Water transparency and color in large rivers observed by Sentinel-2 MSI and its implications for SDG 6.3.2 monitoring

• Z SD and FUI of five large rivers across the world assessed using Sentinel-2 MSI. • Danube is the clearest river, while Amazon is the most turbid in the five rivers. • 37.8 % of the sharp changes in river Z SD are situated within 20 km of dams. • Z SD and FUI show a strong inverse correlation and complementary sensitivity. • A satellite-based framework is proposed for SDG 6.3.2 evaluation in rivers. Rivers are vital to Earth’s water cycle and human societies, yet their water quality is increasingly threatened by climate change and human activities. While satellite remote sensing has emerged as a powerful tool for large-scale water quality monitoring across diverse aquatic ecosystems, a systematic analysis of water optical properties in rivers remains limited, restricting its use in supporting Sustainable Development Goal (SDG) monitoring. This study presents the first comprehensive analysis of water transparency (Secchi disk depth, Z SD ) and color (Forel-Ule Index) in the five large rivers (Yangtze, Danube, Mississippi, Nile, and Amazon) using Sentinel-2 MSI data (2019–2021). Results reveal significant spatial-seasonal variations: Danube had the highest transparency (Z SD ) and bluest color (FUI), followed by Nile, Yangtze, Mississippi, and Amazon. These differences were primarily driven by basin-specific soil erodibility and precipitation. Spatially, the Yangtze, Mississippi, and Amazon exhibited decreasing Z SD and increasing FUI from their upper to lower reaches, contrasting with different trends in Danube and Nile, highlighting the influence of large dams. Seasonally, two different patterns were observed in the five rivers, underscoring the hydrological influences on water optical properties. Furthermore, as two key optical water quality parameters, Z SD and FUI were analyzed for their complementary roles in characterizing river turbidity across varying water conditions. By quantifying spatiotemporal patterns, this study establishes a global baseline for river optical properties and supports SDG 6.3.2 monitoring. Our findings offer new insights into large-scale river ecosystem dynamics under environmental change.