Recognizing harmful algal bloom based on remote sensing reflectance band ratio

Abstract

We present a band ratio algorithm based on remote sensing reflectance (RRS) data to detect an algal bloom composed of cyanobacteria (Planktothrix spp.) and chrysophytes in Lake Idro, a small meso-eutrophic lake situated in the subalpine region (northern Italy). The bloom started around the first week of September 2010 and persisted for about 1 month, with highest mean chlorophyll-a concentrations (17.5 ± 1.6 mgm−3) and phytoplankton cellular density (7,250,000 cell·l−1) measured on September 14, 2010. RRS data obtained from in situ measurements were first investigated to select the diagnostic wavelengths (i.e., 560 and 620 nm) of both phycoerythrin (present in the Planktothrix spp.) and other pigments (e.g., fucoxanthin, common to several species of chrysophyte). Testing the algorithm on RRS data derived from atmospherically corrected image data showed the ability of the medium resolution imaging spectrometer (MERIS) to detect the bloom also. The results demonstrate that a combination of in situ and MERIS data is a valuable tool to monitor the extent and duration of phytoplankton blooms.