Spatio-temporal changes to our world’s surface water resources are escalating. Translating how these changes impact communities and ecosystems requires time-varying data of Global Surface Water Extents (GSWE). Traditionally, GSWE mapping has been limited to static estimates, with recent efforts focusing on annual averages, frequency and occurrence of long-term variations. Building upon these foundational capabilities, we harnessed remotely sensed Sentinel-2 based near real-time Dynamic World (DW) land cover products to produce the first-of-its-kind 10 m resolution GSWE dataset representing 2015–2023. Our dataset estimated 2.5 million km2 of permanent waters and 8 million km2 of seasonal waters worldwide. Comparing our Sentinel-2 based data to contemporary Landsat-based GSWE, we observed that our data mapped less water within the >50% probability of occurrence range, suggesting a lower presence of open permanent water especially in high latitudes, deviating from what we previously learnt from Landsat data. Statistical analysis compared to well-established observational products and widely used GSWE datasets across some of the world’s most ecologically significant regions, including Pantanal in South America and Haor in South Asia, supports the overall physical realism of our data in predicting global open surface water dynamics. Our key contribution is a prototype Open Science operational framework that extracts routinely available DW products, runs geospatial analytics, and creates actionable water information for educators, researchers, and stakeholders at any scale of practical interest. We present examples of this operational capability through instant mapping of flood in Spain and drought in Lake Urmia, Central Asia, frequent monitoring of river extent changes at the Ganges–Brahmaputra confluence, and above all, interoperability with other existing GSWE applications.

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