Abstract
Early detection of dense harmful algal blooms (HABs) is possible using ocean colour remote sensing. Some algorithms require a training dataset, usually constructed from satellite images with a priori knowledge of the existence of the bloom. This approach can be limited if there is a lack of in situ observations, coincident with satellite images. A laboratory experiment collected biological and bio-optical data from a culture of Karenia mikimotoi, a harmful phytoplankton dinoflagellate. These data showed characteristic signals in chlorophyll-specific absorption and backscattering coefficients. The bio-optical data from the culture and a bio-optical model were used to construct a training dataset for an existing statistical classifier. MERIS imagery over the European continental shelf were processed with the classifier using different training datasets. The differences in positive rates of detection of K. mikimotoi between using an algorithm trained with purely manually selected areas on satellite images and using laboratory data as training was overall <1%. The difference was higher, <15%, when using modeled optical data rather than laboratory data, with potential for improvement if local average chlorophyll concentrations are used. Using a laboratory-derived training dataset improved the ability of the algorithm to distinguish high turbidity from high chlorophyll concentrations. However, additional in situ observations of non-harmful high chlorophyll blooms in the area would improve testing of the ability to distinguish harmful from non-harmful high chlorophyll blooms. This approach can be expanded to use additional wavelengths, different satellite sensors and different phytoplankton genera.
Highlights
Toxic phytoplankton species impact human health and the economy world wide (Kudela et al, 2015; Sanseverino et al, 2016)
In the context of harmful algal blooms (HABs) species, some authors have focused on the absorption coefficient, showing potential for detection using the fourthderivative of the phytoplankton absorption coefficient (Millie et al, 1995, 1997; Stæhr and Cullen, 2003)
The results demonstrate that the novel approach based on laboratory experiments can be used for simulation of Rrs values and training of a HAB classifier with only slight degradation of the classification accuracy while reducing false positives due to turbid coastal waters
Summary
Toxic phytoplankton species impact human health and the economy world wide (Kudela et al, 2015; Sanseverino et al, 2016). Events of enhanced growth of toxic phytoplankton (or Harmful algal blooms, HABs) are expected to be more frequent in a climate change scenario (Griffith and Gobler, 2020). In the context of HAB species, some authors have focused on the absorption coefficient, showing potential for detection using the fourthderivative of the phytoplankton absorption coefficient (aphy, m−1) (Millie et al, 1995, 1997; Stæhr and Cullen, 2003) This technique has been applied to the discrimination among phytoplankton groups using hyperspectral remote sensing reflectance, (Rrs, sr−1), in preparation for new sensors (Xi et al, 2015, 2017). Other laboratory studies have taken into account the optical backscattering coefficient (bbp, m−1) (Vaillancourt et al, 2004; Whitmire et al, 2010; Harmel et al, 2016), including the effects of different light regimes (Stramski and Morel, 1990; Poulin et al, 2018)
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