The gSSURGO dataset provides detailed soil survey mapping in raster format with ready-to-map attributes organized in statewide tiles for desktop GIS. gSSURGO is derived from the official Soil Survey Geographic (SSURGO) Database. SSURGO generally has the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes and are derived from properties and characteristics stored in the National Soil Information System (NASIS).

The gSSURGO data were prepared by merging the traditional vector-based SSURGO digital map data and tabular data into statewide extents, adding a statewide gridded map layer derived from the vector layer, and adding a new value-added look up table (valu) containing ready-to-map attributes. The gridded map layer is in an ArcGIS file geodatabase in raster format, thus it has the capacity to store significantly more data and greater spatial extents than the traditional SSURGO product. The raster map data have a 10-meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link the raster cells and polygons to attribute tables.

For more information, see the gSSURGO webpage: https://www.nrcs.usda.gov/resources/data-and-reports/description-of-gridded-soil-survey-geographic-gssurgo-database

This dataset is called the Gridded SSURGO (gSSURGO) Database and is derived from the Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into State-wide extents, and adding a State-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format. The raster and vector map data have a State-wide extent. The recently released (2011) gSSURGO value added look up (valu) table (created by USDA-NRCS) contains attribute data summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria.Summarized description of the Format and organization of SSURGO:Adjacent soil surveys may have been composed by different individuals, and may be of widely different vintages. Any given survey must comply with basic standards, but older surveys reflect a more generalized approach than more modern surveys. The figure to the right illustrates such differences.Polygons represent a repeating pattern of legend entries: groups of map-able soil concepts called map unitsMap unit data is stored in the mapunit table, and is referenced by the field mukeyPre-aggregated map unit data is stored in the muaggatt table, and is referenced by the field mukeyMap units are comprised of multiple, unmapped soil types called 'components'Component data is stored in the component table, and is referenced by the field cokeySoil components (or soil type) are associated with multiple horizonsHorizon data is stored in the chorizon table, and is referenced by the field cokeySince there is a 1:many:many (mapunit:component:horizon) relationship between spatial and horizon-level soil property data two aggregation steps are required in order to produce a thematic mapSource: http://casoilresource.lawr.ucdavis.edu/drupal/book/export/html/335Summary Descriptions of gSSURGO Soil Survey Attributes contained within the MUAGGATT table.MUAGGATT Table: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 Average

The SSURGO database contains information about soil as collected by the National Cooperative Soil Survey over the course of a century. The information can be displayed in tables or as maps and is available for most areas in the United States and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS (Natural Resources Conservation Service). The information was gathered by walking over the land and observing the soil. Many soil samples were analyzed in laboratories. The maps outline areas called map units. The map units describe soils and other components that have unique properties, interpretations, and productivity. The information was collected at scales ranging from 1:12,000 to 1:63,360. More details were gathered at a scale of 1:12,000 than at a scale of 1:63,360. The mapping is intended for natural resource planning and management by landowners, townships, and counties. Some knowledge of soils data and map scale is necessary to avoid misunderstandings. The maps are linked in the database to information about the component soils and their properties for each map unit. Each map unit may contain one to three major components and some minor components. The map units are typically named for the major components. Examples of information available from the database include available water capacity, soil reaction, electrical conductivity, and frequency of flooding; yields for cropland, woodland, rangeland, and pastureland; and limitations affecting recreational development, building site development, and other engineering uses. SSURGO datasets consist of map data, tabular data, and information about how the maps and tables were created. The extent of a SSURGO dataset is a soil survey area, which may consist of a single county, multiple counties, or parts of multiple counties. SSURGO map data can be viewed in the Web Soil Survey or downloaded in ESRI® Shapefile format. The coordinate systems are Geographic. Attribute data can be downloaded in text format that can be imported into a Microsoft® Access® database. A complete SSURGO dataset consists of:

GIS data (as ESRI® Shapefiles) attribute data (dbf files - a multitude of separate tables) database template (MS Access format - this helps with understanding the structure and linkages of the various tables) metadata

Resources in this dataset:Resource Title: SSURGO Metadata - Tables and Columns Report. File Name: SSURGO_Metadata_-_Tables_and_Columns.pdfResource Description: This report contains a complete listing of all columns in each database table. Please see SSURGO Metadata - Table Column Descriptions Report for more detailed descriptions of each column.

Find the Soil Survey Geographic (SSURGO) web site at https://www.nrcs.usda.gov/wps/portal/nrcs/detail/vt/soils/?cid=nrcs142p2_010596#Datamart Title: SSURGO Metadata - Table Column Descriptions Report. File Name: SSURGO_Metadata_-_Table_Column_Descriptions.pdfResource Description: This report contains the descriptions of all columns in each database table. Please see SSURGO Metadata - Tables and Columns Report for a complete listing of all columns in each database table.

Find the Soil Survey Geographic (SSURGO) web site at https://www.nrcs.usda.gov/wps/portal/nrcs/detail/vt/soils/?cid=nrcs142p2_010596#Datamart Title: SSURGO Data Dictionary. File Name: SSURGO 2.3.2 Data Dictionary.csvResource Description: CSV version of the data dictionary

