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 attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

This dataset contains soil type and soil classification, by area. If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (http://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (http://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below. Category: Environment Organization: Allegheny County Department: Geographic Information Systems Group; Department of Administrative Services Temporal Coverage: 2000 Data Notes: Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot Development Notes: 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 attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties. The soil map and data used in the SSURGO product were prepared by soil scientists as part of the National Cooperative Soil Survey Other: none Related Document(s): Data Dictionary for SOIL_CODE Related Document(s): https://www.nrcs.usda.gov/Internet/FSE_MANUSCRIPTS/pennsylvania/PA003/0/legends.pdf - the last page includes the soil legend for this dataset. Frequency - Data Change: As needed Frequency - Publishing: As needed Data Steward Name: Eli Thomas Data Steward Email: gishelp@alleghenycounty.us

This dataset 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 dataset 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 attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties. 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.

For large areas, like Washington State, download as a file geodatabase. Large data sets like this one, for the State of Washington, may exceed the limits for downloading as shape files, excel files, or KML files. For areas less than a county, you may use the map to zoom to your area and download as shape file, excel or KML, if that format is desired.Information for SOILS data layer was derived from the Private Forest Land Grading system (PFLG) and subsequent soil surveys. PFLG was a five-year mapping program completed in 1980 for the purpose of forestland taxation. It was funded by the Washington State Department of Revenue. The Department of Natural Resources, Soil Conservation Service (now known as the Natural Resources Conservation Service or NRCS), USDA Forest Service and Washington State University conducted soil mapping cooperatively following national soil survey standards. Private lands having the potential of supporting commercial forests were surveyed along with interspersed small areas of State lands, Indian tribal lands, and federal lands. Because this was a cooperative soil survey project, agricultural and non-commercial forestlands were included within some survey areas. After the Department of Natural Resources originally developed its geographic information system, digitized soil map unit delineations and a few soil attributes were transferred to the system. Remaining PFLG soil attributes were later added and are now available through associated lookup tables. SCS (NRCS) soils data on agricultural lands also have been subsequently added to this data layer. The SOILS data layer includes approximately 1,100 townships with wholly or partially digitized soils data. State and private lands which have the potential of supporting commercial forest stands were surveyed. Some Indian tribal and federal lands were surveyed. Because this was a cooperative soils survey project, agricultural and non-commercial forestlands were also included within some survey areas. After the Department of Natural Resources originally developed its geographic information system, digitized soils delineations and a few soil attributes were transferred to the system. Remaining PFLG soil attributes were added at a later time and are now available through associated lookup tables. SCS soils data on agricultural lands also have subsequently been added to this data layer. This layer includes approximately 1, 100 townships with wholly or partially digitized soils data (2,101 townships would provide complete coverage of the state of Washington).-

The soils_sv resolves one to many relationships and as such is one of those special "DNR" spatial views ( ie. is implemented similar to a feature class). Column names may not match between SOILS_SV and the originating datasets. Use limitations

This Spatial View is available to Washingotn DNR users and those with access to the Washington State Uplands IMS site.

The following cautions only apply to one-to-many and many-to-many spatial views! Use these in the metadata only if the SV is one-to-many or many-to-many.

CAUTIONS: Area and Length Calculations: Use care when summarizing or totaling area or length calculations from spatial views with one-to-many or many-to-many relationships. One-to-many or many-to-many relationships between tabular and spatial data create multiple features in the same geometry. In other words, if there are two or more records in the table that correspond to the same feature (a single polygon, line or point), the spatial view will contain an identical copy of that feature's geometry for every corresponding record in the table. Area and length calculations should be performed carefully, to ensure they are not being exaggerated by including copies of the same feature's geometry.

Symbolizing Spatial Features:
Use care when symbolizing data in one-to-many or many-to-many spatial views. If there are multiple attributes tied to the same feature, symbolizing with a solid fill may mask other important features within the spatial view. This can be most commonly seen when symbolizing features based on a field with multiple table records.

