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.

Metadata Data are available for the following Chemical properties: pH (measured in H2O) pH (n CaCl2 0.01 M solution) Cation Exchange Capacity (CEC) Calcium carbonates (CaCO3) C:N ratio Nitrogen (N) Phosphorus (P) Potassium (K) Note that these data are based on the LUCAS topsoil data for ca 22,000 samples across EU. Resolution: 500m; Format: geo TIFF; projection information: ETRS89 / LAEA EuropeGeographical Coverage: European Union (EU-26 excluding Cyprus and Croatia)Input data: LUCAS 2009 Topsoil 22,000 sample point dataModel: Gaussian process regression (GPR) Description The Physical proerty maps ( clay, silt and salt content; coarse fragments; bulk density; USDA soil textural class; available water capacity) are available in ESDAC from september 2015. In September 2019, we conluded the development of the LUCAS Chemical parameters [pH, pH CaCl, Cation Exchange Capacity (CEC), Calcium carbonates (CaCO3), C:N ratio, Nitrogen (N), Phosphorus (P) and Potassium (K)] and we made them available for download together with the scientific publications. With 22,000 sampled locations the LUCAS soil database is unique in Europe for the number of available observations, its spatial coverage and its temporal resolution. While LUCAS point data are available upon request from the European Soil Data Centre (ESDAC), the interpolated maps of chemical properties offer a better overview of the distribution of soil chemical properties in the EU to the scientific community and to policy makers. The derived maps will establish baselines that will help monitor soil quality and provide guidance to agro-environmental research and policy developments in the European Union. The chemical properties datasets, together with the physical properties, contribute to one of the main objectives of the GlobalSoilMap project. The modelling is based on Gaussian Process Regression technique that allows the estimation of prediction uncertainty. The best performing prediction was obtained for the C:N ratio (R2=0.91), followed by phosphorus and potassium (R2=0.75). The performance prediction of the rest of chemical properties in terms of R2 is higher than 0.60 with the exception of CEC (R2= 0.35). Topsoil pH is influenced by soil parent material, erosional effects, climate and vegetation. The calcium carbonate content is highly correlated with pH, having similar influencing factors. Soil nitrogen distribution is dependent on soil organic carbon, vegetation and climate and soil texture. The land use appears to be the main driver for phosphorus content in soils, as agricultural areas have higher concentrations due to fertilizer application. CEC is influenced by the clay distribution in soils, topography and parent material. References Ballabio, C., Lugato, E., Fernández-Ugalde, O., Orgiazzi, A., Jones, A., Borrelli, P., Montanarella, L. and Panagos, P., 2019. Mapping LUCAS topsoil chemical properties at European scale using Gaussian process regression. Geoderma, 355: 113912.

Metadata Data are available for the following Physical properties: Clay content (%) in topsoil (0-20cm) modelled by Multivariate Additive Regression Splines Silt content (%) in topsoil modelled by Multivariate Additive Regression Splines Sand content (%) in topsoil modelled by Multivariate Additive Regression Splines Coarse fragements (%) content in topsoil modelled by Multivariate Additive Regression Splines Bulk density derived from soil texture datasets (obtained from the packing density and themapped clay content following the equation of Jones et al. 2003). Important note: Please use the Bulk density and the packing density data as well as they have been produced with 6,000 measured LUCAS points USDA soil textural classes derived from clay, silt and sand maps Available Water Capacity (AWC) for the topsoil fine earth fraction Note that these data are based on the LUCAS topsoil data for ca 20,000 samples across EU. Resolution: 500m Geographical Coverage: European Union Input data: LUCAS 2009 Topsoil 20,000 sample point data Model: Multivariate Additive Regression Splines (MARS) Description The Land Use and Cover Area frame Statistical survey (LUCAS) aimed at the collecting harmonised data about the state of land use/cover over the extent of European Union (EU).Among these 2 · 105 land use/cover observations selected for validation, a topsoil survey was conducted at about 10% of these sites. Topsoil sampling locations were selected as to be representative of European landscape using a Latin hypercube stratified random sampling, taking into account CORINE land cover 2000, the Shuttle Radar Topography Mission (SRTM) DEMand its derived slope, aspect and curvature. The LUCAS topsoil database was used to map soil properties at continental scale over the geographical extent of Europe. Several soil properties were predicted using hybrid approaches like regression kriging. For those datasets, we predicted topsoil texture and related derived physical properties. Regression models were fitted using, along other variables, remotely sensed data coming from the MODIS sensor. The high temporal resolution of MODIS allowed detecting changes in the vegetative response due to soil properties, which can then be used to map soil features distribution.We will also discuss the prediction of intrinsically collinear variables like soil texture which required the use of models capable of dealing with multivariate constrained dependent variables like Multivariate Adaptive Regression Splines (MARS). Cross validation of the fitted models proved that the LUCAS dataset constitutes a good sample for mapping purposes leading to cross-validation R2 between 0.47 and 0.50 for soil texture and normalized errors between 4 and 10%. References Ballabio C., Panagos P., Montanarella L. Mapping topsoil physical properties at European scale using the LUCAS database (2016) Geoderma, 261 , pp. 110-123.

