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.

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 Land Use/Cover Area frame Survey (LUCAS) in the European Union (EU) was set up to provide statistical information. It represents a triennial in-situ landcover and land-use data-collection exercise that extends over the whole of the EU's territory. LUCAS collects information on land cover and land use, agro-environmental variables, soil, and grassland. The surveys also provide spatial information to analyse the mutual influences between agriculture, environment, and countryside, such as irrigation and land management. The dataset presented here is the harmonized version of all yearly LUCAS surveys with a total of 106 attributes. Each point's location is using the fields 'th_lat' and 'th_lon', that is, the LUCAS theoretical location (THLOC), as prescribed by the LUCAS grid. For more information please see Citations. Note that not every field is present for every year - see the "Years" section in property descriptions. The text "C1 (Instructions)" in the table schema descriptions refers to this document. See also the 2018 LUCAS polygons dataset.

Assessing the benefits of crop diversification – a pillar of the agroecological transition – on a large scale requires a description of current crop sequences as a baseline, which is lacking at the scale of the European Union (EU). This work is based on the Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union (doi: 10.2905/f85907ae-d123-471f-a44a-8cca993485a2) to fill this gap, We completed this dataset with a crop sequence type information for each point under non-perennial agricultural land cover in 2012, 2015 and 2018.

The dataset lucas_classified.csv includes 31 159 points. Variables "point_id", "nuts0", "nuts2", "th_lat", "th_long", "LC1_2012", "LC1_2015", "LC1_2018" are inherited from the Harmonised LUCAS databse. Variables "cereals", "corn", "rapeseed", "sunflower", "pulses", "rootCrops", "forageLeg", "grassland" correspond to the temporal frequencies of respectively cereals, corn, rapeseed, sunflower, pulses, root crops, forage legumes and grassland within the 2012, 2015 and 2018 crop sequence for each point. Variable "crop_sequence_type" is the crop sequence type assigned to each point, among eight options: cereals, corn and cereals, forage legumes and cereals, pulses and cereals, rapeseed and cereals, root crops and cereals, sunflower and cereals, temporary grasslands.

This dataset could be used to map current dominant crop sequences in the European Union, as illustrated in the map attached, and to assess the benefits of future crop diversification.

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).

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.

The data published are distributed along with the ESSD manuscript entitled "LUCAS Copernicus 2018: Earth Observation relevant in-situ data on land cover and use throughout the European Union"by Raphaël d’Andrimont, Astrid Verhegghen, Michele Meroni, Guido Lemoine, Peter Strobl, Beatrice Eiselt, Momchil Yordanov, Laura Martinez-Sanchez and Marijn van der Velde.----The repository contains the following files: LUCAS_2018_Copernicus_ReadMe.txt : short description of the data repository LUCAS_2018_Copernicus.zip : compressed files containing the dataset LUCAS_2018_Copernicus_attributes.csv : CSV containing the 120 variables including the "POINT_ID" LUCAS_2018_Copernicus_polygons.shp : Shapefile of polygons with the "POINT_ID" attribute LUCAS_2018_Copernicus_polygons.dbf LUCAS_2018_Copernicus_polygons.shx LUCAS_2018_Copernicus_polygons.prj ESSD_create_LUCAS_polygons.Rmd : R markdown script used to generate the data ESSD_manuscript_Tables_and_Figures.Rmd : R markdown script used to generate the figures and tables of the manuscript

Topsoil data for 18,984 samples from LUCAS 2018 are available as a CSV file and, to facilitate use of the data, an ESRI shapefile containing the theoretical points to which the taken samples should be associated; you may preview the readme file to find a description of these data.

This dataset provides annual raster maps of historical and projected future land use and land cover (LULC) for California, USA. Changes in LULC over time were simulated using the Land Use and Carbon Scenario Simulator (LUCAS) model. The model was run at 1-km resolution on an annual timestep for historical (1985-2020) and projected future time periods (2021-2100). Simulations for the projected future time period were run under all combinations of four climate scenarios, two urbanization scenarios, and two vegetation management scenarios with 40 Monte Carlo realizations for each simulation.

Analysis of ‘Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/f85907ae-d123-471f-a44a-8cca993485a2 on 18 January 2022.

--- Dataset description provided by original source is as follows ---

Accurately characterizing land surface changes with Earth Observation requires geo-localized ground truth. In the European Union (EU), a tri-annual surveyed sample of land cover and land use has been collected since 2006 under the Land Use/Cover Area frame Survey (LUCAS). A total of 1,351,293 observations at 651,780 unique locations for 117 variables along with 5.4 million photos were collected during five LUCAS surveys. Until now, these data have never been harmonised into one database, limiting full exploitation of the information. This paper describes the LUCAS point sampling/surveying methodology, including collection of standard variables such as land cover, environmental parameters, and full resolution landscape and point photos, and then describes the harmonisation process. The resulting harmonised database is the most comprehensive in-situ dataset on land cover and use in the EU. The database is valuable for geo-spatial and statistical analysis of land use and land cover change. Furthermore, its potential to provide multi-temporal in-situ data will be enhanced by recent computational advances such as deep learning.

