Comprehensive dataset of 12 Tax assessors in Alabama, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Geospatial data about St. Clair County, Alabama Parcels. Export to CAD, GIS, PDF, CSV and access via API.

Map displaying Jefferson County Parcel data. This map is used by several of the open data apps

Geospatial data about Lawrence County, Alabama Parcels. Export to CAD, GIS, PDF, CSV and access via API.

These products are derived from disaggregation of legacy soil mapping in the agricultural zone of South Australia using the DSMART tool (Odgers et al. 2014a); produced for the Soil and Landscape Grid of Australia Facility. There are 10 soil attribute products available from the Soil Facility: Available Water Capacity (AWC); Bulk Density - Whole Earth (BDw); Cation Exchange Capacity (CEC); Clay (CLY); Coarse Fragments (CFG); Electrical Conductivity (ECD); Organic Carbon (SOC); pH - CaCl2( pHc); Sand (SND); Silt (SLT).

Each soil attribute product is a collection of 6 depth slices (except for effective depth and total depth). Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm & 100-200cm, consistent with the specifications of the GlobalSoilMap.

The DSMART tool was used in a downscaling process to translate legacy soil landscape mapping to 3” resolution (approx. 100m cell size) raster predictions of soil classes and corresponding soil properties. Legacy mapping was performed at 1:50,000 and 1:100,000 scales to delineate associated soils within polygons however individual soils were not explicitly spatially defined. These new disaggregated map products aim to incorporate expert soil surveyor knowledge embodied in legacy polygon soil maps, while providing re-interpreted soil spatial information at a scale that is more suited to on-ground decision making.

Note: The DSMART-derived dissagregated legacy soil mapping products provide different spatial predictions of soil properties to the national TERN Soil Grid products derived by Cubist (data mining) kriging based on site data by Viscarra Rossel et al. (2014). Where they overlap, the national prediction layers and DSMART products can be considered complementary predictions. They will offer varying spatial reliability (/ uncertainty) depending on the availability of representative site data (for national predictions) and the scale and expertise of legacy mapping. The national predictions and DSMART disaggregated layers have also been merged as a means to present the best available (lowest statistical uncertainty) data from both products (Clifford et al. 2014).

Previous versions of this collection contained Depths layers. These have been removed as the units do not comply with Global Soil Map specifications. Lineage: The soil attribute maps are generated using novel spatial modelling and digital soil mapping techniques to disaggregate legacy soil mapping.

Legacy soil mapping: Polygon-based soil mapping for South Australia’s agricultural zone was developed via SA’s State Land and Soil Mapping Program (DEWNR 2014, Hall et al. 2009). Sixty one soil classes (termed ‘subgroup soils’) have been defined to capture the range of variation in soil profiles across this area. While legacy soil mapping does not explicitly map the distribution of these soil classes, estimates of their percentage composition and associated soil properties are available for each soil landscape map unit (polygon).

Disaggregation of soil classes: The DSMART algorithm (version 1, described in Odgers et al. 2014) was used to produce fine-resolution raster predictions for the probability of occurrence of each soil class. This uses random virtual sampling within each map unit (with sampling weighted by the expected proportions of each soil class) to build predictions for the distribution of soil classes based on relationships with environmental covariate layers (e.g. elevation, terrain attributes, climate, remote sensing vegetation indices, radiometrics). The algorithm was run 100 times then averaged to create probabilistic estimates for soil class spatial distributions.

Soil property predictions: The PROPR algorithm (Odgers et al. 2015b) was used to generate soil property maps (and their associated uncertainty) using reference soil property data and the soil class probability maps create through the above DSMART disaggregation step.

South Australia’s national- or ASRIS-format soil mapping was used to provide reference soil properties. This dataset was previously developed to meet the specifications of McKenzie et al. (2012) and provides expert soil surveyor estimates for map unit area composition and representative profile properties of approximately 1500 regional variants of the original sixty one ‘subgroup soil’ classes. Equal area depth smoothing splines were applied to the regional variant profile data to obtain property values at the specified GlobalSoilMap depth intervals. Then area-weighted soil property averages were calculated for each subgroup soil class. This process is documented further in Odgers et al. (2015a).

