Research on the Detection Method of Cyanobacteria in Lake Taihu Based on Hyperspectral Data from ZY-1E

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.