Lakes are crucial for ecosystems1, greenhouse gas emissions2 and water resources3, yet their surface-extent dynamics, particularly seasonality, remain poorly understood at continental to global scales owing to limitations in satellite observations4,5. Although previous studies have focused on long-term changes6–8, comprehensive assessments of seasonality have been constrained by trade-offs between spatial resolution and temporal resolution in single-source satellite data. Here we show that seasonality is the dominant driver of lake-surface-extent variations globally. By leveraging a deep-learning-based spatiotemporal fusion of MODIS and Landsat-based datasets, combined with high-performance computing, we achieved monthly mapping of 1.4 million lakes (2001–2023). Our approach yielded basin-level median user’s and producer’s accuracies of 93% and 96%, respectively, when validated against the Global Surface Water dataset7. Seasonality-dominated lakes constitute 66% of the global lake area and approximately 60% of total lake counts, with over 90% of the world’s population residing in regions where such lakes prevail. During seasonality-induced extreme events, the impacts can exceed the combined magnitude of 23-year long-term changes and regular seasonal variations, doubling the contraction of 42% of shrinking lakes and fully offsetting the expansion of 45% of growing lakes. These results uncover previously hidden seasonal dynamics that are crucial for understanding hydrospheric responses to environmental changes9, protecting lacustrine systems10–12 and improving global climate models13,14. Our findings underscore the importance of incorporating seasonality into future research and suggest that advancements in the fusion of multisource remote-sensing data offer a promising path forward.
