Provisional Agricultural Land Classification Grade. Agricultural land classified into five grades. Grade one is best quality and grade five is poorest quality. A number of consistent criteria used for assessment which include climate (temperature, rainfall, aspect, exposure, frost risk), site (gradient, micro-relief, flood risk) and soil (depth, structure, texture, chemicals, stoniness) for England only. Digitised from the published 1:250,000 map which was in turn compiled from the 1 inch to the mile maps.More information about the Agricultural Land Classification can be found at the following links:http://webarchive.nationalarchives.gov.uk/20130402200910/http://archive.defra.gov.uk/foodfarm/landmanage/land-use/documents/alc-guidelines-1988.pdfhttp://publications.naturalengland.org.uk/publication/35012.Full metadata can be viewed on data.gov.uk.

A 24"x28" PDF Map of Soil Classifications in Denton County.

This data set provides a digital map of soil orders for the Ji-Parana River Basin, in the state of Rondonia, Brazil (Western Amazonia). Soil orders were manually digitized from a 1:500,000 map from EMBRAPA originally published in 1983. Oxisols and Ultisols are the predominant soil types in the basin, encompassing 47% and 24% of the total drainage area, respectively. Entisols cover 14%, Alfisols 13% and Eptisols 2% of the basin (Ballester et al., 2003). One data file is provided in ESRI ArcGIS Shapefile format compressed into a single zip file (*.zip).

Mineral Land Classification studies are produced by the State Geologist as specified by the Surface Mining and Reclamation Act (SMARA, PRC 2710 et seq.) of 1975. To address mineral resource conservation, SMARA mandated a two-phase process called classification-designation. Classification is carried out by the State Geologist and designation is a function of the State Mining and Geology Board. The classification studies contained here evaluate the mineral resources and present this information in the form of Mineral Resource Zones. The objective of the classification-designation process is to ensure, through appropriate local lead agency policies and procedures, that mineral materials will be available when needed and do not become inaccessible as a result of inadequate information during the land-use decision-making process.

This data set consists of land cover classification data derived from satellite imagery and of data obtained in the field as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).

This data set consists of land cover classification data derived from satellite imagery as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12). Images from the RADARSAT-2, Système Pour l'Observation de la Terre (SPOT-4), and DMC International Imaging Ltd (DMCii) of the study area were retrieved for the summer of 2012. The land use classification image provides information about vegetation present in the study area at a resolution of 20 meters.

This map identifies the dominant soil types across NSW using the Australian Soils Classification (ASC) at Order level. It uses the best available soil resource mapping coverage incorporating over 55 different datasets of multiple scales across NSW. \r \r The formal ASC classification has been slightly modified in this map to further identify 2 extra sub-classes - soils with alluvial origins in the Rudosol order and soils with sodium-rich subsoils in the Kurosol order category. \r \r Soil types are representative of the dominant facet (sub-landscape) of each map unit and allocated using a lookup table system, linking a Great Soil Group classification soil type to the most appropriate Australian Soil Classification (ASC) class (see LUT table in data package). In some areas (north coast region and Cobargo area), an ASC classification has been assigned to map units directly without using a lookup system. These areas are identified in the ASC confidence map found within in the data package. While the ASC classification commonly equates to a particular GSG soil type classification, this is is not always the case and therefore ASC classifications allocated manually, will have a higher accuracy. \r \r Online Maps: This dataset can be viewed using eSPADE (NSW’s soil spatial viewer), which contains a suite of soil and landscape information including soil profile data. Many of these datasets have hot-linked soil reports. An alternative viewer is the SEED Map; an ideal way to see what other natural resources datasets (e.g. vegetation) are available for this map area.\r \r Reference: Department of Planning, Industry and Environment, 2021, Australian Soil Classification (ASC) Soil Type map of NSW, Version 4.5, Department of Planning, Industry and Environment, Parramatta.

