Remote Sensing of Environment 112 (2008) 3153–3159
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Remote Sensing of Environment
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / r s e
An absorption model to determine phytoplankton size classes from
satellite ocean colour
T. Hirata a,b,⁎, J. Aiken a,b, N. Hardman-Mountford a,b, T.J. Smyth a,b, R.G. Barlow c
a
b
c
Plymouth Marine Laboratory, Plymouth, UK
Centre for observation of Air–Sea Interactions and fluXes (CASIX), Plymouth, UK
Marine and Coastal Management, Cape Town, South Africa
A R T I C L E
I N F O
Article history:
Received 5 November 2007
Received in revised form 7 March 2008
Accepted 9 March 2008
Keywords:
Absorption
Phytoplankton
Size, Ocean colour
Remote sensing
Pigment
A B S T R A C T
We have developed a model linking phytoplankton absorption to phytoplankton size classes (PSCs) that uses
a single variable, the optical absorption by phytoplankton at 443 nm, aph(443), which can be derived from
the inversion of ocean colour data. The model is based on the observation that the absolute value of aph(443)
co-varies with the spectral slope of phytoplankton absorption in the range of 443–510 nm, which is also a
characteristic of phytoplankton size classes. The model when used for analysis of SeaWiFS global data,
showed that picoplankton dominated ~79.1% of surface waters, nanoplankton ~ 18.5% and microplankton the
remainder (2.3%). The N. and S. Atlantic and the N. and S. Pacific Oceans showed seasonal cycles with both
micro and nanoplankton increasing in spring and summer in each hemisphere, while picoplankton,
dominant in the oligotrophic gyres, decreased in the summer. The PSCs derived from SeaWiFS data were
verified by comparing contemporary 8-day composites with PSCs derived from in situ pigment data from
quasiconcurrent Atlantic Meridional Transect cruises.
© 2008 Elsevier Inc. All rights reserved.
1. Introduction
Satellite remotely sensed observations of ocean colour have spatial–
temporal resolutions that are appropriate for modelling global marine
ecosystem dynamics. Presently chlorophyll-a (Chla) is the main
biological variable that is derived operationally. The derivation of other
biological variables (e.g. pigment composition) or other interpretations
(e.g. phytoplankton taxa or species) could provide data to improve
ecosystem models. There have been several empirical approaches that
have used ocean colour data to derive phytoplankton functional types
(size classes or representative species), most of which were linked to
taxa-specific phytoplankton pigments (Ciotti et al., 2002; Sathyendranath et al., 2002; Alvain et al., 2005). Aiken et al. (2007) partitioned
MERIS data of the Benguela ecosystem into size classes, microplankton
(diatoms and dinoflagellates), nano-flagellates and picoplankton, using
ranges of Chla and other bio-optical variables associated with each size
class. Currently there is no systematic definition for phytoplankton
functional types, but there are established links between phytoplankton
size classes (PSCs) and major taxa (see Table 1).
Recent oceanographic observations have shown links between
optical properties, phytoplankton pigment composition and photosynthetic parameters (Aiken et al., 2004; Barlow et al., 2002, 2004;
Moore et al., 2005; Fishwick et al., 2006). Besides the links between
⁎ Corresponding author. Plymouth Marine Laboratory Prospect Place, The Hoe,
Plymouth, PL1 3DH, UK.
E-mail address: tahi@pml.ac.uk (T. Hirata).
0034-4257/$ – see front matter © 2008 Elsevier Inc. All rights reserved.
