Soil Classification Datasets

Please ensure to cite the paper when utilizing the dataset in a research study. Refer to the paper link or BibTeX provided below.

This repository contains comprehensive datasets for soil classification and recognition research. The Original Dataset comprises soil images sourced from various online repositories, which have been meticulously cleaned and preprocessed to ensure data quality and consistency. To enhance the dataset's size and diversity, we employed Generative Adversarial Networks (GANs), specifically the CycleGAN architecture, to generate synthetic soil images. This augmented collection is referred to as the CyAUG Dataset. Both datasets are specifically designed to advance research in soil classification and recognition using state-of-the-art deep learning methodologies.

This dataset was curated as part of the research study titled "An advanced artificial intelligence framework integrating ensembled convolutional neural networks and Vision Transformers for precise soil classification with adaptive fuzzy logic-based crop recommendations" by Farhan Sheth, Priya Mathur, Amit Kumar Gupta, and Sandeep Chaurasia, published in Engineering Applications of Artificial Intelligence.

Links

• Paper: An advanced artificial intelligence framework integrating ensembled convolutional neural networks and Vision Transformers for precise soil classification with adaptive fuzzy logic-based crop recommendations
• Code: Development Code (model and recommendation system)

Application produced by this research is available at:

• Agro Companion: Agro Companion
• Server Code: Agro Companion Server

Note: If you are using any part of this project; dataset, code, application, then please cite the work as mentioned in the Citation section below.

Dataset

Both dataset consists of images of 7 different soil types.

The Soil Classification Dataset is structured to facilitate the classification of various soil types based on images. The dataset includes images of the following soil types:

• Alluvial Soil
• Black Soil
• Laterite Soil
• Red Soil
• Yellow Soil
• Arid Soil
• Mountain Soil

The dataset is organized into folders, each named after a specific soil type, containing images of that soil type. The images vary in resolution and quality, providing a diverse set of examples for training and testing classification models.

Original Dataset Details

• Total Images: 1189 images
• Image Format: JPG/JPEG
• Image Size: Varies
• Source: Collected from various online repositories and cleaned for consistency.

CyAUG Dataset Details

• Total Images: 5097 images
• Image Format: JPG/JPEG
• Image Size: Varies
• Source: Generated using CycleGAN to augment the original dataset, enhancing its size and diversity.

Input and Output Parameters

• Input Parameters:
  • Image: The images of the soils (JPG/JPEG format).
  • Label: The labels are in the format 'soil types' (folder names).
• Output Parameter:
  • Classification: The predicted class (soil type) based on the input image.

Citation

If you are using any of the derived dataset, please cite the following paper:

@article{SHETH2025111425,
  title = {An advanced artificial intelligence framework integrating ensembled convolutional neural networks and Vision Transformers for precise soil classification with adaptive fuzzy logic-based crop recommendations},
  journal = {Engineering Applications of Artificial Intelligence},
  volume = {158},
  pages = {111425},
  year = {2025},
  issn = {0952-1976},
  doi = {https://doi.org/10.1016/j.engappai.2025.111425},
  url = {https://www.sciencedirect.com/science/article/pii/S0952197625014277},
  author = {Farhan Sheth and Priya Mathur and Amit Kumar Gupta and Sandeep Chaurasia},
  keywords = {Soil classification, Crop recommendation, Vision transformers, Convolutional neural network, Transfer learning, Fuzzy logic}
}

This dataset is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information.

This dataset consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

SSURGO depicts information about the kinds and distribution of soils on the landscape. The soil map and data used in the SSURGO product were prepared by soil scientists as part of the National Cooperative Soil Survey.

This data set provides soil maps for the United States (US) (including Alaska), Canada, Mexico, and a part of Guatemala. The map information content includes maximum soil depth and eight soil attributes including sand, silt, and clay content, gravel content, organic carbon content, pH, cation exchange capacity, and bulk density for the topsoil layer (0-30 cm) and the subsoil layer (30-100 cm). The spatial resolution is 0.25 degree.

The Unified North American Soil Map (UNASM) combined information from the state-of-the-art US General Soil Map (STATSGO2) and Soil Landscape of Canada (SLCs) databases. The area not covered by these data sets was filled by using the Harmonized World Soil Database version 1.21 (HWSD1.21). The Northern Circumpolar Soil Carbon (NCSCD) database was used to provide more accurate and up-to-date soil organic carbon information for the high-latitude permafrost region and was combined with soil organic carbon content derived from the UNASM (Liu et al., 2013).

