Description

In this dataset, there are soil data analyses with properties such as pH, organic matter (OM), salinity (EC), etc., major elements (N, P, K, Mg) as well as some microelements (Fe, Zn, Mn, Cu, B) with significant impact on plant nutrition.

Categories

Agricultural Soil

Acknowledgements & Source

Panagiotis Tziachris

Data Source

View Details

Image Source

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.

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 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.

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.

Soil Survey Geographic Database (SSURGO) consists of spatial data and a comprehensive relational database with tables that describe soil properties, interpretations and productivity values. The USDA Natural Resources Conservation Service (NRCS, formerly Soil Conservation Service) provides a download of the statewide SSURGO database that includes vector and raster spatial data, database tables and their relationship classes, and a user guide.Access SSURGO through the Web Soil Survey (WSS).

Soil texture classes (USDA system) for 6 soil depths (0, 10, 30, 60, 100 and 200 cm) at 250 m Derived from predicted soil texture fractions using the soiltexture package in R. Processing steps are described in detail here. Antarctica is not included. To access and visualize maps outside of Earth Engine, use this page. If you discover a bug, artifact or inconsistency in the LandGIS maps or if you have a question please use the following channels: Technical issues and questions about the code General questions and comments

The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) is an ongoing multiinstitutional, international effort addressing the response of biogeography and biogeochemistry to environmental variability in climate and other drivers in both space and time domains. The objectives of VEMAP are the intercomparison of biogeochemistry models and vegetationtype distribution models (biogeography models) and determination of their sensitivity to changing climate, elevated atmospheric carbon dioxide concentrations, and other sources of altered forcing. Soil properties were based on a 10-km gridded EPA soil database developed by Kern (1994, 1995). Two soil coverages are provided in the Kern data set: one from the USDA Soil Conservation Service (SCS) national soil database (NATSGO) and the other from the United Nations Food and Agriculture Organization soil database (FAO 1974- 78). Only the SCS NATSGO soils are included in the VEMAP set. Physical consistency in soils data was incorporated by representing a grid cell's soil by a set of dominant (modal) soil profiles, rather than by a simple average of soil properties. Because soil processes, such as soil organic matter turnover and water balance, are non-linearly related to soil texture and other soil parameters, simulations based on dominant soil profiles and their frequency distribution can account for soil dynamics that would be lost if averaged soil properties were used. To spatially aggregate Kern data to the 0.5 degree grid, we used cluster analysis to group the subgrid 10-km elements into up to 4 modal soil catagories (Kittel et al. 1995). In this statistical approach, cell soil properties are represented by the set of modal soils, rather than by an "average soil." We also provide cell- averaged soil data. Please see the associated Data Set Revision page for an explanation of recent changes made to this data set. A complete users guide to the VEMAP Phase I database which includes more information about this data set can be found at ftp://daac.ornl.gov/data/vemap-1/comp/Phase_1_User_Guide.pdf. ORNL DAAC maintains additional information associated with the VEMAP Project. Data Citation: This data set should be cited as follows: Kittel, T. G. F., N. A. Rosenbloom, T. H. Painter, D. S. Schimel, H. H. Fisher, A. Grimsdell, VEMAP Participants, C. Daly, and E. R. Hunt, Jr. 2002. VEMAP Phase I Database, revised. Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.

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 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 More information on the Harmonized World Soil Database is available here.

The Soil Environment Analysis Dataset contains a collection of 5 subfolders, each representing a distinct type of soil environment. The dataset is designed for analysis and classification tasks, particularly focused on different soil types under varying environmental conditions. Each image in the dataset is resized to 256x256 pixels and saved in the .jpg format for consistency and ease of use in machine learning and computer vision tasks.

Dataset Overview: Total number of folders: 5 Image format: JPG Image dimensions: 256x256 pixels Folder Details: coal_soil

Number of images: 991 Represents soil in coal-dominant environments. drought_soil

Number of images: 991 Represents soil in drought-affected environments. normal_soil

Number of images: 984 Represents soil in typical, non-stressed environmental conditions. sandy_soil

Number of images: 991 Represents soil in sandy terrain, often characterized by low moisture retention. soil_insects_soil

Number of images: 990 Represents soil conditions affected by the presence of insects, potentially impacting soil quality and structure.

