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

Tünde Takáts, László Pásztor (), Mátyás Árvai, Gáspár Albert and János Mészáros
Additional contact information
Tünde Takáts: Doctoral School of Earth Sciences, Faculty of Science, Eötvös Loránd University, Pázmány Péter sétány 1/A, 1117 Budapest, Hungary
László Pásztor: Institute for Soil Sciences, HUN-REN Centre for Agricultural Research, Fehérvári út.132-144, 1116 Budapest, Hungary
Mátyás Árvai: Institute for Soil Sciences, HUN-REN Centre for Agricultural Research, Fehérvári út.132-144, 1116 Budapest, Hungary
Gáspár Albert: Institute of Cartography and Geoinformatics, Faculty of Informatics, Eötvös Loránd University, Pázmány Péter sétány 1/A, 1117 Budapest, Hungary
János Mészáros: Institute for Soil Sciences, HUN-REN Centre for Agricultural Research, Fehérvári út.132-144, 1116 Budapest, Hungary

Land, 2025, vol. 14, issue 1, 1-23

Abstract: 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.

Keywords: soil erosion; spatial modeling; USLE; machine learning; vineyards (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/14/1/163/pdf (application/pdf)
https://www.mdpi.com/2073-445X/14/1/163/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:14:y:2025:i:1:p:163-:d:1566902

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().