LiDAR-based high resolution soil mapping in a Hungarian lowland area

High resolution (HR) soil information is critical for a broad range of applications, including agriculture and sustainable land practice, environmental modelling, nature conservation and resource management. Its importance is particularly pronounced at local scales and in low-relief landscapes where subtle topographic variations can strongly influence soil properties.

Our aim was to evaluate the contribution of LiDAR-derived topographic information to HR soil property mapping and to assess its added value compared to conventional digital elevation models (DEMs) on a low-relief, alluvial plain in Hungary. Soil pH was modelled using a hybrid, machine learning and geostatistical approach that integrates LiDAR-derived DEM and its derivatives, Sentinel-2 imagery, geological map and land cover information, achieving acceptable predictive performance (RMSE = 0.55, ME = 0.01). The results indicate that the LiDAR-derived topographic covariates were the most important predictors. Comparisons with existing large-scale soil pH maps revealed very weak spatial agreement in both spatial patterns and value distributions, which can be largely attributed to the limited capability of conventional DEMs to represent microtopography and subtle elevation changes in low-relief areas, particularly under forest cover.

These findings demonstrate the substantial added value of LiDAR-derived data for HR soil mapping. When used as environmental covariates, LiDAR-DEM and its derivatives, effectively represent topographic features and capture soil moisture and drainage effects that influence soil formation even in low-relief areas, regardless of vegetation cover. Since topographic parameters are decisive factors in soil property modelling, the quality of the applied DEM directly determines the quality of the resulting soil maps.