This data release reformats data fields from the STATSGO soil characteristics dataset (Schwarz and Alexander, 1995) as cloud-optimized GeoTIFFs (COGs). The COG format allows standard software tools to efficiently access the datasets over an internet connection. Please refer to the documentation of the source archive (Schwarz and Alexander, 1995) for additional details on the underlying dataset. This data release includes COGs for the KF-factor (KFFACT) and soil thickness (THICK) data fields. KF-factors are defined as the saturated hydraulic conductivity of the fine soil (< 2mm) fraction in units of inches per hour. The soil thickness dataset has units of inches. Each COG raster spans the continental US at a nominal 30 meter resolution. The spatial reference is EPSG:5069. Each COG uses a float32 precision, and the NoData value (NaN) indicates raster pixels not covered by the original STATSGO dataset. The COGs also include non-physical values of -0.1, which were used by the source STATSGO archive to mark large water bodies. The COG format uses compression internally to reduce file size. As such, reading large portions of a COG into memory can require much more RAM than the nominal file size. The COGs in this dataset will require ~60GB of memory to read in full. This dataset can be reproduced by running the included `rasterize_statsgo.py` Python script. Please refer to the script for documentation and usage instructions. Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References: Schwarz, G.E. and Alexander, R.B., 1995, Soils data for the Conterminous United States Derived from the NRCS State Soil Geographic (STATSGO) Data Base. [Original title: State Soil Geographic (STATSGO) Data Base for the Conterminous United States.]: U.S. Geological Survey data release, https://doi.org/10.5066/P94JAULO.

This data release reformats the STATSGO soil thickness (THICK) dataset (Schwarz and Alexander, 1995) as a cloud-optimized GeoTIFF (COG). The COG format allows standard software tools to efficiently access the datasets over an internet connection. The soil thickness values have units of inches. Please refer to the documentation of the source archive (Schwarz and Alexander, 1995) for additional details on the underlying dataset. The COG dataset spans the continental US at a nominal 30 meter resolution. The spatial reference is EPSG:5069. Each COG uses a float32 precision, and the NoData value (NaN) indicates raster pixels not covered by the original STATSGO dataset. The COG also includes non-physical values of -0.1, which were used by the source dataset to mark large water bodies. The COG format uses compression internally to reduce file size. As such, reading large portions of a COG into memory can require much more RAM than the nominal file size. This COG will require ~60GB of memory to read in full. This dataset can be reproduced by running the `rasterize_statsgo.py` Python script included in this dataset's parent folder (https://doi.org/10.5066/P13WAPYV). Please refer to the script for documentation and usage instructions. Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References: Schwarz, G.E. and Alexander, R.B., 1995, Soils data for the Conterminous United States Derived from the NRCS State Soil Geographic (STATSGO) Data Base. [Original title: State Soil Geographic (STATSGO) Data Base for the Conterminous United States.]: U.S. Geological Survey data release, https://doi.org/10.5066/P94JAULO.

This data release reformats the STATSGO soil thickness (THICK) dataset (Schwarz and Alexander, 1995) as a cloud-optimized GeoTIFF (COG). The COG format allows standard software tools to efficiently access the datasets over an internet connection. The soil thickness values have units of inches. Please refer to the documentation of the source archive (Schwarz and Alexander, 1995) for additional details on the underlying dataset. The COG dataset spans the continental US at a nominal 30 meter resolution. The spatial reference is EPSG:5069. Each COG uses a float32 precision, and the NoData value (NaN) indicates raster pixels not covered by the original STATSGO dataset. The COG also includes non-physical values of -0.1, which were used by the source dataset to mark large water bodies. The COG format uses compression internally to reduce file size. As such, reading large portions of a COG into memory can require much more RAM than the nominal file size. This COG will require ~60GB of memory to read in full. This dataset can be reproduced by running the `rasterize_statsgo.py` Python script included in this dataset's parent folder (https://doi.org/10.5066/P13WAPYV). Please refer to the script for documentation and usage instructions. Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References: Schwarz, G.E. and Alexander, R.B., 1995, Soils data for the Conterminous United States Derived from the NRCS State Soil Geographic (STATSGO) Data Base. [Original title: State Soil Geographic (STATSGO) Data Base for the Conterminous United States.]: U.S. Geological Survey data release, https://doi.org/10.5066/P94JAULO.

This data release reformats the STATSGO KF-factor (KFFACT) dataset (Schwarz and Alexander, 1995) as a cloud-optimized GeoTIFF (COG). The COG format allows standard software tools to efficiently access the datasets over an internet connection. KF-factors are defined as the saturated hydraulic conductivity of the fine soil (< 2mm) fraction. Units are inches per hour. Please refer to the documentation of the source archive (Schwarz and Alexander, 1995) for additional details on the underlying dataset. The COG dataset spans the continental US at a nominal 30 meter resolution. The spatial reference is EPSG:5069. Each COG uses a float32 precision, and the NoData value (NaN) indicates raster pixels not covered by the original STATSGO dataset. The COG also includes non-physical values of -0.1, which were used by the source dataset to mark large water bodies. The COG format uses compression internally to reduce file size. As such, reading large portions of a COG into memory can require much more RAM than the nominal file size. This COG will require ~60GB of memory to read in full. This dataset can be reproduced by running the `rasterize_statsgo.py` Python script included in this dataset's parent folder (https://doi.org/10.5066/P13WAPYV). Please refer to the script for documentation and usage instructions. Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References: Schwarz, G.E. and Alexander, R.B., 1995, Soils data for the Conterminous United States Derived from the NRCS State Soil Geographic (STATSGO) Data Base. [Original title: State Soil Geographic (STATSGO) Data Base for the Conterminous United States.]: U.S. Geological Survey data release, https://doi.org/10.5066/P94JAULO.

