Climate change, declining sediment supply, and global population growth in the coastal zone are projected to result in unprecedented socio-economic losses and environmental changes in the coming decades. Coastal management and planning require improved understanding of past and future shoreline evolution and its drivers. However, both observations and models have provided inconsistent or fragmented insight so far. The major cross-discipline advances in the modelling and remote sensing of large-scale (O(1-100 km)) and long-term (O(10 years)) shoreline change, together with the potential of data assimilation to optimally combine satellite imagery and shoreline modelling, calls for an ambitious and innovative research project. In SHORMOSAT we will both improve a state-of-the-art hybrid shoreline model (LX-Shore) and apply well-adapted data-assimilation techniques using more than 35 years of satellite-derived shoreline data. We will further address shoreline change and the primary drivers and processes and their interactions, and will explore the future of beaches where accommodation space is limited by e.g. coastal structures. We will apply this new framework to seven carefully selected national and international field sites distributed across three continents, representing the most widespread sandy coast environments and where a wealth of field data are collected by our consortium and international collaborators: (i) coastal embayments (O(1 km)) with various degrees of headland/groyne sand bypassing and wave exposure, (ii) wave-dominated deltas (O(10-100 km)), (iii) long sandy barriers (O(100 km)) interrupted by tidal inlets and estuary mouths. Improvements to LX-Shore and extension of its scope of application will be achieved by including obstacle sand bypassing, sediment source, a beach profile change module and a new wave module. Approximately 35 years of time series of satellite-derived waterline, shoreline position and associated errors will be generated at our seven study sites by developing and applying advanced image analysis technics. LX-Shore will be calibrated on our study sites based on a non-linear optimisation method and we will further develop a new data-assimilation framework in LX-Shore using satellite-derived shorelines. These developments will allow addressing the dominant spatial and temporal modes of shoreline variability and identifying the respective contributions of the different drivers and links between model parameter variability and wave forcing variability for the different coastal environments over the past ~35 years. Such an assessment will guide the preferred model configurations to address future shoreline change. Building on Intergovernmental Panel on Climate Change (IPCC) wave and sea-level-rise projections, future shoreline change will be estimated up to 2100 using ensemble simulations, together with the uncertainties related mainly to sea-level rise and model parameters. We will address if, where, when and why critical changes may occur (e.g., potential demise of beaches where accommodation space is limited). Overall, SHORMOSAT will provide fresh insight into past and future multi-decadal shoreline change and trajectory shifts in a context of climate change and increasing anthropogenic pressure, with important overarching implications for society and coastal planning.