The continual damage to ecosystem integrity, functions, and processes caused by increasing anthropogenic pressures on a global scale are threatening the supply of ecosystem services fundamental for human well-being. Marine and coastal protected areas (MCPAs) are an essential bastion of ecosystem services. Their special management and conservation status aims to ensure the maintenance of key ecosystem functions and the sustained supply of ecosystem services in the face of increasing pressures. This chapter deals with the contribution of Earth observation (EO) for the enhancement of knowledge-based conservation, management, and restoration policies in MCPAs, to ensure the sustainable provision of ecosystem services. H2020 European Project ECOPOTENTIAL has led to a novel unified framework for improving the monitoring and management of MCPAs, based on the use of EO. The framework constitutes a significant progress beyond the state of the art, due to its unique capacity to blend EO from remote sensing and field measurements. It also maximizes return on investments due to its capabilities for data mining and inclusion of versatile modeling approaches utilizing information from EO. These tools allow for assessing ecosystem service supply in current and future conditions, designing and executing needs assessment of new measurement protocols, and evaluating the requirements of new MCPAs. Open and interoperable access to data and knowledge will be ensured to researchers, managers, policy makers, and stakeholders by a GEO Ecosystem Virtual Laboratory Platform, fully integrated in GEOSS. To show how this unified framework is applied we showcase two MCPAs that include marine areas and coastal wetlands: Wadden Sea and Doñana, with specific focus on their relevance for bird conservation.

Spatiotemporal ecological modelling of terrestrial ecosystems relies on climatological and biophysical Earth observations. Due to their increasing availability, global coverage, frequent acquisition and high spatial resolution, satellite remote sensing (SRS) products are frequently integrated to in situ data in the development of ecosystem models (EMs) quantifying the interaction among the vegetation component and the hydrological, energy and nutrient cycles. This review highlights the main advances achieved in the last decade in combining SRS data with EMs, with particular attention to the challenges modellers face for applications at local scales (e.g. small watersheds). We critically review the literature on progress made towards integration of SRS data into terrestrial EMs: (1) as input to define model drivers; (2) as reference to validate model results; and (3) as a tool to sequentially update the state variables, and to quantify and reduce model uncertainty. The number of applications provided in the literature shows that EMs may profit greatly from the inclusion of spatial parameters and forcings provided by vegetation and climatic-related SRS products. Limiting factors for the application of such models to local scales are: (1) mismatch between the resolution of SRS products and model grid; (2) unavailability of specific products in free and public online repositories; (3) temporal gaps in SRS data; and (4) quantification of model and measurement uncertainties. This review provides examples of possible solutions adopted in recent literature, with particular reference to the spatiotemporal scales of analysis and data accuracy. We propose that analysis methods such as stochastic downscaling techniques and multi-sensor/multi-platform fusion approaches are necessary to improve the quality of SRS data for local applications. Moreover, we suggest coupling models with data assimilation techniques to improve their forecast abilities. This review encourages the use of SRS data in EMs for local applications, and underlines the necessity for a closer collaboration among EM developers and remote sensing scientists. With more upcoming satellite missions, especially the Sentinel platforms, concerted efforts to further integrate SRS into modelling are in great demand and these types of applications will certainly proliferate.