Determining the Spectral Requirements for Cyanobacteria Detection for the CyanoSat Hyperspectral Imager with Machine Learning

Mark W Matthews; Jeremy Kravitz; Joshua Pease; Stephen Gensemer

doi:10.3390/s23187800

Determining the Spectral Requirements for Cyanobacteria Detection for the CyanoSat Hyperspectral Imager with Machine Learning

Sensors (Basel). 2023 Sep 11;23(18):7800. doi: 10.3390/s23187800.

Authors

Mark W Matthews¹, Jeremy Kravitz^{2

3

4}, Joshua Pease⁵, Stephen Gensemer⁵

Affiliations

¹ CyanoLakes (Pty) Ltd., Cherrybrook, NSW 2126, Australia.
² NASA Postdoctoral Program, Oak Ridge Associated Universities, NASA Ames Research Center, Moffett Field, CA 94035, USA.
³ Bay Area Environmental Research Institute, Moffett Field, CA 94035, USA.
⁴ NASA Ames Research Center, Moffett Field, CA 94035, USA.
⁵ CSIRO Manufacturing, Urrbrae, SA 5064, Australia.

Abstract

This study determines an optimal spectral configuration for the CyanoSat imager for the discrimination and retrieval of cyanobacterial pigments using a simulated dataset with machine learning (ML). A minimum viable spectral configuration with as few as three spectral bands enabled the determination of cyanobacterial pigments phycocyanin (PC) and chlorophyll-a (Chl-a) but may not be suitable for determining cyanobacteria composition. A spectral configuration with about nine ideally positioned spectral bands enabled estimation of the cyanobacteria-to-algae ratio (CAR) and pigment concentrations with almost the same accuracy as using all 300 spectral channels. A narrower spectral band full-width half-maximum (FWHM) did not provide improved performance compared to the nominal 12 nm configuration. In conclusion, continuous sampling of the visible spectrum is not a requirement for cyanobacterial detection, provided that a multi-spectral configuration with ideally positioned, narrow bands is used. The spectral configurations identified here could be used to guide the selection of bands for future ocean and water color radiometry sensors.

Keywords: CyanoSat; chlorophyll-a; cyanobacterial blooms; hyperspectral; linear variable filter; machine learning; phycocyanin; spectral requirements.

MeSH terms

Chlorophyll / analysis
Chlorophyll A
Cyanobacteria*
Environmental Monitoring*
Machine Learning
Water

Substances

Chlorophyll
Chlorophyll A
Water

Grants and funding

This work was internally funded by CSIRO. M.M. was contracted to CSIRO. J.K. was supported by an appointment to the NASA Postdoctoral Program at the NASA Ames Research Center, administered by Oak Ridge Associated Universities under contract with NASA.