--- Original source retains full ownership of the source dataset ---

d'Andrimont, R., Yordanov, M., Martinez-Sanchez, L., Eiselt, B., Palmieri, A., Dominici, P., Gallego, J., Reuter, H.I., Joebges, C., Lemoine, G. and van der Velde, M., 2020. Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union.LUCAS_harmonised├── 0_releaseNote.txt├── 1_table│ ├── lucas_harmo_exif.zip│ ├── lucas_harmo_uf_2006.zip│ ├── lucas_harmo_uf_2009.zip│ ├── lucas_harmo_uf_2012.zip│ ├── lucas_harmo_uf_2015.zip│ ├── lucas_harmo_uf_2018.zip│ └── lucas_harmo_uf.zip├── 2_geometry│ ├── LUCAS_gps_geom.zip│ ├── LUCAS_th_geom.zip│ └── LUCAS_trans_geom.zip├── 3_supporting│ ├── C3_legends.xls│ ├── lucas_harmo_record_descriptor.xls│ └── LUCAS-Variable_and_Classification_Changes.xlsx└── 4_mappings ├── 2006_lucas_harmo_allVars.csv ├── 2009_lucas_harmo_allVars.csv ├── 2012_lucas_harmo_allVars.csv ├── 2015_lucas_harmo_allVars.csv ├── 2018_lucas_harmo_allVars.csv ├── columnRename.csv ├── manChangedVars.csv └── RecodeVars.csv

Data from the 2018 LUCAS campaign soil component containing soil properties data for 18,984 samples: pH (CaCl2 and H2O), organic carbon content, CaCO3, nitrogen, phosphorous, potassium, EC (Electrical conductivity), Oxalate extractable Fe and Al . Soil dataset collected as part of the 2018 Land Use/Cover Area frame statistical Survey’ (generally referred to as LUCAS Soil Module). It presents an overview of the various laboratory analysis and describes the spatial variability of soil properties by land cover (LC) class and a comparative analysis of the soil properties for NUTS 2 regions. The LUCAS Soil Module is the only mechanism that currently provides a harmonised and regular collection of soil data for the entire territory of the European Union, addressing all major land cover types simultaneously, in a single sampling period (April – October). At the same time, the LUCAS Soil module can support further policy needs through a flexibility that permits both the collection of new field data, if required, from new sampling sites. In turn, this can be complemented with additional laboratory analysis (e.g. micronutrients, specific pollutants).

ST_LUCAS is a harmonized dataset derived from the LUCAS (Land Use and Coverage Area frame Survey) dataset. LUCAS is an Eurostat activity that has performed repeated in situ surveys over Europe every three years since 2006. Original LUCAS data (https://ec.europa.eu/eurostat/web/lucas/data) starting with the 2006 survey were harmonized into common nomenclature based on the 2018 survey. ST_LUCAS dataset is provided in two versions: lucas_points: each LUCAS survey is represented by single record lucas_st_points: each LUCAS point is represented by a single location calculated from multiple surveys and by a set of harmonized attributes for each survey year Harmonization and space-aggregation of LUCAS data were performed by ST_LUCAS system available from https://geoforall.fsv.cvut.cz/st_lucas. The methodology is described in Landa, M.; Brodský, L.; Halounová, L.; Bouček, T.; Pešek, O. Open Geospatial System for LUCAS In Situ Data Harmonization and Distribution. ISPRS Int. J. Geo-Inf. 2022, 11, 361. https://doi.org/10.3390/ijgi11070361. List of harmonized LUCAS attributes: https://geoforall.fsv.cvut.cz/st_lucas/tables/list_of_attributes.html ST_LUCAS dataset is provided under the same conditions (“free of charge”) as the original LUCAS data (https://ec.europa.eu/eurostat/web/lucas/data). This work is co-financed under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 by the European Union.

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 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 data set is a harmonized collection of existing data from GBIF, the EU-Forest project and the LUCAS survey. It has about 3 million observations and is supplemented by variables (e.g. location accuracy, land cover type, canopy height, etc.) which enable precise filtering for specific user applications.

The RDS file is created from an sf-object and suitable for fast reading in the R-programming environment. The CSV.GZ file contains records as a table with Easting and Northing in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035) and can be fed in a GIS after being unzipped.

