The gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS (Natural Resources Conservation Service) Soil & Plant Science Division (SPSD) composite ESRI file geodatabase 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 gNATSGO database contains 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. 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. The current completion status of SSURGO mapping is displayed (PDF). 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. The first version of gNATSGO was created in 2019. It is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. Three RSSs have been published as of 2019. These were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years. Resources in this dataset:Resource Title: Website Pointer for Gridded National Soil Survey Geographic Database (gNATSGO). File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625 The gNATSGO website provides an Overview slide presentation, Download links for gNATSGO databases (CONUS or States), ArcTools, Metadata, Technical Information, and Recommended Data Citations.

The dataset consists of three raster GeoTIFF files describing the following soil properties in the US: available water capacity, field capacity, and soil porosity. The input data were obtained from the gridded National Soil Survey Geographic (gNATSGO) Database and the Gridded Soil Survey Geographic (gSSURGO) Database with Soil Data Development tools provided by the Natural Resources Conservation Service. The soil characteristics derived from the databases were Available Water Capacity (AWC), Water Content (one-third bar) (WC), and Bulk Density (one-third bar) (BD) aggregated as weighted average values in the upper 1 m of soil. AWC and WC layers were converted to mm/m to express respectively available water capacity and field capacity in 1 m of soil, and BD layer was used to produce soil porosity raster assuming that the average particle density of soils is equal to 2.65 g/cm3. For each soil property, soil maps with CONUS, Alaska, and Hawaii geographic coverages were derived from separate databases and combined into one file. To replace no data values within a raster, we used data values statistically derived from neighboring cell values. The final product is provided in a GeoTIFF format and therefore can be easily integrated into raster-based models requiring estimates of soil characteristics in the US.

Data dictionary for Gridded National Soil Survey Geographic Database (gNATSGO). https://data.nal.usda.gov/node/23067gNATSGO has a schema that is very similar to that of SSURGO and STATSGO2. A CSV version of the data dictionary is presented.A data dictionary typically provides a detailed description for each element or variable in a dataset or data model. Data dictionaries are used to document important and useful information such as a descriptive name, the data type, allowed values, units, and text description.Dataset citation: (dataset) Soil Survey Staff. Gridded National Soil Survey Geographic (gNATSGO) Database for [State name -or- the Conterminous United States]. United States Department of Agriculture, Natural Resources Conservation Service. Available online at https://nrcs.app.box.com/v/soils. Month, day, year.

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

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/

SSURGO PortalThe newest version of SSURGO Portal with Soil Data Viewer is available via the Quick Start Guide. Install Python to C:\Program Files. This is a different version than what ArcGIS Pro uses.If you need data for multiple states, we also offer a prebuilt large database with all SSURGO for the entire United States and all Islands. The prebuilt saves you time but it’s large and takes a while to download.You can also use the prebuilt gNATSGO GeoPackage database in SSURGO Portal – Soil Data Viewer. Read the ReadMe.txt in the folder. More about gNATSGO here. You can also import STATSGO2 data into SSURGO Portal and create a database to use in Soil Data Viewer – Available for download via the Soils Box folder. SSURGO Portal NotesThis 10 minute video covers it all, other than installation of SSURGO Portal and the GIS tool. Installation is typically smooth and easy.There is also a user guide on the SSURGO Portal website that can be very helpful. It has info about using the data in ArcGIS Pro or QGIS. SQLite SSURGO database be opened and queried with DB Browser. It’s essentially free Microsoft Access.Guidance about setting up DB Browser to easily open SQLite databases is available in section 4 of this Installation Guide.Workflow if you need to make your own databaseInstall SSURGO PortalInstall SSURGO Downloader GIS tool (Refer to the Installation and User Guide for assistance)There is one for QGIS and one for ArcGIS Pro. They both do the same thing. Quickly download California SSURGO data with toolEnter two digit state symbol followed by asterisk in “Search by Areasymbol” to download all data for state.For example, enter CA* to batch download all data for CaliforniaOpen SSURGO Portal and create a new SQLite SSURGO Template database (Refer to the User Guide for assistance)Import SSURGO data you downloaded into databaseYou can import SSURGO data from many states at once, building a database that spans many statesAfter SSURGO data is done importing, click on Soil Data Viewer tab and run ratingsThese are the exact same ratings as Web Soil SurveyA new table is added to your database for each ratingYou can search for ratings by keywordIf desired, open database in GIS and make a map (Refer to the User Guide for assistance)Workflow if you need use large prebuilt database (don’t make own database) Install SSURGO PortalIn SSURGO Portal, browse to unzipped prebuilt GeoPackage database with all SSURGOprebuilt large database with all SSURGOgNATSGO GeoPackage databaseIn SSURGO Portal, click on Soil Data Viewer tab and run ratingsThese are the exact same ratings as Web Soil SurveyA new table is added to your database for each ratingYou can search for ratings by keywordIf desired, open database in GIS and make a mapIf you have trouble installing SSURGO Portal. Its usually the connection with Python. Create Desktop short cut that tells SSURGO Portal which Python to useThese were created for Windows 11 Right click anywhere on your desktop and choose New > ShortcutIn the text bar enter your path to the python.exe and your path to the SSURGO Portal.pyz. Notes:Example of format:"C:\Program Files\Python310\python.exe" "C:\SSURGO Portal\SSURGO_Portal-0.3.0.8.pyz"Include quotation marks.Paths may be different on your machine. To avoid typing, you can browse to python.exe in windows explorer, right click and select "Copy as Path and paste results into box. Paste into short location and then do the same for SSURGO Portal.pyz file, but paste to the right of the python.exe path. Click NextName the shortcut anything you want.