We complied the European topsoil bulk density and organic carbon stock database (0-20 cm) using LUCAS Soil 2018. This database inlcudes 18,945 and 15,389 soil samples (0-20 cm) with bulk density in fine fraction (Bdfine) and soil organic cabron stock (SOCS) for the EU and UK using the best traditional pedotransfer function (T-PTF-4) and machine leanring based PTFs (Local-RFFRFS). It also contains the POINTID linked to LUCAS Soil 2018, coarse fragements in volume (coarse_vol) and coordinates (GPS_LAT, GPS_LONG). For more information, please refer to LUCAS 2018 TOPSOIL data (https://esdac.jrc.ec.europa.eu/content/lucas-2018-topsoil-data).

This dataset is asscoated to the "European soil bulk density and organic carbon stock database using machine learning based pedotransfer function" by Chen et al. (2024).

Manuscript citation: Chen, S., Chen, Z., Zhang, X., Luo, Z., Schillaci, C., Arrouays, D., Richer-de-Forges, A.C., Shi, Z. , 2024. European topsoil bulk density and organic carbon stock database (0-20 cm) using machine learning based pedotransfer functions. Earth System Science Data, 16, 2367–2383.

When using the data, please cite repositories as well as the original manuscript.

For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).

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.

Download .zipA soil mapping unit designates a specific type of soil which has unique characteristics including texture, slope and erosion class.

This coverage was digitized from soil survey sheets. These sheets were taped together to form an area covering each of the USGS quadrangle maps in the county. The areas for each quadrangle were then digitized using a run length encoding technique sampling along horizontal lines which represented the midline of cells with a height of 250 feet. The measurement increment along these horizontal lines was one decafoot(10 feet). The quadrangle files were then merged into a county raster file which was subsequently converted into ARC/INFO format.

The user should bear in mind that this coverage is only an approximation of the original soil survey and should not be used for site specific analysis. Additional details on the digitizing process are available on request.

Original coverage data was converted from the .e00 file to a more standard ESRI shapefile(s) in November 2014.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesReal Estate & Land ManagementReal Estate and Lands Management2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

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.

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).

