Distinguishing Cyanobacterial Bloom From Floating Leaf Vegetation in Lake Taihu Based on Medium-Resolution Imaging Spectrometer (MERIS) Data

Abstract

Based on field measurements of water surface reflectance spectra in Lake Taihu, we construct a model for distinguishing between cyanobacterial bloom and floating leaf vegetation by combining a chlorophyll spectral index with a baseline of phycocyanin. In situ $R_{{\rm{rs}}}$ measurements validation results show that this model performs well in distinguishing cyanobacterial bloom from floating leaf vegetation in Lake Taihu. We apply this model to 52 remote sensing images from the Medium-Resolution Imaging Spectrometer (MERIS) from 2003 to 2011. Using two different accuracy evaluation methods, we find an average recognition accuracy of more than 80% for cyanobacterial bloom and floating leaf vegetation when using optimal index thresholds. Using an average index threshold to extract cyanobacterial bloom and floating leaf vegetation from the images, the relative accuracies are 78.8% and 74.6%, respectively. If more efficiency is desired, these average thresholds can be used, which is convenient for batch processing and automated extraction of cyanobacterial bloom and floating leaf vegetation from remote sensing data. The overall distribution of cyanobacterial bloom and floating leaf vegetation in Lake Taihu from 2003 to 2011 is determined by overlapping the distribution maps from individual images, and the results of our analysis are consistent with previously published results. In addition, our analysis shows that this model is immune to perturbations from thin clouds and aerosols.