Cyanobacterial pigment concentrations in inland waters: Novel semi-analytical algorithms for multi- and hyperspectral remote sensing data

Pravin Jeba Dev; Assaf Sukenik; Deepak R Mishra; Ilia Ostrovsky

doi:10.1016/j.scitotenv.2021.150423

Cyanobacterial pigment concentrations in inland waters: Novel semi-analytical algorithms for multi- and hyperspectral remote sensing data

Sci Total Environ. 2022 Jan 20:805:150423. doi: 10.1016/j.scitotenv.2021.150423. Epub 2021 Sep 20.

Authors

Pravin Jeba Dev¹, Assaf Sukenik¹, Deepak R Mishra², Ilia Ostrovsky³

Affiliations

¹ Israel Oceanographic and Limnological Research, The Yigal Allon Kinneret Limnological Laboratory, Migdal 14950, Israel.
² Department of Geography, University of Georgia, Athens 30602, GA, USA.
³ Israel Oceanographic and Limnological Research, The Yigal Allon Kinneret Limnological Laboratory, Migdal 14950, Israel. Electronic address: ostrovsky@ocean.org.il.

PMID: 34818810
DOI: 10.1016/j.scitotenv.2021.150423

Abstract

Cyanobacteria are notorious for producing harmful algal blooms that present an ever-increasing serious threat to aquatic ecosystems worldwide, impacting the quality of drinking water and disrupting the recreational use of many water bodies. Remote sensing techniques for the detection and quantification of cyanobacterial blooms are required to monitor their initiation and spatiotemporal variability. In this study, we developed a novel semi-analytical approach to estimate the concentration of cyanobacteria-specific pigment phycocyanin (PC) and common phytoplankton pigment chlorophyll a (Chl a) from hyperspectral remote sensing data. The PC algorithm was derived from absorbance-concentration relationship, and the Chl a algorithm was devised based on a conceptual three-band structure model. The developed algorithms were applied to satellite imageries obtained by the Hyperspectral Imager for the Coastal Ocean (HICO™) sensor and tested in Lake Kinneret (Israel) during strong cyanobacterium Microcystis sp. bloom and out-of-bloom times. The sensitivity of the algorithms to errors was evaluated. The Chl a and PC concentrations were estimated with a mean absolute percentage difference (MAPD) of 16% and 28%, respectively. Sensitivity analysis shows that the influences of backscattering and other water constituents do not affect the estimation accuracy of PC (~2% MAPD). The reliable PC/Chl a ratios can be obtained at PC concentrations above 10 mg m^-3. The computed PC/Chl a ratio depicts the contribution of cyanobacteria to the total phytoplankton biomass and permits investigating the role of ambient factors in the formation of a complex planktonic community. The novel algorithms have extensive practical applicability and should be suitable for the quantification of PC and Chl a in aquatic ecosystems using hyperspectral remote sensing data as well as data from future multispectral remote sensing satellites, if the respective bands are featured in the sensor.

Keywords: Chlorophyll a; CyanoHAB; HICO™; Lake Kinneret; Microcystis; Phycocyanin.

MeSH terms

Algorithms
Chlorophyll / analysis
Chlorophyll A
Cyanobacteria*
Ecosystem*
Environmental Monitoring
Hyperspectral Imaging
Lakes
Remote Sensing Technology

Substances

Chlorophyll
Chlorophyll A