Cyanobacteria in Inland Waters: Remote Sensing

Abstract

Remote sensing plays important roles in managing harmful cyanobacterial blooms. Remote sensing algorithms for monitoring cyanobacterial blooms are grouped into empirical, semi-empirical, and semi-analytical methods. In this chapter, 12 of these methods were selected to be reviewed for their performances when applied to in situ measured field reflectance spectra and airborne or satellite sensor collected image spectra. Five empirical PC algorithms based on either band ratio or baseline calculation showed data-dependent performances, empirical band ratios and the baseline can be used to build semi-empirical models such as double three band baseline (DTBB) and four band baseline model (FBBM) showing stronger performance than the three band model (TBM), and the DTBB even performing stronger than the nested band ratio (NBR). As far as three semi-analytical models concern, the NBR and EIIMIW consistently performed well compared to the QAA pc , but care or recalibration should be practiced for applying both EIIMIW and NBR given caring inherent optical property of non-phycocyanin (PC) constituent in the water column. Although neither DTBB nor FBM cannot be evaluated with satellite MEdium Resolution Imaging Spectrometer (MERIS) and Ocean and Land Color Instrument (OLCI) images, they should be tested in future with hyperspectral satellite images acquired by PRISMA, EnMAP, and HyspIRI.