Recent developments in satellite processing tools allow noncost, fast and automatic processing of a large amount of information from Earth observation, enhancing the capability of detecting coastal changes from space at different temporal scales. However, these automatic procedures are usually based on processors calibrated with information from a limited set of beaches, and the application of these tools to areas with different conditions may lead to significant errors in coastal change assessment. In this work, we evaluate the capability to monitor changes in coastal morphology at various temporal and spatial scales using 1D (coastlines) and 3D (bathymetry) satellite-derived data obtained from site-specific processing methods. Local characteristics were included in several phases of the development of the satellite products used here: i) VHR images from each pilot site were used in the coregistration process to guarantee high geolocation accuracy in images from different missions, ii) different spectral indices were tested at each pilot site to guarantee reliable detection of the coastline at all sites and iii) measured topobathymetry data were used to obtain datum-based satellite shorelines and bathymetry. The accuracy and skill of those satellite products were assessed at several pilot sites in Spain. The results indicated high horizontal accuracy, with errors on the order of half of the pixel size. Time-series analysis using satellite-derived shorelines showed that coastal change processes can be detected at several temporal and spatial scales, such as short-term erosion and accretion events on a small beach, seasonal beach rotation, and long-term trends at local and regional scales. However, the results from satellite-derived bathymetry indicated that the quantitative assessment of the coastal morphology with 3D products is still limited. Some in situ measurements are necessary to obtain satellite data that represent site-specific conditions. However, the quantity of required data measured in situ is significantly lower than the quantity required by traditional monitoring methods.