Read more here: SSURGO On-Demand ArcProThe purpose of these tools are to give users the ability to get Soil Survey Geographic Database (SSURGO) properties and interpretations in an efficient manner. They are very similiar to the United States Department of Agriculture - Natural Resource Conservation Service's Soil Data Viewer (SDV) application, although there are distinct differences. The most important difference is the data collected with the SSURGO On-Demand (SOD) tools are collected in real-time via web requests to Soil Data Access (https://sdmdataaccess.nrcs.usda.gov/). This means that the information collected is the most up-to-date possible. SOD tools do not require users to have the data found in a traditional SSURGO download from the NRCS's official repository, Web Soil Survey (https://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm). The main intent of both SOD and SDV are to hide the complex relationships of the SSURGO tables and allow the users to focus on asking the question they need to get the information they want. This is accomplished in the user interface of the tools and the subsequent SQL is built and executed for the user. Currently, the tools packaged here are designed to run within the ESRI ArcGIS Pro software and developed under version 2.8.3.NOTE: The queries in these tools only consider the major components of soil map units.There are currently 2 tools in this package, 1 for SSURGO properties and the other for SSURGO interpretations. Both tools require the user to provide a feature layer based upon a WGS84, NAD83, or NAD83(2011) geographic coordinate system. This feature layer determines the area of interest for which both SSURGO geometry and either property or interpretation are collected. The feature layer must have a selection. Even if there is only 1 feature in the layer, it must be selected. The output workspace is required to be a file geodatabase (gdb). The geometry collected is in WGS84 (4326). Each property or interpretations requested will output an individual table. Users have the option of updating the spatial attribute table with each property or interpretation requested.It is very important to consider that Soil Data Access is limited in the number characters it can return. Due to this, there is an unknown constraint on how large an AOI can be requested because the characters (coordinates/vertices) can reach this threshold fairly quickly. This is locally dependent on polygon (mapping) density and vertex density. When this threshold is exceeded Soil Data Access returns nothing which will cause SSURGO On-Demand tools to exit.

Download In State Plane Projection Here See the Lake County Soils Documentation for additional information -- including an explanation of data fields. This product includes the United States Department of Agriculture - Natural Resources Conservation Service 2004 Soil Survey data and related information. An intergovernmental funding agreement between the USDA-NRCS, the State of Illinois Department of Agriculture and Lake County made this product possible. This soil information is referred to as SSURGO by NRCS. The spatial (soil polygons) and tabular (physical and chemical properties) data for all soil survey areas are available free from the Web Soil Survey website. Helpful information about soils can be found through the NRCS Illinois website. The McHenry-Lake County Soil and Water Conservation District staff can also provide assistance with questions related to soils.

The DSL SWI Soils dataset represents two selected subsets of the USDA NRCS gNATSGO dataset for Oregon. The “SWI Predominantly Hydric Soil Map Units” layer represents soil map units that are comprised of greater than 50 percent hydric soil components. The Agate-Winlo Soil Map Units layer is associated with vernal pools in Jackson County. These two subsets indicate areas where unmapped wetlands may be present for the purpose of planning, scoping projects, and coordination with DSL.The gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS-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.Consult the gNATSGO home page for more information: https://www.nrcs.usda.gov/resources/data-and-reports/gridded-national-soil-survey-geographic-database-gnatsgo and the web soil survey: http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm.NRCS description of SSURGO Database:The SSURGO database contains information about soil as collected by the National Cooperative Soil Survey over the course of a century. The information can be displayed in tables or as maps and is available for most areas in the United States and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS. The information was gathered by walking over the land and observing the soil. Many soil samples were analyzed in laboratories. The maps outline areas called map units. The map units describe soils and other components that have unique properties, interpretations, and productivity. The information was collected at scales ranging from 1:12,000 to 1:63,360. More details were gathered at a scale of 1:12,000 than at a scale of 1:63,360. The mapping is intended for natural resource planning and management by landowners, townships, and counties. Some knowledge of soils data and map scale is necessary to avoid misunderstandings.The maps are linked in the database to information about the component soils and their properties for each map unit. Each map unit may contain one to three major components and some minor components. The map units are typically named for the major components. Examples of information available from the database include available water capacity, soil reaction, electrical conductivity, and frequency of flooding; yields for cropland, woodland, rangeland, and pastureland; and limitations affecting recreational development, building site development, and other engineering uses.SSURGO datasets consist of map data, tabular data, and information about how the maps and tables were created. The extent of a SSURGO dataset is a soil survey area, which may consist of a single county, multiple counties, or parts of multiple counties. SSURGO map data can be viewed in the Web Soil Survey or downloaded in ESRI® Shapefile format. The coordinate systems are Geographic. Attribute data can be downloaded in text format that can be imported into a Microsoft® Access® database.https://www.nrcs.usda.gov/resources/data-and-reports/soil-survey-geographic-database-ssurgoNRCS description of STATSGO2 Database:The Digital General Soil Map of the United States or STATSGO2 is a broad-based inventory of soils and non-soil 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 Rico, and the Virgin Islands and 1:1,000,000 in Alaska. The level of mapping is designed for broad planning and management uses covering state, regional, and multi-state areas. The U.S. General Soil Map is comprised of general soil association units and is maintained and distributed as a spatial and tabular dataset.The U.S. General Soil Map was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) dataset. 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 and related to Land Remote Sensing Satellite (LANDSAT) images. Soils of similar 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 then statistically expanding the data to characterize the whole map unit.The dataset consists of georeferenced, vector and tabular data. The map data were collected in 1- by 2-degree topographic quadrangle units and merged into a seamless national dataset. The dataset is distributed in state, territorial, and national extents. The spatial units are linked to attributes in the tabular data, which give the proportionate extent of the component soils and their properties.The tabular data contains estimates of physical and chemical soil properties, soil interpretations, and static and dynamic metadata. Most of the tabular data exists in the database as a range of values for soil properties. The values depict the range for the geographic extent of the map unit. For most properties, the data include high, low, and representative values.Spatial data are available in ESRI® shapefile format. Spatial reference is decimal degrees, World Geodetic System 1984 (WGS84). Tabular data are available as ASCII text files (.txt). Fields are pipe delimited, and text is double-quote delimited. A Microsoft® Access® template database is available for use with the tabular data.https://www.nrcs.usda.gov/resources/data-and-reports/description-of-statsgo2-databaseCitation: Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. Available online at. http://websoilsurvey.nrcs.usda.gov/

