ABSTRACT Wei, C. and Theuerkauf, S.J., 2021. A novel multitemporal approach for satellite-derived bathymetry for coastal waters of Palau. Journal of Coastal Research, 37(2), 336–348. Coconut Creek (Florida), ISSN 0749-0208. Shallow water bathymetry is important for understanding biogeophysical and socioeconomic processes in coastal areas. In recent years, satellite-derived bathymetry (SDB) methods have been increasingly used to provide high-resolution bathymetry estimation in different regions using single remote sensing images. To tackle the common issues of single-image SDB, such as data gaps due to cloud coverage and false bathymetry due to water turbidity, this study applied a novel multitemporal workflow for SDB estimation in Palau, a Pacific Island nation. This workflow implements the typical empirical SDB steps to calculate relative water depth ( i.e . log ratio of the blue and green band) of 20 Landsat 8 images that were composited and subsequently related to in situ depth measurements to estimate true depth. Before composition, a histogram equalization approach was employed to normalize the images and identify clear water areas of each image pair by applying a 1% difference threshold. To achieve better performance, different methodological options at three key steps were evaluated, including temporal composition (mean vs . median), point data extraction (direct vs . bilinear interpolation), and regression (linear vs . piecewise vs . polynomial). Among 12 models, the polynomial model built upon bilinearly interpolated mean composition data performed the best, accurately estimating water depth up to the extinction depth of 13.7 m (45 ft), with a root mean square error of 1.76 m (5.77 ft). This multitemporal approach, with proper methodological choices according to local circumstances, could be applied to other regions to derive gap-free and accurate bathymetry estimations.