Data Description: To improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively. Files in collection (32): Collection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts gNATSGO TIF files: ├── available_water_storage_30arc_30cm_us.tif [30 cm depth soil available water storage]
├── available_water_storage_30arc_100cm_us.tif [100 cm depth soil available water storage]
├── caco3_30arc_30cm_us.tif [30 cm depth soil CaCO3 content]
├── caco3_30arc_100cm_us.tif [100 cm depth soil CaCO3 content]
├── cec_30arc_30cm_us.tif [30 cm depth soil cation exchange capacity]
├── cec_30arc_100cm_us.tif [100 cm depth soil cation exchange capacity]
├── clay_30arc_30cm_us.tif [30 cm depth soil clay content]
├── clay_30arc_100cm_us.tif [100 cm depth soil clay content]
├── depthWT_30arc_us.tif [depth to water table]
├── kfactor_30arc_30cm_us.tif [30 cm depth soil erosion factor]
├── kfactor_30arc_100cm_us.tif [100 cm depth soil erosion factor]
├── ph_30arc_100cm_us.tif [100 cm depth soil pH]
├── ph_30arc_100cm_us.tif [30 cm depth soil pH]
├── pondingFre_30arc_us.tif [ponding frequency]
├── sand_30arc_30cm_us.tif [30 cm depth soil sand content]
├── sand_30arc_100cm_us.tif [100 cm depth soil sand content]
├── silt_30arc_30cm_us.tif [30 cm depth soil silt content]
├── silt_30arc_100cm_us.tif [100 cm depth soil silt content]
├── water_content_30arc_30cm_us.tif [30 cm depth soil water content]
└── water_content_30arc_100cm_us.tif [100 cm depth soil water content] SOC TIF files: ├──30cm SOC mean.tif [30 cm depth soil SOC]
├──100cm SOC mean.tif [100 cm depth soil SOC]
├──30cm SOC CV.tif [30 cm depth soil SOC coefficient of variation]
└──100cm SOC CV.tif [100 cm depth soil SOC coefficient of variation] site observations csv files: ISCN_rmNRCS_addNCSS_30cm.csv 30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data ISCN_rmNRCS_addNCSS_100cm.csv 100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data
Data format: Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution. Geospatial projection:

GEOGCS['GCS_WGS_1984', DATUM['D_WGS_1984', SPHEROID['WGS_1984',6378137,298.257223563]], PRIMEM['Greenwich',0], UNIT['Degree',0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS['wgs84', DATUM['WGS_1984', SPHEROID['WGS_1984',6378137,298.257223563]], PRIMEM['Greenwich',0], UNIT['degree',0.0174532925199433]] 

