Reproducible and Scalable cloud-native EO data analysis using openEO

Earth Observation (EO) data plays a crucial role in research and applications related to environmental monitoring, enabling informed decision-making. However, the continuously increasing volume and diversity of EO data, distributed across multiple platforms and varying formats, pose challenges for easy access and the development of scalable and reproducible workflows.

openEO addresses these challenges by providing a community-driven, open standard for unified access to EO data and cloud-native processing capabilities. It supports researchers to develop interoperable, scalable and reproducible workflows that can be executed using various programming languages (Python, R or JavaScript).

openEO has become a cornerstone technology across major initiatives in agriculture, natural capital accounting, and land-cover monitoring. In ESA’s WorldCereal project, it provides the scalable framework needed to process global Sentinel-1 and Sentinel-2 time series and integrate advanced machine-learning models, enabling dynamic 10-meter cropland and crop-type maps. It also supports the Copernicus Global Land Cover service and its tropical forestry component by delivering consistent and repeatable processing chains for annual 10-meter land-cover products, which are crucial for policy reporting and SDG monitoring. Beyond land cover, openEO supports efforts like ESA's World Ecosystem Extent Dynamics project by creating reproducible ecosystem-extent mapping and change detection maps — key elements for biodiversity and environmental management.

Building on this foundation, the openEO Federation, now integrated within the Copernicus Data Space Ecosystem (CDSE), provides seamless access to distributed Earth observation data and processing resources through a single, unified interface. By connecting multiple backends, it removes the need to juggle separate accounts or APIs and enables cross-platform workflows over datasets hosted by platforms such as Terrascope and CDSE.

openEO also strongly supports FAIR (Findable, Accessible, Interoperable, Reusable) principles. It exposes rich metadata, relies on standardised processes, and encourages the use of reusable workflow definitions. This promotes transparency, reproducibility, and the sharing of algorithms and data across research and operational communities. The approach has been validated in several large-scale implementations, including ESA’s WorldCereal and the JRC’s Copernicus Global Land Cover and Tropical Forestry Mapping and Monitoring Service (LCFM), demonstrating its maturity for both research and production environments.

By enabling reusable, federated, and reproducible Earth observation workflows, openEO is helping to build a more interoperable and efficient computational ecosystem, one that supports scalable innovation, collaboration, and long-term operational monitoring. Therefore, in this session, we aim to spark discussion on how openEO enables federated, FAIR-compliant, and reproducible workflow approaches for large-scale Earth observation applications.