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

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