See the Lake County Soils Documentation for additional information -- including an explanation of data fields. This product includes the United States Department of Agriculture - Natural Resources Conservation Service 2004 Soil Survey data and related information. An intergovernmental funding agreement between the USDA-NRCS, the State of Illinois Department of Agriculture and Lake County made this product possible. This soil information is referred to as SSURGO by NRCS. The spatial (soil polygons) and tabular (physical and chemical properties) data for all soil survey areas are available free from the Web Soil Survey website. Helpful information about soils can be found through the NRCS Illinois website. The McHenry-Lake County Soil and Water Conservation District staff can also provide assistance with questions related to soils.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. userdata and unzip the LayerFiles.zip folder.Data from the four SSURGO tables were assembled into the single table included in each map package. Data from the component table were aggregated using a dominant component model (listed below under Component Table - Dominant Component) or a weighted average model (listed below under Component Table - Weighted Average) using custom Python scripts. The the Mapunit table - the MUAGATTAT table and the processed Component table data were joined to the Mapunit Feature Class. Field aliases were added and indexes calculated. A field named Map Symbol was created and populated with random integers from 1-10 for symbolizing the soil units in the map package.For documentation of the SSURGO dataset see:http://soildatamart.nrcs.usda.gov/SSURGOMetadata.aspxFor documentation of the Watershed Boundary Dataset see: http://www.nrcs.usda.gov/wps/portal/nrcs/main/national/water/watersheds/datasetThe map packages contain the following attributes in the Map Units layer:Mapunit Feature Class:Survey AreaSpatial VersionMapunit SymbolMapunit KeyNational Mapunit SymbolMapunit Table:Mapunit NameMapunit KindFarmland ClassHighly Erodible Lands Classification - Wind and WaterHighly Erodible Lands Classification - WaterHighly Erodible Lands Classification - WindInterpretive FocusIntensity of MappingLegend KeyMapunit SequenceIowa Corn Suitability RatingLegend Table:Project ScaleTabular VersionMUAGGATT Table: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 - Weighted Average:Mean Annual Air Temperature - High Value Mean Annual Air Temperature - Low Value Mean Annual Air Temperature - Representative Value Albedo - High Value Albedo - Low Value Albedo - Representative Value Slope - High Value Slope - Low Value Slope - Representative Value Slope Length - High Value Slope Length - Low Value Slope Length - Representative Value Elevation - High Value Elevation - Low Value Elevation - Representative Value Mean Annual Precipitation - High Value Mean Annual Precipitation - Low Value Mean Annual Precipitation - Representative Value Days between Last and First Frost - High Value Days between Last and First Frost - Low Value Days between Last and First Frost - Representative Value Crop Production Index Range Forage Annual Potential Production - High Value Range Forage Annual Potential Production - Low Value Range Forage Annual Potential Production - Representative Value Initial Subsidence - High Value Initial Subsidence - Low Value Initial Subsidence - Representative Value Total Subsidence - High ValueTotal Subsidence - Low Value Total Subsidence - Representative Value Component Table - Dominant Component:Component KeyComponent Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoffSoil Loss Tolerance FactorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupForage Suitability 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 Class NameOrderSuborderGreat GroupSubgroupParticle SizeParticle Size ModifierCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoisture SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilThe U.S. Department of Agriculture - Natural Resources Conservation Service - should be acknowledged as the data source in products derived from these data. This data set is not designed for use as a primary regulatory tool in permitting or citing decisions - but may be used as a reference source. This is public information and may be interpreted by organizations - agencies - units of government - or others based on needs; however - they are responsible for the appropriate application. Federal - State - or local regulatory bodies are not to reassign to the Natural Resources Conservation Service any authority for the decisions that they make. The Natural Resources Conservation Service will not perform any evaluations of these maps for purposes related solely to State or local regulatory programs. Photographic or digital enlargement of these maps to scales greater than at which they were originally mapped can cause misinterpretation of the data. If enlarged - maps do not show the small areas of contrasting soils that could have been shown at a larger scale. The depicted soil boundaries - interpretations - and analysis derived from them do not eliminate the need for onsite sampling - testing - and detailed study of specific sites for intensive uses. Thus - these data and their interpretations are intended for planning purposes only. Digital data files are periodically updated. Files are dated - and users are responsible for obtaining the latest version of the data.The attribute accuracy is tested by manual comparison of the source with hard copy plots and/or symbolized display of the map data on an interactive computer graphic system. Selected attributes that cannot be visually verified on plots or on screen are interactively queried and verified on screen. In addition - the attributes are tested against a master set of valid attributes. All attribute data conform to the attribute codes in the signed classification and correlation document and amendment(s). Last Updated: Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/Geoscientific/MD_SSURGOSoils/MapServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Two seamless soils datasets based on USDA’s SSURGO and STATSGO databases were created for the entire DRB region, and information pertaining to various soil-related factors such as erodibility (k factor), available water - holding capacity, texture, etc. were compiled and summarized for discrete mapping units at these two scales. The SSURGO (Soil Survey Geographic) database is compiled at the detailed county-level survey scale that most soil information users are familiar with, and has two basic components: 1) digital boundaries of the detailed soil mapping units, and 2) tabular information on a wide range of soil parameters associated with each mapping unit. The STATSGO (State Soil Geographic) database summarizes similar soils information at a much more generalized “soil association” scale. Both of these datasets for the DRB area were downloaded from USDA’s “geospatial data” site at http://datagateway.nrcs.usda.gov. Once downloaded, considerable effort was then expended to first seam together the data from the separate states overlapping the DRB, and then to “populate” both soil databases by linking a number of “attribute tables” to the soils polygons contained within the DRB boundary. In this case, over 325,000 soil polygons were populated with information extracted from about a dozen different attribute tables.

This data is hosted at, and may be downloaded or accessed from PASDA, the Pennsylvania Spatial Data Access Geospatial Data Clearinghouse http://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=1506

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 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 NYSERDA 2023 SOILS DATA IS TO BE USED FOR NYSERDA’S RENEWABLE ENERGY STANDARD (RES) REQUEST FOR PROPOSAL (RFP) ISSUED AFTER THE PUBLICATION OF THIS DATA OR THE NY-SUN PROGRAM AND IS NOT INTENDED TO REPRESENT ACTUAL IN SITU SOIL CONDITIONS.

