Abstract

The severe threat to coastal waters by Cyanobacterial blooms is increasing, particularly in the regions with fish and shrimp farming activities. It is comparatively easy to detect the blooms optically, due to the surface abundance of cells, the existence of Phycocyanin pigments, and the raised backscatter associated with cell size and gas vacuoles. Remote sensing methods play imminent role in detecting the abundance cyanobacteria cells. Multispectral and hyperspectral sensors have showed adequate performance and become pertinent means of detecting these cells in the Bushehr (Iran) coastal waters within a 5-day period. Sentinel-2 satellite images over the Bushehr region showed that developed algorithms could meaningfully estimate the abundance of cyanobacteria cells in the coastal waters of Bushehr area, where there are high activities of fish and shrimp farming in the region. Indeed, springtime detections showed that the abundance of cyanobacterial cells varied between1.62×10-4 and 3.91×10-3 per liter. The maximum abundance occurred within the band near the coastline and lower abundances offshore. Using these data, we demonstrated that a spectral-shape algorithm requiring minimal atmospheric correction could be used to detect cyanobacterial blooms. Likewise, with the availability of high spectral resolution data and appropriate atmospheric correction, it is possible to develop a potential early warning system based on the abundance of Microcystis at the coastal surface waters of the Persian Gulf.

Keywords: Hyperspectral; Cyanobacteria; Phycocyanin pigments; Microcystis; Photosynthetic; Microorganisms; Algal bloom

Abbreviations: MODIS: Moderate Resolution Imaging Spectrometer; MERIS: Medium Resolution Imaging Spectrometer