The UNASM data were utilized in the North American Carbon Program (NACP) Multi-Scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as model input driver data (Huntzinger et al., 2013). The driver data were used by 22 terrestrial biosphere models to run baseline and sensitivity simulations. The compilation of these data was facilitated by the NACP Modeling and Synthesis Thematic Data Center (MAST-DC). MAST-DC was a component of the NACP (www.nacarbon.org) designed to support NACP by providing data products and data management services needed for modeling and synthesis activities.

Soil Type

## Overview

Soil Type is a dataset for classification tasks - it contains Soil annotations for 158 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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 map depicts soils data from the USDA NRCS SSURGO dataset. The soil type is indicated in the MUSYM field. The data was downloaded from the NRCS website.The full Kansas geospatial catalog is administered by the Kansas Data Access & Support Center (DASC) and can be found at the following URL: https://hub.kansasgis.org/

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 s ...

(Link to Metadata) This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties. Survey Dates - https://www.nrcs.usda.gov/wps/portal/nrcs/surveylist/soils/survey/state/?stateId=VT

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

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

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

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

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

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

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

The U.S. Department of Agriculture, Agriculture and Agri-Food Canada, the Russian Academy of Agricultural Sciences, the University of Copenhagen Institute of Geography, the European Soil Bureau, the University of Manchester Institute of Landscape Ecology, MTT Agrifood Research Finland, and the Agricultural Research Institute Iceland have shared data and expertise in order to develop the Northern and Mid Latitude Soil Database (Cryosol Working Group, 2001). This database was the source of data for the current product. The spatial coverage of the Northern and Mid Latitude Soil Database is the polar and mid-latitude regions of the northern hemisphere: Alaska, Canada, Conterminous United States, Eurasia (except Italy), Greenland, Iceland, Kazakstan, Mexico, Mongolia, Italy, and Svalbard. The Northern and Mid Latitude Soil Database represents the proportion (percentage) of polygon encompassed by the dominant soil or nonsoil. Soils include turbels, orthels, histels, histosols, mollisols, vertisols, aridisols, andisols, entisols, spodosols, inceptisols (and hapludolls), alfisols (cryalf and udalf), natric great groups, aqu-suborders, glaciers, and rocklands. Also included are data on the circumpolar distribution of gelisols (turbels, orthels, and histels), and the ice content (low, medium, or high) of circumpolar soil materials (from the International Permafrost Association, 1997). The resulting maps show the dominant soil of the spatial polygon unless the polygon is over 90 percent rock or ice. Data are in the U.S. soil classification system and includes the distribution of soil types (%) within a map unit (polygon). Data are available in ESRI shapefile format and include the same attribute values with the exception of Italy, which does not contain distribution values.

This layer shows soil type, based on the result of a classification established from Kalimantan RePPProT data on 'SL_ORDER' field (1990, 1:250,000 scale) . This data was provided and processed by Daemeter Consulting. Soil categories from RePPProT were then re-classified by the World Resources Institute according to the FAO Digital Soil Map of the World, for use in the Suitability Mapper (2012). The FAO data is available at http://www.fao.org/geonetwork/srv/en/metadata.show?id=14116 . Data separated into categories: Inceptisol; Oxisol; Alfisol; Ultisol; Spodosol; Entisol; Histosol.

