The Peak Value Method (PVM): A Phenology-Guided, Training-Independent Approach for Large-Area Time-Series Winter Wheat Mapping in the Cross-Border Region Between Bulgaria and Türkiye

Accurate, field-scale mapping of crop-type areas across large and diverse regions remains challenging due to the need for extensive training data, inconsistent ground surveys, and the computational demands of supervised methods. This study presents the Peak Value Method (PVM)—a dynamic, phenology-guided, and training-independent classification framework for mapping winter wheat extent over 20 years (2001–2020) across the socioeconomically distinct but agroecologically similar cross-border region between Bulgaria and Türkiye. By leveraging Landsat-derived Enhanced Vegetation Index (EVI) time series, PVM uses region-specific, empirically optimized thresholds for peak vegetation intensity (MaxEVI) and its timing (MaxDOY) aligned with local cropping calendars, to classify wheat pixels at 30-meter resolution. The method achieved strong validation performance, with overall accuracies ranging from 86% to 98.5% and an averageR2of 0.85, based on comparisons with official wheat statistics and ground-collected data. Final thresholds were calibrated through a sensitivity analysis of MaxEVI and MaxDOY, ensuring regional adaptability. PVM incorporated annual cropland masks, generated using a generalized classifier (∼88% accuracy), which reduced wheat commission errors by ∼30%. Analysis of spatiotemporal dynamics showed distinct trends in the cross-border regions. This study delivers the first consistent, high-resolution, two-decade winter wheat maps for this region and provides a scalable and reproducible solution for long-term operational agricultural monitoring without training data dependency. All data and code are openly available at https://doi.org/10.5281/zenodo.15612128.