Cross-Water-Body Validation of Chlorophyll-a Retrieval Using Synergistic UAV Hyperspectral and Satellite Multispectral Data in Eutrophic Inland Waters

Eutrophication driven by algal blooms underscores the need for reliable chlorophyll-a (Chl-a) monitoring. Multi-source remote sensing, integrating Sentinel-2 multispectral and UAV hyperspectral data, provides complementary information but its applicability across optically diverse inland waters remains limited. This study evaluates the cross-water-body transferability of Chl-a inversion models using a “single training area with three validation areas” experimental design. Multiple empirical and machine learning models were constructed, and several hyperparameter optimization strategies were tested. Among all modes, the Extreme Gradient Boosting (XGB) model optimized using the Genetic Algorithm (GA) achieved the best performance for UAV data (R2 = 0.98, MAPE = 18.59%, RMSE = 2.15 μg/L). The Sentinel-2 counterpart also performed well (R2 = 0.86, MAPE = 50.03%, RMSE = 7.89 μg/L). While cross-water-body validation caused moderate performance declines, all models maintained R2 > 0.71. Overall, integrating multi-source remote sensing with cross-water-body validation enhances the robustness and transferability of Chl-a inversion models for eutrophic inland waters.