In order to facilitate the protection of agricultural lands, developers participating in RESRFPs or the NY-Sun program may be responsible for making an agricultural mitigation payment to a designated fund based on the extent to which the solar project’s facility area overlaps with an Agricultural District and New York’s highly productive agricultural soils, identified as Mineral Soil Groups (MSG) classifications 1 through 4 (MSG 1-4). This mitigation approach is designed to discourage solar projects from siting on MSG 1-4. Furthermore, this mitigation approach is designed to encourage retaining agricultural productivity on the project site. Instances where Proposers cannot avoid or minimize impacts on MSG 1-4 will result in a payment to a fund administered by NYSERDA. Disbursement of collected agricultural mitigation payment funds will be informed by consultation with the New York State Department of Agriculture and Markets (AGM) to support ongoing regional agricultural practices and/or soil conservation initiatives.

This dataset contains a combination of soils data from multiple sources to serve participants of NYSERDA’s Large-Scale Renewable and NY-Sun programs. The NYSERDA 2023 Soils Data was created by converting the 2023 New York State Agricultural Land Classification (https://agriculture.ny.gov/system/files/documents/2023/01/masterlistofagriculturalsoils.pdf) master list of soils maintained by AGM to a tabular form and providing a corresponding unique identifier for each listed soil that enables the user to link the soils to the Natural Resources Conservation Service (NRCS) SSURGO soils database, allowing for a geographical representation. When the NYSERDA 2023 Soils Data is joined with spatial data from the Natural Resources Conservation Service (NRCS) SSURGO soils database (https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_053627), the corresponding soil unit can be mapped in a geographic information system software. The latest version of the SSURGO database (https://nrcs.app.box.com/v/soils) should be used to get the most accurate join. Data is updated yearly from both NRCS and from AGM, however, NYSERDA will not update this dataset and it will remain intact for future reference. NYSERDA intends on creating new soils datasets for future procurements on an annual basis.

The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

November 2021

THE NYSERDA 2022 SOILS DATA IS TO BE USED FOR NYSERDA’S PROCUREMENT PURPOSES OR THE NY-SUN PROGRAM AND IS NOT INTENDED TO REPRESENT ACTUAL IN SITU SOIL CONDITIONS.

NYSERDA has launched the Renewable Energy Standard (RES) request for proposals, RESRFP22-1, to continue accelerating progress towards New York’s goal of generating 70 percent of its electricity from renewable sources by 2030. Through RESRFP22-1, NYSERDA seeks to procure approximately 4.5 million Tier 1 eligible Renewable Energy Certificates (RECs) from eligible facilities that enter commercial operation on or after January 1, 2015 and on or before November 30, 2024, unless extended to November 30, 2027.

In order to facilitate the protection of agricultural lands, developers participating in RESRFP22-1 or the NY-Sun program may be responsible for making an agricultural mitigation payment to a designated fund based on the extent to which the solar project’s facility area overlaps with an Agricultural District and New York’s highly productive agricultural soils, identified as Mineral Soil Groups (MSG) classifications 1 through 4 (MSG 1-4). This mitigation approach is designed to discourage solar projects from siting on MSG 1-4. Furthermore, this mitigation approach is designed to encourage retaining agricultural productivity on the project site. Instances where Proposers cannot avoid or minimize impacts on MSG 1-4 will result in a payment to a fund administered by NYSERDA. Disbursement of collected agricultural mitigation payment funds will be informed by consultation with the New York State Department of Agriculture and Markets (AGM) to support ongoing regional agricultural practices and/or soil conservation initiatives.

This dataset contains a combination of soils data from multiple sources to serve participants of NYSERDA’s Large-Scale Renewable and NY-Sun programs. The NYSERDA 2022 Soils Data was created by converting the 2022 New York State Agricultural Land Classification master list of soils maintained by AGM to a tabular form and providing a corresponding unique identifier for each listed soil that enables the user to link the soils to the Natural Resources Conservation Service (NRCS) SSURGO soils database, allowing for a geographical representation. When the NYSERDA 2022 Soils Data is joined with spatial data from the Natural Resources Conservation Service (NRCS) SSURGO soils database, the corresponding soil unit can be mapped in a geographic information system software. The latest version of the SSURGO database should be used to get the most accurate join. Data is updated yearly from both NRCS and from AGM, however, NYSERDA will not update this dataset and it will remain intact for future reference. NYSERDA intends on creating new soils datasets for future procurements on an annual basis.

The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

Defining the pre-European range of vegetation communities can enhance our understanding of the role soil, hydrology, and climate had on climax plant communities within southwest Louisiana. Coastal prairie grasslands were in a perpetual state of succession due to two primary disturbances; grazing, primarily by bison and other ungulates, and fires ignited by lightning and Native Americans. Along its borders, prairie vegetation blended into adjacent plant communities forming biologically diverse ecotones that may have fluctuated between a prairie, marsh, or forest dominated community as a result of variable conditions including climate cycles, disturbance and soil characteristics. Since European settlement, this landscape has undergone dramatic change with less than 1% of intact coastal prairie remaining. Conservation entities across the Western Gulf Coastal Plain are taking a collaborative, strategic, landscape scale approach to pollinator conservation. This effort encourages communication and implementation of restoration and habitat enhancement actions within water sheds. We have produced a spatial dataset which considers landscape position and soil type, based on Soil Survey Geographic Database (SSURGO) data, to predict appropriate vegetation associations for plantings across southwest Louisiana based on expert elicitation, and historic references. Methods to produce this product begin with soil boundaries and identification information using Map Unit Keys (MUKEY) which were gathered from SSURGO data (Soil Survey Staff, NRCS 2017). Each mukey number was reviewed on the SOIL WEB to obtain information about components. Components include the proportion and general geomorphic features associated with soil series. Natural vegetation associations were examined and documented for each soil series individually using multiple references, including USDA Soil Series descriptions, expert elicitation, and historical spatial references. Professional reference maps contributed to this spatial dataset and include an 1863 work by Henry L. Abbot and numerous General Land Office surveyor maps and surveyor descriptions from the early 1800s drawn at the scale of a township. General vegetation categories associated with Soil Types (Mukey) were derived from reviewing the vegetation associations of the dominant components, or soil series. These general categories include: anthropogenic, prairie, transition, forest, marsh, swamp, uncertain, and water. Anthropogenic categories were generally due to significant dredging, or other industrial activities. Transitional areas included savannas and areas which may have significantly changed from prairie to forest dominated communities due to rainfall and/or fire frequency and intensity. Forest and swamp includes a range of forest types from which the distinction between these two categories primarily depend upon relative elevation and hydrology. There were a few soil series in which we are uncertain of their pre-settlement vegetation. These areas are anomalies on the landscape and include salt domes and old, disjunct river meanders which are largely comprised of Pleistocene soils and were most likely marais, yet currently much of it is heavily forested as bottomlands, and we are therefore uncertain if this result is solely due to absence of fire. Attribute data include MUKEYs within the parishes which are included in the Louisiana portion of the Gulf Coastal Plain Ecoregion. Information in the table includes symbols, common names, and components which were compiled from SSURGO dataset and Soil Web online resources (Soil Survey Staff, NRCS, accessed 2/2017). For more detailed vegetation associations for individual soil series, please refer to 'VegSoilAssoc_SWLA.pdf' or 'VegSoilAssoc_SWLA.csv'.