Erosion, the loss of soil due to the effects of water and wind, can lead to serious degradation of lands and the loss of agricultural productivity.This layer classifies the amount of soil loss in the top soil layers in 5 classes:None: Area of soil deposition.Class 1: In this map unit,1 to 25 percent of the original topsoil has been lost to erosion. Class 2: In this map unit, 1 to 25 percent of the original topsoil has been lost to erosion.Class 3: In this map unit, 75 to 99 percent of the original topsoil has been lost to erosion.Class 4: In this map unit, all of the original topsoil has been lost to erosionDataset SummaryPhenomenon Mapped: Top soil loss due to erosionUnits: ClassesCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa)Mosaic Projection: Web Mercator Auxiliary SphereExtent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaSource: Natural Resources Conservation ServicePublication Date: December 2021ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/Data 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).This layer is derived from the 30m (contiguous U.S.) and 10m rasters (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for runoff is derived from the gSSURGO component table field Erosion Class (erocl). The value in this layer is the dominant condition found within the map unit.What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "erosion class" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "erosion class" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

When rain falls over land, a portion of it runs off into stream channels and storm water systems while the remainder infiltrates into the soil or returns to the atmosphere directly through evaporation.Physical properties of soil affect the rate that water is absorbed and the amount of runoff produced by a storm. Hydrologic soil group provides an index of the rate that water infiltrates a soil and is an input to rainfall-runoff models that are used to predict potential stream flow.For more information on using hydrologic soil group in hydrologic modeling see the publication Urban Hydrology for Small Watersheds (Natural Resources Conservation Service, United States Department of Agriculture, Technical Release–55).Dataset SummaryPhenomenon Mapped: Soil hydrologic groupUnits: ClassesCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa)Mosaic Projection: Web Mercator Auxiliary SphereExtent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaSource: Natural Resources Conservation ServicePublication Date: December 2021ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/Data 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).This layer is derived from the 30m (contiguous U.S.) and 10m rasters (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for hydrologic group is derived from the gSSURGO map unit aggregated attribute table field Hydrologic Group - Dominant Conditions (hydgrpdcd).The seven classes of hydrologic soil group followed by definitions:Group A - Group A soils consist of deep, well drained sands or gravelly sands with high infiltration and low runoff rates.Group B - Group B soils consist of deep well drained soils with a moderately fine to moderately coarse texture and a moderate rate of infiltration and runoff.Group C - Group C consists of soils with a layer that impedes the downward movement of water or fine textured soils and a slow rate of infiltration.Group D - Group D consists of soils with a very slow infiltration rate and high runoff potential. This group is composed of clays that have a high shrink-swell potential, soils with a high water table, soils that have a clay pan or clay layer at or near the surface, and soils that are shallow over nearly impervious material.Group A/D - Group A/D soils naturally have a very slow infiltration rate due to a high water table but will have high infiltration and low runoff rates if drained.Group B/D - Group B/D soils naturally have a very slow infiltration rate due to a high water table but will have a moderate rate of infiltration and runoff if drained.Group C/D - Group C/D soils naturally have a very slow infiltration rate due to a high water table but will have a slow rate of infiltration if drained.What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "soil hydrologic group" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "soil hydrologic group" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

Albedo measures the reflectivity of an object. Surfaces that are black reflect little light and have low albedo values while white surfaces reflect most of the light striking them and have high albedo values. Albedo is measured on a scale of 0 (no light reflected) to 1 (100% of light reflected). Albedo is measured using a scale of 0 (no light reflected) to 1 (100% of the light is reflected). Divide each integer"s raw pixel value by one hundred to find its representative albedo value. Thus, a pixel with the value of 24 represents an albedo value of 0.24 while a pixel with the value of 38 represents the albedo value 0.38. Dataset SummaryPhenomenon Mapped: Soil albedoGeographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands, Republic of Palau, Republic of the Marshall Islands, Federated States of Micronesia, and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: WKID 5070 USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WKID 3338 WGS 1984 Albers (Alaska), WKID 4326 WGS 1984 Decimal Degrees (Guam, Republic of the Marshall Islands, Northern Mariana Islands, Republic of Palau, Federated States of Micronesia, American Samoa, and Hawaii).Units: NoneCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerSource: Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 Typical albedo values:Fresh asphalt 0.04Worn asphalt 0.12Confier forest 0.08 – 0.15Deciduous trees 0.15 – 0.18Bare soil 0.17Green grass 0.25Desert sand 0.4New concrete 0.55Ocean ice 0.5 – 0.7Fresh snow 0.8-0.9 Albedo is used in climate and water cycle models. Estimates of evapotranspiration rate and prediction of soil water balances require albedo values. Soil hydrology models that are part of water quality and resource assessment programs also require albedo. Data from the gNATSGO database was used to create the layer. This layer is derived from the 30m rasters produced by the Natural Resources Conservation Service (NRCS). The value for soil albedo is derived from the gSSURGO component table field Albedo Dry - Representative Value (albedodry_r). The value in this layer is the average value for all components of each map unit weighted by component percent (comppct_r). What can you do with this layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map: In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "albedo" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "albedo" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro. Online you can filter the layer to show subsets of the data using the filter button and the layer"s built-in raster functions. The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

