gippsland Project boundary

• Derived From Bioregional Assessment areas v04

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From Bioregional Assessment areas v05

• Derived From GEODATA TOPO 250K Series 3

• Derived From Soil and Landscape Grid National Soil Attribute Maps - Clay 3 resolution - Release 1

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v06

• Derived From Victoria - Seamless Geology 2014

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

This is Version 2 of the Australian Soil Clay Content product of the Soil and Landscape Grid of Australia.

It supersedes the Release 1 product that can be found at https://doi.org/10.4225/08/546EEE35164BF

The map gives a modelled estimate of the spatial distribution of clay 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: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf). 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 - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html

Attribute Definition: 2 μm mass fraction of the less than 2 mm soil material determined using the pipette method; Units: %; Period (temporal coverage; approximately): 1950-2021; Spatial resolution: 3 arc seconds (approx 90m); 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; Lineage: The approach, based on machine learning, predicts each soil texture fraction at 90 m grid cell resolution, at depths 0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm and 100–200 cm. The approach accommodates uncertainty in converting field measurements to quantitative estimates of texture fractions. Existing methods of bootstrap resampling were exploited to predict uncertainties, which are expressed as 90% prediction intervals about the mean prediction at each grid cell. The models and the prediction uncertainties were assessed by an external validation dataset. Results were compared with Version 1 Soil and Landscape Grid of Australia (v1.SLGA) (Viscarra Rossel et al. 2015). All predictive and functional accuracy diagnostics demonstrate improvements compared with v1.SLGA. Improvements were noted for the sand and clay fraction mapping with average improvement of 3% and 2%, respectively, in the RMSE estimates. Marginal improvements were made for the silt fraction mapping, which was relatively difficult to predict. We also made comparisons with recently released World Soil Grid products (v2.WSG) and made similar conclusions.

All processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Code - https://github.com/AusSoilsDSM/SLGA Observation data - https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html Covariate rasters - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/GetData-COGSDataStore.html

Important Note: This item is in mature support as of April 2024 and will be retired in December 2026. Please use the following layers at replacements: World Soils 250m Percent Sand, World Soils 250m Percent Silt, World Soils 250m Percent Clay. Esri recommends updating your maps and apps to use the new version. Soil is a key natural resource that provides the foundation of basic ecosystem services. Soil determines the types of farms and forests that can grow on a landscape. Soil filters water. Soil helps regulate the Earth's climate by storing large amounts of carbon. Activities that degrade soils reduce the value of the ecosystem services that soil provides. For example, since 1850 35% of human caused green house gas emissions are linked to land use change. The Soil Science Society of America is a good source of of additional information.Soil texture is an important factor determining which kinds of plants can be grown in a particular location. Texture determines a soil's susceptibility to erosion or compaction and how well a soil holds nutrients and water. For example sandy soils tend to be well drained and dry quickly often holding few nutrients while clay soils may hold much more water and many more plant nutrients.Dataset SummaryThis layer provides access to a 30 arc-second (roughly 1 km) cell-sized raster with attributes related to soil texture derived from the Harmonized World Soil Database v 1.2. The values in this layer are for the dominant soil in each mapping unit (sequence field = 1).Fields for topsoil (0-30 cm) and subsoil (30-100 cm) are available for each of these attributes related to soil texture:USDA Texture ClassGravel - % volumeSand - % weightSilt - % weightClay - % weightThe layer is symbolized with the topsoil texture class.The document Harmonized World Soil Database Version 1.2 provides more detail on the soil texture attributes contained in this layer.Other attributes contained in this layer include:Soil Mapping Unit Name - the name of the spatially dominant major soil groupSoil Mapping Unit Symbol - a two letter code for labeling the spatially dominant major soil group in thematic mapsData Source - the HWSD is an aggregation of datasets. The data sources are the European Soil Database (ESDB), the 1:1 million soil map of China (CHINA), the Soil and Terrain Database Program (SOTWIS), and the Digital Soil Map of the World (DSMW).Percentage of Mapping Unit covered by dominant componentMore information on the Harmonized World Soil Database is available here.Other layers created from the Harmonized World Soil Database are available on ArcGIS Online:World Soils Harmonized World Soil Database - Bulk DensityWorld Soils Harmonized World Soil Database – ChemistryWorld Soils Harmonized World Soil Database - Exchange CapacityWorld Soils Harmonized World Soil Database – GeneralWorld Soils Harmonized World Soil Database – HydricThe authors of this data set request that projects using these data include the following citation:FAO/IIASA/ISRIC/ISSCAS/JRC, 2012. Harmonized World Soil Database (version 1.2). FAO, Rome, Italy and IIASA, Laxenburg, Austria.What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. The source data for this layer are available here.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