The code producing this data set is publicly available on GitLab.

Variables:

• id = unique point identifier
• easting = x coordinate
• northing = y coordinate
• country = ISO country code
• species = Latin species name
• genus = genus name
• scientific_name = long species name
• gbif_taxon_key = taxon key from GBIF
• gbif_genus_key = genus key from GBIF
• taxon_rank = species or genus
• year = year of observation
• accessed_through = database through which data was accessed (GBIF, LUCAS, EU-Forest)
• dataset_info = data set name (individual sub-data-set)
• citation = DOI citation of the individual data set
• license = distribution license
• location_accuracy = spatial accuracy of observation (meters)
• flag_location_issue = known location issues present
• flag_date_issue = known date issues present
• eoo = Extent of occurrence (applying the concept of natural geographical range used for the EU-Forest data set (Mauri et al., 2017) to all other data points. 1 = point inside species range; 0 = point outside; NA = EOO polygon not available for this species)
• dbh = Diameter Breast Height (only recorded for observations from the EU-Forest data set (Mauri et al., 2017))
• lc1 = LUCAS land cover type 1 (only recorded for observations from LUCAS data)
• lc2 = LUCAS land cover type 2 (only recorded for observations from LUCAS data)
• landmask_country = land mask overlay 30 meters (NA = not on land)
• corine = CORINE 2018 land cover type (extracted from the 100 meter raster data set)
• nightlights = light pollution observed by VIIRS (proxy for remoteness / distance to human structures)
• canopy_height = canopy height derived from GEDI waveform LiDAR point data
• natura_2000 = Natura 2000 site code (if a point falls inside a protected area (GIS-layer) this variable contains the site identification code; all sites can be explored on an interactive map)
• freq_location = number of points with identical location (in some cases one location has multiple observation, differing in species and/or year. This may lead to difficulties in certain modeling tasks)
• geometry = point geometry in ETRS89 / LAEA Europe

See this detailed documentation for more insights into each variable.

If you would like to know more about the creation of this data set, see

1. the R-Markdown documenting the process (GitLab repository)
2. the talk at OpenGeoHub Summer School 2020 (Youtube)

Some advice: This data set is a puzzle with pieces from many different sources. Take some time to explore before including it in your work. Use summary statistics to see which variables have NAs and how many. Choose your filtering criteria wisely. For example, some points with the highest location accuracy have no record for the year of observations. You would exclude these, if "year > 1990" was your criteria.

This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).

"Data from the 2018 LUCAS campaign soil component containing soil properties data for 18,984 samples: pH (CaCl2 and H2O), organic carbon content, CaCO3, nitrogen, phosphorous, potassium, EC (Electrical conductivity), Oxalate extractable Fe and Al . Topsoil data for 18,984 samples from LUCAS 2018 are available as a CSV file and, to facilitate use of the data, an ESRI shapefile containing the theoretical points to which the taken samples should be associated; you may preview the readme file to find a description of these data. In 2018, the LUCAS Soil survey will include the additional analyses a) Bulk density (i.e. weight of dry soil in a given soil volume). b) Visual assessment of soil erosion and c) Measurement of the thickness of the organic horizon in organic-rich soil. Soil biodiversity analysis: The most extensive EU assessment of soil biodiversity, based on DNA metabarcoding will be included as part of the LUCAS Soil survey. For this, 1000 points were selected. Analysis will target the following attributes: Bacteria and Archaea (16S rDNA), Fungi (ITS), Eukaryotes (18S rDNA), Microfauna (nematodes), Mesofauna (arthropods), Macrofauna (earthworms), Metagenomics. Bulk density will be measured at 9000 points. Points were selected from the total set based on the heterogeneity of soil texture and organic carbon content, land use and land cover, topography and soil type. A CLHS approach was used to select candidate points, as for the biodiversity. Bulk density data points coincided with soil biodiversity points to explore possible correlation between these properties. Visual assessment of erosion.Surveyors will provide a qualitative assessment of soil erosion by indicating the type of erosion (i.e. sheet, rill, gully, mass movement, re-deposition and wind erosion), and the distance and direction from the LUCAS point, together with an estimate of the number of rills or gullies observed. Measurement of thickness of organic horizon in organic-rich soil. The thickness of the organic horizon in effectively or potentially organic-rich soil will be measured at 1470 locations."

Note: Metadata relates to LiDAR point clouds. This layer has been provided to enable users to explore coverage and capture dates of the LiDAR. To enquire about ordering the LiDAR and/or related orthophotography, please e-mail lucas[at]mfe.govt.nz.

Ministry for the Environment, Land Use Carbon Analysis System collection of swaths of LiDAR over planted forests of interest (raw and classified returns) from 2006 to 2015.

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.

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