A global data set of soil types is available at 0.5-degree latitude by 0.5-degree longitude resolution. There are 106 soil units, based on Zobler?s (1986) assessment of the FAO/UNESCO Soil Map of the World. This data set is a conversion of the Zobler 1-degree resolution version to a 0.5-degree resolution. The resolution of the data set was not actually increased. Rather, the 1-degree squares were divided into four 0.5-degree squares with the necessary adjustment of continental boundaries and islands. The computer code used to convert the original 1-degree data to 0.5-degree is provided as a companion file. A JPG image of the data is provided in this document. The Zobler data (1-degree resolution) as distributed by Webb et al. (1993) [http://www.ngdc.noaa.gov/seg/eco/cdroms/gedii_a/datasets/a12/wr.htm#top] contains two columns, one column for continent and one column for soil type. The Soil Map of the World consists of 9 maps that represent parts of the world. The texture data that Webb et al.(1993) provided allowed for the fact that a soil type in one part of the world may have different properties than the same soil in a different part of the world. This continent-specific information is retained in this 0.5-degree resolution data set, as well as the soil type information which is the second column. A code was written (one2half.c) to take the file CONTIZOB.LER distributed by Webb et al. (1993) [http://www.ngdc.noaa.gov/seg/eco/cdroms/gedii_a/datasets/a12/wr.htm#top] and simply divide the 1-degree cells into quarters. This code also reads in a land/water file (land.wave) that specifies the cells that are land at 0.5 degrees. The code checks for consistency between the newly quartered map and the land/water map to which the quartered map is to be registered. If there is a discrepancy between the two, an attempt was made to make the two consistent using the following logic. If the cell is supposed to be water, it is forced to be water. If it is supposed to be land but was resolved to water at 1 degree, the code looks at the surrounding 8 cells and picks the most frequent soil type and assigns it to the cell. If there are no surrounding land cells then it is kept as water in the hopes that on the next pass one or more of the surrounding cells might be converted from water to a soil type. The whole map is iterated 5 times. The remaining cells that should be land but couldn't be determined from surrounding cells (mostly islands that are resolved at 0.5 degree but not at 1 degree) are printed out with coordinate information. A temporary map is output with -9 indicating where data is required. This is repeated for the continent code in CONTIZOB.LER as well. A separate map of the temporary continent codes is produced with -9 indicating required data. A nearly identical code (one2half.c) does the same for the continent codes. The printout allows one to consult the printed versions of the soil map and look up the soil type with the largest coverage in the 0.5-degree cell. The program manfix.c then will go through the temporary map and prompt for input to correct both the soil codes and the continent codes for the map. This can be done manually or by preparing a file of changes (new_fix.dat) and redirecting stdin. A new complete version of the map is outputted. This is in the form of the original CONTIZOB.LER file (contizob.half) but four times larger. Original documentation and computer codes prepared by Post et al. (1996) are provided as companion files with this data set. Image of 106 global soil types available at 0.5-degree by 0.5-degree resolution. Additional documentation from Zobler?s assessment of FAO soil units is available from the NASA Center for Scientific Information.

General Urban Planning Plan. Soil classification. Map

The vector data set is based on the FAO-UNESCO soil map of the world. The digitized soil map of the world, at 1:5.000.000 scale, is in the geographic projection (latitude - longitude) intersected with a template containing water related features (coastlines, lakes, glaciers and double-lined rivers). The digital soil map of the world (except for the continent of Africa) was intersected with the country boundaries map from the world data bank ii (with country boundaries updated to January 1994 at 1:3 000 000 scale), obtained from the US government. For Africa, the country boundaries are derived from the FAO country boundaries on the original FAO/UNESCO soil map of the world. Country boundaries in both cases were checked and adjusted in certain places on the basis of FAO and UN conventions.

**Suggested to use 'Download' button instead of 'Open in ArcGIS Pro'The REST service page displays all data provided in this layer package: https://arcgis.dnr.alaska.gov/arcgis/rest/services/Mapper/Surface_Classification/MapServer

Traditional soil maps have helped us to better understand soil, to form our concepts and to teach and transfer our ideas about it, and so they have been used for many purposes. Although, soil maps are available in many countries, there is a need for them to be updated because they are often deficient in that their spatial delineations and their descriptions are subjective and lack assessments of uncertainty. Updating them is a priority for federal soil surveys worldwide as well as for research, teaching and communication. New data from sensors and quantitative ‘digital’ methods provide us with the tools to do so. Here, we present an approach to update large scale, national soil maps with data derived from a combination of traditional soil profile classifications, classifications made with visible–near infrared (vis–NIR) spectroscopy, and digital soil class mapping (DSM). Our results present an update of the Australian Soil Classification (ASC) orders map. The overall error rate of the DSM model, tested on an independent validation set, was 55.6%, and a few of the orders were poorly classified. We discuss the possible reasons for these errors, but argue that compared to the previous ASC maps, our classification was derived objectively, using currently best available data sets and methods, the classification model was interpretable in terms of the factors of soil formation, the modelling produced a 1×1 km resolution soil map with estimates of spatial uncertainty for each soil order and our map has no artefacts at state and territory borders.