Complete LUCAS soil organic matter (SOM) fractions data (352 samples) (29.4.2021) This folder contains the original measured SOM fractions of cropland, grassland, shrubland and forest subsamples of the LUCAS survey (2009). The SOM was divided by size into: Particulate organic matter (POM, >53 mm) Mineral-associated organic matter (MAOM, <53 mm) The corresponding fractions were also analyzed for the Nitrogen content, Additional LUCAS original soil properties are also included. This dataset is an extension and revision of the previous dataset reporting only forest and grassland land cover (see below). Soil carbon sequestration is seen as an effective means to draw down atmospheric CO2, but at the same time warming may accelerate the loss of extant soil carbon, so an accurate estimation of soil carbon stocks and their vulnerability to climate change is required. Here we demonstrate how separating soil carbon into particulate and mineral-associated organic matter (POM and MAOM, respectively) aids in the understanding of its vulnerability to climate change and identification of carbon sequestration strategies. By coupling European-wide databases with soil organic matter physical fractionation, we assessed the current geographical distribution of mineral topsoil carbon in POM and MAOM by land cover using a machine-learning approach. Further, using observed climate relationships, we projected the vulnerability of carbon in POM and MAOM to future climate change. Arable and coniferous forest soils contain the largest and most vulnerable carbon stocks when cumulated at the European scale. Although we show a lower carbon loss from mineral topsoils with climate change (2.5 ± 1.2 PgC by 2080) than those in some previous predictions, we urge the implementation of coniferous forest management practices that increase plant inputs to soils to offset POM losses, and the adoption of best management practices to avert the loss of and to build up both POM and MAOM in arable soils. Metadata: File name: LCS.csv Spatial coverage: 25 European Union Member States (excluded Romania, Bulgaria, Croatia) Input data source: LUCAS point data Fields: Coarse, clay, silt, sand = % pH_in_H2O, pH_in_CaCl OC = organic carbon (g C kg-1); N = soil nitrogen (g N kg-1) CaCO3 = g kg-1; available P and K = mg kg-1; CEC = cmol(+) kg-1 MAT =mean annual temperature (°C), RAIN = annual precipitation (mm); Ndep_WD_tx = total N deposition (kg ha-1); EROS = soil erosion (Mg ha-1); WT = water table depth (m)* s_c_prc = % of silt+clay; OC_pom_g_kg = organic carbon in POM (g/kg); OC_sc_g_kg = organic carbon in MAOM (g/kg); N_pom_g_kg = nitrogen in POM (g/kg); N_sc_g_kg = nitrogen in MAOM; OC_tf = POMC+MAOMC; N_tf= POMN+MAOMN The database contains a field called ‘POINT_ID’, which can be used to join the data with the general LUCAS soil survey (https://esdac.jrc.ec.europa.eu/projects/lucas). Nevertheless, geographical coordinates of LUCAS points (in WSG84) are provided. *see table S1 of supplementary for data sources R scripts All the .R files contain the basic elaborations reported in the paper: “Different climate sensitivity of particulate and mineral-associated organic carbon” in press in Nature Geoscience. ‘1_RF_MAOMpred.R’ Random Forest regression models to predict C and N in MAOM fraction from measured data (LCS.csv) ‘ 2_RF_cross_valid.R’ cross validation of the RF models ‘ 3_dMAOM_POM_pred.R’ multiregression models to predict change in POM and MAOM in relation to temperature, precipitation, sand X precipitation and land cover. Due to the number of spatial layers necessary to upscale the above models, we provide only the final maps (in raster format). However, all the elaboration steps can be seen in this repository: https://github.com/elugato/SOC_saturation If you need further assistance and information, contact: ec-esdac@ec.europa.eu Raster Layers Resolution= 1 km, projection =LAEA, format = Geotiff MAOM_g_kg = C content in MAOM (g C kg-1 soil) in the top 20 cm POM_g_kg = C content in PAOM (g C kg-1 soil) in the top 20 cm dMAOM_Mg_ha = cumulative change in C stock of MAOM (Mg C ha-1 in 0-20 cm) at ~2080 dPOM_Mg_ha = cumulative change in C stock of PAOM (Mg C ha-1 in 0-20 cm) at ~2080 Additional raster multilayers reporting ensemble estimates (see Supplementary Table 2): MAOM_ENS_g_kg = C content in MAOM (g C kg-1 soil) in the top 20 cm MAOM_ENS_ Mg_ha = C stock in MAOM (Mg C ha-1) in the top 20 cm POM_ENS_g_kg = C content in POM (g C kg-1 soil) in the top 20 cm POM_ENS_ Mg_ha = C stock in POM (Mg C ha-1) in the top 20 cm Cite as: Lugato, E., Lavallee, J.M., Haddix, M.L., Panagos, P., Cotrufo, F. 2021. Different climate sensitivity of particulate and mineral-associated soil organic matter. Nat. Geosci. (2021). https://doi.org/10.1038/s41561-021-00744-x SOM fractions for grassland and forest (186 samples) 18.11.2019 This folder contains the original measured Soil Organic Matter (SOM) fractions of grassland and forest subsamples of the LUCAS survey (2009). The SOM was divided by size into: Particulate organic matter (POM, >53 mm) Mineral-associated organic matter (MAOM, <53 mm) The corresponding fractions were also analyzed for the Nitrogen content. Metadata Description: Land management for C sequestration is most often informed by bulk soil C inventories, without considering the form in which C is stored, its capacity, persistency and N demand. Recent frameworks suggest that soil C accrual, its persistence and response to N availability can be better described if SOM is broadly divided into a Particulate Organic Matter (POM) and a Mineral Associated Organic Matter (MAOM) pool. POM, being predominantly of plant origin, contains many structural C-compounds with low N content and persists in soil through inherent biochemical recalcitrance, physical protection in aggregates and/or microbial inhibition. MAOM is largely made of microbial products richer in N, and persists in soil because of chemical bonding to minerals and physical protection in small aggregates. In this study, we used the Land Use/Land Cover Area Frame Survey (LUCAS) database to determine topsoil C and N storage in European forests and grasslands on 9415 geo-referenced points and separate by size POM (2000-53 μm) and MAOM (<53 μm) in more than 180 subsamples. File name: SOM_fraction.csv Spatial coverage: 25 European Union Member States (excluded Romania, Bulgaria, Croatia) Input data source: LUCAS point data Fieds: s_c_prc = % of silt+clay; OC_pom_g_kg = organic carbon in POM (g/kg); OC_sc_g_kg = organic carbon in MAOM (g/kg); N_pom_g_kg = nitrogen in POM (g/kg); N_sc_g_kg = nitrogen in MAOM The database contains a field called ‘POINT_ID’, which can be used to join the data with the general LUCAS soil survey (https://esdac.jrc.ec.europa.eu/projects/lucas). Nevertheless, geographical coordinates of LUCAS points (in WSG84) are provided. R workspace and scripts ll the .R files contain the basic data and elaborations reported in the paper: “Soil carbon storage informed by particulate and mineral-associated organic matter” in press in Nature Geoscience Please, refers to the instruction contains in the ‘1_master_script.R’ to run the different scripts that reproduce the statistical procedure and results contained in the paper.