Labeling Spatial Features: Spatial views with one-to-many or many-to-many relationships may present duplicate labels for those features with multiple table records. This is because there are multiple features in the same geometry, and each one receives a label.Soils Metadata

A global data set of soil types is available at 0.5-degree latitude by 0.5-degree longitude resolution. There are 106 soil units, based on Zobler?s (1986) assessment of the FAO/UNESCO Soil Map of the World. This data set is a conversion of the Zobler 1-degree resolution version to a 0.5-degree resolution. The resolution of the data set was not actually increased. Rather, the 1-degree squares were divided into four 0.5-degree squares with the necessary adjustment of continental boundaries and islands. The computer code used to convert the original 1-degree data to 0.5-degree is provided as a companion file. A JPG image of the data is provided in this document. The Zobler data (1-degree resolution) as distributed by Webb et al. (1993) [http://www.ngdc.noaa.gov/seg/eco/cdroms/gedii_a/datasets/a12/wr.htm#top] contains two columns, one column for continent and one column for soil type. The Soil Map of the World consists of 9 maps that represent parts of the world. The texture data that Webb et al.(1993) provided allowed for the fact that a soil type in one part of the world may have different properties than the same soil in a different part of the world. This continent-specific information is retained in this 0.5-degree resolution data set, as well as the soil type information which is the second column. A code was written (one2half.c) to take the file CONTIZOB.LER distributed by Webb et al. (1993) [http://www.ngdc.noaa.gov/seg/eco/cdroms/gedii_a/datasets/a12/wr.htm#top] and simply divide the 1-degree cells into quarters. This code also reads in a land/water file (land.wave) that specifies the cells that are land at 0.5 degrees. The code checks for consistency between the newly quartered map and the land/water map to which the quartered map is to be registered. If there is a discrepancy between the two, an attempt was made to make the two consistent using the following logic. If the cell is supposed to be water, it is forced to be water. If it is supposed to be land but was resolved to water at 1 degree, the code looks at the surrounding 8 cells and picks the most frequent soil type and assigns it to the cell. If there are no surrounding land cells then it is kept as water in the hopes that on the next pass one or more of the surrounding cells might be converted from water to a soil type. The whole map is iterated 5 times. The remaining cells that should be land but couldn't be determined from surrounding cells (mostly islands that are resolved at 0.5 degree but not at 1 degree) are printed out with coordinate information. A temporary map is output with -9 indicating where data is required. This is repeated for the continent code in CONTIZOB.LER as well. A separate map of the temporary continent codes is produced with -9 indicating required data. A nearly identical code (one2half.c) does the same for the continent codes. The printout allows one to consult the printed versions of the soil map and look up the soil type with the largest coverage in the 0.5-degree cell. The program manfix.c then will go through the temporary map and prompt for input to correct both the soil codes and the continent codes for the map. This can be done manually or by preparing a file of changes (new_fix.dat) and redirecting stdin. A new complete version of the map is outputted. This is in the form of the original CONTIZOB.LER file (contizob.half) but four times larger. Original documentation and computer codes prepared by Post et al. (1996) are provided as companion files with this data set. Image of 106 global soil types available at 0.5-degree by 0.5-degree resolution. Additional documentation from Zobler?s assessment of FAO soil units is available from the NASA Center for Scientific Information.

This data set consists of general soil association units. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) data set published in 1994. It consists of a broad based inventory of soils and nonsoil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. The data set was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined.

Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the whole map unit.

This data set consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1-by 2-degree topographic quadrangle units and merged into a seamless national data set. It is distributed in state/territory and national extents. The soil map units are linked to attributes in the National Soil Information System data base which gives the proportionate extent of the component soils and their properties.

MnGeo adapted the NRCS metadata record to create this record.

The maps show the soil cover of the Czech Republic. They express the typological affiliation of the soil in the "Taxonomic classification system of soils of the Czech Republic" (Němeček et al., 2011), or the version for forest soils (Vokoun et al., 2002), i.e. soil type, subtype, soil variety, or subvariety. The data for the selected map sheets also contain data on the soil parent material in a classification based on the legend of the Geological Map of the Czech Republic 1:50,000, supplemented by classification codes (Schuler et al,. 2013). This soil map on a scale of 1:50,000 is the most detailed soil map so far, which maps both agricultural and forest land together in the area of the entire territory of the Czech Republic (in process), in the same and up-to-date soil classification system. In the GIS environment, the maps are processed after the map sheets of the ZM50 division, so that they create a continuous thematic layer of the soil-typology map.

For more information on this dataset please go to https://gis.epa.ie/geonetwork/srv/eng/catalog.search#/metadata/2cd0c5e9-83b2-49a9-8c3e-79675ffd18bfSIS 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 (https://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'.

