The weakness of global metrics for assessing the accuracy of satellite-derived bathymetry (SDB) is demonstrated and the magnitude of local accuracy variability quantified. SDB was developed for three small areas (500 m-by-1500 m) near Key West Florida (United States) that varied in depth and geomorphometric complexity. Data employed were 2016 and 2019 airborne lidar and three 2016 and four 2019 Landsat images. The modelling methodology was a widely used quasi-empirical linear regression-based approach. Local accuracy of SDB depth change - which was expected to be pronounced between 2016 and 2019 due to the occurrence of Category 5 Hurricane Irma in 2017 - was also calculated and examined. The global correlation (R2) between lidar-based depth and SDB for 2016 and 2019 varied from about 0.1 to 0.9 which produced relative (to depth) root mean square errors (RMSEs) between 5% and 25%. No image was consistently best for all three areas nor for either 2016 and 2019. SDB-based depth change was poorly correlated to the reference lidar-based depth change for all three areas. Spatial depiction of SDB accuracy showed considerable variability even within the three relatively small areas employed for 2016 and 2019 SDB depth estimates and 2016-2019 SDB depth change estimates.