Satellite-derived shoreline observations combined with dynamic shoreline models enable fine-scale predictions of coastal change across large spatiotemporal scales. Here, we present a satellite-data-assimilated, "littoral-cell"-based, ensemble Kalman-filter shoreline model to predict coastal change and uncertainty due to waves, sea-level rise, and other natural and anthropogenic processes. We apply the developed ensemble model to the entire California coastline (approximately 1,350 km), much of which is sparsely monitored with traditional survey methods (e.g., Lidar/GPS). Water-level-corrected, satellite-derived shoreline observations (obtained from the CoastSat toolbox) offer a nearly unbiased representation of in-situ surveyed shorelines (e.g., Mean Sea Level elevation contours) at Ocean Beach, San Francisco. We demonstrate that model calibration with satellite observations during a 20-year hindcast period (1995 to 2015) provides a nearly equivalent model forecast accuracy during a validation period (2015 to 2020) compared to model calibration with monthly in-situ observations at Ocean Beach. When comparing model-predicted shoreline positions to satellite-derived observations, the model achieves an accuracy of <10 m RMSE for nearly half of the entire California coastline for the validation period. The calibrated/validated model is then applied for multi-decadal simulations of shoreline change due projected wave and sea-level conditions while holding the model parameters fixed. By 2100, the model estimates that 25 to 70% of California's beaches may become completely eroded due to sea-level rise scenarios of 0.5 to 3.0 m, respectively. The satellite-data-assimilated modeling system presented here is generally applicable to a variety of coastal settings around the world owing to the global coverage of satellite imagery.