The Percentage Potential Wetland Area on Cultivated Cropland (PWAC) layer shows areas where conditions may be suitable for wetland restoration or creation at a 10-m resolution. Since the 1600's, an estimated 53% of wetlands in the Conterminous United States have been lost, with many areas being converted for agricultural or urban use. The ecosystems services provided by wetlands are extremely valuable, providing flood attenuation, water filtration, nutrient sequestration, vital habitat, and many others. Wetland restoration or creation can help restore these benefits for the surrounding community. There are several government and community projects that can utilize these data to assist in site selection for wetland restoration projects. This layer was created using the Random Forest (RF) machine learning algorithm in Google Earth Engine (GEE). The RF model utilized 17 data inputs to identify areas where attributes on the landscape are similar to the attributes found in existing wetlands. The input data for this layer fall into three categories: topographic variables, soils, and satellite imagery. Topographic - DEM's sourced from USGS 3D Elevation Program (10-m) -Elevation -Aspect -Slope -Compound Topographic Index (CTI) -Vertical Overland Flow Distance (VOFD) -Horizontal Overland Flow Distance (HOFD) -Pythagoras Overland Flow Distance (POFD) -Soils - Natural Resource Conservation Service's gNATSGO and gSSURGO products · Potential Wetland Soils (PWS) -European Space Agency's Sentinel-1 Synthetic Aperture Radar (10-m) Using these variables, the Random Forest model was run for each 2 digit Hydrologic Unit Code (HUC) in Google Earth Engine. The model used wetlands from the National Wetland Inventory (NWI) to create training data, masking out deep water areas such as the centers of lakes and rivers, and excluding estuarine and marine wetlands. For each HUC an equal number of wetland an non-wetland training points proportional to the size of the HUC were generated, with 30% of those points being reserved for accuracy assessment. The model results were then intersected with NLCD's land cover class Cultivated Cropland. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This is a dataset download, not a document. The Open button will start the download.Detailed soil units from Soils Surveys covering nonfederal land conducted by the U.S. Natural Resource Conservation Service (NRCS) that differentiates mapped units on the basis of a range of physical, topographic, and chemical properties.

The National Commodity Crop Productivity Index (NCCPI) ranks the inherent capability of soils to produce agricultural crops without irrigation. For more information on how the NCCPI is calculated see User Guide for the National Commodity Crop Productivity Index.Dataset SummaryPhenomenon Mapped: National Commodity Crop Productivity Index version 3.0Units: Thousandths of nccpi3all index value, served as integers (this layer's value of 889 equals 0.889 in the nccpi3all)Cell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa)Mosaic Projection: Web Mercator Auxiliary SphereExtent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaSource: Natural Resources Conservation ServicePublication Date: December 2021, except Puerto Rico and US Virgin Islands which are July 2020.ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/Data 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).This layer is derived from the 30m (contiguous U.S.) and 10m rasters (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for the National Commodity Crop Productivity Index is derived from the gSSURGO valu1 table field nccpi3all.Note: This layer serves the National Commodity Crop Productivity Index value from the 2021 version for Puerto Rico and the US Virgin Islands. In 2022 the gNATSGO source was missing its Valu1 table for Puerto Rico and the US Virgin Islands.What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "soil crop production" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "soil crop production" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

