Surface water resources are widely distributed and undergo rapid fluctuations, necessitating large-scale, highfrequency monitoring. Remote sensing technologies provide critical data for this purpose, but challenges such as data gaps and contamination hinder the effective observation of surface water dynamics at fine temporal scales. This limitation can obscure the recording of short-term water variations, ultimately leading to misclassified inundation extents. This study aims to develop a framework for large-scale and short-interval surface water dynamic monitoring using Sentinel-1/2 data, and generate surface water dynamic product for detailed analysis of water distribution and changes in East Africa (EA). Specifically, we proposed a novel water mapping algorithm including water extraction, integration and filtering techniques for Sentinel-1/2 data to map semimonthly surface water dynamic across EA. We then used a simple similarity-based gap-filling method to fill the data gaps in these water maps. Using this framework, we generated semimonthly and seamless surface water dynamic product covering EA from 2017 to 2023. A comprehensive spatiotemporal analysis of surface water distribution and dynamics in EA was then conducted using the product. The results showed that the water mapping algorithm achieved an overall accuracy of 0.9746, with precision (0.9815) higher than recall (0.9706). The gap-filling algorithm proved highly robust, with overall accuracy exceeding 0.98 under different scenarios. The spatial distribution of surface water in EA is heterogeneous, with dominant permanent water area (66.57 %), followed by temporary water area (22.04 %), and seasonal water area (11.39 %). The overall surface water area in EA shows fluctuation, with an increase from 2017 to 2021, followed by a decrease from 2021 to 2023. By incorporating SAR data and increasing observation frequency, our product revealed finer-scale surface water dynamics than previous product. This large-scale, short-interval mapping framework provides new insights for regional and global water resource monitoring, while the EA dataset serves as a key reference for water management in the region.