doi:10.1016/j.rse.2008.03.011
specific pigments and specific taxa (Jeffrey et al., 1997), distinctive biooptical characteristics have been observed for different taxonomic
groups and PSCs. Notably, the Chla to accessory pigment ratio (Chla/
AP) was greatest for phytoplankton with highest Chla and the largest
taxa (diatoms and dinoflagellates). Smaller taxa (flagellates) had lower
Chla and Chla/AP. The smallest taxa (cyanobacteria and picoeukaryotes) had very low Chla and low Chla/AP. The photosynthetic
quantum efficiency was positively correlated with Chla/AP and
increased with phytoplankton size. The inference is that phytoplankton synthesise Chla preferentially when conditions are conducive to
growth. These size, pigment and photosynthetic relationships are
consistent with data from the Atlantic Meridional Transect (AMT,
Aiken et al., 2000; Robinson et al., 2006) on: phytoplankton
distributions (Zubkov et al., 1998; 2000; Tarran et al., 2006; Heywood
et al., 2006); pigments (Gibb et al., 2000; Barlow et al., 2002, 2004;
Aiken et al., in press); productivity (Maranon and Holligan, 1999;
Maranon et al., 2000; Maranon, 2005; Poulton et al., 2006);
photosynthetic activity (Suggett et al., 2006). Scaling of photosynthesis
and cell size (Maranon et al., 2007; Maranon, 2008) shows a linear
relationship over 9 orders of cell volume, from picoplankton to the
large microplankton, is particularly significant in the context of the
present research and by inference there should be a scaling of Cbiomass, Chla-biomass, photosynthesis and cell size. Chla has a
distinctive blue absorption spectrum (400–470 nm, peak 443 nm),
whereas carotenoids (both photosynthetic and photoprotective) have
similar chemical structures, identical chromophores (similar absorption spectra) and are blue–green absorbers (400–550 nm, peak
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T. Hirata et al. / Remote Sensing of Environment 112 (2008) 3153–3159
Table 1
Taxonomic groups, marker pigments and size classes
Taxonomic group
Marker pigments (abbreviation)
Size class
Diatoms
Dinoflagellates
Flagellates
Prymnesiophytes
Chrysophytes
Cryptophytes
Chlorophytes
Pico-flagellates or
Pico-eukaryotes
Cyanobacteria (prokaryotes; prok)
Synechococcus spp.
Prochlorococcus spp.
Fucoxanthin (Fuc)
Peridinin (Per); also Fuc
Micro
Micro
19′-Hexanoyloxyfucoxanthin (Hex)
19′-butanoyloxyfucoxanthin (But)
Alloxanthin (Allo)
Chlorophyll b (Chlb)
(Hex, But, Allo, Chlb)
Nano
Nano
Nano
Nano
Pico
Zeaxanthin (Zea)
Zeaxanthin (Zea) + DVChlb
Pico
Pico
Pico
Diagnostic pigments (DP) = 0.6Allo + 0.35But + 1.01Chlb + 1.41Fuc + 1.27Hex + 1.41Per +
0.86Zea.
Micro (M) = 1.41(Fuc + Per)/DP.
Nano (N) = (0.6Allo + 0.35But + 1.27Hex + 1.01Chlb)/DP.
Prokaryotes (Cyanobacteria) = 0.86Zea/DP.
PE = pico-eukaryotes (size b 2 μm, Chla b 0.25 mg m− 3).
Pico (P) = Prok + PE.
490 nm). Phycobilins are major light-harvesting pigments in cyanobacteria (Synechococcus spp.) absorbing 580–630 nm, but occur in very
low concentrations in the surface ocean. The peak of Chla absorption at
443 nm is the dominant influence on the shape and absolute
magnitude of phytoplankton absorption spectra in the blue–green
region (400–550 nm). This distinctive optical signature should be
detectable in ocean colour data, as the basis of a model to classify PSCs.
Here we explore the relationships between direct optical properties,
phytoplankton absorption, aph(λ) and PSCs, using exclusively the
NASA bio-optical data set NOMAD (Werdell & Bailey, 2006) that
combines pigment and optical data acquired concurrently (contemporary and co-located) from widespread geographically distributed
sources (new release 2007). NOMAD is a unique internationally
accepted reference data set that is continuously expanding, benefiting
the global bio-optical community.
1.1. Rationale for absorption model for phytoplankton size classes
Fig. 1 shows phytoplankton absorption spectra, aph(λ), m− 1, for
samples acquired in the Benguela (Fishwick et al., 2006) and AMT
(Aiken et al., in press) for a range of Chla values and phytoplankton of
different size classes: micro (diatoms and dinoflagellates); nano
(several flagellate taxa); pico (prokaryotes and pico-eukaryotes).
Fig. 1 inset shows the lower range of aph(λ) at expanded scale for
nano and pico samples. All spectra peak at aph(443) and the blue–
green difference δ ( = aph(443) − aph(510)) or the slope S ( = δ/|443–510|)
have greatest values at largest aph(443) and largest Chla. Both δ and S
decrease with decreasing aph(443) and Chla. Larger microplankton
have the largest aph(443) and slope S, nanoplankton have mid-range
aph(443) and S and picoplankton have the smallest aph(443) and S.
