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Spatially explicit environmental variables at 25m resolution for spatial modelling in the Netherlands

  • Helfenstein, Anatol
  • Mulder, Vera L.
  • Hack-ten Broeke, Mirjam
  • van Doorn, Maarten
  • Teuling, Kees
  • Walvoort, Dennis
  • Heuvelink, Gerard
Publication Date
Jan 01, 2024
Wageningen University and Researchcenter Publications
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This dataset contains 206 spatially explicit environmental variables, also termed covariates, at 25m resolution that cover the entire Netherlands (national scale). The raster data are comprised of covariates related to the soil-forming factors (climate, organism/land use/land cover, relief/topography, parent material/geology) for the purpose of using them for digital soil mapping. However, since the covariates cover a wide range of environmental variables, they can potentially be used for spatial modelling in the Netherlands also outside the field of soil science. All covariates can also be found from the original source, but the potential strength and practicality of this dataset lies in the broad range of readily available, collected, prepared and harmonized raster data. The metadata of all the covariates in this dataset can be found in the "00_covariates_metadata.csv" file, including information about the names, category, value types, specific value types, type of geospatial data, file type, whether its static or dynamic, temporal coverage, date/version, resolution (all 25m), origin, source, access/license, description, processing steps and comments. The dataset includes 3 different types of files: GeoTIFF (.tif): the covariates as raster data at 25m resolution in the EPSG:28992 (Amersfoort / RD New) spatial projection Text (.txt): README files for each covariate with additional metadata information (filename ending in "_readme.txt") Tabular data (.csv): Classification and re-classification table for categorical covariates (filename ending in "_reclassify.csv") Note that the reclassification tables contain potential ways to reclassify the data provided, but can be altered by the user. Reclassification may be useful for categorical covariates with a large number of classes/categories. Note that covariates with CC BY-ND 4.0 licenses, covariates that are not open data or for which the license was unknown are not shared in this dataset. More information about these covariates can be found in the associated scientific paper "BIS-4D: Mapping soil properties and their uncertainties at 25m resolution in the Netherlands" (Helfenstein et al., 2024, under review). Different ways of pre-processing and preparing the covariates for subsequent modelling can be found in R scripts 20-25 in the associated code repository on GitLab. This includes assembling and preparing covariates using GDAL ("20_cov_prep_gdal.R"), computing digital elevation model (DEM) derivatives using SAGA GIS ("21_cov_dem_deriv_saga.R"), deriving spectral indices from RGBNIR bands of Sentinel 2 images ("22_cov_sensing_deriv.R"), preparing categorical covariates using GDAL ("23_cov_cat_recl_gdal.R"), deriving dynamic covariates ("24_cov_dyn_prep_gdal.R") and exploratory analysis of the covariates ("25_cov_expl_analysis_clorpt.Rmd", "25_cov_expl_analysis_cont_cat.Rmd").

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