## 3 Results
### Adjusting the Algorithm to Regional Properties
#### 3.3.2 Bio-Optical Properties  
As with the latitudinal dynamic boundaries described before, coastal proximity is anticipated to influence Chlorophyll-_a_ concentration due to various factors, including the continental outflow of inorganic matter carried hundreds of miles from the coast by Santa Ana winds [30; 31] and upper layer mixing and thermocline shoaling originated by coastal upwelling, among others. These physical processes bring about nutrient enrichment in the euphotic zone and the subsequent increase of Chl-_a_. In order to explore the influence of near-shore coastal processes and given the correlation between station number and proximity to the coast (Figure 1), the station number was plotted against the green/blue ratio, \(\mathfrak{R}^{-1}\) (Figure 4a). A discernible pattern emerged with green/blue ratios higher than or equal to 1 nearer to the coast. This is likely due to the fact that these stations are nutrient-enriched and contain more Chl-_a_ due to the photon absorption by phytoplanktonic pigments [32]. When the green/blue ratio decreases, the mChl-_a_ concentration also decreases. This offshore trend suggests that the data could be categorized based on bio-optical properties (green/blue reflectance ratio) in three categories: coastal waters with a green/blue ratio of reflectance of 1 or higher, transitional waters with a green/blue ratio between 0.5 and 1, and oceanic waters with a green/blue ratio of 0.5 and lower. Figure 4b shows the locations of the stations according to these three categories.  
Figure 4: (**a**) Variation of green/blue reflectance ratio (\(\mathfrak{R}^{-1}\)) with the station number (Figure 1) that increases with the distance from the coast. The stations were classified as coastal waters with a green/blue ratio of reflectance of 1 or higher (red squares, 105 stations), transitional waters with a green/blue ratio between 0.5 and 1 (green crosses, 653 stations), and oceanic waters with a green/blue ratio of 0.5 and under (blue circles, 2607 stations). (**b**) Station locations.  
Polynomial algorithms were then fitted for each category (Figure 5), with the order determined by quantitative analysis metrics (\(R^{2}\), \(R^{2}_{\alpha}\), SSE, RMSLE, and AIC)  
\[\log_{10}\left(\text{mChl}-a\right)= 0.2138-2.6481\text{F} \tag{8}\] \[\log_{10}\left(\text{mChl}-a\right)= 0.2501-1.7957\text{F}-0.4325\text{F}^{2}\] (9) \[\log_{10}\left(\text{mChl}-a\right)= 0.2786-2.1925\text{F}+0.8474\text{F}^{2} \tag{10}\]  
The results of these algorithms, depicted in Figure 5, align with the general data trend and closely resemble the regional algorithm (lower mChl-\(a\) prediction than OCx for coastal waters (small \(\mathfrak{R}\), large \(\mathfrak{R}^{-1}\)) and higher mChl-\(a\) predictions than OCx for oceanic waters (small \(\mathfrak{R}^{-1}\)). These algorithms consider the bio-optical properties of each area; coastal stations have a greater abundance of microhyptoplankton (diatoms, dinoflagellates, siliconflagellates) associated with a larger phytoplankton absorption coefficient, whereas, in oceanic waters, smaller phytoplankton cells are found (nano and picophytoplankton) with smaller absorption coefficients [33]. They also offer the advantage of simpler inversion and reduced overfitting risk. The regional algorithm, on the other hand, provides a unified function for the entire \(\mathfrak{R}\) range.