We used 2071 forest soil profiles to model a wide range of soil properties for the forested area of Switzerland. The spatial prediction is based on the principle of «digital soil mapping». This involves linking soil profiles with soil forming factors using statistical or machine learning methods. A quantile regression forest (QRF) approach was applied to predict the following soil properties at six depth ranges: clay, gravel, sand, fine earth density, SOC. The depth ranges correspond to the standard depths of the GlobalSoilMap.Net specification: 0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm. In addition, the total soil depth down to a non-root-permeable layer or solid rock soil thick was predicted. To quantify the uncertainty for each predicted pixel, the upper and lower limit of the 90% prediction interval derived from QRF was calculated. More details on the methods and results are described in Baltensweiler et al. 2021 and Baltensweiler et al 2022. The soil property maps, and the uncertainty maps are provided as a GeoTIFF files at 25 m resolution. The excel file (xlsx) provides a short description of the raster layers. The soil and the uncertainty maps can be viewed in a simple web-GIS application available at: www.wsl.ch/soilmaps.

These are products of the Soil and Landscape Grid of Australia Facility generated through disaggregation of the Western Australian soil mapping. There are 9 soil attribute products available from the Soil Facility: Available Water Holding Capacity - Volumetric (AWC); Bulk Density - Whole Earth (BDw); Bulk Density - Fine Earth (BDf); Clay (CLY); Course Fragments (CFG); Electrical Conductivity (ECD); pH Water (pHw); Sand (SND); Silt (SLT).

Each soil attribute product is a collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm & 100-200cm, consistent with the Specifications of the GlobalSoilMap.

The DSMART tool (Odgers et al. 2014) tool was used in a downscaling process to translate legacy soil landscape mapping to 3” resolution (approx. 100m cell size) raster predictions of soil classes (Holmes et al. Submitted). The soil class maps were then used to produce corresponding soil property surfaces using the PROPR tool (Odgers et al. 2015; Odgers et al. Submitted). Legacy mapping was compiled for the state of WA from surveys ranging in map scale from 1:20,000 to 1:2,000,000 (Schoknecht et al., 2004). The polygons are attributed with the soils and proportions of soils within polygons however individual soils were not explicitly spatially defined. These new disaggregated map products aim to incorporate expert soil surveyor knowledge embodied in legacy polygon soil maps, while providing re-interpreted soil spatial information at a scale that is more suited to on-ground decision making.

Note: The DSMART-derived dissagregated legacy soil mapping products provide different spatial predictions of soil properties to the national TERN Soil Grid products derived by Cubist (data mining) and kriging based on site data by Viscarra Rossel et al. (Submitted). Where they overlap, the national prediction layers and DSMART products can be considered complementary predictions. They will offer varying spatial reliability (/ uncertainty) depending on the availability of representative site data (for national predictions) and the scale and expertise of legacy mapping. The national predictions and DSMART disaggregated layers have also been merged as a means to present the best available (lowest statistical uncertainty) data from both products (Clifford et al. In Prep).

Previous versions of this collection contained Depths layers. These have been removed as the units do not comply with Global Soil Map specifications. Lineage: The soil attribute maps are generated using novel spatial modelling and digital soil mapping techniques to disaggregate legacy soil mapping.

Legacy soil mapping: Polygon-based soil mapping for Western Australia’s agricultural zone was developed via WA’s Department of Agriculture and Food (Schoknecht et al., 2004). Seventy-three soil classes (termed ‘WA soil groups’ Schoknecht and Pathan, 2013) have been defined to capture the range of variation in soil profiles across this area. While legacy soil mapping does not explicitly map the distribution of these soil classes, estimates of their percentage composition and associated soil properties are available for each soil landscape map unit (polygon).

