EVOLUTION OF THE COPERNICUS LAND SERVICE PORTFOLIO INTEGRATING NOVEL EO DATA AND LATEST MACHINE LEARNING ALGORITHMS TO CONTINUOUSLY MONITOR THE STATUS, DYNAMICS AND BIOMASS OF THE LAND SURFACE

The Horizon Europe EvoLand (Evolution of the Copernicus Land Service portfolio) proposal aims at developing innovative methods, algorithms and candidate Copernicus Land Monitoring Service (CLMS) prototypes monitoring the status and changes of land use/land cover and various land surface characteristics at high spatial and temporal resolution.

Specifically, EvoLand will address eleven next-generation CLMS product candidates by integrating innovative approaches in data fusion, Machine Learning, continuous monitoring and biomass mapping, as well as through integration of novel EO and in-situ data.

EvoLand will analyse policy, data and infrastructure requirements for the prototype services by interacting with the relevant Entrusted Entities as well as the main Copernicus Land stakeholders and users. These requirements will steer all methodological developments.

Algorithms will be developed, tested and implemented in an efficient, modular and scalable way on open-source infrastructure on various representative test sites, located both within and outside Europe.

In a subsequent demonstration phase, the candidate services will be prototypically implemented over larger regions, focusing on key thematic domains such as agriculture, forest, water, urban and general land cover.

Finally, all candidate services will be regularly analysed in view of their innovation and policy support potential, technical excellence, and operational readiness, and a strategy to transferring these services to operations will be proposed.

The ambition of EvoLand is thus to support the Entrusted Entities by dedicated research, providing tangible proof of the evolution potential of the CLMS in terms of improved information content, quality and timeliness, and enable well-informed and facts-based decision-making on the future of the CLMS.