A Method for Assessing Trophic Status of Inland Lakes Based on the Forel–Ule Index and Red-edge Band Hue Angle

Eutrophication in inland lakes has become increasingly prominent, often accompanied by frequent algal blooms and risks of degraded aquatic ecosystem functions. Therefore, broad and dynamic monitoring of lake trophic status is crucial for aquatic ecosystem protection and refined water-resources management. Satellite remote sensing enables rapid, large-area monitoring of lakes. Previous studies have developed large-scale trophic status assessment methods based on the visible-band water color index, the Forel–Ule Index (FUI), to retrieve a chlorophyll-a-referenced trophic state index (TSI(Chl-a)). However, inland waters are optically complex; high concentrations of chromophoric dissolved organic matter (CDOM) or suspended matter can inflate FUI values. Consequently, the single-index approach using FUI alone to assess TSI(Chl-a) (Model1) tends to misclassify mesotrophic waters as eutrophic. To mitigate this interference, most studies have adopted an improved strategy in which FUI serves as the primary indicator and specific spectral bands provide auxiliary discrimination. In this study, we incorporate two medium-to-high resolution satellites with red-edge bands, GF-6 and Sentinel-2, and design Red-edge Band Hue Angle α’(RHA α’) based on bands Red（630nm-690nm/650nm-680nm）,Red-edge1（690nm-730nm/698nm-713nm）,Red-edge2（730nm-770nm/733nm-748nm）. We then develop a coupled lake trophic status assessment method integrating FUI and RHA α’ (Model2).

The results indicate that: (1) RHA α’ can characterize the reflectance-peak feature of chlorophyll-a near 700 nm. For waters with FUI ≥ 11, if elevated FUI is primarily driven by high chlorophyll-a concentrations, RHA α’ tends to be high; conversely, if elevated FUI is mainly caused by high suspended matter concentrations, RHA α’ tends to be low. Thus, Model2 can effectively distinguish high-chlorophyll waters from highly turbid waters by leveraging RHA α’. (2) Using the IOCCG Hydrolight simulated dataset (including 500 synthetic water spectra under varying concentrations of phytoplankton pigments, CDOM, and non-pigmented suspended matter across 400–800 nm). For simulated Gaofen-6 data, the eutrophic-state monitoring assessment accuracies of Model1 and Model2 were respectively 84.1% and 95.8%, and the overall accuracies were respectively 88.6% and 90.4%; for simulated Sentinel-2 data, the corresponding eutrophic-state monitoring assessment accuracies were respectively 84.9% and 99.1%, and the overall accuracies were respectively 88.0% and 89.8%. Overall, Model2 markedly improves the accuracy of eutrophic-state assessment. (3) Taking 252 spatially representative lakes across China as monitoring targets, we produced lake trophic status products for 2021–2022 using Model2 and validated them against the National Surface Water Quality Report released by the Ministry of Ecology and Environment of the People’s Republic of China, achieving an overall accuracy of 79.84%.

In the next step, we will extend this method to long-term spatiotemporal analysis of TSI(Chl-a) for Chinese lakes with an area of 1 km² and above. The data preparation has been largely completed, and the related analyses are currently underway.