This study applies a pragmatic multi-criteria approach to identify areas with elevated probability of agricultural land abandonment in Vyhoda territorial hromada (Pre-Carpathians, Ukraine). Using Sentinel-2 L2A imagery, a 1-arcsec SRTM DEM and infrastructure layers from OpenStreetMap, seven spectral indices were computed and screened, and NDVI, MSAVI2 and a red-edge chlorophyll index (RECI) were retained as core spectral predictors. These indices were combined with three contextual layers (slope, Euclidean distance to roads, Euclidean distance to settlements). All rasters were normalised, reclassified to a common 1–5 suitability scale and integrated by Weighted Overlay in ArcGIS Pro to produce a five-class abandonment-probability surface. The resulting map shows clear spatial structure: high-probability clusters are concentrated mainly in the southern and north-eastern parts of the hromada, where steep terrain, fragmented parcels and limited accessibility coincide, while central areas near settlements and roads are dominated by low-probability classes. Targeted photographic field evidence and visual checks in very-high-resolution imagery corroborate successional overgrowth and loss of field boundaries on several parcels flagged as high probability, providing qualitative validation. It is recommended to interpret the map as a prioritisation tool for targeted field verification and local planning rather than as conclusive parcel-level proof. To strengthen inference and enable wider application, we recommend a parcel-level accuracy assessment, multi-date time-series analysis to separate persistent abandonment from temporary fallow, and replication of the workflow in a scalable environment.
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