The importance of biodiversity (BD) and healthy ecosystems and the services they deliver has increasingly been acknowledged. Policy initiatives, such as the EU BD Strategies 2020 and 2030, IPBES, IPCC and CBD were launched to address BD, natural capital and related values. Research has created a knowledge base to better understand nature-human interactions that are at the base of ecosystem services (ES) delivery. The key aim of related actions is to provide robust information that can be harnessed to support protection, restoration and sustainable as well as climate-neutral use of ecosystems in the EU by 2030. MAES has provided the conceptual, methodological and data base for comprehensive assessments on different spatial scales, including the EU-wide assessment (2020) and assessments in EU member states. Knowledge and data for different ecosystem types (including protected and marine areas) are increasingly available. The next step is to integrate the different MAES components (ecosystem mapping, condition, ES, accounting) and to enable the uptake of ES in decision making. Key challenges include the proof of BD-ecosystem condition-ES relationships and to link them to EU policies. The consortium brings together experts from all EU member states, associated countries and EU overseas regions with stakeholders from various public and private sectors. The expertise in the consortium includes leading experts (ecologists, economists, social scientists) on ES science, ecosystem accounting and on science-policy-business interfaces from related actions (OpenNESS, ESMERALDA, MAIA, MAES, ESP, IPBES, BD Partnership). Therefore, the project will successfully address the call's challenges and provide applicable tools and models together with guidance how to use them. The project will deliver real cases for an evidence-, ES-based and harmonised decision making across Europe, enabling transformative change to halt BD decline and to secure essential ES-sustainable supply and use.

MAIA (Mapping and Assessment for Integrated ecosystem Accounting) aims to mainstream natural capital and ecosystem accounting (NCA) in EU member states. MAIA uses the System of Environmental Economic Accounting – Experimental Ecosystem Accounting (SEEA-EEA) as the methodological basis for NCA. The SEEA EEA provides a consistent framework for analysing and storing information on ecosystem assets and flows of ecosystem services, in both physical and monetary units. The SEEA EEA follows a spatial approach, requiring extensive (spatial) data and models as inputs into the accounts. In MAIA, a flexible approach will be followed, allowing for adaptation of the SEEA EEA framework to the conditions of individual EU countries. In particular, MAIA will: (i) assess policy priorities for accounting; (ii) test, pilot and mainstream NCA in 10 countries; (iii) test innovative approaches for NCA in the European context; and (iv) support NCA in the EU through various communication and dissemination activities including the

DINOSAR aims to develop Copernicus based algorithms to support smart farming applications that can be used worldwide, clouds, or no clouds. At the moment, most EO based crop monitoring tools are based on optical satellite inputs. In areas with substantial cloud cover the use of these applications is extremely limited. To be able to introduce more sustainable crop management practices, reliable and continuous time series on crop phenology and health throughout the growing season are needed. This will support farmers to match agricultural inputs (fertilisers, pesticides, water) with what the crop actually needs, decreasing their environmental footprint. DINOSAR will do this by integrating the diagnostic power of optical, infrared and Synthetic Aperture Radar (SAR) signals. With the DINOSAR project we intend to kickstart a revolution in EO-based solutions that tackle challenges in agriculture (under clouds) by making full use of the Copernicus infrastructure. We intend to take the existing methodology a step further by designing a multi-sensor operational monitoring method for a single crop (sugarcane) capable of operating on large data volumes, and then extrapolating this approach to practical field cases and to other crops (and geographies) for which the application of EO-based applications has been underexplored. Rather than looking at optical and SAR based data as two parallel signals, we will focus on integrating the two early on in the processing chain. This has not been done before. Sugarcane in Colombia is our initial test-case, but we will not stop there. DINOSAR will also develop a methodology integrating the combined observations from optical, infrared and SAR EO satellites to monitor other crops in other geographies.