The Food and Agriculture Organization of the United Nations (FAO) and its partners -- the United Nations Environment Programme (UNEP), the International Soil Reference and Information Centre (ISRIC), the International Institute for Applied Systems Analysis (IIASA), the European Soil Bureau (ESB), and national soil institutes -- are working towards a common World Soil and Terrain Resources [SOTER (SO for soils, TER for terrain)] Map covering the globe at 1:5 million scale. The main objectives of this work are: (1) to provide sound soil and terrain resources information for use in global and continental models to simulate food production potentials, climatic change, river flow simulation, livestock distribution, research priorities, land constraints, and (very) general land management advice; (2) to provide an educational tool to the soil science community; and (3) to provide harmonized norms for soil mapping, soil classification, soil analysis and interpretation of soil resources information.

SOTER is a methodology for storing and handling soils and terrain data. The SOTER program provides an orderly arrangement of natural resource data in such a way that these data can be readily accessed, combined, and analyzed from the point of view of potential use and production, in relation to food requirements, environmental impact, and conservation. Fundamental in the SOTER approach is the mapping of areas with a distinctive, often repetitive pattern of landform, morphology, slope, parent material, and soils at 1:1 million scale (SOTER units). Each SOTER unit is linked through a geographic information system (GIS) with a computerized database containing, in theory, all available attributes on topography, landform and terrain, soils, climate, vegetation, and land use. In this way, each type of information or each combination of attributes can be displayed spatially as a separate layer or overlay or in tabular form. The methodology is explained in the SOTER Procedures Manual (ftp://ftp.fao.org/agl/agll/docs/sotereng.pdf).

The SOTER concept was originally developed for application at country (national) scale and national SOTER maps have been prepared, with ISRIC's assistance, for Uruguay (1:1 M), Kenya (1:1 M), Hungary (1:500,000), and Jordan and Syria (1:500,000).

Several continental SOTER databases have been prepared and are available on CD-ROMs from FAO. These include SOTER databases for northeastern Africa, Latin America and the Caribbean, north and central Eurasia, and central and eastern Europe. [Note: Separate metadata reports for the continental SOTER databases have been registered in the ORNL DAAC Mercury Metadata Database for Global and Regional Environmental Data.] SOTER Europe and SOTER Southern Africa are in preparation.

The different SOTER puzzle-pieces will eventually be correlated and integrated into a unique 1:5 million scale WORLD SOTER, using whatever best soil profile information is available, resulting in a significant update of the global soil and terrain information source. In the interim, FAO, ISRIC, and the European Commission Joint Research Centre have recently updated the 1990 FAO Map of World Soil Resources (1:25 Million Scale) that is generalized from the FAO/Unesco Soil Map of the World (1:5 Million Scale) (FAO, 1971-1981). The Map of World Soil Resources has been redrawn under the SOTER Programme to incorporate the findings of all regional SOTER projects and additional data provided by the United States and Canada. In addition, the FAO legend has been replaced by the World Reference Base for Soil Resources (WRB) as a classification system, and the updated map also contains topographical information. The updated Map of World Soil Resources (January 2003) can be viewed and downloaded at [http://www.fao.org/ag/agl/agll/wrb/wrbmaps/htm/soilres.htm].

Version 3.1 of the ISRIC-WISE database (WISE3) was compiled from a wide range of soil profile data collected by many soil professionals worldwide. All profiles have been harmonized with respect to the original Legend (1974) and Revised Legend (1988) of FAO-Unesco. Thereby, the primary soil data ─ and any secondary data derived from them ─ can be linked using GIS to the spatial units of the digitized Soil Map of the World as well as more recent digital Soil and Terrain (SOTER) databases through the soil legend code.

WISE3 holds selected attribute data for some 10,250 soil profiles, with some 47,800 horizons, from 149 countries. Individual profiles have been sampled, described, and analyzed according to methods and standards in use in the originating countries. There is no uniform set of properties for which all profiles have analytical data, generally because only selected measurements were planned during the original surveys. Methods used for laboratory determinations of specific soil properties vary between laboratories and over time; sometimes, results for the same property cannot be compared directly. WISE3 will inevitably include gaps, being a compilation of legacy soil data derived from traditional soil survey, which can be of a taxonomic, geographic, and soil analytical nature. As a result, the amount of data available for modelling is sometimes much less than expected. Adroit use of the data, however, will permit a wide range of agricultural and environmental applications at a global and continental scale (1:500 000 and broader).

