An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters

Abstract

A novel algorithm is presented for detecting trophic status (chlorophyll-a), cyanobacterial blooms (cyano-blooms), surface scum and floating vegetation in coastal and inland waters using top-ofatmosphere data from the Medium Resolution Imaging Spectrometer (MERIS). The Maximum Peak Height algorithm (MPH) uses a baseline subtraction procedure to calculate the height of the dominant peak across the red and near-infrared (NIR) MERIS bands between 664 and 885 nm caused by sun-induced chlorophyll fluorescence (SICF) and particulate backscatter. Atmospheric correction of the MERIS TOA reflectance data for gaseous absorption and Rayleigh scattering proved adequate given the spectral proximity of the relevant bands and the sufficiently large di erential spectral signal. This avoided the need to correct for atmospheric aerosols, a procedure which is typically prone to large errors in turbid and high-biomass waters. A combination of switching algorithms for estimating chl-a were derived from coincident in situ chl-a and MERIS bottom-of-rayleigh reflectance measurements. These algorithms are designed to simultaneously handle a wide trophic range, from oligotrophic/mesotrophic waters (chl-a < 20 mg.m-3), to eutrophic/hypertrophic waters (chl-a > 20 mg.m-3) and surface scums or dry floating algae or vegetation (dystrophic, chl-a > 500 mg.m 3). In addition, cyanobaceria-dominant waters were di erentiated from those dominated by prokaryote species (dinoflagellates/diatoms) on the basis of the magnitude of the MPH variable. This is supported by evidence that vacuolate cyanobacteria (e.g. Microcystis aeruginosa) possess enhanced chl-a specific backscatter which is an important bio-optical distinguishing feature. This enables these broad algal classes to be distinguish with some certainty from space. A flag based on cyanobacteria-specific spectral pigmentation and fluorescence features was also used to identify cyanobacterial dominance in eutrophic waters. An operational algorithm for use with prokaryote-algae for chl-a in the range 0.5 - 350.4 mg.m-3 gave a coefficient of determination of 0.71 and a mean absolute percentage error (mape) of 60% (N=48). An algorithm for cyano-dominant waters had an r2 of 0.58 for chl-a between 33 and 362.5 mg.m-3 and an error of 33.7% (N=17). Example applications demonstrate how the MPH algorithm can o er rapid and e ective assessment of trophic status, cyano-blooms, surface scums and floating vegetation in inland and coastal waters.