A unified framework for large-scale, operational data processing in Earth Observation

The current era of Earth Observation (EO) is marked by an unprecedented increase in data volume and a growing number of satellite missions, driving a transition from dedicated processing infrastructure to cloud-native, distributed, and scalable orchestration. As Earth System Science, industry, and society increasingly rely on near-real-time EO data, efficient processing and workflow management have become critical components of modern ground segments. This presentation introduces an operational framework designed to meet the challenges of large-scale EO data processing. Examples from the Copernicus Sentinel programme and ESA’s Earth Explorer missions illustrate the framework’s scalable cloud deployment and operational performance. Common challenges - such as handling geospatial data formats, managing ground-segment anomalies, ensuring cybersecurity, providing standardized service interfaces, and leveraging public-cloud infrastructure - are addressed through a unified workflow approach. Operational experience from Copernicus payload data ground segment services, including monitoring via dashboards and control procedures, serves as a model for scientific missions and initiatives adopting these proven concepts. Scalability has emerged as a key feature, enabling efficient data transfers for the Copernicus Long-Term Archive, data access for Copernicus services, and higher-level processing workflows for scientific missions like BIOMASS. These orchestration strategies optimize resource use and energy efficiency for on-demand processing. The generic processing concepts demonstrated in the Copernicus and Earth Explorer programmes offer inspiration for new applications within the Earth System Science community, including hybrid approaches that integrate observations and simulation data.