This dataset, "S4A-CyL," provides a comprehensive, multi-annual (2020-2024) collection of analysis-ready data patches for the Castilla y León region in Spain. It is specifically designed to support the development and validation of deep learning models for agricultural applications, with a primary focus on crop type classification.

The dataset integrates dense Sentinel-2 L2A time series with meticulously processed agricultural parcel geometries and harmonized crop type labels derived from the Spanish Land Parcel Identification System (SIGPAC). A key feature is the implementation of a persistent parcel identification system, ensuring the temporal traceability of agricultural plots across the five-year period.

The data is structured in a format inspired by the Sen4AgriNet project, with individual NetCDF files for each spatial patch. Each patch is a self-contained unit that includes the multi-spectral Sentinel-2 time series alongside the corresponding parcel ID and crop type reference layers., This work is part of the project: "Uso de imágenes Sentinel para la monitorización de prácticas agrícolas y su contribución a la iniciativa «4 por 1000» de incremento de carbono orgánico en el suelo" (TED2021-131638B-I00), from the "proyectos de transición ecológica y digital" (TED) program of 2021, funded by the Spanish Ministry of Science and Innovation, through the "Plan de recuperación, transformación y resiliencia" of the European Union.