This service is available to all ArcGIS Online users with organizational accounts. For more information on this service, including the terms of use, visit us online at https://goto.arcgisonline.com/landscape11/USA_Soils_Hydric_Class.Hydric soils are soils that form under conditions of saturation, flooding, or ponding long enough during the growing season to develop anaerobic conditions in the upper part of the soil. Hydric soils are poorly or very poorly drained and under natural conditions, these soils are either saturated or inundated long enough during the growing season to support the growth and reproduction of wetland vegetation. Hydric soils are part of the legal definition for wetlands in the United States and are used to identify wetland areas that require a permit issued by the Army Corps of Engineers under Section 404 of the Clean Water Act prior to any ground disturbing activities. For more information on hydric soils see the Natural Resources Conservation Service’s publication Field Indicators of Hydric Soils in the United States.Dataset SummaryPhenomenon Mapped: Hydric soilsUnits: PercentCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa)Mosaic Projection: Web Mercator Auxiliary SphereExtent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaSource: Natural Resources Conservation ServicePublication Date: July 2020ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/Data 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).This layer is derived from the 30m (contiguous U.S.) and 10m rasters (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for hydric class is derived from the gSSURGO map unit aggregated attribute table field Hydric Classification - Presence (hydclprs).What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "hydric" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "hydric" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

Soil erodibility factor, also known as K factor, is one of the 5 inputs to the Universal Soil Loss Equation. Soil erodibility factor quantifies the susceptibility of soil particles to detachment and movement by water. For more information on how soil erodibilty factor is calculated see the National Soil Survey Handbook. The Universal Soil Loss Equation is a mathematical model commonly used to estimate soil erosion rates. Originally designed for the management and conservation of farmland soils, the USLE is now used for a variety of other projects such as managing non-point pollution and sediment load in streams. In the United States, the equation is frequently used by federal agencies. For example federal regulations require that the Department of Agriculture identify highly erodible land based on the Universal Soil Loss Equation and its derivative models. Dataset SummaryPhenomenon Mapped: Erodibility factor (not adjusted for rock fragments)Geographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands, Republic of Palau, Republic of the Marshall Islands, Federated States of Micronesia, and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: WKID 5070 USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WKID 3338 WGS 1984 Albers (Alaska), WKID 4326 WGS 1984 Decimal Degrees (Guam, Republic of the Marshall Islands, Northern Mariana Islands, Republic of Palau, Federated States of Micronesia, American Samoa, and Hawaii).Units: NoneCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerSource: Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 Data from the gNATSGO database was used to create the layer. This layer is derived from the 30m rasters produced by the Natural Resources Conservation Service (NRCS). This field was calculated by selecting the least transmissive horizon of the dominant component for each mapunit. The values are in units of Micrometers per second (μm/s). In the past this layer used to display an average of components, but this is no longer the case. What can you do with this layer?This layer is suitable for both visualization and analysis acrossthe ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "erodibility factor" in the search box and browse to the layer. Select the layer then clickAdd to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "erodibility factor" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro. Online you can filter the layer to show subsets of the data using the filter button and the layer"s built-in raster functions. The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one. Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

