Spatial modelling of vineyard erosion using machine learning methods

Sustainable agriculture is seriously threatened by severe soil erosion, which is also occurred in the Neszmély Wine Region, in the northern part of the Gerecse Hills in Hungary. In the region, three vineyards with visible signs of erosion were chosen to quantify the amount of eroded soil. The empirically based Universal Soil Loss Equation (USLE) model was utilized first to determine the soil loss. The study sites were monitored with an unmanned aerial vehicle (UAV) to create high-resolution models of seasonal and annual soil loss. After the empirically based, spatially detailed quantification of erosion, we have tested the applicability of machine learning methods to predict soil erosion for the selected parcels during the same time period. The primary concept was to use the empirically inferred erosion values as observation data to construct parcel-specific prediction models and test them on the remaining two parcels. In the model we have used (i) Sentinel 2 satellite data in the form of both native spectral bands and its derived spectral indices; (ii) terrain features derived from digital surface model created and aggregated from the UAV flights and (iii) formerly elaborated digital soil property maps as auxiliary data. Various machine learning methods (ranger, ridge, xgbLinear, enet, pls, brnn) have been tested to find the best performing predictions. Observation data were generated in the form of random points, in 100 representations. Model performances have been tested by proper measures to evaluate the applicability of the applied machine learning techniques for soil erosion mapping.

 

Acknowledgment: Our research has been supported by the Hungarian National Research, Development and Innovation Office (NRDI; Grant No: K 131820).