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.

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.

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.

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.

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.

LUCAS is the acronym of Land Use and Cover Area frame Survey.

The aim of the LUCAS survey is to gather harmonised information on land cover, land use and environmental parameters. The survey also provides territorial information to analyse the interactions between agriculture, environment and countryside, such as irrigation and land management.

From 2006 to 2018, EUROSTAT has carried out LUCAS surveys every three years. 2006 data is considered pilot and has not been used to produce estimates. The most recent surveys happened in the spring-summer of 2009, 2012, 2015, 2018 and 2022.

Since the LUCAS surveys are carried out in-situ, this means that observations are made and registered on the ground by field surveyors. A mixed panel approach is used, so some points are visited in subsequent years.

In the field, the surveyor classifies the land cover and the visible land use according to the harmonized LUCAS Survey land cover and land use classifications. Landscape pictures are taken in the four cardinal directions. Till 2015 a transect of 250m was walked from the point to the east direction, where the surveyor records all transitions of land cover and existing linear features. The above-mentioned transect was not surveyed during the 2018 survey and in 2022 a landscape feature module was introduced instead, to assess agricultural landscapes. A specific topsoil module was implemented in 2009, in 2012 (partly), in 2015, in 2018 and in 2022. In addition, a contribution of points for validation of Copernicus Programme had also been part of the LUCAS 2018 survey and in a slightly simplified form in 2022.

From the LUCAS survey in situ data collection, different types of information are obtained:

• Micro data;
• Images;
• Statistical tables.

1. Micro data

Land cover, land use and environmental parameters associated to the single surveyed points are available freely for download in the http://ec.europa.eu/eurostat/web/lucas/overview" target="_blank">LUCAS dedicated section. Transect indicators on landscape features related to the single point (diversity and richness) are also part of the information available for free download for survey 2009. Specific ad hoc modules have been included in some surveys such as the 2009, 2015, 2018 and 2022 topsoil samples taken on approximately 10% of total LUCAS points. Soil results for the EU countries are available via the JRC Land resource management unit under license agreement. In 2012 the soil module was implemented in Bulgaria and Romania. The soil samples of the 2022 collection are currently being analyzed in laboratories. Copernicus was added in 2018 and 2022 and the results are published together with the land cover and land use information.

2. Images

Point and landscape photos taken in the four cardinal directions at each point are available freely.

3. Statistical tables

Statistical tables with aggregated results by land cover, land use at geographical level are available in Eurobase under the domain land cover, land use and landscape (LUCAS). The statistics are presented at NUTS0, NUTS1 and NUTS2 levels using the classification for NUTS 2013. For 2018 statistical tables at NUTS1 and NUTS2 are aligned by using the classification for NUTS 2016. These estimates are based on the point data conveniently weighted. For further information on weighting refer to chapter 18.5 Data compilation and the Quality Reports.

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).

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

Plot-level biomass and ecosystem driver data as used in the following analysis: Holdaway RJ, Easdale T, Carswell FE, Richardson SJ, Peltzer DA, Mason NWH, Brandon A, Coomes DA. Nationally representative plot network reveals contrasting drivers of net biomass change in secondary and old-growth forests. Ecosystems (2017) 20: 944. (also see related dataset "Species wood density data")

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).

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

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).

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).

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.

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

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.

The LUCAS LUC historical dataset consists of annual land use and land cover maps from 1950 to 2015 for Europe. It is based on land cover data from the LANDMATE PFT dataset that was generated from ESA-CCI LC data. The ESA-CCI LC land cover classes are converted into 16 plant functional types and non-vegetative classes employing the method of Reinhart et al. (2022). For version 1.1 of the LUCAS LUC dataset, the improved LANDMATE PFT map version 1.1 was employed. The land use change information from the Land-Use Harmonization Data Set version 2 (LUH2 v2h, Hurtt et al. 2020) was imposed using the land use translator developed by Hoffmann et al. (2021). For each year, a map is provided that contains 16 fields. Each field holds the fraction of the respective plant functional types and non-vegetative classes in the total grid cell (0-1). The LUCAS LUC dataset was constructed within the HICSS project LANDMATE and the WCRP flagship pilot study LUCAS to meet the requirements of downscaling experiments within EURO-CORDEX and other CORDEX regions. Plant functional types and non-vegetated classes: 1 - Tropical broadleaf evergreen trees 2 - Tropical deciduous trees 3 - Temperate broadleaf evergreen trees 4 - Temperate deciduous trees 5 - Evergreen coniferous trees 6 - Deciduous coniferous trees 7 - Coniferous shrubs 8 - Deciduous shrubs 9 - C3 grass 10 - C4 grass 11 - Tundra 12 - Swamp 13 - Non-irrigated crops 14 - Irrigated crops 15 - Urban 16 - Bare

LUCAS is the acronym of Land Use and Cover Area frame Survey.

The aim of the LUCAS survey is to gather harmonised information on land cover, land use and environmental parameters. The survey also provides territorial information to analyse the interactions between agriculture, environment and countryside, such as irrigation and land management.

From 2006 to 2018, EUROSTAT has carried out LUCAS surveys every three years. 2006 data is considered pilot and has not been used to produce estimates. The most recent surveys happened in the spring-summer of 2009, 2012, 2015, 2018 and 2022.

Since the LUCAS surveys are carried out in-situ, this means that observations are made and registered on the ground by field surveyors. A mixed panel approach is used, so some points are visited in subsequent years.

In the field, the surveyor classifies the land cover and the visible land use according to the harmonized LUCAS Survey land cover and land use classifications. Landscape pictures are taken in the four cardinal directions. Till 2015 a transect of 250m was walked from the point to the east direction, where the surveyor records all transitions of land cover and existing linear features. The above-mentioned transect was not surveyed during the 2018 survey and in 2022 a landscape feature module was introduced instead, to assess agricultural landscapes. A specific topsoil module was implemented in 2009, in 2012 (partly), in 2015, in 2018 and in 2022. In addition, a contribution of points for validation of Copernicus Programme had also been part of the LUCAS 2018 survey and in a slightly simplified form in 2022.

From the LUCAS survey in situ data collection, different types of information are obtained:

• Micro data;
• Images;
• Statistical tables.

1. Micro data

Land cover, land use and environmental parameters associated to the single surveyed points are available freely for download in the http://ec.europa.eu/eurostat/web/lucas/overview" target="_blank">LUCAS dedicated section. Transect indicators on landscape features related to the single point (diversity and richness) are also part of the information available for free download for survey 2009. Specific ad hoc modules have been included in some surveys such as the 2009, 2015, 2018 and 2022 topsoil samples taken on approximately 10% of total LUCAS points. Soil results for the EU countries are available via the JRC Land resource management unit under license agreement. In 2012 the soil module was implemented in Bulgaria and Romania. The soil samples of the 2022 collection are currently being analyzed in laboratories. Copernicus was added in 2018 and 2022 and the results are published together with the land cover and land use information.

2. Images

Point and landscape photos taken in the four cardinal directions at each point are available freely.

3. Statistical tables

Statistical tables with aggregated results by land cover, land use at geographical level are available in Eurobase under the domain land cover, land use and landscape (LUCAS). The statistics are presented at NUTS0, NUTS1 and NUTS2 levels using the classification for NUTS 2013. For 2018 statistical tables at NUTS1 and NUTS2 are aligned by using the classification for NUTS 2016. These estimates are based on the point data conveniently weighted. For further information on weighting refer to chapter 18.5 Data compilation and the Quality Reports.