Preferred citation: Batjes NH 2009. Harmonized soil profile data for applications at global and continental scales: updates to the WISE database. Soil Use and Management 5:124–127, http://dx.doi.org/10.1111/j.1475-2743.2009.00202.x

The maps show the soil cover of the Czech Republic. They express the typological affiliation of the soil in the "Taxonomic classification system of soils of the Czech Republic" (Němeček et al., 2011), or the version for forest soils (Vokoun et al., 2002), i.e. soil type, subtype, soil variety, or subvariety. The data for the selected map sheets also contain data on the soil parent material in a classification based on the legend of the Geological Map of the Czech Republic 1:50,000, supplemented by classification codes (Schuler et al,. 2013). This soil map on a scale of 1:50,000 is the most detailed soil map so far, which maps both agricultural and forest land together in the area of the entire territory of the Czech Republic (in process), in the same and up-to-date soil classification system. In the GIS environment, the maps are processed after the map sheets of the ZM50 division, so that they create a continuous thematic layer of the soil-typology map.

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.Dataset SummaryPhenomenon Mapped: Soils of the United States and associated territoriesCoordinate System: Web Mercator Auxiliary SphereExtent: The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaVisible Scale: 1:144,000 to 1:1,000Resolution/Tolerance: 1 meter/2 metersNumber of Features: 36,543,233Feature Request Limit: 10,000Source: USDA Natural Resources Conservation ServicePublication Date: October 1, 2019ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/rest/servicesData from the gNATSGO database was used to create the layer for the contiguous United States, Alaska, Puerto Rico, and the U.S. Virgin Islands. The remaining areas were created with the gSSURGO database (Hawaii, Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa).AttributesKey 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 SymbolMap 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 RatingLegend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field.Project ScaleSurvey 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 VersionMap 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 - PresenceRating for Manure and Food Processing Waste - Weighted AverageComponent 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 KeyComponent 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 - High ValueTotal Subsidence - Low ValueTotal Subsidence - Representative ValueTotal Subsidence - High ValueCrop Productivity IndexEsri SymbologyThis field was created to provide symbology based on the Taxonomic Order field (taxorder). Because some mapunits have a null value for soil order, a custom script was used to populate this field using the Component Name (compname) and Mapunit Name (muname) fields. This field was created using the dominant soil order of each mapunit.Esri SymbologyHorizon TableEach map unit polygon has one or more components and each component has one or more layers known as horizons. To incorporate this field from the Horizon table into the attributes for this layer, a custom script was used to first calculate the mean value weighted by thickness of the horizon for each component and then a mean value of components weighted by the Component Percentage Representative Value field for each map unit. K-Factor Rock FreeEsri Soil OrderThese fields were calculated from the Component table using a model that included the Pivot Table Tool, the Summarize Tool and a custom script. The first 11 fields provide the sum of Component Percentage Representative Value for each soil order for each map unit. The Soil Order Dominant Condition field was calculated by selecting the highest value in the preceding 11 soil order fields. In the case of tied values the component with the lowest average slope value (slope_r) was selected. If both soil order and slope

In order to use the standard color legend for Romanian soil type maps in the ESRI ArcMap-10 electronic format, a dataset consisting a shapefile set (.dbf, .shp, .shx, .sbn, and .sbx files), four different .lyr files, and three different .style files have been prepared (ESRI, 2016). The shapefile set is not a “real” georeferenced layer/coverage; it is designed only to handle all the instants of soil types from the standard legend. This legend contains 67 standard items: 63 proper colors (different color hues, each of them having, generally, 2 - 4 degrees of lightness and/or chroma, four shades of grey, and white color), and four hatching patterns on white background (ESRI, 2016). The “color difference DE*ab” between any two legend colors, calculated with the color perceptually-uniform model CIELAB , is greater than 10 units, thus ensuring acceptably-distinguishable colors in the legend. The 67 standard items are assigned to 60 main soils existing in Romania, four main nonsoils, and three special cases of unsurveyed land. The soils are specified in terms of the current Romanian system of soil taxonomy, SRTS-2012+, and of the international soil classification system WRB-2014. The four different .lyr files presented here are: legend_soilcode_srts_wrb.lyr, legend_soilcode_wrb.lyr, legend_colourcode_srts_wrb.lyr, and legend_colourcode_wrb.lyr. The first two of them are built using as value field the ‘Soil_codes’ field, and as labels (explanation texts) the ‘Soil_name’ field (storing the soil types according to SRTS/WRB classification), respectively, the ‘WRB’ field (the soil type according to WRB classification), while the last two .lyr files are built using as value field the ‘colour_code’ field (storing the color codes) and as labels the soil name in SRTS and WRB, respectively, in WRB classification. In order to exemplify how the legend is displayed, two .jpg files are also presented: legend_soil_srts_wrb.jpg and legend_colour_wrb.jpg. The first displays the legend (symbols and labels) according to the SRTS classification order, the second according to the WRB classification. The three different .style files presented here are: soil_symbols.style, wrb_codes.style, and colour_codes.style. They use as name the soil acronym in SRTS classification, soil acronym in WRB classification, and, respectively, the color code.