SOLUS maps use a digital soil mapping framework that combines multiple sources of soil survey data with environmental covariate data and machine learning. The SOLUS100 maps are a set of 100 m spatial resolution maps that predict 20 soil properties (listed below with units) at 7 depths with uncertainty estimates for the contiguous United States. Very Fine Sand (%)Fine Sand (%)Medium Sand (%)Coarse Sand (%)Very Coarse Sand (%)Total Sand (%)Silt (%)Clay (%)pHSoil Organic Carbon (%)Calcium Carbonate Equivalent (%)Gypsum Content (% by weight)Electrical Conductivity (mmhos/cm)Sodium Adsorption RatioCation Exchange Capacity (meq/100 g)Effective Cation Exchange Capacity (meq/100 g)Oven Dry Bulk Density (g/cm^3)Depth to Bedrock (cm)Depth to Restriction (cm)Rock Fragment Volume (%)Each property listed above is predicted at 0, 5, 15, 30, 60, 100, and 150 cm depths. Each property-depth prediction has an estimate of uncertainty expressed as the relative prediction interval (RPI) that ranges from 0 to 1. RPI is a relative measure of uncertainty with high values being more uncertain. It is computed as the ratio of the 95 percent prediction interval width to the training set 95 percent quantile width (97.5 percent quantile value minus 2.5 percent quantile value). Values closer to 0 indicate lower uncertainty and values closer to 1 indicate higher uncertainty. Values greater than 1 indicate that the prediction at that location is outside the range of the training data used for that property at that depth. Prediction interval high and low are also provided. Property prediction and uncertainty layers follow the naming convention: propertyname_depth_cm_p (predicted property values)propertyname_depth_cm_rpi (relative prediction interval)propertyname_depth_cm_l (prediction interval low)propertyname_depth_cm_h (prediction interval high)Select property, prediction interval, and RPI layers have been rescaled to provide the data in integer format. Information about scaling factors is provided in the Fields section of this document. A summary table with layer filename, property, depth, filetype, scalar, description, and units is also included with the maps: Final_Layer_Table_20231215.csvSOLUS webpage: https://www.nrcs.usda.gov/resources/data-and-reports/soil-landscapes-of-the-united-states-solus

Soil Landscapes of the United States, or SOLUS, is a national map product developed by the National Cooperative Soil Survey that is focused on providing a consistent set of spatially continuous soil property maps to support large scope soil investigations and land use decisions. SOLUS maps use a digital soil mapping framework that combines multiple sources of soil survey data with environmental covariate data and machine learning. Digital soil mapping is the production of georeferenced soil databases based on the quantitative relationships between soil measurements made in the field or laboratory and environmental data. Numerical models use the quantitative relationships to predict the spatial distribution of either discrete soil classes, such as map units, or continuous soil properties, such as clay content.

SOLUS maps use continuous property mapping, which predicts soil physical or chemical properties in horizontal and vertical dimensions. The soil properties are represented across a continuous range of values. Raster datasets of select soil properties can be predicted at specified depths or depth intervals. Continuous soil property maps such as SOLUS provide critical natural resource information to support environmental researchers and modelers, conservationists, and others making land management decisions. SOLUS will be updated annually with improved data and methodology.

The SOLUS dataset includes 20 different soil properties (listed below) with most properties predicted for seven standard depths (0, 5, 15, 30, 60, 100, and 150 cm).

Properties included in SOLUS100:

Bulk density (oven dry) Calcium carbonate Cation Exchange Capacity (pH 7) Clay Coarse sand Electrical Conductivity (sat. paste) Effective cation exchange capacity Fine sand Gypsum (in <20 mm fraction) Medium sand pH (1:1 method) Rock content Sand Sodium adsorption ratio Silt Soil organic carbon Very coarse sand Very fine sand Depth to bedrock Depth to restriction

These Soil Mapping Data Packages include 1. a Soil Map dataset which includes the equivalents to Soil Project Boundaries, Soil Survey Spatial View mapping polygons with attributes from the Soil Name and Layer Files, plus + A Soil Site dataset which includes soil pit site information and detailed soil pit descriptions and any associated lab analyses, and + The Soil Data Dictionary which documents the fields and allowable codes within the data. The Soil Map geodatabase contains the 'best available' data ranging from 1:20,000 scale to 1:250,000 scale with overlapping data removed. The choice of the datasets that remain is based on connectivity to the soil attributes (soil name and layer files), map scale and survey date. (Note: the BC Soil Landscapes of Canada (BCSLC) 1:1,000,000 data has not been included in the Soil_Map or SIFT, but is available from: CANSIS. (A complete soils data package with overlapping soil survey mapping and BCSLC is available on request. Note that the soil survey data with attributes can also be viewed interactively in the [Soil Information Finder Tool](The Soil Map dataset is also available for interactive map viewing or as KMZs from the Soil Information Finder Tool website.

