Training Sample Migration for Temporal Cropland Mapping in Central Asia

Accurate cropland mapping in data-scarce regions remains challenging due to limited field data, strong interannual climatic variability, and heterogeneous cropping systems. This study proposes an NDVI-based training sample migration framework that transfers labeled samples from reference years in irrigated and rainfed agricultural systems to a target year using time-series similarity analysis. Ten similarity metrics representing geometric, temporal alignment, and correlation-based families were systematically evaluated to identify optimal thresholds and robust hybrid combinations for stable cropland transfer. The migrated samples were used to train a Random Forest classifier to generate binary cropland maps for 2021. Independent validation yielded overall accuracies of 86% in Kazakhstan and 95% in Uzbekistan. Comparisons with global cropland products (WorldCereal 2021 and WorldCover 2021) demonstrated improved spatial coherence and reduced misclassification, particularly in semi-arid environments. The proposed framework extends the temporal utility of existing labeled datasets and supports scalable cropland mapping without the need for repeated annual field surveys.