Development of a zero-shot classification method for cross-regional crop mapping demonstrating domain transferability in Sentinel-2 imagery

The object of the study is zero-shot crop-type classification in a data-poor target region (Karabakh, Azerbaijan) using a single-date Sentinel-2 composite, with the classifier trained on labeled parcels from a data-rich source region (central France). Cross-regional deployment of crop classifiers is impeded by domain shift differences in phenology, management, and sensor-band responses and by the absence of local labels, which together degrade accuracy and trust in operational maps. Cloud-free July-2021 median composites were produced in Google Earth Engine, a fourteen-band stack (core optical bands plus NDVI, NDRE, NDWI, NDMI) was assembled, four supervised algorithms were trained on balanced French parcels, validated using overall accuracy and Cohen’s κ, and then applied zero-shot to Karabakh. Random Forest yielded 94.6% accuracy on French validation and, after instance reweighting and feature normalization, delivered spatially coherent predictions in Karabakh. The pipeline’s combination of harmonized inputs, index-augmented spectra, and lightweight domain correction enabled transfer without target-region labels, generating confidence-aware maps suitable for rapid decision support. Growth-stage mismatch and spectral sensitivity are the main causes of performance differences, red-edge information was essential for distinguishing structurally similar crops, and moisture indices helped with irrigation-induced discrimination. The approach is most effective under peak-season, cloud-free conditions with comparable agro-ecological settings and a harmonized crop taxonomy, it requires only open Sentinel-2 data, a cropland mask, and standard ML tools in GEE, supporting scalable, repeatable assessments where ground truth is scarce