The HUMERIS Global Soil Properties Dataset offers a comprehensive, 39‐year time series (1985–2023) of annual global soil maps at a 1 km resolution for topsoil (0–30 cm) covering four key soil properties: pH, soil salinity (ECe), nitrogen (N), and organic carbon (OC). The dataset is based on a harmonized collection of soil pedons compiled from the ISRIC‐WISE, OpenLandMap and LUCAS datasets, ensuring a wide coverage of in-situ measurements. Advanced machine learning techniques were employed using 41 environmental predictors, which include climatic variables, topographic indices, land cover data, and remotely sensed vegetation indices, to capture the spatio-temporal dynamics of soil properties. Users accessing the repository will find freely downloadable multiband maps with descriptive statistics of the reference period. These maps allow for an assessment of the overall state and variability of soil health indicators across different regions and can serve as benchmarks for environmental monitoring and sustainable land management. For those interested in examining annual variations, single-year maps are available exclusively via a Google Earth Engine interface, which offers the flexibility to explore detailed temporal changes and spatial patterns. To download a series of Yearly data at Global scale or for further request please contact the corresponding author.

All the informations can be found in the article "A large-scale framework for estimating soil carbon, nitrogen, pH, and salinity dynamics for 1985–2023" https://doi.org/10.1073/pnas.2534913123.

Article citation: M. Dalle Vaglie, S. Francini, G. Chirici, & F. Martellozzo, A large-scale framework for estimating soil carbon, nitrogen, pH, and salinity dynamics for 1985–2023, Proc. Natl. Acad. Sci. U.S.A. 123 (22) e2534913123, https://doi.org/10.1073/pnas.2534913123 (2026).

Ontario Ministry of Agriculture, Food, and Rural Affairs and Agriculture and Agri-Food Canada, in cooperation with the Ministry of Natural Resources, have compiled a geo-spatial soils database for Southern Ontario. The database consolidated the existing digital soil data mapped on a county basis into a digitally stitched and standardized product. The soil survey data was mapped by a number of soil surveyors from the 1920s to the 1990s. The Soil Ontario product incorporates soil information from a variety of map scales. The project has brought the individual county or regional municipality surveys together in a digitally stitched database which reveals inconsistencies in soil data across county boundaries. Using GIS and NRVIS (Natural Resource Values Information System) a GIS Specialist matched the soil polygons that crossed boundaries using the best available resources.Additional Metadata Location: Agriculture and AgriFood Canada - Canada Soils Information SystemAdditional Metadata:Canadian Soil Information Service Additional DocumentationSoil Survey Complex - Data Description (PDF)Soil Survey Complex - Documentation (Word)StatusOn going: data is being continually updated Maintenance and Update Frequency As needed: data is updated as deemed necessary Contact Daniel Saurette, omafra.gis@ontario.ca

The Harmonized World Soil Database version 2.0 (HWSD v2.0) is a unique global soil inventory providing information on the morphological, chemical and physical properties of soils at approximately 1 km resolution. Its main objective is to serve as a basis for prospective studies on agro-ecological zoning, food security and climate change. The Harmonized World Soil Database (HWSD) was established in 2008 by the International Institute for Applied Systems Analysis (IIASA) and FAO, and in partnership with International Soil Reference and Information Centre (ISRIC), the European Soil Bureau Network (ESBN) and the Institute for Soil Sciences Chinese Academy of Sciences (CAS). The data entry and harmonization within a Geographic Information System (GIS) was carried out at IIASA, with verification of the database undertaken by all partners. HWSD was then updated in 2013 (HWSD v1.2) and in 2023 (HWSD v2.0). This updated version (HWSD v2.0) is built on the previous versions of HWSD with several improvements on (i) the data source that now includes several national soil databases, (ii) an enhanced number of soil attributes available for seven soil depth layers, instead of two in HWSD v1.2, and (iii) a common soil reference for all soil units (FAO1990 and the World Reference Base for Soil Resources). This contributes to a further harmonization of the database. The GIS raster image file is linked to the soil attribute database. The HWSD v2.0 soil attribute database provides information on the soil unit composition for each of the near 30 000 soil association mapping units. The HWSD v2.0 Viewer, provided with the database, creates this link automatically and provides direct access to the soil attribute data and the soil association information. Note: - A tutorial for accessing HWSD ver. 2.0 using R (prepared by David Rossiter, June 2023) has been added as an 'associated resource' (NOTE: Needs the SQLite version of HWSD v2 as provided below). - Soil property estimates in HWSDv2 were derived from Batjes (2016), Geoderma (https://doi.org/10.1016/j.geoderma.2016.01.034).