This implies that the absolute magnitude of aph(443) or S, has a
taxonomic link that can be used to classify phytoplankton by size. This
conceptual model is unique in that it uses functions solely of aph, i.e.
aph(443) (or S or δ), and aph is a physical observable, directly derivable
from remotely sensed water-leaving radiance (Smyth et al., 2006). The
slope S represents the spectral shape in terms of absolute difference,
while the commonly used ratio, aph(443)/aph(510) is a measure of
spectral shape in relative terms.
controlled (QC) independently, by regression analysis of Chla vs AP, (as
per Aiken et al., in press) and comparably by regression analysis of aph
(443) vs aph(490); only a few ‘outlier’ data (N3 standard deviation)
were deleted. The merged pigments and aph dataset that had been
sampled and analysed separately by different processes were highly
correlated (r2 =0.77), but had obvious ‘outlier’ data, possibly arising form
small differences or errors of water sampling (timing) or data labelling
(from different depths or stations). The merged data were quality
controlled (de-spiked) by successively regressing aph and Chla, three
times and deleting outliers (2 standard deviation) at each stage (final
r2 =0.91). This reduced the data set to 256 stations, i.e. 84% of original
merged data. Some miss-aligned aph and Chla data may remain in the
merged dataset after QC, which adds noise to the inter-relationships and
reduces the precision of the prediction from the final regression equations.
NOMAD data were classified using the Diagnostic Pigment Analysis
(DPA, Vidussi et al. 2000; revised by Uitz et al., 2006) based on marker
pigments (MP, see Table 1), modified as described below. The major
taxa were merged into size classes. Diatoms and dinoflagellates can
not be separated absolutely, as Fuc (the MP for diatoms) often replaces
Per as the major pigment in dinoflagellates. DPA is not definitive, but it
is a useful approximation; its accuracy and utility is assessed by
analyses of the NOMAD pigment data. Fig. 2 shows the fractional
occurrence (MP/DP) of the 5 marker pigments (Zea, Fuc, Hex, Chlb,
Per) in NOMAD, as a function of Chla, smoothed by a 5 point runningmean to reduce the remnant ‘spikiness’; the distributions of the MPs
relative to aph(443) are similar, though more ‘spiky’. The occurrences
of Allo and But (not shown) are very low (~0.02). The dominance of
Hex in NOMAD suggests that most nanoplankton are prymnesiophytes and that chrysophytes and cryptophytes are not abundant in
the global ocean. Fig. 2a shows that there is a step drop in the
occurrence of Zea at ~ 0.25 mg m− 3, signifying the upper limit of
prokaryote dominance (major picoplankton component). Fuc, the MP
for microplankton, is distributed across the full range of Chla, partly
because of its co-occurrence in nano-flagellates as the pre-cursor
pigment for Hex and But, giving a potential anomaly in the DPA. There
is a step increase of Fuc to N0.4 at Chla ~0.95 mg m− 3 and to ~0.6 at
Chla N 1.8 mg m− 3. The abundance of Per (Fig. 2b) is generally low
(b0.1) and very low (b0.03) for Chla b 0.25 mg m− 3. Hex is abundant
(0.2–0.3) for Chla b 0.25 mg m− 3, indicating that these data probably
correspond to small flagellates (b2 μm, e.g. prymnesiophytes); i.e.
pico-eukaryotes as reported by Not et al. (2004); Fuller et al. (2006);
Tarran et al. (2006). Supporting evidence comes from pigment
2. Data and methods
A sub-set of NOMAD comprising contemporary, co-located aph
data at key wavelengths and the key pigments, reduced the data total
from N1000 to 306 stations. Pigments and aph data were quality
Fig. 1. Phytoplankton absorption spectra for a range of Chla (24.6, 18.9, 13.0, 1.91, 0.68,
0.21 mg m− 3) and taxonomic size classes (pico, nano and micro) with decreasing slope
from high to low aph(λ) and Chla; inset spectra of pico and nanoplankton at expanded
range.