This data set contains land cover classification data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.

Talbert Marsh is a wet land in Huntington Beach, California. It has open water, wet meadow, reeds, shrubs, and trees. This data is produced using 6 bands of MicaSense Dual Camera image by SVM classification and majority filtering. There are 12 classes here defined as

0: Water

1: Pebbles

2: Tall vegetations

3: Medium vegetations

4: Short vegetations

5: Dry reeds

6: Soil

7: Unpaved road

8: Shadow

9: Paved road

10: Reeds

11: Fens

NZSC Order is the first level of the NZ soil classification (Hewitt, 2010). This layer is a "dissolved" representation of the NZSC soil order attribute for S-map, where neighbouring S-map polygons have been combined if they have the same value of the attribute. Refer to document Smap Data Dictionary Dissolved Layers.pdf at https://lris.scinfo.org.nz/document/22129-smap-data-dictionary-dissolved-layers/

The Belgian soil map was drawn up on the basis of the results of intensive soil mapping during the 1950s and 1970s. This Belgian soil map is based on the Belgian soil classification system. It is a national system that has been set up exclusively for Belgian soils. The soil map of Belgium according to the international World Reference Base (WRB) soil classification system is a translation of the soil map according to the Belgian soil classification system. WRB consists of 2 levels of detail: on the one hand the 32 'Reference Soil Groups' (RSGs) and on the other hand a combination of RSGs with prefixes and suffixes, i.e. 'qualifiers', which allow to classify an individual soil profile. By using 'Reference Soil Groups' (colored areas) and 'Main qualifiers' (blue-outlined polygons with labels), the most important information from the soil map of Belgium can be translated into the WRB system. To ensure that the information about texture, drainage and substrates is not lost, 3 more sub-maps have been added: 'Texture map', 'Drainage qualifiers' and 'Abruptic/Ruptic qualifiers'. The 'Texture map' shows the texture class as it appears on the soil map of Belgium. The maps are provisional versions and are the result of the project 'Development and application of a method for converting the Belgian soil map according to the international Word Reference Base system' carried out by S. Dondeyne, J. Deckers and E. Van Ranst of the K.U.Leuven and U.Gent. More explanation can be found in the report of the study (https://www.dov.vlaanderen.be/themas/grond). Comments or suggestions are welcome via dov@vlaanderen.be.

The maps show the soil cover of the Czech Republic. They express the typological affiliation of the soil in the "Taxonomic classification system of soils of the Czech Republic" (Němeček et al., 2011), or the version for forest soils (Vokoun et al., 2002), i.e. soil type, subtype, soil variety, or subvariety. The data for the selected map sheets also contain data on the soil parent material in a classification based on the legend of the Geological Map of the Czech Republic 1:50,000, supplemented by classification codes (Schuler et al,. 2013). This soil map on a scale of 1:50,000 is the most detailed soil map so far, which maps both agricultural and forest land together in the area of the entire territory of the Czech Republic (in process), in the same and up-to-date soil classification system. In the GIS environment, the maps are processed after the map sheets of the ZM50 division, so that they create a continuous thematic layer of the soil-typology map.

The maps show the soil cover of the Czech Republic. They express the typological affiliation of the soil in the "Taxonomic classification system of soils of the Czech Republic" (Němeček et al., 2011), or the version for forest soils (Vokoun et al., 2002), i.e. soil type, subtype, soil variety, or subvariety. The data for the selected map sheets also contain data on the soil parent material in a classification based on the legend of the Geological Map of the Czech Republic 1:50,000, supplemented by classification codes (Schuler et al,. 2013). This soil map on a scale of 1:50,000 is the most detailed soil map so far, which maps both agricultural and forest land together in the area of the entire territory of the Czech Republic (in process), in the same and up-to-date soil classification system. In the GIS environment, the maps are processed after the map sheets of the ZM50 division, so that they create a continuous thematic layer of the soil-typology map.

The polygon data layer in this map service consists of an updated National Earthquake Hazards Reduction Program (NEHRP) soil classification map of Massachusetts at 100-meter resolution. This is a statewide coverage that classifies soils according to the NEHRP soil categories A, B, C, D, and E. The category into which a soil is classified is determined by the average shear wave velocity in the top 30 meters (100 feet) of the earth’s surface. The classification is also dependent on the thickness of the soil cover that lies over the bedrock. This updated NEHRP soil classification map incorporates overburden thickness into the soil category determination that was previously unavailable.Feature service also available.See the full metadata page.