Hydrolight allows only one user-provided chlorophyll-specific absorption spec- trum as standard. However, as discovered in Chapter 2, a two phytoplankton component (Procholorococcus and Diatom) model fits the observed variations in pigment packaging and was shown to be useful for retrieving TChl concentrations based on aφ spectra.
The contribution of the Diatom spectrum (relative to Prochlorococcus) was of- ten seen to increase as TChl increased (see Fig. 2.20), and in Chapter 2, this trend was modelled with Eq. 2.48. In order to model a realistic phytoplankton assemblage for the GBR to be input into Hydrolight, chlorophyll-specific absorp- tion spectra needed to be determined for these different relative abundances of Procholorococcus and Diatoms. From Chapter 2:
SP ro=
M
8.94T Chl + 0.0214M − 0.1125, (3.26)
To model the observed (see Fig. 2.20) shift in assemblage, and allow for the natural variability evident in the data, M was varied by a uniformly-distributed random number < so that it spanned from 0.75 to 3:
M = 0.75 + 2.25<. (3.27)
For low TChl values, the maximum fractional value of SP ro was restricted to 1.
The fraction of TChl attributable to Diatoms was then determined by:
SDiat = 1 − SP ro. (3.28)
The chlorophyll specific absorption model input was then calculated as:
a∗φ(λ) = a∗P ro(λ)SP ro+ a∗Diat(λ)(1 − SP ro), (3.29)
where a∗P ro(λ) and a∗Diat(λ) and the chlorophyll specific absorption spectral end- members for Prochlorococcus and Diatoms (respectively).
Due to the Case 2 nature of coastal waters and the relatively low numbers of stations available in Case 1 conditions (i.e. outer shelf samples), the data set collected in the GBR was not sufficient to determine the spectral scattering prop- erties of phytoplankton in isolation (also see Section 2.3.3.2.3). Instead, the Chl-specific scattering data of Morel (1987) (his Fig. 5b) was used to estimate a TChl-specific scattering end-members for oligotrophic waters (b∗P ro) and eutrophic waters (b∗Diat). These can then be mixed together in an equation analogous to Eq. 2.26:
b∗φ(λ) = b∗P ro(λ)SP ro+ b∗Diat(λ)(1 − SP ro), (3.30)
where the aforementioned graph of Morel (1987) was used to estimate b∗P ro(550) = 1.2 and b∗Diat(550) = 0.12.
This b∗φ(550) = (1.2SP ro + 0.12SDiat) mixing relationship satisfactorily recon-
error of approximately 13% for TChl values ranging from 0.15 to 10 µgl−1.
For a given TChl value consistent with that used in Eq. 3.26, a phytoplankton scattering spectrum was formulated based on Eq. 2.26:
bφ(λ) = [T Chl]b∗φ(550) λ 550 −γφ , (3.31)
where γφ = 2σ(2< − 1) + 0.65; i.e. γφ was centered on the value of 0.65, but
was allowed to randomly vary within two standard deviations 2σ = 0.14 of the observed γp values calculated by Eq. 2.22. This assumes that γp ≈ γφ.
As is the case for phytoplankton scattering, blending was also performed on the Mie-modelled Prochlorococcus ( gβP ro(λ, ψ)) and Diatom ( gβDiat(λ, ψ)) phase
functions determined in Section 2.3.3.2.5, and shown in Figs. 2.27a-c. After blending, the phase functions were re-normalised, as is required for the definition of phase functions (see Eq. 2.5 and 2.8):
f
βφ(λ, ψ) =
(1.2SP roβgP ro(λ, ψ) + 0.12SDiatβgDiat(λ, ψ)) 2πRπ
0 (1.2SP roβgP ro(λ, ψ) + 0.12SDiatβgDiat(λ, ψ)) sin (ψ)
3.2.2.2 Non-algal Particulates
In Chapter 2, two different mass-specific absorption spectra were reduced from the GBR dataset; representing terrestrially-sourced mineral particles (a∗min(λ)) and the other for pseudo-detrital or calcareous particles (a∗det(λ)) (see Fig. 2.22). A blending scheme analogous to Eq. 2.44 was used to model the total aN AP spec-
trum based on the two mass-specific basis vectors, and yielded TSS retrievals with 9% RMS error (see Fig. 2.22b):
a∗N AP(λ) = a∗det(λ)sdet+ a∗min(λ)smin, (3.33)
smin = 1 − sdet, (3.34)
where a∗N AP is the mass specific non-algal particulate spectrum, Smin is the frac-
tional contribution of minerals to aN AP and sdet is the fractional contribution of
detritus to aN AP.