1. Table: Soil associations and soil mapping units (including their clay and silt contents) in the study area (according to the Revised FAO Legend (1990)). The Table shows the areas that the six soil associations and their assigned soil mapping units cover in the investigated regions of the three German Federal States of Thuringia, Saxony-Anhalt and Saxony. The table also includes information about the clay and silt contents of these soils, which is necessary for the assessment of the soil organic matter (Franko et al., 2011). This high degree of soil information is necessary because it allows a spatial differentiation in the landscape (not all soil associations are homogeneous) - which is important with regard to the designation of hot spots of high carbon demand. Large parts of the arable land in the study area are located on loess sediments. Soils in loess areas – where also chernosems can be found - cover 31.9% of the study area. All of these soils show high clay and silt contents, reaching from over 50 to 90%. The regional pattern of soil texture has been taken from the German soil map (BUEK1000; scale 1:1,000,000; Hartwich et al., 1998), which includes 72 soil mapping units in total. The mapping units are summarized to seven soil associations. This is a genetic-oriented approach, where the soils are grouped due to their different parent material and conditions relevant for soil formation (topography, climate, etc.) in the corresponding landscape.49 of these 72 different soil mapping units can be found in our study area. Each soil unit has been assigned a characteristic soil profile (“Leitprofil”) where soil texture was derived by using the guidelines for soil mapping (Eckelmann et al., 2005). References: Eckelmann. W., Sponagel, H., Grottenthaler, W., Hartmann, K.-J., Hartwich, R., Janetzko, P., Joisten, H., Kühn, D., Sabel, K.-J., Traidl, R. (2005): Bodenkundliche Kartieranleitung. 5. verbesserte und erweiterte Auflage, Schweizerbart'sche Verlagsbuchhandlung, Stuttgart. Franko, U., Kolbe, H., Thiel, E., Ließ, E. (2011): Multi-site validation of a soil organic matter model for arable fields based on generally available input data. Geoderma 166, 119-134. Hartwich, R., Behrens, J., Eckelmann, W., Haase, G., Richter, A., Roeschmann, G., Schmidt, R. (1998): Bodenübersichtskarte der Bundesrepublik Deutschland. Bundesanstalt für Geologie und Rohstoffe, Hannover.
2. Figure: CDI (Carbon Demand Index) values for the soil mapping units within the study area. We define a Carbon Demand Index (CDI) as a factor that relates the required C demand for a sustainable SOM reproduction in future (Crep(future) ) to the carbon reproduction flux in the past (Crep(past)): Crep(future) = CDI*Crep(past) (1) that can be calculated from the turnover conditition in future (BATfuture) and past (BATpast) values: CDI= BATfuture/BATpast (2) The distribution of the CDI data is nearly Gaussian. Using the breaks of the 1st and 3rd quartile we can identify 3 classes: low: (CDI

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.

Soil fractions of Argentina in g/100g, Clay, Silt and Sand, for 4 standard depth intervals (0–15, 15-30, 30–60, 60–100) at 1000 m resolution. Including textural classes for the four standard layers and error estimation using random forest. Global accuracy based on cross-validation sp RMSE Rsquared MAE Sand 0-15 cm 16.189 0.640 11.069 Sand 15-30 cm 16.320 0.629 11.213 Sand 30-60 cm 16.676 0.618 11.364 Sand 60-100 cm 16.762 0.587 11.472 silt 0-15 cm 12.011 0.638 8.352 silt 15-30 cm 11.807 0.608 8.388 silt 30-60 cm 11.504 0.561 8.168 silt 60-100 cm 11.728 0.583 8.263 clay 0-15 cm 8.766 0.475 5.721 clay 15-30 cm 10.723 0.452 7.432 clay 30-60 cm 11.211 0.557 7.842 clay 60-100 cm 11.005 0.536 7.734 Schulz G.A., Rodríguez D.M., Angelini M., Moretti L.M., Olmedo G.F., Tenti Vuegen L.M., Colazo, J.C., & Guevara M. (2022). Digital Soil Texture Maps Of Argentina And Their Relationship With Soil-Forming Factors And Processes. In Geopedology second edition (pp. XXXX). Springer, Cham.

Clay content (0-2 micro meter) mass fraction in ‰ 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.

CLSOILMAPS presents a newly gridded database of soil physical properties and soil hydraulic parameters based on digital soil mapping (DSM) techniques and a pedotransfer function (Rosetta V3) at close to 100m of spatial resolution covering the continental area of Chile and binational basins shared with Argentina for six standardized depths following GlobalSoilMap project standards. Maps were based on a newly compiled soil profile database covering different land use conditions (e.g. agricultural, forest, peatland, shrubland, and Andean grassland), and several environmental covariates based on the SCORPAN soil forming factors. DSM models showed moderate to good accuracies with R2 ranging from 0.76-0.88 for bulk density, 0.50-0.76 for clay, and 0.67-0.84 for sand. Silt maps were derived from clay and sand predictions taking advantage of the compositional nature of the particle size fraction. Field capacity, permanent wilting point, total available water capacity, and Van Genuchten´s soil hydraulic parameters were derived with Rosetta V3 algorithm.

The mapping of the hay removal of clays in the commune of Caumont in the Gers department is extracted from the departmental mapping from the BRGM. This mapping is a zoning of the probability of occurrence of the phenomenon of shrink-inflating of clay soils. A susceptibility map was first drawn up on the basis of purely physical criteria by the BRGM from the geological maps of the department, which were interpreted taking into account the following factors for each geological formation: — the proportion of clay material within the formation (lithic analysis); — the proportion of blowing minerals in the clay phase (mineralogical composition); — the geotechnical behavior of the material. For each of the clay formations identified, the hazard level is ultimately the result of the level of susceptibility thus obtained with the density of sinister swelling, reported to 100 km² of actual urbanised outcropping surface.

This map layer contains the selection of all soils containing heavy clay, clay and complexes with clay in the Soil Map. This selection was made based on the texture class. This is a representation of the situation at the time of the soil mapping.