Foreword of the Editors

Earth Observation archives now encompass multispectral imagery, yet producing analysis-ready Sentinel-2 time series remains a critical bottleneck. We present an open-source Python package that builds data cubes from Spatio-Temporal Asset Catalog catalogues and integrates cloud masking, co-registration, and super-resolution in one workflow. Validated on high-performance computing, it enables rapid, reproducible cube construction and updates, improving temporal consistency in a braided river.