LAUSR

There is no standard methodology which allows the incorporation of geological information into digital soil mapping (DSM) despite the great potential of geology as environmental covariate in DSM. To fill this gap, in this study, a geochemical parent material classification scheme was tested on the watershed area of Lake Balaton, for which soil maps at a finer scale have not yet been created. A parent material map was prepared on the basis of a 1:100 000 surface geology map in order to make the incorporation of soil modelling and mapping possible. Legacy data of 12400 soil sample points was used in order to examine the role of geology in the quantitative distribution of some soil properties and element content (liquid limit, soil organic carbon, pH(KCL), CaCO3, Mg, Cu, Zn, Mn). Results confirm that the SiO2 content of the parent material influences the properties of the derived soils. In the second part of the study Random Forest models were developed for three major soil properties (liquid limit, soil organic carbon, pH) with the use of additional environmental covariates: elevation, slope, aspect, curvature, topographic position index (TPI), annual average temperature, annual average precipitation, remote sensing based normalized difference vegetation index (NDVI) and land cover information. The performance and accuracy of the models were evaluated on the basis of the coefficient of determination (R2) and root mean square error (RMSE), calculated on a randomly selected validation dataset (20% of the database). The models performed with R2 values of 0.72, 0.6 and 0.68 for liquid limit, soil organic carbon and pH respectively. The importance of variables was also examined in the RF models, and this demonstrated that while geology is among the best-performing predictors, in neither case is it the most important variable. Ninety metre resolution maps of the three major soil properties were compiled by making spatial predictions with the RF models developed here. For validation of the maps, an independent soil database was used, which showed that the prediction performed well on the cultivated area where the concordance correlation coefficients (CCC) were 0.73, 0.73 and 0.69 for liquid limit, pH and soil organic carbon respectively.