Even for a perfectly sampled population of non-mineral particulates, the appli- cation of the term ‘detrital’ to non-mineral absorption measurements determined with the filter-pad depigmentation method (Tassan & Ferrari 1995) is misleading. This is because the filter pad retains components of previously-living phytoplank- ton cells (i.e. that were living immediately prior to sampling), so a portion of the aN AP spectrum will be correlated with TChl. By comparing the outer-reef
TSS measurements with TChl, one finds that there is evidence of a relationship between the lower limit of TSS and TChl (see Fig. 3.10). By assuming that in these waters, smin = 0, then modelling the lower limit provides an estimate
of the functional relationship between sdet, TSS and TChl. By considering that
sdet must be zero when TSS and TChl both equal, a simple square-root function
was manually chosen to estimate the fraction of the oceanic/detrital basis vectors that are used to contribute to the overall aN AP spectrum, namely
sdet =
0.85√T Chl
T SS , (3.35)
In real life, however, it is expected that there could be significant variability around this assumed relationship. In order to add more variability in the simu- lations, the random (right hand) term was added:
sdet= 0.85√T Chl T SS 1 + (2< − 1) 5 , (3.36)
where the Sdet fraction was limited between 0 and 1.
Figure 3.10: Outer-reef TSS and TChl, with the approximate lower-limit rela- tionship (Eq. 3.35) overplotted.
As shown in Fig. 2.24, the TChl-corrected mineral scattering at 660 nm was determined by the linear relationship:
bmin(660) = 0.339[T SS] − 0.11. (3.37)
It is assumed that, due to the considerable spread of the data points that was used to derive b∗min, the y-intercept of 0.11 was ignored and assumed to be a con- sequence of methodological uncertainty. This uncertainty is prominent because four separate measurements (cpg, aφ, aN AP, aCDOM) needed to be combined (Eq.
2.49), so the uncertainties are additive. It is also worth mentioning at this point that, although never highlighted, if one is to regress the mean TSS and mean bp(555) values from Blondeau-Patissier et al. (2009) (their Table 2), the relation-
to analyse their dataset to estimate an equivalent b∗min to compare with Eq. 3.37.
The mineral phase function was generated using the Fournier-Forand phase func- tion, with a randomised input of µ = 2σ(2< − 1) + 0.65 + 3 and an input spectral backscattering ratio generated by Eq. 3.38, which is a modification of Eq. 2.56 to include a randomised spectral slope based on the 2σ = 0.14 variability of γb.
Bmin(λ) = 0.0257 λ 660 −2σ(2<−1)−0.578 . (3.38) 3.2.2.3 CDOM
The absorption coefficient of CDOM was modelled as a function of DOC, as described below in Eq 3.39.
aCDOM(λ) = a∗CDOM(λ)[DOC − DOCinv], (3.39)
where the estimated optically inactive fraction of DOC, DOCinv is approximately
0.65mgl−1 (see Eq. 2.38). Accordingly, DOC concentration, in the modelling exercise of this section, was varied from 0.65 to 5.65mgl−1 to provide aCDOM(λ)
coefficients greater than zero.
3.2.2.4 Other Inputs
Hydrolight runs were performed for clear skies, for a variety of solar zenith angles (θs = 0◦, 15◦, 30◦, 45◦ and 60◦) and wind speeds (w =1, 2, 5 and 10 ms−1).
Computations were performed from 400 to 750 nm in 10 nm increments (36), so the total number of individual Hydrolight runs with unique IOP combinations was 86,400 (consisting of 5(θs) × 4(w) × 5(T Chl) × 6(T SS) × 4(DOC) × 36(λ)).
Calculations were performed to provide data for just below the surface (0-), and just above the surface (0+). Due to the IOP blending approach, input phase functions needed to be re-discretised for each IOP combination. As a result of the wavelength dependence of, in particular, the Prochlorococcus phase function (see Fig. 2.27), each new wavelength also required a new discretisation.
3.3
Results
3.3.1
DALEC R
rsMeasurements
Figure 3.11 shows the spatial distribution of quality-controlled DALEC measure- ments where coincident flow-though measurements were also made (described later in Section 2.2.4.4). The flow-through measurements are discussed later in Section 4.2.1.2. Figure 3.12a shows example spectra measured during a transect from Lodestone Reef towards Magnetic Island, near Townsville on the 31st of
January 2006. As the ship approached the coast, the DALEC Rrs measurements
decreased in the blue (400 - 440 nm) and increased in the green (500 - 550 nm) as the particulate load increased. Explaining and understanding the changing shapes of the Rrs spectrum is assisted by reducing the Rrs spectrum into the
Figure 3.11: The locations of quality-controlled DALEC Rrsmeasurements where
ancillary flow-through measurements were made. All transect data was recorded between Jan 4th and Jan 31st, 2006.
a)
b)
Figure 3.12: a) Examples of DALEC Rrs measurements made on the 31st of
January 2006 from deep blue mid-lagoon water (label A), transitioning to brown water near Magnetic Island (label D). b) Locations of the example DALEC Rrs