## 1 Introduction
It is then overlaid with further complexity, given that a potential PFT signal from the phytoplankton component of a water body's optical constituents must be considered in the context of the other components in the water, recognising the contributions from the non-algal sources of optical variability: absorption due to CDOM (Coloured Dissolved Organic Matter) and detrital particles, i.e., \(a_{gd}(\lambda)\), and non-algal backscatter i.e., \(b_{bmap}(\lambda)\)[13]. The blue spectral region of maximum phytoplankton absorption is also the region most affected by CDOM and detrital absorption. (It should also be noted that, in the context of satellite radiometry, blue spectral bands display the largest absolute measurement uncertainties [7; 14]. While the blue water-leaving signal may be large in oceanic regions, resulting in a small relative uncertainty, this is when the signal is overwhelmingly dominated by the backscattering of water, decreasing confidence in the lesser contributions of \(a_{gd}(\lambda)\) and \(a_{\phi}\). Generally, \(a_{gd}\) product retrievals from the satellite tend to be less robust than those of other IOPs [15; 16]. )A comprehensive guide to PFT approaches is given by [PERSON] et al. [17], dividing them into four categories: abundance-based e.g., [PERSON] et al. [18] and [PERSON] et al. [19; 20]; radiance based e.g., the PHYSAT method: [PERSON] et al. [21; 22]; absorption based e.g., [PERSON] et al. [23], [PERSON] and [PERSON] [24], and PhytoDOAS: [PERSON] et al. [25]; and scattering based e.g., [PERSON] et al. [10; 26] (all references in [17]). Existing scattering-based approaches [10; 11] assume a Jungian (exponential) particle size distribution and rely on Mie modelling, which does not adequately represent phytoplankton angular scattering [27], and there are consequently high uncertainties in PSD retrieval where the particle size distribution slope is low, i.e., highly productive and coastal areas dominated by relatively large cells [10]. Low biomass (Chl \(a<1\) mg\(\cdot\)m\({}^{3}\)) oceanic conditions with an absorption-dominated phytoplankton component of the water-leaving signal can exhibit good relationships with differential pigment absorption e.g., the diagnostic pigment approach used on satellite \(R_{rs}\) in [PERSON] et al. [28]. It follows that, in the context of additional non-algal absorption, differentiated spectra show better similarity than non-differentiated [29], and also that high spectral resolution measurements show better potential for retrieving phytoplankton assemblage information than multi-spectral \(R_{rs}\)[29] when retrieving diagnostic features of the first derivative of \(R_{rs}\). However, other methods using the fourth derivative of pigment absorption and \(R_{rs}\) to identify fine-scale phytoplankton absorption features have found that objective discrimination of pigment groups from hyperspectral \(R_{rs}\) may not be feasible at low biomass \(<1\) mg\(\cdot\)m\({}^{3}\) due to the high similarities in the derivative spectra [30]. This suggests that the primary phytoplankton signal in \(R_{rs}\) is due to biomass (Chl \(a\)) rather than the accessory pigments, and that with the exception of uniquely diagnostic pigment absorption outside the spectral regions of that of Chl \(a\), phytoplankton information cannot be retrieved without assumptions about PFT relationships with biomass. The PhytoDOAS method also employs a fourth derivative analysis [25; 31] but is performed on hyperspectral top-of-atmosphere satellite measurements, avoiding the uncertainties associated with poor atmospheric correction, but the sensitivity of this high spectral resolution approach to biomass and both algal and non-algal scattering contributions to the IOP budget has yet to be determined. Generally, phytoplankton absorption- and abundance-based methods rely on empirical relationships between biomass, functional type and CDOM. Where these quantities co-vary predictably or are exactly known, empirical PFT algorithms may be successful. However, [PERSON] et al.