A Semianalytical Algorithm for Mapping Proportion of Cyanobacterial Biomass in Eutrophic Inland Lakes Based on OLCI Data

The proportion of cyanobacterial biomass (PCB) can indicate the dominance of cyanobacteria in water, and it provides important information regarding the phytoplankton composition. Variations in the PCB can clarify the cyanobacteria accumulation process. Therefore, using remote sensing to obtain the spatial–temporal distribution of the PCB in inland lakes is very important for understanding the cyanobacterial bloom process. A total of 357 water samples was collected from three Chinese eutrophic inland lakes: Taihu Lake, Dianchi Lake, and Chaohu Lake, over eight field campaigns from 2013 to 2018. A normalized index of the PCB (NIPCB) was proposed using the absorption of chlorophyll-a (Chl-a) at 665 nm ( $a_{\mathrm {chl}}$ (665)) and the absorption of phycocyanin (PC) at 620 nm ( $a_{\mathrm {pc}}$ (620)) to indicate the PCB. A classification method using the PC index (PCI) was developed to identify the water types using the different algae composition features, and the relationship between the NIPCB and PCB in two water types was extensively examined. Finally, a semianalytical algorithm was developed to estimate the PCB based on the NIPCB index and the OLCI bands. The result of the estimated accuracy assessment using the independent validating data set demonstrated that the algorithm performance is satisfactory with mean absolute percentage error (MAPE) of 31.01% and root mean square error (RMSE) of 0.20. The MUMM method was then applied to conduct an OLCI image atmospheric correction with a satisfactory performance with the MAPE calculated between the measured remote sensing reflectance and the retrieved remote sensing from a satellite image of less than 25%. Consequently, the developed algorithm was successfully applied to three OLCI images of Dianchi Lake on April 12, 2017, Taihu Lake on May 18, 2017, and Chaohu Lake on September 8, 2018. The spatial distribution of the PCB in these three inland lakes is reasonable, and it demonstrated that the developed algorithm could be used to effectively monitor PCB at a large scale and provide important assistance in predicting the cyanobacterial blooms in eutrophic inland lakes by using remote sensing.