Descriptive statistics of LUCAS soil properties and predictor variables.

The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques, accepted for publication in:

Cristian Valeriu Patriche, Bogdan Roșca, Radu Gabriel Pîrnău, Ionuț Vasiliniuc, Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning, PLOS ONE, 2023

The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques. The file names indicate the soil variable and the method used for interpolation (RK – regression - kriging, EML – ensemble machine learning, GWR_OK – Geographically Weighted Regression – Ordinary kriging).

The raster data is classified and saved in tif format with a resolution of 100 x 100 m. The spatial reference is Stereographic projection 1970 (Pulkovo_1942_Adj_58_Stereo_70).

The soil variables are classified as follows:

Variable	Classes
1	2	3	4	5	5	7
pH	≤ 5 (strongly acid)	5.1 – 5.8 (moderately acid)	5.9 – 6.8 (weakly acid)	6.9 – 7.2 (neutral)	7.3 – 8.4 (weakly alkaline)	8.5 – 8.8 (moderately alkaline)
EC (mS m-1)	≤ 12.75	12.76 – 16.49	16.50 – 20.04	20.05 – 24.18	24.19 – 29.11	29.12 – 35.23	≤ 35.24
OC (g kg-1)	< 7.5 (very low)	7.5 – 17.4 (low)	17.4 – 37.8 (moderate)	37.8 – 61.0 (high)	> 61 (very high)
CaCO3 (g kg-1)	0 (no carbonates)	1 – 10 (low)	11 – 40 (medium 1)	41 – 80 (medium 2)	81 – 107 (medium 3)
P (mg kg-1)	< 4 (extremely low)	4 – 8 (very low)	8 – 18 (low)	18 – 36 (medium)	36 – 72 (high)	> 72 (very high)
N (g kg-1)	≤ 1 (very low)	1.1 – 1.4 (low)	1.5 – 2.0 (medium 1)	2.1 – 2.7 (medium 2)	2.8 – 6.0 (high)	> 6 (very high)
K (mg kg-1)	≤ 40 * (extremely low)	41 – 65 * (very low)	66 – 130 (low)	131 – 200 (medium)	201 – 300 (high)	> 300 (very high)
Clay (%)	≤ 25 (low 1)	26 – 32 (low 2)	33 – 40 (medium 1)	41 – 45 (medium 2)	≥ 46 (high)
Silt (%)	< 25 (medium 1)	25 – 32 (medium 2)	33 – 40 (high 1)	41 – 50 (high 2)	> 50 (high 3)
Sand (%)	< 15 (low 1)	15 – 25 (low 2)	26 – 35 (low 3)	36 – 56 (medium)	> 56 (high)

* classes not present on the Romanian territory

Average values of soil parameters for soil reference groups of Romania.