This dataset was produced by the US Geological Survey (USGS) as a supporting dataset to be used for the purpose of calculating stream gage basin characteristics in preparation for the South Carolina StreamStats application. This integer raster dataset represents runoff curve numbers for combinations of hydrologic soils groups and land cover classes within the South Carolina StreamStats study area. The source of the soils data was the United States Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) Soil Survey Geographic Database (SSURGO). The source of the land cover data was the USGS 2019 National Land Cover Database (NLCD) data. This dataset will be used in peak flow regression equations that are used to predict flow in South Carolina streams. The StreamStats application provides access to spatial analytical tools that are useful for water-resources planning and management, as well as for engineering and design purposes. Inventory of data release: SC_RCN_LU_CO.tiff (one .tif file): Cloud optimized GeoTIFF containing integer rasters of runoff curve numbers for soils/land cover combinations SoilLU_RCN_Cross.csv (one .csv file): Table of 239 land cover/soils combinations showing sources used to make RCN assignments RevisionHistory_SouthCarolina_RunoffCurveNumbers.txt (one .txt file): Text file documenting revision history to this data release. First posted - August 29, 2022 Revised - September 24, 2025 (version 1.1)

For a series of studies on the ecosystem service values of chaparral in Southern California, we developed a raster data layer providing an ecological unit classification of the Southern California landscape. This raster dataset is at a 30 meter pixel resolution and partitions the landscape into 37 different ecological unit types. This dataset was derived through a GIS-based cluster analysis of 10 different physiographic variables, namely soil suborder type, terrain geomorphon type, flow accumulation, slope, solar irradiation, annual precipitation, annual minimum temperature, actual evapotranspiration, and climatic water deficit. This partitioning was based on physiographic variables rather than vegetation types because of the wish to have the ecological units reflect biophysical characteristics rather than the historical land use patterns that may influence vegetation. The cluster analysis was performed across a set of 10,000 points randomly placed on a GIS layer stack for the 10 variables. These random points were grouped into 37 discrete clusters using an algorithm called partitioning around medoids. This assignment of points to clusters was then used to train a random forest classifier, which in turn was run across the GIS stack to produce the output raster layer.

This dataset is described in the following book chapter publication:

Underwood, Emma C., Allan D. Hollander, Patrick R. Huber, and Charlie Schrader-Patton. 2018. “Mapping the Value of National Forest Landscapes for Ecosystem Service Provision.” In Valuing Chaparral, 245–70. Springer Series on Environmental Management. Springer, Cham. https://doi.org/10.1007/978-3-319-68303-4_9.

Methods Summary of Methods for Developing Ecological Units in Southern California

Allan Hollander and Emma Underwood, University of California Davis.

1) Compiling GIS layers. These data were compiled from a variety of sources and resolutions (Table 1) for the southern California study area (see Methods_figure_1.png for the study area). The original resolution of these raster layers ran from 10 meters to 270 meters, and resampling was conducted so all analyses were performed at a 30 meter raster resolution. We decided not to include vegetation in the data stack as the aim was to capture biophysical characteristics and vegetation will reflect current landscape history and land use patterns (e.g. fire history, type conversion from shrubland, or agricultural use). Lakes and reservoirs were omitted from the subsequent analysis. Data compiled:

a) Soil suborders. This was a discretely-classified raster layer with 22 soil suborder classes included in the southern California region. This was derived from the gridded Soil Survey Geographic Database (gSSURGO, available at http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcs142p2_053628). This product is a rasterization at a 10-meter resolution of the county-scale SSURGO data published by the USDA Natural Resources Conservation Service.

b) Terrain geomorphons. This raster layer derives from a DEM surface and classifies the landscape into 10 discrete landform types, examples being ridges, slopes, hollows, and valleys. The algorithm for geomorphon classification uses a pattern recognition approach based on line of sight analysis (Jasiewisc and Stepinski 2013). This layer was created from a 30 meter DEM in GRASS 7.0.0, using the extension r.geomorphon (https://grass.osgeo.org/grass70/manuals/addons/r.geomorphon.html).

c) Annualized solar irradiation. This layer uses the r.sun model available in GRASS 7.0.0 (https://grass.osgeo.org/grass70/manuals/r.sun.html) which calculates direct, diffuse, and reflected solar irradiation for a given day, location, topography, and atmospheric conditions. This layer was created from a 30 meter DEM and assumes clear-sky conditions. To estimate the total annual irradiation, the model was run for every 15th day and these values were integrated over the year.