This dataset contains high-quality images of soil surfaces categorized into three moisture levels—Wet, Moderate, and Dry—captured under natural outdoor lighting in Sirajganj, Bangladesh. Images were collected at seven time intervals (0 min, 30 min, 1 hr, 2 hr, 4 hr, 5 hr, and 7+ hr after saturation) using a Sony Xperia 1 Mark II smartphone. A total of 1,177 raw images were captured, with blurry, noisy, and low-quality photos removed during pre-processing. The dataset reflects real-world agricultural conditions and serves as a benchmark for training machine learning and deep learning models for non-invasive soil moisture classification. Subject Areas: Computer Science, Agriculture Science, AI, Computer Vision, Environmental Monitoring, Pattern Recognition Data Format: JPG images (raw and filtered) Data Collection: Captured using Sony Xperia 1 Mark II under natural outdoor lighting in multiple soil locations. Organized into three labeled categories (Wet, Moderate, Dry) based on time intervals after saturation. Can be split into training and testing sets (recommended 80:20 ratio). Usage Notes: Ideal for developing AI models in soil moisture classification, precision irrigation scheduling, and image-based environmental monitoring. Supports affordable, sensor-free soil analysis for sustainable farming practices, particularly in resource-limited settings.

Retirement Notice: This item is in mature support as of April 2024 and will be retired in December 2026. Please use the following layers at replacements: World Soils 250m Percent Sand, World Soils 250m Percent Silt, World Soils 250m Percent Clay. Esri recommends updating your maps and apps to use the new version.Soil is a key natural resource that provides the foundation of basic ecosystem services. Soil determines the types of farms and forests that can grow on a landscape. Soil filters water. Soil helps regulate the Earth's climate by storing large amounts of carbon. Activities that degrade soils reduce the value of the ecosystem services that soil provides. For example, since 1850 35% of human caused green house gas emissions are linked to land use change. The Soil Science Society of America is a good source of additional information. Soil texture is an important factor determining which kinds of plants can be grown in a particular location. Texture determines a soil's susceptibility to erosion or compaction and how well a soil holds nutrients and water. For example sandy soils tend to be well drained and dry quickly often holding few nutrients while clay soils may hold much more water and many more plant nutrients. Dataset SummaryThis layer provides access to a 30 arc-second (roughly 1 km) cell-sized raster with attributes related to soil texture derived from the Harmonized World Soil Database v 1.2. The values in this layer are for the dominant soil in each mapping unit (sequence field = 1). Fields for topsoil (0-30 cm) and subsoil (30-100 cm) are available for each of these attributes related to soil texture:USDA Texture ClassGravel - % volumeSand - % weightSilt - % weightClay - % weight The layer is symbolized with the topsoil texture class. The document Harmonized World Soil Database Version 1.2 provides more detail on the soil texture attributes contained in this layer. Other attributes contained in this layer include:Soil Mapping Unit Name - the name of the spatially dominant major soil groupSoil Mapping Unit Symbol - a two letter code for labeling the spatially dominant major soil group in thematic mapsData Source - the HWSD is an aggregation of datasets. The data sources are the European Soil Database (ESDB), the 1:1 million soil map of China (CHINA), the Soil and Terrain Database Program (SOTWIS), and the Digital Soil Map of the World (DSMW).Percentage of Mapping Unit covered by dominant component

The Web Soil Survey (WSS) provides soil survey maps and information through an online, interactive mapping tool. It is operated by the USDA Natural Resources Conservation Service (NRCS). The WSS is used by agricultural producers, conservationists, planners, engineering firms, government agencies, and others to explore properties, features, suitabilities, and limitations of soils and to view descriptions of ecological sites. Soil maps and associated data are available for more than 95 percent of the counties in the United States. A user is able to select an area of interest through a variety of methods, including defining an area on a map, selecting an area from menus, searching on keywords, or importing a shapefile. The user can access a wide range of detailed information, including chemical properties, physical properties, erosion factors, water features, and ratings for building site development, sanitary facilities, and vegetative productivity. The resulting soil map and associated information can be printed, downloaded, or saved for download later. Resources in this dataset:Resource Title: Web Soil Survey. File Name: Web Page, url: https://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx