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Remote Sens. 2009, 1(4), 758-775; doi:10.3390/rs1040758

A Novel Algorithm for Predicting Phycocyanin Concentrations in Cyanobacteria: A Proximal Hyperspectral Remote Sensing Approach

1
Geosystems Research Institute and Department of Geosciences, P.O. Box 5448, Mississippi State, MS 39762-5448, USA
2
Department of Biological Sciences, University of New Orleans, New Orleans, LA 70148, USA
*
Author to whom correspondence should be addressed.
Received: 26 August 2009 / Revised: 8 September 2009 / Accepted: 9 October 2009 / Published: 19 October 2009
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Abstract

The purpose of this research was to evaluate the performance of existing spectral band ratio algorithms and develop a novel algorithm to quantify phycocyanin (PC) in cyanobacteria using hyperspectral remotely-sensed data. We performed four spectroscopic experiments on two different laboratory cultured cyanobacterial species and found that the existing band ratio algorithms are highly sensitive to chlorophylls, making them inaccurate in predicting cyanobacterial abundance in the presence of other chlorophyll-containing organisms. We present a novel spectral band ratio algorithm using 700 and 600 nm that is much less sensitive to the presence of chlorophyll.
Keywords: phycocyanin; cyanobacteria; hyperspectral remote sensing; spectral reflectance phycocyanin; cyanobacteria; hyperspectral remote sensing; spectral reflectance
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Mishra, S.; Mishra, D.R.; Schluchter, W.M. A Novel Algorithm for Predicting Phycocyanin Concentrations in Cyanobacteria: A Proximal Hyperspectral Remote Sensing Approach. Remote Sens. 2009, 1, 758-775.

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