d) Flow accumulation. This layer is another product of 30 meter DEM data and measures the upslope area in pixel count that conceivably drains into a given pixel. This was calculated using the accumulation option in the GRASS 7.0.0 command r.watershed (https://grass.osgeo.org/grass70/manuals/r.watershed.html)

e) Slope. This was derived from 30 meter DEM data using the GRASS 7.0.0 command r.slope.aspect, and is measured in degrees.

f) Annual precipitation. This layer came from the 2014 Basin Characterization Model (BCM) for California (Flint et al. 2013) and gives the average annual precipitation between 1981 and 2010 at a 270-meter resolution.

g) Annual minimum temperature. This layer also came from BCM (Flint et al. 2013) and gives the average annual minimum temperature between 1981 and 2010 at a 270-meter resolution. Minimum temperature was included in the set of climate variables to represent montane winter conditions.

h) Climatic water deficit. This layer also came from the BCM (Flint et al. 2013) and gives the average climatic water deficit between 1981 and 2010 at a 270-meter resolution. The two evapotranspiration variables (climatic water deficit and actual evapotranspiration) are included in this set because they are strong drivers of vegetation distribution (Stephenson 1998).

i) Actual evapotranspiration. This layer also came from the BCM (Flint et al. 2013) and gives the average actual evapotranspiration between 1981 and 2010 at a 270-meter resolution.

Table 1. Summary of GIS data stack

    LAYER


    ORIGINAL SOURCE


    ORIGINAL RESOLUTION


    THEME








    Soil suborders


    gSSURGO


    10 meters


    Soil type




    Terrain geomorphons


    Digital elevation model


    30 meters


    Geomorphometry




    Solar irradiation


    Digital elevation model


    30 meters


    Energy balance




    Flow accumulation


    Digital elevation model


    30 meters


    Geomorphometry




    Slope


    Digital elevation model


    30 meters


    Geomorphometry




    Annual precipitation


    Basin Characterization Model


    270 meters


    Climate




    Annual min temperature


    Basin Characterization Model


    270 meters


    Climate




    Climatic water deficit


    Basin Characterization Model


    270 meters


    Climate




    Actual evapotranspiration


    Basin Characterization Model


    270 meters


    Climate

2) Generating 10,000 random points. A mask was imposed to limit analyses to the 35,158 square study area and 10,000 random points were generated to create a data table of the values of each GIS layer at each of the random points. This data table was the basis for sorting the random points into a limited number of clustered types. The first step in doing this is calculating in multivariate space the distance with respect to these environmental variables each random point is from every other point, in other words creating a dissimilarity matrix.

3) Assigning weights to variables. Because the 9 environmental variables use completely different metrics and are a combination of numerical and categorical types, calculating an environmental distance between any two of these random points requires some weighting to be assigned to each of the environmental variables to sum up their relative distances. A subanalysis to determine these weightings used a subset of the study area, the Santa Clara River watershed. Since these ecological units are intended to summarize a diverse set of ecological services, we chose three different proxy variables from the GIS data available for this area to represent biomass, hydrological response, and biodiversity. These proxies included mean annual MODIS Enhanced Vegetation Index (EVI) value for biomass, recharge for hydrological response, and habitat type in the California Wildlife Habitat Relations (CWHR) classification for biodiversity.

The MODIS EVI data was derived by averaging over the 2000-2014 period the maximum EVI value in a single year. The MODIS index used was MOD13Q1 (https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod13q1) at a 250 meter resolution, available at 16-day intervals.


The hydrological recharge data were extracted from the 2014 Basin Characterization Model (Flint et al. 2013) at 270 meter resolution.


The CWHR habitat type came from the 2015 FRAP vegetation layer (FVEG15_1, from http://frap.fire.ca.gov/data/frapgisdata-sw-fveg_download), available at a 30 meter resolution.

a) We used random forest regression and classification (Hastie et al. 2009) to determine a ranking of importance values of these predictor variables using random forest regression for EVI and recharge and random forest classification for the habitat type. These were calculated using the randomForest package in R (Liaw and Wiener 2002).

b) We then averaged these three sets of importance values to create an overall set of weightings to enter into the dissimilarity matrix (Table 2).

Table 2. Weightings for each variable to reflect their relative importance to the ecological units