This data set is a digital general soil association map developed by the National Cooperative Soil Survey. It consists of a broad based inventory of soils and nonsoil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. The soil maps for STATSGO are compiled by generalizing more detailed soil survey maps. Where more detailed soil survey maps are not available, data on geology, topography, vegetation, and climate are assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas are studied, and the probable classification and extent of the soils are determined. Map unit composition for a STATSGO map is determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the whole mapunit. This data set consists of georeferenced digital map data and computerized attribute data. The map data are collected in 1- by 2-degree topographic quadrangle units and merged and distributed as statewide coverages. The soil map units are linked to attributes in the Map Unit Interpretations Record relational data base which gives the proportionate extent of the component soils and their properties. For use with the GeoStac Database, STATSGO spatial and tabular data files were imported from the source file format to geodatabase feature class format, and converted to a common GeoStac map projection. No changes were made to the geometric or tabular data. Mohawk River Watershed Processing: The original dataset was clipped for use in the Mohawk River Watershed Management Plan. The data was re-projected from Albers to UTM 18N, NAD 83. Attributes of interest were extracted and summarized. STATSGO depicts information about soil features on or near the surface of the Earth. These data are collected as part of the National Cooperative Soil Survey. For use with the GeoStac database, this data set was compiled in order to simplify presticide risk assessment and to provide a common data set upon which to perform analysis for all stakeholders. This data was collected by Stone Environmental, Inc. for the New York State Department of State with funds provided under Title 11 of the Environmental Protection Fund.View Dataset on the Gateway

ABSTRACT There is an ever-growing need for soil maps, since detailed soil information is directly related to agricultural activities, urbanization and environmental protection. However, there is a lack of large-scale soil maps in developing tropical countries such as Brazil. Albeit there are soil maps for small areas, large regions usually have undetailed maps. Considering the importance of finding low-cost alternatives to overcome the lack of detailed soil information, the main objective of this work was to manually create a local soil map and extrapolate it to similar larger areas that lack detailed soil information. The Anhumas River Basin, in the municipality of Itajubá, southeast Brazil, was manually mapped and this map was used to predict soils distribution for the entire municipality. First, the prediction model was tested in the same basin and provided sufficient results, achieving 67% global accuracy and 0.62 Kappa coefficient. Second, the resulting map was used together with the soil map of the larger José Pereira Basin to map the entire municipality, achieving 54% global accuracy and 0.40 Kappa coefficient. Low resolution parent material information was found to confuse models; maps showed better results when this variable was removed. The Minas Gerais soil map presents general mapping units only for the Acrisol class and its associations with other soil classes in the area. The soil map predicted by this work identified more soil classes. Mapping representative areas and extrapolating these maps to larger similar areas constitute a promising alternative to overcome the lack of detailed soil maps.

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 attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

This data set consists of general soil association units. It was develped by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) data set published in 1994. It consists of a broad based inventory of soils and nonsoil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. The data set was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined. Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the whole map unit. This data set consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1-by 2-degree topographic quadrangle units and merged into a seamless national data set. It is distributed in state/territory and national extents. The soil map units are linked to attributes in the National Soil Information System data base which gives the proportionate extent of the component soils and their properties.

Soil survey maps were examined to better understand the surficial materials at each study site. Soil maps provide information about soil properties and their behavioral characteristics. Soil survey maps were generated using the United States Department of Agriculture-Natural Resources Conservation Service’s (USDA-NRCS) Web Soil Survey (WSS) tool (http://websoilsurvey.sc.egov.usda.gov/). A USDA-NRCS WSS map of the vicinity of each culvert installation study site is presented as a separate Portable Document Format (PDF) file, with the abbreviated site name embedded in the filename.

This dataset consists of general soil association units. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) dataset published in 1994. It consists of a broad based inventory of soils and nonsoil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped of 1:250,000 in the continental U.S., Hawaii, Puerto, and the Virgin Islands and 1:1,000,000 in Alaska. The dataset was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined.

Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the entire map unit.

This dataset consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1- by 2-degree topographic quadrangle units. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

These data provide information about soil features on or near the surface of the Earth. Data were collected as part of the National Cooperative Soil Survey. These data are intended for geographic display and analysis at the state, regional, and national level. The data should be displayed and analyzed at scales appropriate for 1:250,000-scale data.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: STATSGO2-State For complete information, please visit https://data.gov.

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.