Metadata: Title: Soil Erodibility in Europe High Resolution dataset (500m)Description: This map provides a complete picture of the soil erodibility in the European Union member states. It is derived from the LUCAS 2009 point survey exercise and the European Soil Database. Spatial coverage: All Member States of the European Union where data available. Due to a number of requests from non-EU users, we also make available the Extrapolated datasets covering also Norway, Switzerland, Balkan states, Moldova and Ukraine. Pixel size: 500m Projection: ETRS89 Lambert Azimuthal Equal Area Temporal coverage: 2014 Input data source: LUCAS point data, European Soil Database K-factor in Europe The soil erodibility dataset overcomes the problems of limited data availability for K-factor assessment and presents a high quality resource for modellers who aim at soil erosion estimation on local/regional, national or European scale. The new proposed dataset has also been verified against local/regional/national studies with very good results. Soil erosion modellers (and not only) may use it for their applications at any scale. The aim of this study is the generation of a harmonised high-resolution soil erodibility map (with a grid cell size of 500 m) for the 25 EU Member States and then for the 28 Member States. Soil erodibility was calculated for the LUCAS survey points using the nomograph of Wischmeier and Smith (1978). A Cubist regression model was applied to correlate spatial data such as latitude, longitude, remotely sensed and terrain features in order to develop a high-resolution soil erodibility map. The mean K-factor for Europe was estimated at 0.032 t ha h ha-1 MJ-1 mm-1 with a standard deviation of 0.009 t ha h ha-1 MJ-1 mm-1. The yielded soil erodibility dataset compared well with the published local and regional soil erodibility data. However, the incorporation of the protective effect of surface stone cover, which is usually not considered for the soil erodibility calculations, resulted in an average 15% decrease of the K-factor. The exclusion of this effect in K-factor calculations is likely to result in an overestimation of soil erosion, particularly for the Mediterranean countries, where highest percentages of surface stone cover were observed. The high-resolution soil erodibility map (500m) version 2014 incorporates certain improvements over the coarse-resolution map (10km) version 2011: High resolution dataset (500m) and application of Cubist regression-interpolation (better spatial accuracy) Soil structure was for the first time included in the K-factor estimation Coarse fragments were taken into account for the better estimation of soil permeability Surface stone content, which acts as protection against soil erosion was for the first time included in the K-factor estimation. This correction is of great interest for the Mediterranean countries where stoniness is an important regulating parameter of soil erosion The estimated soil erodibility dataset is verified against local, regional and national data found in the literature (21 Studies) Cyprus and Malta have been included in the analysis Data The Soil Erodibility Dataset is in Raster format. The public user can download 3 different datasets: a) Soil erodibility in Europe (K-factor), b) Soil Erodibility incorporating Stoniness (Kst Factor) and c) the Effect of Stoniness in K-factor (% reduction). To get access to the data, please compile the online form; instructions will then follow how to download the data. (Mar 2015) K-factor values are available for the 28 European Union Member States (including Bulgaria, Romania, Croatia). Due to a number of requests from non-EU users, we also make available the Extrapolated datasets covering also Norway, Switzerland, Balkan states, Moldova and Ukraine. More information about Soil Erodibility in the corresponding section. References A complete description of the methodogoly (High resolution - 2014 version) and the application in Europe is described in the paper: Panagos, P., Meusburger, K., Ballabio, C., Borrelli, P., Alewell, C. Soil erodibility in Europe: A high-resolution dataset based on LUCAS, Science of Total Environment, 479–480 (2014) pp. 189–200 Download the article (Open Access): 10.1016/j.scitotenv.2014.02.010

aStandard Deviation;blower quartile;cmedian;dupper quartile,ecorrelation of PC1-3 scores with the soil properties;fnumber of samples.

We complied the China soil bulk density and organic carbon stock database. This database inlcudes 18,945 and 15,389 soil samples with bulk density in fine fraction (Bdfine) and soil organic cabron stock (SOCS) for the EU and UK using the best traditional pedotransfer function (T-PTF-4) and machine leanring based PTFs (Local-RFFRFS). It also contains the POINTID linked to LUCAS Soil 2018, coarse fragements in volume (coarse_vol) and coordinates (GPS_LAT, GPS_LONG).

This dataset is asscoated to the "A soil organic carbon density database (2010-2024) using ensemble modelling-based pedotransfer functions in China" by Chen et al. (2025).

Manuscript citation: Chen, Z., Chen, L., Lu, R., Lou, Z., Zhou, F., Jin, Y., Xue, J., Guo, H., Wang, Z., Wang, Y., Liu, F., Song, X., Zhang, G., Su, Y., Ye, S., Shi, Z., Chen, S., 2025. A soil organic carbon density database (2010-2024) using ensemble modelling-based pedotransfer functions in China. Earth System Science Data, 16, 2367–2383.

When using the data, please cite repositories as well as the original manuscript.

For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).