    VARIABLE NAME


    WEIGHT




    Precipitation


    1.00




    Annual minimum temperature


    0.600




    Slope


    0.507




    Climatic water deficit


    0.413




    Annualized solar radiation


    0.404




    Soil suborder

This data archive is a collection of GIS files and FGDC metadata prepared in 1995 for the Northampton County Planning Office by the Virginia Coast Reserve LTER project at the University of Virginia with support from the Virginia Department of Environmental Quality (DEQ) and the National Science Foundation (NSF). Original data sources include: 1:100,000-scale USGS digital line graph (DLG) hydrography and transportation data; 1:6,000-scale boundary, road, and railroad data for the town of Cape Charles from VDOT; 1:190,000-scale county-wide general soil map data and 1:15,540-scale detailed soil data for the Cape Charles area digitized from printed USDA soil survey maps; a land use and vegetation cover dataset (30 m. resolution) created by the VCRLTER derived from a 1993 Landsat Thematic Mapper satellite image; 1:20,000-scale plant association maps for 10 seaside barrier and marsh islands between Hog and Smith Islands, inclusive, prepared by Cheryl McCaffrey for TNC in 1975 and published in the Virginia Journal of Science in 1990; and 1993 colonial bird nesting site data collected by The Center for Conservation Biology (with partners The Nature Conservancy, College of William and Mary, University of Virginia, USFWS, VA-DCR, and VA-DGIF). Contents: HYDROGRAPHY Based on USGS 1:100,000 Digital Line Graph (DLG) data. Files: h100k_arc_u84 (streams, shorelines, etc.) and h100k_poly_u84 (marshes, mudflats, etc.). Note that the hydrographic data has been superseded by the more recent and more detailed USGS National Hydrography Dataset, available for the entire state of Virginia at "ftp://nhdftp.usgs.gov/DataSets/Staged/States/FileGDB/HighResolution/NHDH_VA_931v210.zip" (see http://nhd.usgs.gov/data.html for more information). A static 2013 version of the NHD data that includes shapefiles extracted from the original ESRI geodatabase format data and covering just the watersheds of the Eastern Shore of VA can also be found in the VCRLTER Data Catalog (dataset VCR14223). TRANSPORTATION Based on USGS 1:100,000 Digital Line Graph (DLG) data for the full county, and 1:6,000 VDOT data for the Cape Charles township. Files: 1:100k Transportation (lines) from USGS DLG data: rtf100k_arc_u84 (roads), rrf100k_arc_u84 (railroads), and mtf100k_arc_u84 (airports and utility transmission lines). Files: 1:6000 street, boundary, and rail line data for the town of Cape Charles, 1984, prepared by Virginia Department of Highways and Transportation Information Services (Division 1221 East Broad Street, Richmond, Virginia 23219). Streets correct through December 31,1983. Georeferencing corrected in 2014 for shapefiles only, using same methodology described for VCR14218 dataset. File : town_u84_adj (town_arc_u84old is the older unadjusted data). Note that the transportation data has been superseded by more recent and more detailed data contained in dataset VCR14222 of the VCRLTER Data Catalog. The VCR14222 data contains 2013 U.S. Census Bureau TIGER/Line road and airfield data supplemented by railroad and transmission lines digitized from high resolution VGIN-VBMP 2013 aerial imagery and additionally has boat launch locations not available here. SOILS General soil map for Northampton county (1:190k), and detailed soil map for Cape Charles and Cheriton areas (1:15,540) from published the USDA Soil Conservation Service's 1989 "Soil Survey of Northampton County, Virginia" digitized at UVA by Ray Dukes Smith: soilorig_poly_u84 (uses original shorelines from source maps), soil_poly_u84 (substitutes shorelines from 1993 landcover classification data), and cc_soil_poly_u84 (Cape Charles & Cheriton detailed data, map sheets 13 and 14). Note that the soil data has been superseded by more recent and more detailed SSURGO soil data from the USDA's Natural Resources Conservation Service (NRCS), which has seamless soil data from the 1:15,540 map series in tabular and GIS formats for the full county, as well as for all counties in VA and other states. A static 2013 version of the SSURGO data that contains merged data for Accomack and Northampton Counties can be found in the VCRLTER Data Catalog (dataset VCR14220). LANDUSE/LANDCOVER VCR Landuse and Vegetation Cover, 1993, created by Guofan Shao (VCRLTER) based on 30m resolution Landsat Thematic Mapper (TM) satellite imagery taken on July 28, 1993. Cropped to include just Northampton County. Landcover is divided into 5 classifications: (1) Forest or shrub, (2) Bare Land or Sand, (3) Planted Cropland, Grassland, or Upland Marsh, (4) Open Water, and (5) Low Salt Marsh. File = nhtm93s3_poly_u84. No spatial adjustments necessary. An outline of the county showing the shorelines based on the above 1993 TM classification is included as the shapefile:outline_poly_u84; however, no spatial adjustment has been applied. Note that a similar landuse/landcover classification based on the same... Visit https://dataone.org/datasets/knb-lter-vcr.223.2 for complete metadata about this dataset.

Gridded SSURGO (gSSURGO) is similar to the standard product from the United States Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) Soil Survey Geographic (SSURGO) Database, but is in the Environmental Systems Research Institute, Inc. (ESRI®) file geodatabase format. A file geodatabase has the capacity to store significantly more data and thus greater spatial extents than the traditional SSURGO product. This allows for statewide or even Conterminous United States (CONUS) tiling of data. gSSURGO contains all of the original soil attribute tables in SSURGO. All spatial data are stored within the geodatabase instead of externally as separate shape files. Both SSURGO and gSSURGO are considered products of the National Cooperative Soil Survey (NCSS). An important addition to the new format is a 10-meter raster (MapunitRaster_10m) of the map unit soil polygons feature class, which provides statewide coverage in a single layer. The CONUS database includes a 30-meter raster because of size constraints. This new addition provides greater performance and important analysis capabilities to users of soils data. Statewide tiles consist of soil survey areas needed to provide full coverage for a given State. In order to create a true statewide soils layer, some clipping of excess soil survey area gSSURGO data may be required. The new format also includes a national Value Added Look Up (valu) Table that has several new “ready to map” attributes.Other Documents to Reference:gSSURGO FactsheetgSSURGO User Guide ArcMap version 2.4Soil Data Development Toolbox User Guide v5 for ArcMapgSSURGO Mapping Detailed GuidegSSURGO Valu1 table column descriptions

Massachusetts Top 20 Soils Data Layer

In an effort to provide a simple, statewide soils data layer, the Massachusetts Top 20 soils data layer is a statewide shapefile of the soil survey data that contains a single set of attributes for each soil survey map unit. The attributes provided are those soil properties or ratings that are most requested by soil survey users through the Web Soil Survey platform.

To create the shapefile, statewide gSSURGO data was downloaded from USDA’s Geospatial Data Gateway. A Soil Data Access query was used to extract certain data elements for these most-commonly requested soil properties and interpretations and exported into an excel file. This excel file was joined with the spatial data using the mukey and the resulting shapefile was exported. Descriptions for each attribute included in the shapefile is listed below.

For more information contact your local NRCS office or visit https://www.nrcs.usda.gov/wps/portal/nrcs/main/ma/soils/

Attribute

Attribute Name

Attribute description

Area Symbol

AREASYMBOL

Soil Survey Area Symbol

Map Unit Symbol

MUSYM

The symbol used to uniquely identify the soil mapunit in the soil survey.

Map Unit Key

MUKEY

The symbol used to uniquely identify the soil mapunit in the national soils information system database.

Area Name

AREANAME

Soil Survey Area name

Map Unit Name

MUNAME

Soil map unit name

Component Name

COMPNAME

Name of the dominant component of the soil map unit

Map Unit Kind

MUKIND

The kind of mapunit

Farmland Classification

FRMLNDCLS

Identification of map units as prime farmland, farmland of statewide importance, or farmland of unique importance.

