Evaluation of digital maps of top-soil properties compared to large-scale laboratory soil data and synergies towards a better European soils’ delineation.

Soil maps describe spatial variability by using traditional or predictive soil mapping techniques. Conventional soil maps group soils based on their similar cartographic properties, as on the legendary soil surveys, while digital soil mapping predicts the values of various soil properties through available soil point datasets and geostatistics or other pedometrical techniques. It is expected that both types of soil mapping contain some degree of uncertainty either due to the subjectivity of conventional mapping, which requires a vast amount of pedological knowledge in the field, or due to insufficient number of soil samples and mathematical errors that are underestimated, in geostatistical and pedometrical methods.

Digital maps of top-soil properties provide global and unified coverage without gaps, especially at broad regional scales like countries or continents, which is essential for understanding large-scale processes and cross-border issues. Accurate soil datasets are critical for understanding and managing Earth's vital resources. For instance, in hydrology, these digital maps improve the accuracy of models predicting water runoff, infiltration, and groundwater recharge, while for agriculture, detailed soil information enables precision farming practices, optimizing irrigation, fertilizer application, and crop selection for increased yields and reduced environmental impact. These maps also support broader applications like climate modeling and disaster response.

This study attempts to investigate the representativeness of European-scale soil maps in relation to official national soil data and to outline the conditions for the development of detailed scale soil data that will improve the European soil cartography. Specifically, the study deals with the comparison of six pan-European soil datasets in raster format for four selected soil properties, namely those of top-soil texture, soil organic carbon, pH and CEC with point data coming from detailed soil surveys that were not used for their construction. The gridded datasets are coming from the European Soil Data Centre (ESDAC) while the detailed laboratory data are coming from the soil map of Greece repository covering the agricultural areas of Greece. The European scale soil digital maps were compared with the soil point data (augers and profiles) of the soil map of Greece initially by spatially overlaying the data and extracting the paired values (raster and point) for each soil attribute followed by comparison of the abovementioned soil attributes by using several geospatial, statistical and geostatistical techniques.

The initial results provided a mixed picture with differences between the datasets greatly varying spatially and with differences to be more profound in areas with distinct soil characteristics (e.g. fine soil types). This study highlights the importance of incorporating detailed national soil data to improve the accuracy and reliability of continental-scale digital soil maps, particularly in regions with heterogeneous soil properties. Findings from this research contribute to the development of more robust and reliable global soil datasets by demonstrating the value of multi-source data integration and providing specific recommendations for future mapping initiatives.