Disaggregation of soil classes: The DSMART algorithm (version 1, described in Odgers et al. 2014) was used to produce fine-resolution raster predictions for the probability of occurrence of each soil class. This uses random virtual sampling within each map unit (with sampling weighted by the expected proportions of each soil class) to build predictions for the distribution of soil classes based on relationships with environmental covariate layers (e.g. elevation, terrain attributes, climate, remote sensing vegetation indices, radiometrics). The algorithm was run 100 times then averaged to create probabilistic estimates for soil class spatial distributions.

Soil property predictions: The PROPR algorithm (Odgers et al. 2015) was used to generate soil property maps (and their associated uncertainty) using reference soil property data and the soil class probability maps create through the above DSMART disaggregation step.

Western Australia’s expert defined typical range of soil properties by soil class was used to provide reference soil properties to PROPR. These estimates were made separately for each physiographic zone across WA, and are based on available profile data and surveyor experience. Uncertainty bounds were determined by the minimum and maximum soil properties at the ‘qualified soil group’ level, and the property value of the most common soil in the map unit was used to define the typical soil property. This methodology was previously developed to meet the specifications of McKenzie et al. (2012) and provides expert soil surveyor estimates for map unit area composition and representative profile properties. Depth averaging was applied to the regional variant profile data to obtain property values at the specified GlobalSoilMap depth intervals. Then area-weighted soil property averages were calculated for each subgroup soil class. This process is documented further in Odgers et al. (Submitted).

