## 3 Results and Discussion  
### Quantifying the Contribution of Phytoplankton to the \(R_{rs}\) Signal  
Remembering that the Remote Sensing Reflectance (\(R_{rs}\)) is grossly proportional to \(b_{b}/a\)[78], it should be noted that, for a given \(D_{eff}\) and phytoplankton group, \(b_{b\phi}/a_{\phi}\) will be constant for any given concentration of Chl \(a\) because the package effect observed with increasing Chl \(a\) concentration is implicit in the model (see the comparison of Bricaud and EAP \(a_{\phi}^{*}\) for varying Chl \(a\) concentrations in [PERSON] et al. [65]). However, the contribution of the phytoplankton IOPs to the total, i.e., \(b_{b\phi}/a_{\phi}\) as a percentage of total \(b_{b}/a\), will vary. The EAP model, used together with Hydrolight, allows the inspection of any component optical quantity of interest, and here, the contribution of the phytoplankton IOPs to the total IOP budget is investigated. EAP phytoplankton IOPs are used with Hydrolight to calculate a full radiative transfer solution resulting in a new theoretical quantity, \(R_{rs}\phi\). This quantity is introduced as an approximate quantification of the phytoplankton contribution to the bulk \(R_{rs}\), in order to more intuitively understand the relative optical contributions in terms of remote sensing. While acknowledging that \(R_{rs}\) is not an additive quantity, \(R_{rs}\phi\) is the calculation of reflectance with only water and phytoplankton IOPs. It does not account for any optical interaction between the phytoplankton and other in-water constituents likely to be present in natural waters, such as CDOM or detrital and mineral particles. These interactions are assumed to be secondary to the contribution of phytoplankton, but have not been quantified. It is anticipated that trans-spectral effects are most likely to suffer from this type of subtractive approach, but a full photon tracing model (such as a Monte Carlo model) would be needed to ascertain this. By modelling the phytoplankton contribution to the water-leaving signal, we can assess the availability of signal for PFT retrieval. Being able to identify the spectral regions sensitive to changes in phytoplankton assemblage (focusing on those due to change in assemblage \(D_{eff}\)) is valuable, especially to identify spectral regions which might be sufficiently independent from the ambiguity introduced by other in-water constituents. This allows the quantification of the phytoplankton signal with confidence, even where these other constituents are not well characterised. The spectral regions of maximum proportional phytoplankton signal are the ones which hold potential for detecting PFT changes from an in-water perspective, as these represent the regions of the largest phytoplankton-related signal variability as the assemblage changes. The resulting contribution of phytoplankton to the total \(R_{rs}\) is shown in Figure 1, for typical Case 1 waters as a simple illustrative example. In this example, \(a_{gd}\) covariates with Chl \(a\) while \(b_{tmap}\) is constant. This represents the combined \(a_{cdm}\), known to approximately covary with Chl \(a\), and detrital absorption \(a_{det}\), which is assumed to be small with respect to \(a_{cdm}\)[79] and approximately constant (\(a_{det}\) is neglected altogether in some IOP models e.g., [PERSON] et al. [9]). As CDOM does not scatter, \(b_{tmap}\) represents the scatter of only the detrital component of the non-algal constituents. Thus, it is assumed that biomass increases together with \(a_{cdm}\), against a relatively unchanging background detrital population whose IOPs are dominated by backscatter. As the phytoplankton contribution to the IOP budget increases (i.e., generally, as biomass increases), the impact of the other constituents is proportionally less in the \(R_{rs}\).