## 3 Results and Discussion
### Algorithm Tuning and Validation
#### 3.2.2 Algorithm Validation  
This work represents the most rigorously validated set of global CDOM and DOC algorithms to date. Optimization/training of algorithms as described in the previous section was conducted on GOCAD field stations with coincident in situ radiometry. These stations were then set aside from validation, which was performed only on those remaining stations in GOCAD that had coincident satellite imagery (i.e., MODIS Aqua, Terra, and SeaWiFS). In addition to the algorithms already mentioned, two other empirical algorithms based on band ratio approaches were included in validation analysis. The approach of [PERSON] (2011) [31] (hereafter Shan11) used a power-law relationship between the ratio of \(R_{ps}(443)/R_{rs}(555)\) and \(a_{g}(350)\) and \(a_{g}(412)\), and performed well using the NOMAD dataset. The ratio of these was then used in another power-law function to solve for \(S_{g}(350\)-\(412)\). [PERSON] and [PERSON] (2011) [32] (hereafter TS11) used linear functions to relate the ratio of \(R_{rs}(670)/R_{rs}(490)\) to \(a_{g}(412)\) and \(a_{g}(443)\), and solved for \(S_{g}(412\)-\(670)\) analytically by inverting Equation (1). As these two algorithms were tuned using SeaWiFS bands, a slight adjustment was made to MODIS input reflectances to obtain the SeaWiFS reflectances required (only MODIS validation is shown here graphically). The performance of all algorithms in independent validation is weaker than for optimization (Tables 6-8, Figures 7-9). This should not be surprising considering satellite imagery is subject to higher uncertainty associated with atmospheric correction, where the atmosphere comprises \(\sim\)90% or more of the signal received by the satellite sensor. Furthermore, satellite match-ups exacerbate the issue of temporal and geographic coincidence with in situ measurements. Any regions of moderate to high variability in surface properties will likely not be well captured by the average of a nominally 5 km \(\times\) 5 km pixel array. Nevertheless, results are encouraging, particularly for the MLR approach and particularly in the UV, where the CDOM signal is strongest (in terms of in situ data) and the SAAs are not currently useful. Figure 7 shows Taylor and target diagrams comparing the CDOM absorption retrieval metrics for various algorithms as described in Section 2.4. In the UVB (275 nm), and UVA (380 nm), only the empirical approaches were feasible, while SAAs (i.e., QAA and GIOP) are also shown at 412 nm. MLR and RFTB perform comparably with respect to correlation between the models and measurements at 275 nm, although MLR does have significantly lower _MAPD_ and bias (Table 6; MLR highlighted in bold), and outperforms the RFTB at 380 nm in all but correlation for all sensors. MLR also outperforms all other CDOM absorption algorithms at 412 nm, although GIOP does not appear significantly worse as seen by its proximity to field data in the Taylor plots and the origin in the target diagrams. MLR shows a relatively strong negative bias in most sensors and channels, which is the result of underestimation in high CDOM waters (data not shown). Figure 7: Taylor diagrams (top row) and target plots (bottom row) depicting comparative algorithm performance for retrieving CDOM absorption at 275 nm, 380 nm, and 412 nm from MODIS Aqua.