Innovative Remote Sensing Identification of Cyanobacterial Blooms Inspired from Pseudo Water Color

Abstract

The identification and monitoring of cyanobacterial blooms (CBs) is critical for ensuring water security. However, traditional methods are time-consuming and labor-intensive and are not ideal for large-scale monitoring. In operational monitoring, the existing remote sensing methods are also not ideal due to complex surface features, unstable models, and poor robustness thresholds. Here, a novel algorithm, the pseudo-Forel-Ule index (P-FUI), is developed and validated to identify cyanobacterial blooms based on Terra MODIS, Landsat-8 OLI, Sentinel-2 MSI, and Sentinel-3 OLCI sensors. First, three parameters of P-FUI, that is, brightness Y, saturation s, and hue angle α, were calculated based on remote sensing reflectance. Then, the robustness thresholds of the parameters were determined by statistical analysis for a frequency distribution histogram. We validated the accuracy of our approach using high-spatial-resolution satellite data with the aid of field investigations. Considerable results were obtained by using water color differences directly. The overall classification accuracy is more than 93.76%, and the user’s accuracy and producer’s accuracy are more than 94.60% and 94.00%, respectively, with a kappa coefficient of 0.91. The identified cyanobacterial blooms’ spatial distribution with high, medium, and low intensity produced consistent results compared to those based on satellite data. Impact factors were also discussed, and the algorithm was shown to be tolerant of perturbations by clouds and high turbidity. This new approach enables operational monitoring of cyanobacterial blooms in eutrophic lakes.