EODC Data Repository

This use case focuses on developing and demonstrating an automated workflow that reduces the data management burden for Earth observation (EO) scientists by enabling on-demand access to pre-configured, cloud-optimised data cubes tailored to specific user access patterns. By automating common remote sensing operations such as NDVI trend analysis and SAR coherence using tools like Zarr, Dask, and xarray, the workflow allows users to efficiently perform time-series analyses without handling individual files, thus enabling them to focus on scientific interpretation rather than data preparation. The solution supports both recent EO datasets (e.g., Sentinel-2 L2A from 2024) and older, non-optimised datasets from previous years by providing reusable workflows that convert legacy data into the same cloud-native, time-series-ready format through automated preprocessing, chunking, and metadata harmonisation. Both the data hosted in the EODC data repository and the resulting data cubes will be made discoverable via the EOSC Matchmaker, and EODC’s processing services will be offered in the Catalogue of tools, allowing users to process data directly on the EODC cloud infrastructure

• Develop automated workflows that generate on-demand, cloud-optimised EO data cubes tailored to user-defined spatial and temporal access patterns.
• Enable scalable and efficient time-series analysis of EO data using modern open-source tools such as xarray, Dask, and Zarr, integrated into user-preferred environments.
• Provide reusable workflows to convert legacy, non-optimised EO datasets into harmonised, cloud-native formats suitable for integration into ongoing analyses.
• Ensure the discoverability of both EO datasets and resulting data cubes through the EOSC Matchmaker and facilitate their processing on the EODC cloud infrastructure.

The use case will deliver automated, scalable workflows that produce cloud-optimised EO data cubes ready for time-series analysis, accessible alongside EODC-hosted datasets through the EOSC Matchmaker. As a result, users will be able to access and analyse both recent and legacy datasets through standardised, FAIR-aligned formats using familiar tools such as xarray and Dask.