Global dominance of seasonality in shaping lake-surface-extent dynamics

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 observations^4,5. Although previous studies have focused on long-term changes^6-8, 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 systems^10-12 and improving global climate models^13,14. 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.