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Our team developed geospatial tools to enhance your geospatial analysis, streamlining processes for greater speed, accuracy, and automation.

This R script enhances soil property mapping accuracy by optimizing terrain attribute scaling using Random Forests (RF). It demonstrated the effectiveness of optimized scaling for nine soil properties.

The script resamples a digital elevation model (DEM) at multiple resolutions and finds the optimal scales for each terrain attribute specific to each soil property.

Reference: Dornik A, Cheţan MA, Drăguţ L, Dicu DD, Iliuţă A, 2022, Optimal scaling of predictors for digital mapping of soil properties, Geoderma (IF 6.1; AIS 1.221; Q1), 405:115453, DOI: 10.1016/j.geoderma.2021.115453

This dataset includes land suitability maps for several crops and land uses (14 crops, 7 fruit trees, 3 land-use types) in the temperate continental climate of Europe. To model the land suitability we used geospatial data depicting seventeen eco-pedological indicators (e.g. soil texture, pH, porosity, temperature, precipitation, slope).

Data can be accessed here: https://esdac.jrc.ec.europa.eu/content/land-suitability-temperate-europe

Reference: Dornik, A., Cheţan, M.A., Crişan, T.E., Heciko, R., Gora, A., Drăguţ, L. and Panagos, P., 2024. Geospatial evaluation of the agricultural suitability and land use compatibility in Europe’s temperate continental climate region. International Soil and Water Conservation Research. DOI: 10.1016/j.iswcr.2024.01.002