**More Dataset:: ** https://www.kaggle.com/shuvokumarbasak4004/datasets

…………………………………..Note for Researchers Using the dataset………………………………………………………………………

This dataset was created by Shuvo Kumar Basak. If you use this dataset for your research or academic purposes, please ensure to cite this dataset appropriately. If you have published your research using this dataset, please share a link to your paper. Good Luck.

iSDAsoil dataset soil texture classes derived from sand, silt and clay fractions at 30 m resolution for 0-20 and 20-50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as COG. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (iSDA points, AfSPDB, and other national and regional soil datasets). Cite as:

Hengl, T., Miller, M.A.E., Križan, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). https://doi.org/10.1038/s41598-021-85639-y.

To open the maps in QGIS and/or directly compute with them, please use the Cloud-Optimized GeoTIFF version.

Layer description:

• sol_texture.class_c_30m_*..*cm_2001..2017_v0.13_wgs84.tif = soil texture class

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 National Soil Database has produced a national database of soil geochemistry including point and spatial distribution maps of major nutrients, major elements, essential trace elements, trace elements of special interest and minor elements. In addition, this study has generated a National Soil Archive, comprising bulk soil samples and a nucleic acids archive each of which represent a valuable resource for future soils research in Ireland. The geographical coherence of the geochemical results was considered to be predominantly underpinned by underlying parent material and glacial geology. Other factors such as soil type, land use, anthropogenic effects and climatic effects were also evident. The coherence between elements, as displayed by multivariate analyses, was evident in this study. Examples included strong relationships between Co, Fe, As, Mn and Cu. This study applied large-scale microbiological analysis of soils for the first time in Ireland and in doing so also investigated microbial community structure in a range of soil types in order to determine the relationship between soil microbiology and chemistry. The results of the microbiological analyses were consistent with geochemical analyses and demonstrated that bacterial community populations appeared to be predominantly determined by soil parent material and soil type.

This database contains measured soil water retention, hydraulic conductivity, and water diffusivity data, as well as pedological information of some 790 soil samples from around the world. Quantifying water flow and chemical transport in the vadose zone typically requires knowledge of the unsaturated soil hydraulic properties. The UNsaturated SOil hydraulic DAtabase (UNSODA) was developed to provide a source of unsaturated hydraulic data and some other soil properties for practitioners and researchers. A first MS-DOS version of the database was released in 1996. It has been applied in numerous studies. In this paper, we describe the second version (UNSODA V2.0) for use with Microsoft Access-97®1. The format and structure of the new database have been modified to provide additional and more convenient options for data searches, to provide compatibility with other programs for easy loading and downloading of data, and to allow users to customise the contents and look of graphical output. This paper reviews the structure and contents of the database as well as the operations that can be performed on the different data types in UNSODA V2.0. The use and application of the new database are illustrated with two examples. The retrieval of data is briefly illustrated, followed by a more detailed example regarding the interpolation of soil particle-size distribution data obtained according to different national definitions of particle-size classes. The interpolation procedure, which is based on finding similar particle-size distribution curves from a large European data set, also performed well for soils that originate from other geographical areas. Resources in this dataset:Resource Title: UNSODA 2.0 README. File Name: UNSODA 2.0 README.pdfResource Title: UNSODA 2.0 zip package. File Name: unsoda.ZIPResource Description: Contains a README file and Microsoft Access database. Files added to original record on June 8, 2020.

The data were derived from scanned soil maps. Attributes include a soil number (2-180), corresponding to runoff coefficient values in a Hydrology Manual, provided by the Los Angeles County Department of Public Works, Water Resources Division.Purpose: For use in DPW’s Modified Rational Method Hydrology Model.Supplemental Information:Stormwater Engineering is a Division of the Los Angeles County Department of Public Works. Please visit their website for posted publications, including the above mentioned Hydrology Manual.

Overview

Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are "ready-to-use" for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1) soil organic matter (SOM) or soil organic carbon (SOC), (2) pH, and (3) clay content, while the features for modeling are dataset-specific. The primary goal of `LimeSoDa` is to enable more reliable benchmarking of machine learning methods in digital soil mapping and pedometrics. All the associated materials and data from LimeSoDa can be downloaded in this data repository. However, for a more in-depth analysis, we refer to the published paper "LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping" by Schmidinger et al. (2025). You may also use our R and Python package likewise called LimeSoDa.

Citation

Upon usage of datasets from LimeSoDa, please cite our associated paper:

Schmidinger, J., Vogel, S., Barkov, V., Pham, A.-D., Gebbers, R., Tavakoli, H., Correa, J., Tavares, T.R., Filippi, P., Jones, E. J., Lukas, V., Boenecke, E., Ruehlmann, J., Schroeter, I., Kramer, E., Paetzold, S., Kodaira, M., Wadoux, A.M.J.-C., Bragazza, L., Metzger, K., Huang, J., Valente, D.S.M., Safanelli, J.L., Bottega, E.L., Dalmolin, R.S.D., Farkas, C., Steiger, A., Horst, T. Z., Ramirez-Lopez, L., Scholten, T., Stumpf, F., Rosso, P., Costa, M.M., Zandonadi, R.S., Wetterlind, J. & Atzmueller, M. (2025). LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping. https://doi.org/10.48550/arXiv.2502.20139

These datasets are soil survey mapping available in Queensland. These surveys were conducted for different purposes and are mapped at different scales. Refer to individual records for more information.

The United States Department of Agriculture, Natural Resource Conservation Service (USDA, NRCS) has developed the U.S. General Soil Map (STATSGO2) Database (formerly known as the State Soil Geographic (STATSGO) database). Data are available for all 50 states as well as for Puerto Rico. Soil maps for the STATSGO2 database are made by generalizing detailed soil survey data. The mapping scale for STATSGO2 map is 1:250,000 (with the exception of Alaska, which is 1:1,000,000). The level of mapping is designed to be used for broad planning and management uses covering state, regional, and multi-state areas. STATSGO2 data are designed for use in a geographic information system (GIS).

Digitizing is done by line segment (vector) format in accordance with Natural Resources Conservation Service (NRCS) digitizing standards. The base map used is the U.S. Geological Survey 1:250,000 topographic quadrangles. The number of soil polygons per quadrangle map is between 100 and 400. The minimum area mapped is about 1,544 acres.

STATSGO2 data are collected in 1:250,000 quadrangle units. Map unit delineations match at state boundaries. States have been joined as one complete seamless data base to form statewide coverage. Composition of soil map units was coordinated across state boundaries, so that component identities and relative extents would match.

Each STATSGO2 map is linked to the Soil Interpretations Record (SIR) attribute database. The attribute database gives the proportionate extent of the component soils and their properties for each map unit. The STATSGO map units consist of 1 to 21 components each. The Soil Interpretations Record database includes over 25 physical and chemical soil properties, interpretations, and productivity. Examples of information that can be queried from the database are available water capacity, soil reaction, salinity, flooding, water table, bedrock, and interpretations for engineering uses, cropland, woodland, rangeland, pastureland, wildlife, and recreation development.

STATSGO2 data are available in the USGS Digital Line Graph (DLG-3) Optional distribution format. NRCS soil map symbols are not normally carried within the DLG-3 file; however, these map symbols are made available as a unique ascii file when NRCS soils data are distributed in the DLG-3 format. STATSGO2 data are also available in ArcInfo 7.0 coverage and GRASS 4.13 vector formats. Both DOS/Windows and UNIX compatible file formats are available. Data are available via ftp by state including Puerto Rico. The complete data set is also available on CD-ROM for a fee.

This data set provides gridded data for selected soil parameters derived from data and methods developed by the Global Soil Data Task, an international collaborative project with the objective of making accurate and appropriate data relating to soil properties accessible to the global change research community. The task was coordinated by the International Geosphere-Biosphere Programme (IGBP-DIS). The data in this data set were produced by the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) staff from data obtained from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC, http://daac.ornl.gov/). See the related data sets section below. Two-dimensional gridded maps of selected soil parameters, including soil texture, at a 1.0 by 1.0 degree spatial resolution and for two soil depths are provided. All data layers have been adjusted to match the ISLSCP II land/water mask. There are 36 data files with this data set.