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

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.

Demonstration of a Modular Prototype End-to-End Simulator for Aquatic Remote Sensing Applications.

This study introduces a prototype end-to-end Simulator software tool for simulating two-dimensional satellite multispectral imagery for a variety of satellite instrument models in aquatic environments. Using case studies, the impact of variable sensor configurations on the performance of value-added products for challenging applications, such as coral reefs and cyanobacterial algal blooms, is assessed. This demonstrates how decisions regarding satellite sensor design, driven by cost constraints, directly influence the quality of value-added remote sensing products. Furthermore, the Simulator is used to identify situations where retrieval algorithms require further parameterization before application to unsimulated satellite data, where error sources cannot always be identified or isolated. The application of the Simulator can verify whether a given instrument design meets the performance requirements of end-users before build and launch, critically allowing for the justification of the cost and specifications for planned and future sensors. It is hoped that the Simulator will enable engineers and scientists to understand important design trade-offs in phase 0/A studies easily, quickly, reliably, and accurately in future Earth observation satellites and systems.

Improved hyperspectral inversion of aquatic reflectance under non-uniform vertical mixing.

Estimating the concentration of water constituents by optical remote sensing assumes absorption and scattering processes to be uniform over the observation depth. Using hyperspectral reflectance, we present a method to direct the retrieval of the backscattering coefficient (bb(λ)) from reflectance (> 600 nm) towards wavebands where absorption by water dominates the reflectance curve. Two experiments demonstrate the impact of hyperspectral inversion in the selected band set. First, optical simulations show that the resulting distribution of bb(λ) is sensitive to particle mixing conditions, although a robust indicator of non-uniformity was not found for all scenarios of stratification. Second, in the absence of spectral backscattering profiles from in situ data sets, it is shown how substituting the median of bb(λ) into a near infra-red / red band ratio algorithm improved chlorophyll-a estimates (root mean square error 75.45 mg m-3 became 44.13 mg m-3). This approach also allows propagation of the uncertainty in bb estimates to water constituent concentrations.

Understanding the contribution of phytoplankton phase functions to uncertainties in the water colour signal.

The accurate description of a water body's volume scattering function (VSF), and hence its phase functions, is critical to the determination of the constituent inherent optical properties (IOPs), the associated spectral water-leaving reflectance, and consequently the retrieval of phytoplankton functional type (PFT) information. The equivalent algal populations (EAP) model has previously been evaluated for phytoplankton-dominated waters, and offers the ability to provide phytoplankton population-specific phase functions, unveiling a new opportunity to further understanding of the causality of the PFT signal. This study presents and evaluates the wavelength dependent, spectrally variable EAP particle phase functions and the subsequent effects on water-leaving reflectance. Comparisons are made with frequently used phase function approximations e.g. the Fournier Forand formulation, as well as with phase functions inferred from measured VSFs in coastal waters. Relative differences in shape and magnitude are quantified. Reflectance modelled with the EAP phase functions is then compared against measured reflectance data from phytoplankton-dominated waters. Further examples of modelled phytoplankton-dominated waters are discussed with reference to choice of phase function for two PFTs (eukaryote and prokaryote) across a range of biomass. Finally a demonstration of the sensitivity of reflectance due to the choice of phase function is presented. The EAP model phase functions account for both spectral and angular variability in phytoplankton backscattering i.e. they display variability which is both spectral and shape-related. It is concluded that phase functions modelled in this way are necessary for investigating the effects of assemblage variability on the ocean colour signal, and should be considered for model closure even in relatively low scattering conditions where phytoplankton dominate the IOPs.

Biophysical modelling of phytoplankton communities from first principles using two-layered spheres: Equivalent Algal Populations (EAP) model: erratum.

We regret that the Rrs spectra shown for the EAP modelled high biomass validation in Fig. 7 [Opt. Express, 22, 16745 (2014)] are incorrect. They are corrected here. The closest match of modelled to measured effective diameter is for a generalised 16 µm dinoflagellate population and not a 12 µm one as previously stated. These corrections do not affect the discussion or the conclusions of the paper.