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This is Version 1 of the …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This is Version 1 of the Australian Soil Clay product of the Soil and Landscape Grid of Australia. The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (approximately 90 x 90 m pixels). These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia. Attribute Definition: 2 micrometre mass fraction of the less than 2 mm soil material determined using the pipette method; Units: %; Period (temporal coverage; approximately): 1950-2013; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 18; Number of pixels with coverage per layer: 2007M (49200 x 40800); Total size before compression: about 8GB; Total size after compression: about 4GB; Data license : Creative Commons Attribution 3.0 (CC By); Target data standard: GlobalSoilMap specifications; Format: GeoTIFF. Dataset History The National Digital Soil Property Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. Two DSM methods have been utilised across and in various parts of Australia, these being: 1) Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2014a); 2) Disaggregation of existing polygon soil mapping using DSMART (Odgers et al. 2014a). Version 1 of the National Digital Soil Property Maps combines mapping from the: 1) Australia-wide three-dimensional Digital Soil Property Maps; 2) Western Australia Polygon Disaggregation Maps; 3) South Australian Agricultural Areas Polygon Disaggregation Maps; 4) Tasmanian State-wide DSM Maps. These individual mapping products are also available in the CSIRO Data Access Portal (https://data.csiro.au). Please refer to these individual products for more detail on the DSM methods used. References: Specifications: Version 1 GlobalSoilMap.net products, Release 2.1, viewed 12/09/2014, http://www.globalsoilmap.net/specifications. Bishop, TFA, McBratney, AB & Laslett, GM 1999, 'Modelling soil attribute depth functions with equal-area quadratic smoothing splines', Geoderma, vol. 91, no. 1-2, pp. 27-45. http://dx.doi.org/10.1016/S0016-7061(99)00003-8. Breiman, L, Friedman, J, Stone, CJ & Olshen, RA 1984, Classification and Regression Trees, Wadsworth statistics/probability series, Wadsworth Belmont, Ca. Clifford, D, Dobbie, MJ & Searle, R 2014, 'Non-parametric imputation of properties for soil profiles with sparse observations', Geoderma, vol. 232-234, pp. 10-8. http://dx.doi.org/10.1016/j.geoderma.2014.04.026. Clifford, D, Searle, R & Holmes, KW 2015, 'Methods to merge disparate spatial estimates of soil attributes', Soil Research, in preparation. de Caritat, P & Cooper, M 2011, National Geochemical Survey of Australia: The Geochemical Atlas of Australia, Geoscience Australia, Record 2011/20 (2 Volumes), Canberra, 557 pp. http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_71973. DEWRN 2014, Mapping soil and land, Department of Environment, Water and Natural Resources, Government of South Australia, viewed 14/04/2014, http://www.environment.sa.gov.au/Knowledge_Bank/Information_data/soil-and-land/mapping-soil-and-land. Grunwald, S 2009, 'Multi-criteria characterization of recent digital soil mapping and modeling approaches', Geoderma, vol. 152, no. 3-4, pp. 195-207. http://dx.doi.org/10.1016/j.geoderma.2009.06.003. Hall, JAS, Maschmedt, DJ & Billing, NB 2009, The Soils of Southern South Australia, The South Australian Land and Soil Book Series, Volume 1; Geological Survey of South Australia, Bulletin 56, Volume 1, Department of Water, Land and Biodiversity Conservation, Government of South Australia. https://data.environment.sa.gov.au/Land/Land-Resources/Pages/Home.aspx. Holmes, KW, Griffin, TG & Odgers, NP 2015, 'Continental scale spatial disaggregation of legacy soil maps: evaluation over Western Australia', Soil Research, in preparation. Jacquier, D, Wilson, P, Griffin, T & Daniel, B 2012, Soil Information Transfer and Evaluation System (SITES) - Database design and exchange protocols, CSIRO Land and Water, Canberra. http://www.clw.csiro.au/aclep/publications/reports.htm. Kidd, D 2015, '80-metre Resolution 3D Soil Attribute Maps for Tasmania', Soil Research, in preparation. Kidd, DB, Malone, BP, McBratney, AB, Minasny, B & Webb, MA 2014, 'Digital mapping of a soil drainage index for irrigated enterprise suitability in Tasmania, Australia', Soil Research, vol. 52, no. 2, pp. 107-19. http://dx.doi.org/10.1071/sr13100. Malone, BP, Minasny, B, Odgers, NP & McBratney, AB 2014, 'Using model averaging to combine soil property rasters from legacy soil maps and from point data', Geoderma, vol. 232, pp. 34-44. http://dx.doi.org/10.1016/j.geoderma.2014.04.033. McBratney, AB, Mendonça Santos, ML & Minasny, B 2003, 'On digital soil mapping', Geoderma, vol. 117, no. 1-2, pp. 3-52. http://dx.doi.org/10.1016/S0016-7061(03)00223-4. McKenzie, NJ, Jacquier, DW, Maschmedt, DJ, Griffin, EA & Brough, DM 2012, The Australian Soil Resource Information System (ASRIS) Technical Specifications, Revised Version 1.6, June 2012, The Australian Collaborative Land Evaluation Program. http://www.asris.csiro.au/downloads/ASRIS_Tech_Specs_201.6.pdf. McKenzie, NJ & Ryan, PJ 1999, 'Spatial prediction of soil properties using environmental correlation', Geoderma, vol. 89, no. 1-2, pp. 67-94. http://dx.doi.org/10.1016/s0016-7061(98)00137-2. Odgers, NP, Holmes, KW, Griffin, T & Liddicoat, C 2015a, 'Derivation of soil attribute estimations from legacy soil maps', Soil Research, in preparation. Odgers, NP, McBratney, AB & Minasny, B 2015, 'Digital soil property mapping and uncertainty estimation using soil class probability rasters', Geoderma, vol. 237-238, pp. 190-8. http://dx.doi.org/10.1016/j.geoderma.2014.09.009. Odgers, NP, Sun, W, McBratney, AB, Minasny, B & Clifford, D 2014, 'Disaggregating and harmonising soil map units through resampled classification trees', Geoderma, vol. 214-215, pp. 91-100. http://dx.doi.org/10.1016/j.geoderma.2013.09.024. Rodríguez, E, Morris, CS & Belz, JE 2006, 'A Global Assessment of the SRTM Performance', Photogrammetric Engineering & Remote Sensing, vol. 72, no. 3, pp. 249-60. Schoknecht, N & Pathan, S 2013, Soil groups of Western Australia: a simple guide to the main soils of Western Australia, 4th ed. Resource Management Technical Report 280, Department of Agriculture and Food Western Australia, Perth. http://archive.agric.wa.gov.au/PC_95446.html. Schoknecht, N, Tille, P & Purdie, B 2004, Soil-landscape mapping in south-western Australia: an overview of methodology and outputs, Resource Management Technical Report 280, Department of Agriculture, Government of Western Australia, Perth. Searle, R 2014, 'The Australian Site Data Collation to Support Global Soil Map', paper presented to GlobalSoilMap Conference 2013, Orleans, France, 7-9 October 2013, https://publications.csiro.au/rpr. Viscarra Rossel, RA 2011, 'Fine-resolution multiscale mapping of clay minerals in Australian soils measured with near infrared spectra', Journal of Geophysical Research: Earth Surface, vol. 116, no. F4, p. F04023. http://dx.doi.org/10.1029/2011JF001977. Viscarra Rossel, RA & Chen, C 2011, 'Digitally mapping the information content of visible-near infrared spectra of surficial Australian soils', Remote Sensing of Environment, vol. 115, no. 6, pp. 1443-55. http://dx.doi.org/10.1016/j.rse.2011.02.004. Viscarra Rossel, RA, Chen, C, Grundy, M, Searle, R, Clifford, D & Campbell, PH 2015a, 'The Australian three-dimensional soil grid: Australia's contribution to the GlobalSoilMap project', Soil Research, in preparation. Viscarra Rossel, RA, Chen, H & Hicks, W 2015b, 'Prediction of spatial distribution of soil attributes to depth from Australian site and covariate data', Soil Research, in preparation. Viscarra Rossel, RA & Webster, R 2012, 'Predicting soil properties from the Australian soil visible-near infrared spectroscopic database', European Journal of Soil Science, vol. 63, no. 6, pp. 848-60. http://dx.doi.org/10.1111/j.1365-2389.2012.01495.x. Viscarra Rossel, RA, Webster, R, Bui, EN & Baldock, JA 2014, 'Baseline map of organic carbon in Australian soil to support national carbon accounting and monitoring under climate change', Global Change Biology, vol. 20, no. 9, pp. 2953-70. http://dx.doi.org/10.1111/gcb.12569. Dataset Citation CSIRO (2014) Soil and Landscape Grid National Soil Attribute Maps - Clay 3 resolution - Release 1. Bioregional Assessment Source Dataset. Viewed 12 March 2019, http://data.bioregionalassessments.gov.au/dataset/f8640540-4bb7-42ee-995a-219881e67705.

