Testing the Applicability and Transferability of Data-Driven Geospatial Models for Predicting Soil Erosion in Vineyards

Empirically based approaches, like the Universal Soil Loss Equation (USLE), are appropriate for estimating mass movement attributed to rill erosion. USLE and its associates become widespread even in spatially extended studies in spite of its original plot-level concept, as well as with certain constraints on the supply of suitable input spatial data. At the same time, there is a continuously expanding opportunity and offer for the application of remote sensing (RS) imagery together with machine learning (ML) techniques to model and monitor various environmental processes utilizing their versatile benefits. The present study focused on the applicability of data-driven geospatial models for predicting soil erosion in three vineyards in the Upper Pannon Wine Region, Central Europe, considering the seasonal variation in influencing factors. Soil loss was formerly modeled by USLE, thus providing non-observation-based reference datasets for the calibration of parcel-specific prediction models using various ML methods (Random Forest, eXtreme Gradient Boosting, Regularized Support Vector Machine with Linear Kernel), which is a well-established approach in digital soil mapping (DSM). Predictions used spatially exhaustive, auxiliary, and environmental covariables. RS data were represented by multi-temporal Sentinel-2 satellite imagery data, which were supplemented by (i) topographic covariates derived from a UAV-based digital surface model and (ii) digital primary soil property maps. In addition to spatially quantifying soil erosion, the feasibility of transferring the inferred models between nearby vineyards was tested with ambiguous outcomes. Our results indicate that ML models can feasibly replace the empirical USLE model for erosion prediction. However, further research is needed to assess model transferability even to nearby parcels.