IMPROVEMENT OF EXISTING AND DEVELOPMENT OF FUTURE COPERNICUS LAND MONITORING PRODUCTS – THE ECOLASS PROJECT

• Main Authors: E. Sevillano Marco, D. Herrmann, K. Schwab, K. Schweitzer, R. Almengor, F. Berndt, C. Sommer, M. Probeck
• Format: Article
• Language: English
• Published: Copernicus Publications 2019-09-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W16/201/2019/isprs-archives-XLII-2-W16-201-2019.pdf
• ISSN: 1682-1750; 2194-9034

The Horizon 2020 project ECoLaSS (Evolution of Copernicus Land Services based on Sentinel data) contributes to improving existing and developing next-generation Copernicus Land Monitoring Service (CLMS) products. The High Resolution Layers (HRLs) are currently produced in regular 3-year intervals at 10–20 meter spatial resolution for 39 European countries (EEA 39). Evolving scientific developments and user requirements are continuously analysed in a close stakeholder interaction process with the European Entrusted Entities (EEE), targeting a future pan-European roll-out of new/improved CLMS products and assessing transferability to global applications. Products and methods are being prototypically demonstrated. Representative sites (60,000–90,000 km²) were selected, covering boreal, Mediterranean, steppic, Atlantic, alpine and continental conditions. Improvements comprise yearly updates of enhanced dominant leaf types and tree cover change layers, better-quality permanent grassland classification and use categorisation. Novel products target agriculture products (i.e., crop mask, crop types). Temporal analysis, based on optical (Sentinel-2) and SAR (Sentinel-1) satellite data, makes use of temporal feature descriptors (multiple temporal statistical metrics) derived from spectral bands and indices (e.g., VV/VH ratio and NDVVVH from SAR data and NDWI, NDVI, Brightness and IRECI from optical data). Overall accuracies range from 77–98%. Rigorous benchmarking is applied to assess the prototypes’ operational readiness and technical maturity for integration into the CLMS architecture.