Hydric Rating by Map Unit

HYDRCRATNG

Hydric soils are defined by the National Technical Committee for Hydric Soils (NTCHS) as soils that formed under conditions of saturation, flooding, or ponding long enough during the growing season to develop anaerobic conditions in the upper part (Federal Register, 1994). Under natural conditions, these soils are either saturated or inundated long enough during the growing season to support the growth and reproduction of hydrophytic vegetation. Reported for the dominant component of the map unit.

Drainage Class

DRAINCLASS

The natural drainage condition of the soil refers to the frequency and duration of wet periods. This column displays the dominant drainage class for the map unit, based on composition percentage of each map unit component.

Mineral Surface texture

MINSURFTEXT

The soil texture description of the first mineral soil horizon. Reported for the dominant component of the map unit.

T Factor

TFACTOR

Soil loss tolerance factor. The maximum amount of erosion at which the quality of a soil as a medium for plant growth can be maintained. Reported for the dominant component of the map unit.

Available Water Storage 0-100 cm

AWS100

Available water storage (AWS). The volume of water that the soil, to a depth of 100 centimeters, can store that is available to plants. It is reported as the weighted average of all components in the map unit and is expressed as centimeters of water. AWS is calculated from AWC (available water capacity) which is commonly estimated as the difference between the water contents at 1/10 or 1/3 bar (field capacity) and 15 bars (permanent wilting point) tension and adjusted for salinity and fragments.

Available Water Storage 0-25 cm

AWS25

Available water storage (AWS). The volume of water that the soil, to a depth of 25 centimeters, can store that is available to plants. It is reported as the weighted average of all components in the map unit and is expressed as centimeters of water. AWS is calculated from AWC (available water capacity) which is commonly estimated as the difference between the water contents at 1/10 or 1/3 bar (field capacity) and 15 bars (permanent wilting point) tension and adjusted for salinity and fragments.

Depth to Water Table

DEP2WATTBL

The shallowest depth to a wet soil layer (water table) at any time during the year expressed as centimeters from the soil surface, for components whose composition in the map unit is equal to or exceeds 15%. *These values are derived from the national database. They are intended for general planning purposes only and are not, in any way, intended to replace or supersede an on-site investigation. On-site investigations by certified soil evaluators are required by MA Environmental Code Title V for siting septic systems.

Dwellings with Basements

DWELLWB

The rating of the map unit as a site for dwellings with basements, expressed as the dominant rating class for the map unit, based on composition percentage of each map unit component.

Hydrologic Soil Group

HYDROLGRP

Hydrologic Group is a grouping of soils that have similar runoff potential under similar storm and cover conditions. This column displays the dominant hydrologic group for the map unit, based on composition percentage of each map unit component.

Nonirrigated Land Capability Class

NIRRLCC

This column displays the dominant capability class and subclass, under non-irrigated conditions, for the map unit based on composition percentage of all components in the map unit.

Local Roads and Streets

ROADS

The rating of the map unit as a site for local roads and streets, expressed as the dominant rating class for the map unit, based on composition percentage of each map unit component.

Septic Tank Absorption Fields

SEPTANKAF

The rating of the map unit as a site for septic tank absorption fields, expressed as the dominant rating class for the map unit, based on composition percentage of each map unit component. *These values are derived from the national database. They are intended for general planning purposes only and are not, in any way, intended to replace or supersede an on-site investigation. On-site investigations by certified soil evaluators are required by MA Environmental Code Title V for siting septic systems.

Representative Slope

SLOPE

The difference in elevation between two points, expressed as a percentage of the distance between those points. This column displays the slope gradient of the dominant component of the map unit based on composition percentage.

Flooding Frequency Class

FLOODING

The annual probability of a flood event expressed as a class. This column displays the dominant flood frequency class for the map unit, based on composition percentage of map unit components whose composition in the map unit is equal to or exceeds 15%.

Ponding Frequency Class

PONDING

The annual probability of a ponding event expressed as a class. This column displays the dominant ponding frequency class for the map unit, based on composition percentage of map unit components whose composition in the map unit is equal to or exceeds 15%

Corrosion of Concrete

CORCONCRET

"Risk of corrosion" pertains to potential soil-induced electrochemical or chemical action that corrodes or weakens concrete. The rate of corrosion of concrete is based mainly on the sulfate and sodium content, texture, moisture content, and acidity of the soil. Special site examination and design may be needed if the combination of factors results in a severe hazard of corrosion. The concrete in installations that intersect soil boundaries or soil layers is more susceptible to corrosion than the concrete in installations that are entirely within one kind of soil or within one soil layer. The risk of corrosion is expressed as "low," "moderate," or "high." Reported for the dominant component of the map unit.

Soil Taxonomy Classification

TAXCLNAME

A concatenation of the Soil Taxonomy subgroup and family for a soil (long name). Reported for the dominant component of the map unit.

Depth to Any Soil Restrictive Layer

CM2RESLYR

The distance in centimeters from the soil surface to the upper boundary of any restrictive layer. Reported for the dominant component of the map unit.

Restriction Kind

RESKIND

Type of nearly continuous layer that has one or more physical, chemical, or thermal properties that significantly reduce the movement of water and air through the soil or that otherwise provides an unfavorable root environment. Reported for the dominant component of the map unit.

Parent Material Name

PARMATNM

Reports the name for each of the parent materials that may occur in a vertical cross section of a soil. Reported for the dominant component of the map unit.

Unified Soil Classification (Surface)

UNIFSOILCL

Reports the Unified soil classification group symbol for the first mineral horizon of the dominant component of the map unit.

AASHTO Group Classification (Surface)

AASHTO

Reports the American Association of State Highway and Transportation Officials (AASHTO) class rating for the first mineral horizon of the dominant component of the map unit.

K Factor, Rock Free

KFACTRF

An erodibility factor which quantifies the susceptibility of soil particles to detachment by water. Reported for the first