GENERAL INFORMATIONThis README.txt file was updated on August 23, 2024Citation: Past and present season abiotic conditions predict pollen nutritional qualityBrief abstract: Plant intraspecific trait variation (ITV) is mostly unknown regarding food rewards that angiosperms offer pollinators, including pollen which bees collect for protein and lipid nutrition. This gap limits our understanding of how spatiotemporal and climate-induced variation in reward quality affects mutualisms. Manipulative experiments show that pollen chemistry changes with extreme stress, yet whether different pollen species in natural populations exhibit ITV related to local conditions is unknown and has ecological implications. Using Bayesian sparse regression, we explored the relationship between site-specific climate variables and variation in pollen protein and lipid content from 35 native wildflower species across multiple sites in sagebrush steppe habitat (NV/CA, USA). We found that pollen nutrient ITV across sites was related to current season below-ground (climatic water deficit) and previous season above-ground (dewpoint) conditions. We discuss the implication of these findings considering plant physiological responses to climate stress and potential downstream effects on pollinator community interactions.Names, institutions of all authors:Anthony D. Vaudo1,2, Eva Lin1, Jillian A. Luthy1, Eliza M. Grames1,3, and Anne S. Leonard11. Department of Biology, University of Nevada, Reno, NV 895572. Rocky Mountain Research Station, USDA Forest Service, Moscow, ID 838433. Department of Biological Sciences, Binghamton University, Binghamton, NY 13902Dates of data collection: 2021Geographic location(s) of data collection: Nevada, California, USAFunding Sources: This work was supported by the National Science Foundation (NSF) grant number NSF IOS-1755096 and NSF REPS Supplement number IOS-1755096 to A.S.L. and in part by the U.S.D.A. Forest Service.ACCESS INFORMATIONThese code and data are licensed under a Creative Commons Attribution 4.0 International License. You are free to share and adapt the material for any purpose, provided that you give appropriate credit, provide a link to the license, and indicate if any changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.Recommended citation for this data/code archiveVaudo, A.D., E. Lin, J.A. Luthy, A.S. Leonard, and E.M. Grames. (2024). Do past and present abiotic conditions explain variation in the nutritional quality of wildflower pollens for bees? Evolutionary Ecology, https://doi.org/10.1007/s10682-024-10313-4DATA & CODE FILE OVERVIEWThis data repository consist of 6 data files, 4 code scripts, and this README document, with the following data and code filenames and variablesData files and variables1. pollen_nutrition_population: a .csv file containing raw data of pollen nutrition samples and metadata2. westernstates: a .rda file containing a spatial polygon of western states in which sites are located3. awc: a .tif raster file containing soil available water content in the region4. dem: a .tif raster file of elevation5. PRISM_ppt_tmin_tmean_tmax_tdmean_vpdmin_vpdmax_stable_4km_20210101_20211231: a .csv file containing weather data for the sites in 2021 downloaded from PRISM5. PRISM_ppt_tmin_tmean_tmax_tdmean_vpdmin_vpdmax_stable_4km_20200101_20201231: a .csv file containing weather data for the sites in 2020 downloaded from PRISMCode scripts and workflow1. pollen-nutrition-climate-analysis: a .R script for the main analyses, including calculations of local abiotic conditions, running stage one and stage two analyses, and generating figures2. jags-pollen-model: a .R file containing JAGS code specifying the sparse regression model for climate variable selection3. jags-step2-model-lipid: a .R file containing JAGS code specifying the stage two model for estimating the effects of selected variables on lipid concentrations4. jags-step2-model-protein: a .R file containing JAGS code specifying the stage two model for estimating the effects of selected variables on protein concentrationsSOFTWARE VERSIONSPRISM (PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu, data created and accessed 11 Nov 2022)U.S. Geological Survey, 2022, USGS 3D Elevation Program Digital Elevation Model, accessed June 7, 2020 at URL https://elevation.nationalmap.gov/arcgis/rest/services/3DEPElevation/ImageServer.USGS Soil Properties Dataset based on the Gridded National Soil Survey (gNATSGO) and Gridded Soil Survey (gSSURGO) Geographic Databases (Boiko et al. 2021)Code was developed in R version 4.1.2 on x86_64-pc-linux-gnu (64-bit)R packagesraster v3.5-11 (Hijmans et al. 2021)saveJAGS v0.0.4.9002 (Meredith 2021)R2jags v0.6-1 (Su and Yajima, 2020)REFERENCESBoiko, O., Kagone S., Senay G.B., 2021, Soil properties dataset in the United States: U.S. Geological Survey data release, https://doi.org/10.5066/P9TI3IS8.Hijmans, R. J., J. van Etten, M. Mattiuzzi, M. Sumner, J. A. Greenberg, O. P. Lamingueiro, A. Bevan, E. B. Racine, and A. Shortridge. 2021. raster: Geographic Data Analysis and Modeling, Version 2.9-23, R package.Lu, Z. and W. Lou. 2022. Bayesian approaches to variable selection: a comparative study from practical perspectives. The International Journal of Biostatistics. 18: 83-108.Lutz, J. A., J. W. van Wagtendonk, and J. F. Franklin. 2010. Climatic water deficit, tree species ranges, and climate change in Yosemite National Park. Journal of Biogeography 37:936–950Meredith, M. 2021. saveJAGS: Run JAGS and Regularly Save Output to Files. R package version 0.0.4.9002.Plummer, M. 2003. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Proceedings of the 3rd international workshop on distributed statistical computing. 124, No. 125.10, pp. 1-10).Redmond, M. D. 2022. CWD and AET function (Version V1.0.3). Zenodo. https://doi.org/10.5281/zenodo.6416352.Roberts, E. and L. Zhao. 2022. A Bayesian mixture model for changepoint estimation using ordinal predictors. The International Journal of Biostatistics. 18: 57-72.Su, Y.S. and M. Yajima. 2021. R2jags: Using R to Run ‘JAGS’. R package version 0.6-1; 2020.