Reported here are physical property records and sedimentological parameters generated from core and downhole logging measurements at ODP Site 909 (Fram Strait). The data are listed vs. depth and a newly derived age model. The composite P-wave velocity (Vp) profile of the uppermost 26 m consists of edited and spliced velocity measurements with the P-wave logger on sediment cores from Holes 909A and 909B. For the interval between 102 and 1000 mbsf in-situ velocity information from downhole logs (Sonic Digital Tool, processed postcruise data) at Hole 909C were used. Wet bulk density (WBD) data stem from nondestructive density measurements of whole-round core sections with a gamma-ray attenuation porosity evaluator (0 - 89 m), and from densities calculated from phasor induction resistivity downhole logging measurements (89 - 1016 m). Based on a new chronology, total mass accumulation rates (MAR) and accumulation rates for various grain size classes of the coarse fraction (>63 µm) were recalculated. Original weight percentage data are from O'Connell et al. (1996) and from Wolf-Welling et al. (1996). Percentage silt data estimated from smear slides were used to calculate accumulation rates of silt. Estimated silt percentages as well as all original physical property measurements are from Myhre et al. (1995).

Pixel scaled collocated AIRS, Aqua MODIS, SNPP CrIS, and SNPP VIIRS cloud retrieval products. These datasets are used to generate analyses and results presented in Yue et al. (2022, AMT): "Evaluating the Consistency and Continuity of Pixel-Scale Cloud Property Data Records From Aqua and SNPP"