This data release reformats the STATSGO KF-factor (KFFACT) dataset (Schwarz and Alexander, 1995) as a cloud-optimized GeoTIFF (COG). The COG format allows standard software tools to efficiently access the datasets over an internet connection. KF-factors are defined as the saturated hydraulic conductivity of the fine soil (< 2mm) fraction. Units are inches per hour. Please refer to the documentation of the source archive (Schwarz and Alexander, 1995) for additional details on the underlying dataset. The COG dataset spans the continental US at a nominal 30 meter resolution. The spatial reference is EPSG:5069. Each COG uses a float32 precision, and the NoData value (NaN) indicates raster pixels not covered by the original STATSGO dataset. The COG also includes non-physical values of -0.1, which were used by the source dataset to mark large water bodies. The COG format uses compression internally to reduce file size. As such, reading large portions of a COG into memory can require much more RAM than the nominal file size. This COG will require ~60GB of memory to read in full. This dataset can be reproduced by running the `rasterize_statsgo.py` Python script included in this dataset's parent folder (https://doi.org/10.5066/P13WAPYV). Please refer to the script for documentation and usage instructions. Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References: Schwarz, G.E. and Alexander, R.B., 1995, Soils data for the Conterminous United States Derived from the NRCS State Soil Geographic (STATSGO) Data Base. [Original title: State Soil Geographic (STATSGO) Data Base for the Conterminous United States.]: U.S. Geological Survey data release, https://doi.org/10.5066/P94JAULO.

This digital data release consists of an ARC/INFO grid and associated INFO tables. The grid is called MUID and has STATSGO (U.S. Department of Agriculture, 1994) soil mapping unit identifiers gridded on a 1-kilometer resolution for the conterminous United States. The INFO tables have soil characteristics data in them. The ITEMS in the tables are weighted average values for several soil characteristics in the STATSGO data base. The weighted average values were computed by aggregating the soil layers and components in the data base. The INFO tables are called MUID.LAYER, MUID.COMP, MUID.KFACT, MUID.TFACT, and MUID.WEG. The INFO tables can be related or joined to the MUID grid or to individual State coverages (grids or polygons) of MUIDs. Joining or relating the tables to the MUID grid creates 1-kilometer resolution grids of the soil characteristics for the conterminous United States. The soil characteristics in MUID.LAYER are permeability (PERML and PERMH in the STATSGO data base), available water capacity (AWCL and AWCH), bulk density (BDL and BDH), and organic matter (OML and OMH). The soil characteristics in MUID.COMP are slope (SLOPEL and SLOPEH), depth to seasonally high water table (WTDEPL and WTDEPH), and depth to bedrock (ROCKDEPL and ROCKDEPH). The soil characteristic in MUID.KFACT is the soil erodibility factor (KFACT), the soil characteristic in MUID.TFACT is the soil loss tolerance factor (TFACT), and the soil characteristic in MUID.WEG is the wind erosion group (WEG). The MUID grid and INFO tables were created with a set of arc macro language (aml) and Fortran programs. Send electronic mail to dwolock@usgs.gov to obtain copies of the computer code. (See Procedures_Used.)

This dataset consists of general soil association units. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) dataset published in 1994. It consists of a broad based inventory of soils and 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, and the Virgin Islands and 1:1,000,000 in Alaska. The dataset was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined. Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the entire map unit. This dataset consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1- by 2-degree topographic quadrangle units and merged into a seamless national dataset. The soil map units are linked to attributes in the National Soil Information system relational database, which gives the proportionate extent of the component soils and their properties. These data provide information about soil features on or near the surface of the Earth. Data were collected as part of the National Cooperative Soil Survey. These data are intended for geographic display and analysis at the state, regional, and national level. The data should be displayed and analyzed at scales appropriate for 1:250,000-scale data.

This dataset consists of general soil association units. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) dataset published in 1994. It consists of a broad based inventory of soils and 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, and the Virgin Islands and 1:1,000,000 in Alaska. The dataset was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined. Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the entire map unit. This dataset consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1- by 2-degree topographic quadrangle units and merged into a seamless national dataset. The soil map units are linked to attributes in the National Soil Information system relational database, which gives the proportionate extent of the component soils and their properties. These data provide information about soil features on or near the surface of the Earth. Data were collected as part of the National Cooperative Soil Survey. These data are intended for geographic display and analysis at the state, regional, and national level. The data should be displayed and analyzed at scales appropriate for 1:250,000-scale data.

This dataset consists of general soil association units. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) dataset published in 1994. It consists of a broad based inventory of soils and nonsoil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped of 1:250,000 in the continental U.S., Hawaii, Puerto, and the Virgin Islands and 1:1,000,000 in Alaska. The dataset was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined. Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the entire map unit. This dataset consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1- by 2-degree topographic quadrangle units. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties. These data provide information about soil features on or near the surface of the Earth. Data were collected as part of the National Cooperative Soil Survey. These data are intended for geographic display and analysis at the state, regional, and national level. The data should be displayed and analyzed at scales appropriate for 1:250,000-scale data.

This 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 a Conterminous US-wide extent, and adding a Conterminous US-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 Conterminous US-wide extent. The raster map data have a 30 meter cell size. 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 to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data. The value added look up (valu) table 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. The Gridded SSURGO dataset was created for use in national, regional, and state-wide resource planning and analysis of soils data. The raster map layer data can be readily combined with other national, regional, and local raster layers, e.g., National Land Cover Database (NLCD), the National Agricultural Statistics Service (NASS) Crop Data Layer, or the National Elevation Dataset (NED).