Algal bloom monitoring in Koka Reservoir, Ethiopia: Application of satellite remote sensing algorithms

Koka Reservoir in Ethiopia, which serves as an essential water source for local inhabitants, has faced severe toxic cyanobacteria blooms. A conventional technique has been employed to monitor the trend. Though this technique has been proven accurate, it has been expensive and laborious. Hence, a satellite remote sensing technique is proposed to offset these challenges. The main objective of this paper is to explore two satellite-derived indices, which involved cross-validation of the floating algal index (FAI) derived from Sentinel-2 MSI and Landsat-8 OLI imagery. We further investigated the link between the FAI and normalized difference chlorophyll index (NDCI) using Sentinel-2 MSI. The findings showed that FAI values derived from MSI imagery were slightly higher than those derived from OLI imagery. A strong positive linear regression coefficient (R2 = 0.82), indicated that the FAI algorithm is a sensor-insensitive-suggesting it could be used for algal bloom monitoring in Koka Reservoir.