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

The Farmland Protection Policy Act, part of the 1981 Farm Bill, is intended to limit federal activities that contribute to the unnecessary conversion of farmland to other uses. The law applies to construction projects funded by the federal government such as highways, airports, and dams, and to the management of federal lands. As part of the implementation of this law, the Natural Resources Conservation Service identifies high quality agricultural soils as prime farmland, unique farmland, and land of statewide or local importance. Each category may contain one or more limitations such as Prime Farmland if Irrigated. For a complete list of categories and definitions, see the National Soil Survey Handbook.All areas are prime farmlandFarmland of local importanceFarmland of statewide importanceFarmland of statewide importance, if drainedFarmland of statewide importance, if drained and either protected from flooding or not frequently flooded during the growing seasonFarmland of statewide importance, if irrigatedFarmland of statewide importance, if irrigated and drainedFarmland of statewide importance, if irrigated and either protected from flooding or not frequently flooded during the growing seasonFarmland of statewide importance, if irrigated and reclaimed of excess salts and sodiumFarmland of statewide importance, if irrigated and the product of I (soil erodibility) x C (climate factor) does not exceed 60Farmland of statewide importance, if protected from flooding or not frequently flooded during the growing seasonFarmland of statewide importance, if warm enoughFarmland of statewide importance, if warm enough, and either drained or either protected from flooding or not frequently flooded during the growing seasonFarmland of unique importanceNot prime farmlandPrime farmland if drainedPrime farmland if drained and either protected from flooding or not frequently flooded during the growing seasonPrime farmland if irrigatedPrime farmland if irrigated and drainedPrime farmland if irrigated and either protected from flooding or not frequently flooded during the growing seasonPrime farmland if irrigated and reclaimed of excess salts and sodiumPrime farmland if irrigated and the product of I (soil erodibility) x C (climate factor) does not exceed 60Prime farmland if protected from flooding or not frequently flooded during the growing seasonPrime farmland if subsoiled, completely removing the root inhibiting soil layerDataset SummaryPhenomenon Mapped: FarmlandUnits: ClassesCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa)Mosaic Projection: Web Mercator Auxiliary SphereExtent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaSource: Natural Resources Conservation ServicePublication Date: December 2021ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/Data 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).This layer is derived from the 30m (contiguous U.S.) and 10m rasters (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for farmland class is derived from the gSSURGO map unit table field Farm Class (farmlndcl).What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "farmland" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "farmland" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

This service is available to all ArcGIS Online users with organizational accounts. For more information on this service, including the terms of use, visit us online at https://goto.arcgisonline.com/landscape11/USA_Soils_Map_Units.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

Soil loss tolerance factor is the maximum rate of soil loss that will permit crop productivity to be sustained economically and indefinitely on a given soil. Soil loss tolerance is expressed as tons/acre/year. The primary use for soil loss tolerance factor is evaluating the effectiveness of erosion control measures on farmland. Soil loss tolerance factor serves as a quantitative standard to compare to erosion rate estimates from models such as the Revised Universal Soil Loss Equation. Farmlands where soil loss tolerance factor is less than modeled erosion rates are considered unsustainable.Dataset SummaryPhenomenon Mapped: Soil loss toleranceUnits: tons/acre/yearCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa)Mosaic Projection: Web Mercator Auxiliary SphereExtent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaSource: Natural Resources Conservation ServicePublication Date: December 2021ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/Data 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).This layer is derived from the 30m (contiguous U.S.) and 10m rasters (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for soil loss tolerance is derived from the gSSURGO component table field T (tfact). The value in this layer is the average value for all components of each map unit weighted by component percent (comppct_r). What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "loss tolerance" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "loss tolerance" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.