Overview of all datasets in ESDAC

Description: 

This list provides a one-page overview of all datasaets available in ESDAC to the public: the name (and link) for the dataset and a short abstract.

Displaying 1 - 80 of 80
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  • This database (2004) is the only harmonized soil database for Europe, extending also to Eurasia. It contains a soil geographical database SGDBE (polygons) to which a number of essential soil attributes are attached, and an associate database PTRDB, with attributes which values have been derived through pedotransfer rules. Also part of the database is the Soil Profile Analytical Database, that contains measured and estimated soil profiles for Europe.
  • This database (2006) is a set of raster data sets that have been derived from the European soil Database v2, for most attributes. The values for the attributes are categorized (non-continuous). These rasters are an interpretation of the data that are contained in the ESDB v2.0
  • Analysis of the spatial footprint of the multiple forms of land degradation in global arable lands. This includes 5 land degradation processes: aridity, soil erosion, vegetation decline, soil salinization and soil organic carbon decline. Data are at global scale.
  • Topsoil Hg concentrations (μg kg−1) across 26 EU countries estimated by deep neural network – regression kriging. We also provide Mercury stocks and mercury fluxes to main riverbasins and sea outlets. The assessment is based on 21591 LUCAS samples (0-20cm) from 26 European Union countries.
  • Data from the 2015 LUCAS campaign soil component containing soil properties data (clay, silt and sand content, coarse fragments, pH (CaCl2 and H2O), organic carbon content, CaCO3, nitrogen, phosphorous, potassium, EC (Electrical conductivity) and multispectral reflectance data for 21,859 samples. These primary data are supplemented by reference ancillary data describing a range of environmental conditions for the LUCAS Soil locations.
  • Maximising climate mitigation potential by carbon and radiative agricultural land management with cover crops
  • Global average phosphorus (P) losses due to soil erosion in kg ha−1 yr−1. Thus we combine the most recent spatially distributed global soil erosion estimates with global P content of cropland soils (data also available for P content)
  • Land use and climate change impacts on global soil erosion by water (2015-2070). This dataset includes the baseline scenario (2015) and the future projections (2070) of soil erosion based on land use changes and climate change effects.
  • Plutonium and Cesium inventories in European topsoils. Includes also the Chernobyl-derived 137Cs and Global-derived 137Cs
  • This dataset contains the original measured Soil Organic Matter (SOM) fractions of a subset of the LUCAS 2009 topsoil dataset. This dataset includes 352 samples for all land uses and 186 samples only for grassland and fores used to derived maps on Particulate Organic Matter (POM) and Mineral-Associated Organic Matter (MAOM)
  • This group of datasets contains 8 chemical properties: pH, pH (CaCl), Cation Exchange Capacity (CEC), Calcium carbonates (CaCO3), C:N ratio, Nitrogen (N), Phosphorus (P) and Potassium (K) using soil point data from the LUCAS 2009/2012 soil surveys (around 22,000 points) for EU-26 (not included Cyprus and Croatia). The chemical properties maps for the European Union were produced using Gaussian process regression (GPR) models. Resolution: 500m. Format: TIFF; projection information: ETRS89 / LAEA Europe
  • The dataset contains the data of physical and chemical properties analysed in samples taken in Switzerland within the context of LUCAS 2015 survey. These data have been used in the study “Comparison of sampling with a spade and gouge auger for topsoil monitoring at the continental scale”, published in the European Journal of Soil Science (https://onlinelibrary.wiley.com/doi/abs/10.1111/ejss.12862). The dataset format is an Excel file.
  • Global Soil Erosion is a re-sampled dataset (25km) of the original Global Soil Erosion map. Both the 2012 and 2001 datasets are provided. The data package includes also the input layers (K, LS, R, C) at 25km resolution and a sample area (in Amazon rainforest) at the original resolution of 250m
  • Soil loss due to crop harvesting in the European Union. The regional estimates of total Soil Loss by Crop Harvesting (SLCH) are presented at country and regional level.
