High-resolution ecosystem maps increase the efficiency of policy implementation. However due to challenges related to both data and methods, such maps of appropriate scale and quality are still rarely available for nationwide analyses. We present solutions to some typical challenges of national-scale ecosystem mapping through the new Ecosystem Map of Hungary. It is a comprehensive, spatially and thematically detailed map with a hierarchical typology. The mapping methodology combined several novel elements from the integration of various large-scale databases in a (theoretical) data cube to the use of image-based predictive mapping (with a Random Forest classifier, using Sentinel 1-2 and environmental data). A participatory method involving local experts was used for validation, addressing the lack of suitable reference data as well as improving map-maker - map user interaction. Besides the original objective of supporting conservation-related decision-making, further uses emerged from a variety of fields including spatial planning, education and recreation.

Spatial information about physical soil properties is in great demand, being basic input data in numerous applications. Soil texture can be characterized by different approaches, such as particle size distribution, plasticity index or soil texture classification. In accordance with the increasing demands for spatial soil texture information, our aim was to compile a topsoil texture class map for Hungary with an appropriate spatial resolution, using the United States Department of Agriculture soil texture classes. The ‘Classification and Regression Trees’ method was applied because it is widely used in Digital Soil Mapping, and has numerous advantages. Primary soil data were provided by the Hungarian Soil Information and Monitoring System. A digital elevation model and its derived components, geological and land cover map, and appropriate remotely sensed products together with the soil map featuring overall physical properties provided by the Digital Kreybig Soil Information System were used as auxiliary environmental co-variables. The resulting map can be used as direct input data in meteorological and hydrological modelling as well as in spatial planning.

Wind erosion susceptibility of Hungarian soils was mapped on the national level integrating three factors of the complex phenomenon of deflation (physical soil features, wind characteristics, and land use and land cover). Results of wind tunnel experiments on erodibility of representative soil samples were used for the parametrization of a countrywide map of soil texture compiled for the upper 5 cm layer of soil, which resulted in a map representing threshold wind velocity exceedance. Average wind velocity was spatially estimated with 0.5′ resolution using the Meteorological Interpolation based on Surface Homogenised Data Basis (MISH) method elaborated for the spatial interpolation of surface meteorological elements. The probability of threshold wind velocity exceedance was determined based on values predicted by the soil texture map at the grid locations. Ratio values were further interpolated to a finer 1 ha resolution using sand and silt content of the uppermost (0–5 cm) layer of soil as spatial co-variables. Land cover was also taken into account, excluding areas that are not relevant to wind erosion (forests, water bodies, settlements, etc.), to spatially assess the risk of wind erosion. According to the resulting map of wind erosion susceptibility, about 10 % of the total area of Hungary can be identified as susceptible to wind erosion. The map gives more detailed insight into the spatial distribution of wind-affected areas in Hungary compared to previous studies.

Recently, the Global Map of Salt-affected Soils (GSSmap) was launched, which pursued a country-driven approach and aimed to update the global and country-level information on salt-affected soils (SAS). The aim of this paper was to present how Hungary contributed to GSSmap by preparing its own SAS maps using advanced digital soil mapping techniques. We used not just a combination of random forest and multivariate geostatistical techniques for predicting the spatial distribution of SAS indicators (i.e., pH, electrical conductivity and exchangeable sodium percentage) for the topsoil (0–30 cm) and subsoil (30–100 cm), but also a number of indices derived from Sentinel-2 satellite images as environmental covariates. The importance plots of random forests showed that in addition to climatic, geomorphometric parameters and legacy soil information, image indices were the most important covariates. The performance of spatial modelling was checked by 10-fold cross validation showing that the accuracy of the SAS maps was acceptable. By this study and by the resulting maps of it, we not just contributed to GSSmap, but also renewed the SAS mapping methodology in Hungary, where we paid special attention to modelling and quantifying the prediction uncertainty that had not been quantified or even taken into consideration earlier.

As soil erosion is still a global threat to soil resources, the estimation of soil loss, particularly at a spatiotemporal setting, is still an existing challenge. The primary aim of our study is the assessment of changes in soil erosion potential in Hungary from 1990 to 2018, induced by the changes in land use and land cover based on CORINE Land Cover data. The modeling scheme included the application and cross-valuation of two internationally applied methods, the Universal Soil Loss Equation (USLE) and the Pan-European Soil Erosion Risk Assessment (PESERA) models. Results indicate that the changes in land cover resulted in a general reduction in predicted erosion rates, by up to 0.28 t/ha/year on average. Analysis has also revealed that the combined application of the two models has reduced the occurrence of extreme predictions, thus, increasing the robustness of the method. Random Forest regression analysis has revealed that the differences between the two models are mainly driven by their sensitivity to slope and land cover, followed by soil parameters. The resulting spatial predictions can be readily applied for qualitative spatial analysis. However, the question of extreme predictions still indicates that quantitative use of the output results should only be carried out with sufficient care.