Linking phytoplankton pigment composition and optical properties: A framework for developing remote-sensing metrics for monitoring cyanobacteria

Abstract

This study has been performed in the framework of a research program aiming to develop a low-cost aerial sensor for the monitoring of cyanobacteria in freshwater ecosystems that could be used for early detection. Several empirical and mechanistic remote-sensing tools have been already developed and tested at large scales and have proven useful in monitoring cyanobacterial blooms. However, the effectiveness of these tools for early detection is hard to assess because such work requires the detection of low concentrations of characteristic pigments amid complex ecosystems exhibiting several confounding factors (turbidity, blooms of other species, etc.). We developed a framework for performing high-throughput measurements of the absorbance and reflectance of small volumes (∼ = 20 mL) of controlled mixtures of phytoplankton species and studied the potential of this framework to validate remote-sensing proxies of cyanobacteria concentration. The absorption and reflectance spectra of single and multiple cultures carried a specific signal that allowed for the quantitative analysis of culture mixes. This specific signal was shown to be related to known pigment absorbance spectra. The concentrations of chlorophyll-a and -b, phycocyanin and phycoerythrin could be obtained from direct absorbance measurements and were correlated with the concentration obtained after pigment extraction (R2 ≥ 0.96 for all pigments). A systematic test of every possible two-band and three-band normalized difference between optical indices was then performed, and the coincidental correlation with chlorophyll-b (absent in cyanobacteria) was used as an indicator of non-specificity. Two-band indices were shown to suffer from non-specificity issues and could not yield strong and specific relationships with phycocyanin or phycoerythrin (maximum R2 < 0.5). On the other hand, the three-band modified normalized difference indices yielded strong specific relationships (R2 > 0.8).