Using a combined topsoil dataset from two soil monitoring systems to create predicted soil-physical property maps and comparing them with the predicted maps of the original datasets

The constant and detailed monitoring of soil properties is crucial for having an up-to-date status of the health of our soils. This requires sufficient sampling points to meaningfully and accurately represent the soils of a whole country. Topsoil datasets can be very different regarding point density, spatial distribution and representativity. Soil sampling is also very cost- and labour-intensive, which is why combining existing national and international datasets is an efficient way to create larger datasets for the creation of accurate soil property maps. In the case of Hungary, these datasets were the Hungarian subset of the topsoil dataset of the Land Use/Cover Area frame Survey (LUCAS) and the Hungarian Soil Monitoring and Information System (SIMS). The purpose of this study is to investigate whether combining harmonized soil data from different soil monitoring systems improves the quality and accuracy of the predicted soil property maps.

The physical soil properties (sand-, silt-, and clay content) were harmonized by converting the SIMS dataset to a uniform 0-20 cm depth using mass preserving splines and matching the particle size limit of the LUCAS dataset (FAO/WRB) to the SIMS dataset (USDA). After the harmonization the two datasets were merged together and Additive Log Ratio transformation was used to assure that the particle fractions add up to 100%. This resulted in y1 and y2 values which were used in Random Forest Kriging to create the predicted maps. These maps were converted back to sand-, silt-, and clay content maps. The same procedure was applied to the LUCAS and SIMS datasets resulting in their respective sand-, silt-, and clay-content maps. The particle maps of the combined dataset were compared directly to the SIMS and LUCAS particle maps using linear regression. The quality of the predicted maps were measured and compared. Soil texture maps were created from the particle fractions using the USDA soil texture triangle. The soil texture map of the combined dataset was directly compared to the LUCAS and SIMS soil texture maps using the taxonomic distance between the predicted values of the map pairs. The result of the study show, that the quality and accuracy of the combined datasets’ predicted soil property maps were only slightly better than the maps predicted by LUCAS and slightly worse than the maps predicted by the SIMS dataset. This lead us to conclude that merging datasets alone won’t improve the quality of the soil property maps and that different approaches are required.