  • Detailed maps of heavy metals in the EU27 (EU-28 except Croatia), based on topsoil HM data from LUCAS 2009
  • Cumulative C budget over the period 2016-20100 in the EU agricultural soils under the accelerated and current soil erosion scenarios.
  • A biological factor to be included in soil erosion modelling. The available data for Earthwork diversity (richness and abundance) introduced a new "earthworm factor" to be incorporated in soil erosion modelling.
  • Soil Profile Analytical Database 14 (SPADE 14) is based on the concept used in previous versions of SPADE (SPADE 1 and 8). It includes 1078 soil profile data from 28 countries.
  • Copper distribution in European Union topsoils based on LUCAS points. The data are at 500m resolution and have been the result of advanced interpolation modelling.
  • Soil fluxes (CO2 and N2O ) under mitigation scenarios using the LUCAS soil-DayCent model integration framework
  • Dataset (GIS map) (218) that shows the net soil erosion and deposition in European Union as a result of an application WaTEM/SEDEM model; resolution 100m; EU28. Data are also available at 25m resolution.
  • These data are the WRB soil data for the whole area covered by the Soil Atlas of the Northern Circumpolar Region. It comes as a single shapefile.
  • Rainfall erosivity dataset (2017) is one of the input layers when calculating the Revised Universal Soil Loss Equation (RUSLE) model, which is the most frequently used model for soil erosion risk estimation; for the whole World; R-factor map at resolutions of 30 arc-sec ((~1 km at the Equator).
  • A consistent spatial soil hydraulic database at 7 soil depths up to 2 m calculated for Europe based on SoilGrids250m and 1 km datasets and pedotransfer functions trained on the European Hydropedological Data Inventory. Saturated water content, water content at field capacity and wilting point, saturated hydraulic conductivity and Mualem-van Genuchten parameters for the description of the moisture retention, and unsaturated hydraulic conductivity curves have been predicted. The derived 3D soil hydraulic layers (EU-SoilHydroGrids ver1.0) can be used for environmental modelling purposes at catchment or continental scale in Europe. Currently, only EU-SoilHydroGrids provides information on the most frequently required soil hydraulic properties with full European coverage up to 2 m depth at 250 m resolution.
  • This dataset derives from the integration of the LUCAS soil survey with the bio-geochemistry process-based model DayCent. The model was ran for more than 11,000 LUCAS sampling points under agricultural use, assessing also the model uncertainty. Meta-models based on model outcomes and the Random Forest algorithm were used to upscale the N2O emissions at 1km resolution.
  • This dataset consists of 3 GIS maps that indicate the soil biomass productivity of grasslands and pasture, of croplands and of forest areas in the European Union (EU27)
  • This dataset (maps)(2016) indicates the availability of Raw Material (organic soil material and soil material for constructions) from soils in the European Union.
  • This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication "Assessment of soil organic carbon stocks under future climate and land cover changes in Europe" . Layers cover the current Soil Organic Carbon Stocks (2016) and the projected Soil Organic Carbon Stocks by 2050, for various Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR MRI-CGCM3) and Representative Concentration Pathways (RCPs).
  • This dataset (map)(2016) presents the suitability of soil as a platform for most human activities in the EU. Calculation of suitability was done using vaious properties of the European Soil database (soil type, soil water regime, limitation to agricultural use, depth to rock, land use) and slope of the terrain.
  • Maps (2016) that indicate the preservation capacity of cultural artefacts and buried materials in soils in the EU, for bones, teeth and shells (bones), organic materials (organics), metals (Cu, bronze and Fe) (metals), stratigraphic evidence (strati).
  • This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA), anion storing capacity (STOR_CAPAN), anion filtering capacity (FILT_CAPAN), solids and pathogenic microorganisms storing capacity (STOR_CAPSO), solids and pathogenic microorganisms filtering capacity (FILT_CAPSO), non-polar organic chemicals storing capacity (STOR_CAPNP), non-polar organic chemicals filtering capacity (FILT_CAPNP), nonaqueous Phase Liquids (NAPL) storing capacity (STOR_NAPL), nonaqueous Phase Liquids (NAPL), filtering capacity (FILT_NAPL). As input, variables from the European Soil Database have been used.
  • Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3); for Hydraulic conductivity of topsoil: saturated hydraulic conductivity (cm/day). Besides the true values in the units mentioned values scaled between 1 and 10 without measurement units were also calculated.
  • This dataset (map) (2016) shows the Soil Organic Carbon (SOC) saturation capacity, expressed as the ratio between the actual and the potential SOC stock in each pixel. Values close to 0 indicate a great potential of soil to store more carbon.
  • These maps of predicted distribution of SOC content in Europe (2016) are based on aggregated 23,835 soil samples collected from the LUCAS Project (samples from agricultural soil), BioSoil Project (samples from forest soil), and Soil Transformations in European Catchments (SoilTrEC) Project (samples from local soil data coming from five different critical zone observatories (CZOs) in Europe).
  • Dataset (2016) containing 2 GIS maps from the Global Soil Biodiversity Atlas: 1) the Soil Biodiversity map showing a simple index describing the potential level of diversity living in soils (with the use of two other datasets: distribution of microbial soil carbon used as a proxy for soil microbial diversity, and the distribution of the main groups of soil macrofauna used as a proxy for soil fauna diversity. 2) the Soil Biodiversity threats showing the potential rather than the actual level of threat to soil organisms. For the development of this map, a number of diverse threats and corresponding proxies were chosen.
  • Dataset that contains 3 GIS maps showing Potential threats to soil biodiversity in Europe (for soil microorganisms, for fauna,forbiological functions), along with 13 input layers (habitat fragmentation, climate change, soil erosion, etc.); resolution 500m.
  • Dataset (GIS map) (2015) that shows the Soil Loss by Water Erosion in Europe and is the result of applying a modified version of the Revised Universal Soil Loss Equation (RUSLE) model, RUSLE 2015; resolution 100m. EU28. Two data points are available: 2010 and 2016
  • This dataset (GIS maps)(2016) contains 7 soil property maps that have been derived using soil point data from the LUCAS 2009 soil survey (around 20,000 points) for EU-25, using hybrid approaches like regression kriging. Properties: clay, silt and sand content; coarse fragments; bulk density; USDA soil textural class; available water capacity. Resolution 500m.
  • This dataset (GIS map) (2015) represents the Cover Management factor (C-factor), one of the input layers when calculating the Universal Soil Loss Equation (USLE) model, which is the most frequently used model for soil erosion risk estimation; for EU28; resolution 100m. The C-factor was estimated for a) arable lands based on crop composition and for b) all other land uses (non-arable) based on the vegetation density and land cover type. The management practices (reduced tillage/no till, plant residues and winter cover crops) were taken into account in estimating C-factor in arable lands.
  • This dataset (GIS maps) represent global lanform classification calculated by the JRC, according to 1) Meybeck et al., presenting relief classes, which are calculated based on the relief roughness, and 2) Iwahashi and Pike, presenting relief classes which are classified using an unsupervised nested-means algorithms and a three part geometric signature.
  • This GIS map (2015) represents the "Support Practices factor" (P-factor) for the EU. At European level, the effect of support practices (compulsory for farmers to receive incentives under the CAP-GAEC) on soil loss were assessed by P-factor estimation taking into account a) contour farming b) maintenance of stone walls and c) grass margins. Resolution 1km.
  • This dataset (GIS maps) (2015) represents the "Slope Length and Steepness factor" (LS-factor), one of the six input layers when calculating the Universal Soil Loss Equation (USLE) model, which is the most frequently used model for soil erosion risk estimation; for EU28; maps at resolutions of 25m (per country) and 100m (Europe-wide).
  • This dataset (2015) provides maps for Topsoil Soil Organic Carbon in EU-25 that are based on LUCAS 2009 soil poibnt data through a generalized additive model. Map of predicted topsoil organic carbon content (g C kg-1) : The map of predicted topsoil organic carbon content (g C kg-1) was produced by fitting a generalised additive model between organic carbon measurements from the LUCAS survey (dependent variable) and a set of selected environmental covariates; namely slope, land cover, annual accumulated temperature, net primary productivity, latitude and longitude. It also includes a Map of standard error of the OC model predictions (g C kg-1).