These datasets were prepared within the scope of the CUP4SOIL (Copernicus user uptake for soil information products) project (Copernicus user update project). The soil property maps were generated at a resolution of 20m, and created with LUCAS and other continental level data using digital soil mapping. Users should assess the local ''predictive'' accuracy of the maps prior to using them for making recommendations at local (or field) level. The datasets contain the following soil properties (for top soil (0-30 cm) depth): Soil organic carbon content, pH in water, Total nitrogen, Bulk density (oven dry), Coarse fragments (volumetric), Soil inorganic carbon content, Sand, Silt, Clay. The SoilSuite products (https://c.geoservice.dlr.de/web/datasets/soilsuite_eur_5y) were used as covariates, together with a range of other copernics products. Measurement units and web links for the above properties can be found be below. The maps are provided as Web Map Service (WMS) layers which load dynamically in a GIS software. For instructions see: https://www.isric.org/instruction-wms . The suffix _mosaic refers to the full data (20m spatial resolution). If you need to use the data, please use layers with the _mosaic suffux. The suffix _overviews refers to spatial overviews for faster visualisation. However datasets with the _overview suffix will not load to the full resolution.

The internal EJP SOIL project SERENA contributed to evaluating soil multifunctionality, aiming to provide assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales. The dataset corresponds to a map of Soil Organic Carbon (SOC) concentration (g kg-1) in Portugal. The map of soil organic carbon, as an indicator of SOC loss threat, was based on ordinary kriging and has a spatial resolution of 50 m. Soil organic carbon (0-0.20 m) data from LUCAS 2018 TOPSOIL data - ESDAC - European Commission database, which is freely available (https://esdac.jrc.ec.europa.eu/content/lucas-2018-topsoil-data).

This database contains diffuse reflectance spectra in the mid-infrared of arable topsoil samples (DRIFT-MIRS). Three different datasets are involved: (i) 1013 soil samples from Germany, Belgium, the Netherlands and Luxembourg taken from the EU LUCAS survey (2009), (ii) a regional dataset (n=385) from Schleswig Holstein, and (iii) 513 soil samples from four heterogeneous arable fields in Germany. Details and the associated ground truth data can be found in aggregated form in the publication Leenen et al. (2022). The individual data of the LUCAS data are the property of the EU-JRC in Ispra/Italy and can be requested there. This table contains the index of all tables forming this data collection.

Related datasets are listed in the metadata element 'Related Identifier'. Dataset version 1.0

This European Soil Data Centre (ESDAC) dataset provides the maps of Topsoil Soil Organic Carbon in EU-25 (reference year 2014) that are based on LUCAS 2009 soil point data through a generalized additive model. The data provided have been prepared for use by the Land Resource Management Unit (Institute for Environment & Sustainability) of the Joint Research Centre (JRC) of the European Commission.

This metadata refers to two maps: a) Map of predicted topsoil organic carbon content (g C kg-1) : The map 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. The format provided is GeoTIFF. b) Map of standard error of the OC model predictions (g C kg-1): This map presents the associated uncertainty to the organic carbon content predictions. The standard error, which shows the theoretical range of deviation in the prediction made by the generalized additive model, was calculated for every pixel of the Map of predicted topsoil organic carbon content, based on the posterior covariance matrix of the fitted parameters. The format provided is GeoTIFF.

The map of predicted OC content had the smallest values in Mediterranean countries and in croplands across Europe, whereas largest OC contents were predicted in wetlands, woodlands and mountainous areas. The map of the predictions' standard error had large uncertainty in northern latitudes, wetlands, moors and heathlands, whereas small uncertainty was mostly found in croplands. The map produced gives the most updated general picture of topsoil OC content at the European Union scale.

IMPORTANT NOTE: this metadata is only for internal EEA use. The original metadata of this dataset prepared by ESDAC can be found here: https://esdac.jrc.ec.europa.eu/content/topsoil-soil-organic-carbon-lucas-eu25. For any problem / question / comment on this dataset, please contact ec-esdac@ec.europa.eu.

About ESDAC: a) Panagos P., Van Liedekerke M., Jones A., Montanarella L., “European Soil Data Centre: Response to European policy support and public data requirements”; (2012) Land Use Policy, 29 (2), pp. 329-338. doi:10.1016/j.landusepol.2011.07.003 b) European Soil Data Centre (ESDAC), esdac.jrc.ec.europa.eu, European Commission, Joint Research Centre

The best models identified by quality statistical indices computed for validation samples.