Valero Jorge, AlexeyCasterad Seral, María AuxiliadoraAlcalá, José Tomás2026-01-032026-01-032025-12-31Valero-Jorge, A., Casterad, M. A., & Alcalá, J.-T. (2025). An innovative approach to developing phenological type curves for extensive crops in large-irrigated areas using Copernicus HR-VPP data (SSRN Scholarly Paper No. 5994564). Social Science Research Network. https://doi.org/10.2139/ssrn.5994564https://doi.org/10.2139/ssrn.5994564https://hdl.handle.net/10532/8099The phenological variability observed in crops is not random, but is determined by systematic factors such as microclimate, management practices, soil properties, and cultivated varieties, highlighting the need to identify specific phenological patterns adapted to the particular conditions of each agricultural area. This study presents an innovative methodology for generating reference phenological curves in extensive crops using NDVI time series from the Copernicus HR-VPP product (2018–2023) in the irrigated areas of Monegros and Zaidín (northeastern Spain) and five herbaceous crops. The methodology integrates advanced time series processing, functional data analysis, and explicit treatment of spatial variability. Missing data were imputed using three methods, with Kalman Filter proving to be the most robust (R² = 0.97–0.81 for 10–40% of missing data). Two-level filtering (Dynamic Time Warping + K-means and functional depth measures) eliminated anomalous profiles, retaining 1,673 of 1,809 plots. Spatial autocorrelation (Moran’s I and LISA) revealed significant phenological clusters in barley and double-cropped corn, justifying early/late curves; for other crops, an average curve was estimated. Representative curves were obtained using double weighting (functional centrality and series quality). The MOD-B, MOD-G, and MOD-K models achieved R² > 0.99, with MOD-G standing out. The results confirm the robustness of the proposed methodology and guarantee its transferability to other regions and crops. In addition, it is a flexible methodology in terms of satellite data sources, allowing the integration of other remote sensing products without the need for structural modifications in the workflow.enAttribution-NonCommercial-NoDerivs 3.0 Spaintexto2026-01-0310.2139/ssrn.5994564TeledetecciónFenologíaPlanta herbáceaCyclic variation (periodicity)Análisis de series cronológicasHambre ceroProducción y consumo responsables