Data files belonging to the paper "Dealing with clustered samples for assessing map accuracy by cross-validation"

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: