## 4 Conclusions
These approaches additionally rely on implicit relationships between Chl \(a\) and \(D_{eff}\) which may not always hold. This work shows that, at low biomass (\(<\) 1 mg\(\cdot\)m\({}^{3}\)), where \(R_{rs}\) is absorption-dominated, it is unlikely that there is sufficient size- or pigment-driven PFT signal to be retrieved from satellite radiometry without making these assumptions. (Phytoplankton whose prominent absorption features are at longer wavelengths, such as phycocyanin-containing cyanobacteria, present a different case). Isolating variability in \(R_{rs}\phi\) as \(D_{eff}\) and biomass vary shows that an example oceanic bloom \(\delta D_{eff}\) from 2 to 6 \(\upmu\)m is only detectable at the satellite measurement threshold of \(1\times 10^{-3}\) sr\({}^{-1}\) when the biomass reaches about 2 mg/m\({}^{3}\) (Figure 10A). Consequently, it is the size-related backscatter-driven signal in the 500 to 570 nm region, appearing at substantial biomass, that is the most useful for PFT identification from satellite radiometry as it is sufficiently insensitive to reasonable variability in both \(a_{gd}(\lambda)\) and \(b_{bmap}(\lambda)\) (when composed of small particles) (Figure 4). Variability in scatter due to non-algal particulate in the same size range as phytoplankton will likely ambiguate the distinctive spectral scatter of PFT changes, and this has not yet been tested. The location of the maximum \(\delta R_{rs}\phi\) size feature shifts between 520 and 570 nm (Figure 10B), suggesting strongly that hyperspectral data in this region would add greater capability here. Further analysis is needed to quantify the potential advantages of hyper- over multi-spectraldata with respect to this shifting maximum signal, and also with respect to the reduced SNR implicit in narrow waveband measurements. Understanding the proportional phytoplankton contribution to the total IOP budget and the resulting water-leaving signal is central to the determination of sufficient phytoplankton-driven signal containing PFT information. The proportional 'net' contribution of phytoplankton i.e., \(b_{bg}/a_{\phi}\) as a percentage of total \(b_{b}/a\), has been identified as the driver of PFT sensitivity in the \(R_{rs}\). Given the detectable differences in \(R_{rs}\) as size and biomass change, a proportional phytoplankton contribution of approximately 40% to the total \(b_{b}\) appears to a reasonable minimum threshold in terms of yielding a detectable optical change. The proportional contribution always varies with the non-algal optical constituents \(a_{gd}(\lambda)\) and \(b_{nap}(\lambda)\). Despite the many sources of model uncertainty and the requirement for model validation in specific regions, these results indicate the necessity of approaching PFTs from a strongly biophysical perspective. There is a great need for better characterisation of phytoplankton community structure and improved handling of the complex spectral and angular nature of phytoplankton scattering. The EAP model code in Matlab R2018a (The Mathworks Inc., Natick, Mass, United States) or in Python 3.7, as well as the Fortran routine for Hydrolight allowing the choice of discretised EAP phase function based on wavelength rather than backscatter fraction (see Appendix D: Uncertainties), are freely available to the community. Please contact the corresponding author. The following are available online at [[http://www.mdpi.com/2076-3417/8/12/2681/s1](http://www.mdpi.com/2076-3417/8/12/2681/s1)]([http://www.mdpi.com/2076-3417/8/12/2681/s1](http://www.mdpi.com/2076-3417/8/12/2681/s1)), Visualisation Tool: Interactive visualisation of spectral \(R_{rs}\) with assemblage \(D_{eff}\) shown for 2-20 um, with user-controlled Chl \(a\) concentration, \(a_{gd}(400)\) and \(b_{nap}(550)\). Conceptualization, L.R.L. and S.B. ; Formal analysis, L.R.L. ; Investigation, L.R.L. ; Project administration, S.B. ; Software, L.R.L. ; Supervision, S.B. ; Writing--original draft, L.R.L. ; Writing--review & editing, S.B.