Hyperspectral remote sensing of Cyanobacteria: successes and challenges

Deepak R. Mishra; Sachidananda Mishra; Sunil Narumalani

doi:10.1117/12.2069320

18 November 2014 Hyperspectral remote sensing of Cyanobacteria: successes and challenges

Deepak R. Mishra, Sachidananda Mishra, Sunil Narumalani

Proceedings Volume 9263, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V; 92630J (2014) https://doi.org/10.1117/12.2069320
Event: SPIE Asia-Pacific Remote Sensing, 2014, Beijing, China

Abstract

Cyanobacterial harmful algal blooms (CHABs) is a major water quality issue in surface water bodies because of its scum and bad odor forming and toxin producing abilities. Terminations of blooms also cause oxygen depletion leading to hypoxia and widespread fish kills. Therefore, continuous monitoring of CHABs in recreational water bodies and surface drinking water sources is highly required for their early detection and subsequent issuance of a health warning and reducing the economic loss. We present a comparative study between a modified quasi-analytical algorithm (QAA) and a novel three-band algorithm (PC3) to retrieve phycocyanin (PC) pigment concentration in cyanobacteria laden inland waters. An extensive dataset, consisting of radiometric measurements, absorption measurements of phytoplankton, organic matter, detritus, and pigment concentration, was used to optimize the algorithms. The QAA algorithm isolates the PC signal from the remote sensing reflectance data using a set of radiative transfer equations and retrieves PC concentration in the water bodies through bio-optical inversion. Validation of the QAA algorithm, using an independent dataset, produced a mean relative error (MRE) of 34%. For the PC₃ algorithm, we propose a coefficient (ψ) for isolating the PC absorption component at 620 nm. Results show that inclusion of the model coefficient relating chlorophyll-a (chla) absorption at 620 nm to 665 nm enables PC₃ to compensate for the confounding effect of chl-a and considerably increases the accuracy of the PC prediction algorithm. The MRE of prediction for PC₃ was 27%. Moreover, PC₃ eliminates the nonlinear sensitivity issue of PC algorithms at high range.

Citation Download Citation

Deepak R. Mishra, Sachidananda Mishra, and Sunil Narumalani "Hyperspectral remote sensing of Cyanobacteria: successes and challenges", Proc. SPIE 9263, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V, 92630J (18 November 2014); https://doi.org/10.1117/12.2069320

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KEYWORDS

Absorption

Remote sensing

Reflectivity

Water

Data modeling

Error analysis

Ocean optics

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