High resolution satellite imagery underpins modern agricultural monitoring, yet generating consistent labeled data for deep learning pipelines remains a critical bottleneck. This study presents the adaptation and application of a reproducible workflow based on NASA Harvest’s OpenMapFlow library for automated extraction of 64 \(\times \) 64 px multiband mosaics from satellite and climate reanalysis data in Ecuador. Starting with 101 436 LULC records, data from 2018–2023 were retained, ambiguous labels removed, 22 classes consolidated into five agronomic categories, and random undersampling applied to yield 38 682 georeferenced observations evenly distributed across forest, cropland, pasture, water, and other classes. For each point, four annual composites were generated: Sentinel-2 (eleven optical bands), Sentinel-1 (VV and VH backscatter), ERA5 (seven monthly climate variables), and SRTM (elevation and slope) which were then reprojected and concatenated into a 22-band tensor, ensuring comprehensive spectral, radar, topographic, and climatic coverage. Extraction was performed in blocks of 500 points to optimize Google Earth Engine quotas and mitigate service errors (HTTP 503, timeouts), ensuring reproducibility and fault tolerance. Validation of a sample mosaic confirmed the physio-agronomic consistency of spectral, radar, and topographic signatures: the B5 distribution, VV backscatter peak ( \(-11\) dB), and elevations (3300–3400 m) matched expectations for active vegetation in the Andean highlands, while seasonal patterns of temperature, precipitation, pressure, and radiation from ERA5 supported these observations. Key limitations included Earth Engine quota restrictions, computation interruptions requiring block processing, and high computational demands for scaling to tens of thousands of mosaics. Nevertheless, the pipeline produces realistic, consistent mosaics and lays a solid foundation for training deep learning models to generate continental scale crop classification maps and integrate them into collaborative web applications, thereby strengthening agricultural monitoring and management.