  • This GIS map (2013), present in the Soil Atlas of Africa, contains the dominant WRB Reference Soil Group and associated qualifiers (shapefile).
  • Dataset (2 GIS-maps) (2016) related to soil erosion in Forestland in Europe. One map is the soil loss potential for EU28; the other map is the European Forest Cover Change for 36 European countries.
  • Data (2014) related to Pan-European SOC stock of agricultural soils, containing GIS maps for a) Pan-European SOC stock of agricultural soils (shapefile), b) Potential carbon sequestration by modelling a comprehensive set of management practices (shapefile), c) Average Eroded SOC in agricultural soils (raster).
  • Data from the 2009 LUCAS campaign soil component containing soil properties data (clay, silt and sand content, coarse fragments, pH, organic carbon content, CaCO3, nitrogen, phosphorous, potassium, cation exchane capacity) and multispectral absorbance data.
  • The spatial dataset (GIS map) shows landslide susceptibility levels at European scale, derived from heuristic-statistical modelling of main landslide conditioning factors using also landslide location data. It covers all EU member states except Malta, in addition to Albania, Andorra, Bosnia and Herzegovina, Croatia, FYR Macedonia, Iceland, Kosovo, Liechtenstein, Montenegro, Norway, San Marino, Serbia, and Switzerland. This dataset is available together with ancillary spatial datasets
  • This dataset consists of various elements related to soil erosion by wind: 1) Soil loss by wind erosion in European agricultural soils (2016); 1km resolution, 2) Land susceptibility to wind erosion (2014), 500m resolution, 3) Wind erosion susceptibility of European soils (2014); 500m resolutiomn, and 4) Agriculture Field Parameters data (containing averaged Field Size, Field Orientation, Field Length, Average Number of Images, Percentage of Large Fields and Length to Width Ratios) for the EU 27 Member states and Switzerland, aggregated to NUTS region.
  • Dataset (GIS map) (2015) and associated products for the "Rainfall erosivity" (R-factor), one of the input layers when calculating the Universal Soil Loss Equation (USLE) model, which is the most frequently used model for soil erosion risk estimation; for EU28+Switzerland; R-factor map at resolutions of 500m. Users can downloads Raw data, Baseline map (2010), Monthly erosivity, Future projections (2050), Past erosivity (1961-2000).
  • Map at 500m resolution (2014) providing a complete picture of the soil erodibility in the European Union member states. It is derived on the basis of the LUCAS 2009 soil survey exercise and the European Soil Database.
  • Data from the FP7 iSOIL research projectdealing with new technologies for assessing soil properties along depth accurately and with high resolution by combining existing sensors as well as exploring emerging technologies (mainly geophysical); data available for the test sites of iSoil, located in Germany, Austria, Czech Republic and Bulgaria.
  • G2 generic model for soil erosion applied to 5 application areas (Crete island,Cyprus, Ishmi-Erzeni watershed, Korce, Strymonas/Struma); available layers: Soil erosion (Total & Monthly) plus Rainfall erosivity (Total & Monthly), vegetation retention (Total & Monthly), soil erodibility, topographic influence and slope intercept.
  • Data and software made public as the result of a collaboration between JRC and EFSA. It contains soil, weather and crop data collected by JRC and organised by EFSA for its purpose. The site also hosts the software tool PERSAM for Predicting Environmental Concentrations in soil in support of the: "EFSA Guidance Document for predicting environmental concentrations of active substances of plant protection products and transformation products of these active substances in soil" which EFSA was asked by the European Commission to prepare. The data/maps are provided as rasters (ASCII grid).
  • These Soil Erosion Risk Assessment in Europe data (2000) have been elaborated by INRA for JRC using the MESAES model and can be seen as an intermediate step towards a "state-of-the-art erosion modelling at the European scale", prior to the initiation of the PESERA project.
