Satellite-Based Machine Learning for Temporal Assessment of Water Quality Parameter Prediction in a Coastal Shallow Lake

Satellite remote sensing increasingly supports water quality monitoring, yet the temporal transferability of machine learning (ML) models remains insufficiently tested, particularly in coastal shallow lakes subject to hydrological variability. This study evaluates the predictive robustness of satellite-based ML models for electrical conductivity (EC), turbidity (TUR), water temperature (WT), and dissolved oxygen (DO) in Vrana Lake, Croatia. A total of 409 in situ measurements collected during 2023–2024 and 2025 were paired with Sentinel-2 and Landsat 8–9 imagery. Pearson, Spearman, and Kendall correlation analyses were applied for parameter-specific band selection using original, inverse, quadratic, and logarithmic feature transformations. Seventeen regression algorithms were evaluated under six training–testing split strategies, including strict temporal projection. WT exhibited high robustness (R2 ≈ 0.90 under temporal projection) due to its strong dependence on thermal bands, while DO achieved moderate temporal stability (R2 = 0.51) using log-transformed predictors. EC and TUR demonstrated substantial performance degradation under temporal separation (R2 = 0.14 and −4.62, respectively), reflecting sensitivity to distribution shifts. For parameters showing sufficient stability, interpretable band-based retrieval equations were derived using the most strongly correlated spectral predictors. These findings highlight the importance of temporally structured validation and demonstrate that model complexity does not guarantee operational robustness in shallow, dynamically evolving lake systems.