Mapping of environmental variables often relies on map accuracy assessment through cross-validation with the data used for calibrating the underlying mapping model. When the data points are spatially clustered, conventional cross-validation leads to optimistically biased estimates of map accuracy. Several papers have promoted spatial cross-validation as a means to tackle this over-optimism. Many of these papers blame spatial autocorrelation as the cause of the bias and propagate the widespread misconception that spatial proximity of calibration points to validation points invalidates classical statistical validation of maps. In the paper related to these data, we present and evaluate alternative cross-validation approaches for assessing map accuracy from clustered sample data. The study area is western Europe, constrained in the north at 52° latitude and at -10° and 24° longitude The projection is IGNF:ETRS89LAEA (Lambert azimuthal equal area projection). Files: agb.tif = above ground biomass (AGB) map from version 3 of the 2017 CCI-Biomass product (https://catalogue.ceda.ac.uk/uuid/5f331c418e9f4935b8eb1b836f8a91b8) AGBstack.tif = covariates used for predicting AGB aggArea.tif = coarse grid used for simulation in the model-based methods ocs.tif = soil organic carbon stock (OCS) map (0-30 cm) from Soilgrids (https://www.isric.org/explore/soilgrids) OCSstack.tif = covariates used for predicting OCS strata.xxx = 100 compact geo-strata (ESRI shape) created with the spcosa package; used for generating clustered samples TOTmask.tif = mask of the area covered by the covariates Details and data sources of the covariates in AGBstack.tif and OCSstack.tif: Name Description Source Note ai Aridity Index https://chelsa-climate.org/downloads/ Version 2.1 bio1 Mean annual air temperature [°C] https://chelsa-climate.org/downloads/ Version 2.1 bio5 Mean daily maximum air temperature of the warmest month [°C] https://chelsa-climate.org/downloads/ Version 2.1 bio7 Annual range of air temperature [°C] https://chelsa-climate.org/downloads/ Version 2.1 bio12 Annual precipitation [kg/m2] https://chelsa-climate.org/downloads/ Version 2.1 bio15 Precipitation seasonality [kg/m2] https://chelsa-climate.org/downloads/ Version 2.1 gdd10 Growing degree days heat sum above 10°C https://chelsa-climate.org/downloads/ Version 2.1 clay Clay content [g/kg] of the 0-5cm layer https://soilgrids.org/ Only used for AGB sand Sand content [g/kg] of the 0-5cm layer https://soilgrids.org/ as above pH Acidity (Ph(water)) of the 0-5cm layer https://soilgrids.org/ as above glc2017 Landcover 2017 https://land.copernicus.eu/global/products/lc, reclassified to: closed forest, open forest, natural non-forest veg., bare & sparse veg. cropland, built-up, water Categorical variable dem Elevation https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-eu-dem cosasp Cosine of slope aspect Computed with the terra package from elevation Computed @25m resolution; next aggregated to 0.5km sinasp Sine of slope aspect Computed with the terra package from elevation as above slope Slope Computed with the terra package from elevation as above TPI Topographic position index Computed with the terra package from elevation as above TRI Terrain ruggedness index Computed with the terra package from elevation as above TWI Topographic wetness index Computed with SAGA from 500m resolution (aggregated) dem gedi Forest height https://glad.umd.edu/dataset/gedi Zone: NAFR xcoord X coordinate Using a mask created from the other covariates ycoord Y coordinate Using a mask created from the other covariates Dcoast Distance from coast Using a land mask created from the other covariates

{"references": ["de Bruin et al., 2022. Dealing with clustered samples for assessing map accuracy by cross-validation. https://doi.org/10.1016/j.ecoinf.2022.101665"]}