## 3 Results and Discussion
### Algorithm Tuning and Validation
#### 3.2.1 Algorithm Structures and Optimization
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Algorithm** & \(\mathbf{g_{0}}\) & \(\mathbf{g_{1}}\) & \(\mathbf{g_{2}}\) & \(\mathbf{g_{3}}\) & \(\mathbf{g_{4}}\) & \(\mathbf{N}\) & \(\mathbf{r^{2}}\) & \(\mathbf{RMSD}\) & \(\mathbf{MAPD}\) & \%_Bias_ \\  & & & & & & & & [umol L\({}^{-1}\)] & [\%] & [\%] \\ \hline \multicolumn{11}{c}{**MODIS**} \\ \hline \([\)nm\(]\) & & 443 & 488 & 531 & 547 & & & & & \\ \hline MLR1 & 4.923 & 0.641 & \(-\)2.424 & 3.503 & 1.692 & 183 & 0.76 & 23.9 & 13.9 & \(-\)1.5 \\ \hline \multicolumn{11}{c}{**SeaWiFS**} \\ \hline \([\)nm\(]\) & & 443 & 490 & 510 & 555 & & & & \\ \hline MLR1 & 5.272 & 0.526 & \(-\)2.982 & 2.623 & 0.089 & 246 & 0.68 & 30.3 & 18.8 & \(-\)3.0 \\ \hline \multicolumn{11}{c}{**\(\mathbf{a_{g}(355)}\) and Salinity**} \\ \hline \([\)nm\(]\) & & \(a_{g}(355)\) & \(\mathit{Sal}\) & & & & & & \\ \hline MLR2 & 192.718 & 26.790 & \(-\)3.558 & - & - & 464 & 0.91 & 15.2 & 10.6 & 0.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Coefficients of the MLR algorithm for retrieving DOC following Equation (8) and metrics of fit for the optimization data set. Figure 6: Random forest tree-bagger (RFTB), quasi-analytical algorithm (QAA), and generalized inherent optical property (GIOP) retrievals for tuning datasets. extend into the UV for the current and historical suite of satellite sensors). In the future, however, data from GOCAD and elsewhere could be used in the development of linear matrix inversion-type semi-analytical algorithms including basis vector models extending into the UV for CDOM, thereby potentially enabling their retrieval directly using SAAs. In fact, using GOCAD to build more globally representational basis models extending CDOM into the UV may not only provide better retrievals of CDOM, but also of the other concurrently retrieved optical properties from linear matrix inversion. Both the GIOP and QAA invert the \(R_{ps}(\lambda)\) to retrieve the water column IOPs following the theory that sea surface reflectance at a given wavelength is proportional to the backscattering coefficient, and inversely proportional to the absorption coefficient [16; 66]. Each uses various assumptions, empirical parameterizations, and mathematical inversion techniques to solve for the IOPs and partition them into their constituents. These include the total backscattering coefficient, \(b_{br}(\lambda)\), backscattering by particles, \(b_{bp}(\lambda)\), absorption by total particles, by phytoplankton, and by the combination of non-algal particles and CDOM, \(a_{dg}(\lambda)=a_{d}(\lambda)+a_{g}(\lambda)\), where \(a_{d}(\lambda)\) is non-algal (or detrital) absorption). These latter properties are similar in spectral shape, and therefore difficult to partition, which presents a challenge if we wish to compare the retrievals of SAAs to the other algorithms presented here. Therefore, while we do not re-develop or re-optimize the SAAs here--using them as published--we do utilize GOCAD to facilitate the separation of dissolved and detrital absorption components. Specifically, we solve for \(a_{g}(\lambda)\) by assuming that \(a_{d}(\lambda)\) is a function of the combined backscattering by water, non-algal particles, and the dissolved absorption by CDOM, which we assume does not backscatter, although there is some evidence supporting backscattering by colloids [46]. These SAAs retrieve only the combined \(b_{bp}(\lambda)\) from phytoplankton and non-algal particles, but the latter tend to have a higher refractive index, and therefore contribute far more strongly to the backscattering signature, e.g., [67] and references therein.