Fig 1.
Study region and sea-ice conditions.
ODV maps [44] showing the sampling stations occupied during the ARK-XXVI/3 (PS-78) cruise where CTD casts (a), water sampling and hyperspectral radiometric measurements (b) were performed. Arrows in (a) represent the main surface circulation patterns in the Arctic Ocean colored as follows: major rivers (green); inflowing currents (red); out flowing currents (blue) [45]. AMRSR-2 sea-ice concentration (http://meereisportal.de) for August (c) and September (d) 2011.
Fig 2.
Hydrography in the surface central and eastern Arctic Ocean.
(a) T-S diagram with depth (m) as color bar. Surface distribution of temperature (°C) (b) and salinity (c) with the approximate occupation of the water masses with the PML within the study region. Produced with Ocean Data View [44].
Fig 3.
Absorption coefficients in the surface waters of central and eastern Arctic Ocean.
(a) Surface distribution of aCDOM(443) (m-1) and correlation between aCDOM(443) and aCDOM(350) (inset graph); produced with Ocean Data View [44]. (b) Ternary plot denoting the contribution of the non-water absorbers [aCDOM(443), aph(443), aNAP(443)] to total non-water absorption [atw(443)] at surface; color bar indicates salinity. (c) Station 207 (indicated by the arrow in a) as example of atw(λ), aCDOM(λ), aph(λ) and aNAP(λ) spectra (m-1). Dashed line indicates the position of 443 nm. (d) Correlation between Chl-a (mg m-3) and aph(443) (m-1); for the colors, please refer to Fig 4.
Table 1.
Relative absorption of non-water absorbers.
Averaged contribution of the absorption coefficients for each of the non-water absorbers (at 443 nm) to atw(443) in this and other studies carried out in different regions.
Fig 4.
Clustering based on environmental matrix.
(top) Dendogram (cophenetic correlation coefficient: c = 0.91) for sampling stations based on surface normalized values of an environmental matrix containing hydrographic and IOP parameters: temperature, salinity, aCDOM(443), aNAP(443) and aph(443). (bottom) ODV map [44] showing the position of each station according to the classification based on the hierarchical clustering. Inset graph shows the correlation between aCDOM(443) and salinity colored with respect to the clusters; black line indicates the best fit (p<0.01).
Table 2.
Average of parameters for the geographic clusters based on the environmental matrix.
Averaged values ± standard deviation of hydrographic/IOP parameters and geographic region for each of the clusters presented in Fig 4. Geographic regions acronyms: BG (Beaufort Gyre); EB (Eurasian Basin–Amundsen and Nansen basins); LS (Laptev Sea); LSS (Laptev Sea Shelf–Lena river influenced); TPD (Transpolar Drift).
Fig 5.
(a) Dendogram (cophenetic correlation coefficient: c = 0.87) for sampling stations based on hierarchical cluster analysis applied to the 2nd derivative of Rrs(λ)/Rrs(555) (following Torrecilla et al., 2011). (b) ODV map [44] showing the position of each station according to the classification based on the hierarchical clustering. (c) 2nd derivative of normalized hyperspectral remote sensing reflectance, Rrs(λ)/Rrs(555), with respect to the wavelength range of 435–510 nm (following Torrecilla et al. [65]). (d) Normalized hyperspectral remote sensing reflectance, Rrs(λ)/Rrs(555) in the visible wavelength range. Colored circles in (a) refer to the environmental clusters presented in Fig 4. Colors in (c) and (d) are in accordance with the clusters presented in (a) and (b).
Table 3.
Hydrographic and IOP parameters for the geographic clusters based on hyperspectral AOP measurements.
Averaged values ± standard deviation of geophysical parameters for each of the clusters presented in Fig 5.
Fig 6.
Distribution of the five Arctic bio-optical provinces defined in this study based on HCA applied to surface hydrographical, IOP bulk and hyperspectral AOP data.
Fig 7.
Evaluation of empirical ocean color algorithms frequently applied to the Arctic Ocean.
(a) Chl-a estimated by empirical algorithms (mg m-3; indicated by different colors) versus in situ Chl-a (mg m-3). Stations belonging to the low aCDOM(443) cluster (Cluster 1) are presented as circles, whereas stars represent stations grouped in the high aCDOM(443) cluster (Cluster 2; Fig 5). (b) Chl-a estimated by empirical algorithms (mg m-3; indicated by different symbols) versus in situ Chl-a (mg m-3), with aCDOM(443) (m-1) as colorbar. (c) In situ Chl-a (mg m-3) versus maximum band ratio [MBR; Rrs(443>490>510/555)]. (d) Chl-a estimated by empirical algorithms relative error (%) versus the ratio between aCDOM(443) and atw(443).
Table 4.
Evaluation of empirical ocean color algorithms.
Regression statistics (including the bias and the mean absolute error–MAE) for retrieved Chl-a from in situ Rrs compared to direct measurements of Chl-a using the correspondent algorithms versus in situ measured parameters. r2 and slope were calculated using log-transformed data for each of the correspondent parameters.
Table 5.
Comparison of Chl-a retrieved from empirical ocean color algorithms versus direct measurements of Chl-a, for low aCDOM(443) sites.
Same as Table IV but for the low aCDOM(443) stations.
Fig 8.
Evaluation of semi-analytical algorithms.
Modeled geophysical parameters calculated from in situ Rrs versus in situ measured parameters: aph(443) (a); Chl-a (b); adg(443) (c) and aCDOM(443) (d). Red points refer to the GIOP [60,61] retrievals, whereas blue points to the retrievals from the GSM model adapted to the Arctic [19].
Table 6.
Evaluation of the semi-analytical ocean color algorithms.
Regression statistics for modeled geophysical parameters calculated used in situ Rrs versus in situ measured parameters. r2 and Slope were calculated using log-transformed data for each of the correspondent parameters.