The rapid growth of phytoplankton and microalgae has presented considerable environmental and societal challenges to the sustainable development of human society. Given the inherent limitations of satellite-based algal bloom detection techniques that rely on chlorophyll and fluorescence methods, this study proposes a method that employs hyperspectral data to calculate water chromatic indices (WCIs), including hue, saturation (S), dominant wavelength (λd), and integrated apparent visual wavelength (IAVW), to identify algal blooms. A global in situ hyperspectral dataset was constructed, comprising 13,110 entries, of which 9595 were for normal waters and 3515 for algal bloom waters. The findings of our investigation indicate statistically significant discrepancies in chromaticity parameters between normal and algal bloom waters, with a p-value of 0.05. It has been demonstrated that different algal blooms exhibit distinct chromatic characteristics. For algae of the same type, the chromaticity parameters increase exponentially with chlorophyll concentration for hue and λd, while S shows low correlation and IAVW displays a good linear relationship with chlorophyll concentration. The application of this method to the Bohai Sea (coastal) and Taihu Lake (inland water) for the extraction of algal blooms revealed a clear separation in chromaticity parameters between normal and algal bloom waters. Moreover, the method can be applied to satellite data, offering an alternative approach for the detection of algal blooms based on satellite data. The indices can serve as ground truth values for colorimetric indices and provide a benchmark for the validation of satellite chromatic products.

Cyanobacterial blooms are a widespread phenomenon in aquatic ecosystems worldwide, causing significant harm to the ecological environment. Lake Taihu is the third-largest freshwater lake in China. The region has been increasingly affected by cyanobacterial blooms, drawing greater attention from people. Currently, numerous models have been developed for detecting algal bloom based on spectral characteristics. However, the intuitive basis of optical detection lies in water color. Therefore, constructing an algal bloom detecting model from the perspective of chromaticity is worth exploring. This study constructed an algal bloom detecting model based on chromatic parameters, DFLH, and IAVW by using hyperspectral data from Lake Taihu. It further applied the model to the ZY-1E hyperspectral satellite for large-scale algal bloom monitoring. The threshold for detecting cyanobacterial blooms is defined as DFLH > 0.013 sr⁻¹ and Hue Angle > 170.58 degrees; the threshold for the normal water is defined as DFLH < 0.013 sr⁻¹. The parameter thresholds for the floating leaf vegetation range were defined as DFLH > 0.013 sr⁻¹, Saturation < 0.07, and IAVW > 598 nm. Through the validation, in the modeling dataset, the overall accuracy (OA) value is 0.81 and the F₁-score is 0.86. In the validation dataset, the overall accuracy (OA) value is 0.83 and the F₁-score is 0.89. The model demonstrates good detecting performance. Regarding its application on the ZY-1E satellite, we validated the detection results accuracy through matching synchronized in situ algal density data. The results are as follows: OA is 0.95, and the F₁-score is 0.95. The results above indicate that the algal bloom detection method developed in this study had a good accuracy in detecting algal blooms in Lake Taihu on 6 September 2020. This study provided the algae bloom detecting model based on water color characteristics in Lake Taihu, which had high detecting accuracy.