  • GIS Maps related to Groundwater resources in Europe, covering 3 themes: Inventory of aquifers; Hydrogeology of aquifers; Groundwater abstraction; for 9 European countries (Belgium, Federal Republic of Germany, Denmark, France, Ireland, Italy, Luxembourg, Netherlands and United Kingdom)
  • A quantitative map of estimated soil pH values across Europe from a compilation of 12,333 soil pH measurements from 11 different sources, and using a geo-statistical framework based on Regression-Kriging. Fifty-four (54) auxiliary variables in the form of raster maps at 5km resolution were used to explain the differences in the distribution of soil pH (CaCl2) and the kriged map of the residuals from the regression model was added.
  • Saline and Sodic Soils Map for EU-27 (2008) showing the area distribution of saline, sodic and potentially salt affected areas within the European Union. The accuracy of input input data only allows the designation of salt affected areas with a limited level of reliability (e.g. < 50 or > 50% of the area); therefore the results represented in the map should only be used for orientating purposes.
  • Map (2008) showing the natural susceptibility of agricultural soils to compaction if they were to be exposed to compaction, based on the creation of logical connections between relevant parameters (using pedotransfer rules), taking as input parameters attributes of the European soil database (soil type, texture, etc.). For EU-27.
  • A 2004 GIS map of Soil Organic Carbon (SOC) content (%) in the surface horizon of soils in Europe, associated to a JRC internal report.
  • A 2003 GIS map of Soil erosion estimates (t/ha/yr) by applying the PESERA GRID (physical) model at 1km, using the European Soil Database, CORINE land cover, climate data from the MARS Project and a Digital Elevation Model. The resulting estimates of sediment loss are from erosion by water.
  • Soil Profile Analytical Database for Europe (2009) (separate from the European Soil Database SPADE-1) aiming to provide sufficient soil property data to support higher tier modeling of pesticide fate at the European level. Joint venture between JRC, ECPA and the ESBN.
  • A database (2007) that is the result of adapting the European Soil Database for the provision of new and specific soil information for the CGMS (Crop Growth Monitoring System) for use in the MARS Crop Yield Forecast Sing System.
  • Google Earth Files (with ".kmz" extension) that correspond to 73 attribute maps derived from the European Soil Database v2 (ESDB v2) for EU27 countries.
  • Global Applications of Soil Erosion Modelling Tracker (GASEMT) includes 3030 individual modelling records from 126 countries, encompassing all continents. GASEMT includes peer-reviewed research literature on soil-erosion modelling published between 1994 and 2017.
  • Qualitative assessment on desertification risk in Greece based on Environmentally Sensitive Areas (ESAs) data of MEDALUS methodology
  • GIS Maps (2008) produced by mapping the concentrations of eight critical heavy metals (arsenic, cadmium, chromium, copper, mercury, nickel, lead and zinc) using the 1588 georeferenced topsoil samples from the FOREGS Geochemical database. The concentrations were interpolated using block regression-kriging over the 26 European countries that contributed to the database.
  • These are the underlying data for the report "Progress in management of contaminated sites (CSI 015)", based on the 2011 EIONET NRC Soil data collection concerning contaminated sites, and stored in an MS Excel file. Some data/information that arrived at JRC after the data collection deadline (for Czech Republic, Greece, Italy, Latvia) are bundled in an additional file.
  • This dataset (2015), an Excel file, contains the data associated to the peer-reveiwed paper: Gardi, C., Panagos, P., Van Liedekerke, M., Bosco, C., de Brogniez, D. 2015. Land take and food security: assessment of land take on the agricultural production in Europe. Journal of Environmental Planning and Management, 58 (5) , pp. 898-912.
  • Database (2004) developed from the measured profiles; present in the Soil Profile Analytical Database of Europe of the European Soil Database v2. It counts 560 profiles within the EU.
  • Data (2011) associated to the publication "A quantitative review of the effects of biochar application to soils on crop productivity using meta-analysis. Agriculture, Ecosystems and Environment. Volume 144, Issue 1, November 2011, Pages 175-187"