Pioneering Test-Time training for robust Fine-Grained chlorophyll-a retrieval from Sentinel-2: Unveiling China’s lake eutrophication dynamics (2018–2025)

Remote sensing retrieval of lake chlorophyll-a (Chl-a) concentration is essential for eutrophication assessment and water quality management, yet conventional models suffer severe degradation in large-scale, long-term monitoring due to domain shifts arising from inconsistent atmospheric correction (regional aerosol variability) and spatiotemporal heterogeneity in aquatic optical properties. This study pioneers Test-Time Training (TTT) for robust lake Chl-a retrieval. The TTT dual-task architecture—a primary regression head paired with a self-supervised spectral reconstruction head—enables real-time adaptation to test-domain distributions without retraining, effectively compensating for residual atmospheric errors and regional apparent optical properties (AOP) distortions. Based on the proposed algorithm, this study comprehensively analyzed the spatiotemporal evolution of Chl-a in 2,621 lakes (≥1 km 2 ) in China from 2018 to 2025. The results showed the proposed model achieved superior performance in Chl-a retrieval ( R 2 = 0.79, RMSE = 9.99 μg/L), outperforming conventional machine learning approaches by approximately 28 %-36 % and underscoring its robustness in optically complex inland waters. National Chl-a distribution exhibits a clear east-high, west-low pattern, with mean concentrations of 31.29 μg/L in the Eastern Plain versus 13.83 μg/L on the Tibetan Plateau. Regional analysis indicated that most regions exhibited a downward trend in Chl-a concentrations, suggesting an improvement in water quality. Bloom risk assessment, based on over 170,000 observations, indicates that the Eastern Plain lakes face “extremely high risk” (44.7 % bloom occurrence), requiring targeted management. The proposed lake Chl-a remote sensing retrieval algorithm provides a new paradigm for fine-grained global water quality monitoring and spatiotemporal analysis, holding landmark significance for global water environment surveillance and proactive management. The code is available at https://github.com/KustAIRS/SR-TTA .