T. Hirata et al. / Remote Sensing of Environment 112 (2008) 3153–3159
3155
Fig. 2. Fractional occurrence of major marker pigments (MP) in NOMAD over aph(443) range: a) Zea (MP for prok) and Fuc (MP for diatoms/microplankton); b) Hex (MP for
prymnesiophytes), Chlb (MP for chlorophytes) and Per (MP for dinoflagellates sometimes); c) Hex/Fuc ratio. Data were smoothed with 5 point moving average.
analyses for 15 AMT cruises (Aiken et al., in press) showing that the
transition out of the sub-tropical gyres into temperate provinces was
marked consistently by a step increase of Chla to N0.25 mg m− 3 with
a coincident change of the dominant PSC from picoplankton (in gyre)
to nanoplankton (out), confirmed by phytoplankton species counts
(unpublished data). Hex is most abundant (0.3–0.4) in mid-range
(N0.25 mg m− 3), with a step down at 1.8 mg m− 3 coincident with a
step increases of Fuc; there is other step down at 0.95 mg m− 3. The
widespread distribution of Chlb shows that it is not exclusively the
MP for pico-eukaryotes (Chla b 0.25 mg m− 3); the step increase (to
N0.20) at Chla N0.25 mg m− 3 arises from the occurrence of Chlb in
larger eukaryotes e.g. chlorophytes (nanoplankton). The Hex/Fuc
ratio (Fig. 2c, near-equivalent to the nano/micro ratio) has a general
decline across the full Chla range, with a step-decrease at Chla ~0.95
and ~1.8 mg m− 3 corresponding to changes in the fractions of nano
and microplankton in this “mixed zone”. The Hex/Fuc ratio is ~ 3.0 ±
0.7, in the picoplankton domain (Chla b0.25 mg m− 3), ~1.5 ± 0.5, in
the mid-range (0.25–0.95 mg m− 3) with a step down to b0.5 (mean
0.20 ± 0.17) for Chla N 0.95 mg m− 3. These step changes in the MP/DP
ratio are indicative of step changes of PSCs and phytoplankton
community structure, which could be used for sub-partitioning
(estimating fractions of nano and micro in mixed zones) though
account must be taken of the likely overestimation of microplankton
(MP Fuc). Fuc is totally dominant at Chla N3.0 mg m− 3, indicative of
‘pure’ microplankton.
Here DPA has been modified to reduce the anomalies identified
above. Firstly, Chlb (MP for chlorophytes) is included in the
nanoplankton class, as it is most abundant at Chla N0.25 mg m− 3 and
is a minor pigment at lower Chla. Secondly, all samples with Chla
b0.25 mg m− 3 are defined as picoplankton, comprising prokaryotes
and pico-flagellates designated pico-eukaryotes. Thirdly, the PSC is
“dominant” if the MP/DP ratio is N0.45 rather than N0.5; this minimises
the number of samples diagnosed as “mixed” (i.e. no size class N0.5).
SeaWiFS monthly composite data of Level 3 mapped normalised
water-leaving radiance (412, 443, 490, 510, 555, 670 nm) for 2004
were obtained from NASA Godard Space Flight Centre. The ocean
colour inversion model (Smyth et al., 2006), was used to compute aph
(443), released through International Ocean Colour Coordinating
Group http://www.ioccg.org/groups/software.html. The original data
had the nominal spatial resolution of 9 × 9 km at the equator, but were
re-sized to the reduced resolution of 18 × 18 km globally. For model
validation, 9 × 9 km 8-day composite images of the normalised waterleaving radiance (and Chla) were matched-up with AMT station
positions, where in situ pigment data were collected. Resultant matchup image data have the maximum gap of 4 days from the station
observation date.
3. Results
3.1. Analyses of NOMAD
The relationship between aph(443) and S (from NOMAD) is a linear
function that increases monotonically, meaning that the value of aph
(443) is representative of S and vice versa:
S ¼ 0:0082 aph ð443Þ þ 0:00002 r 2 ¼ 0:984
Clearly aph(443) and S have some degree of auto-correlation, which
applies to all aph at all wavelengths and also aph(443) with ∫ aph(λ) dλ.
Fig. 3 shows the log-transformed data, marked for the dominant PSC
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T. Hirata et al. / Remote Sensing of Environment 112 (2008) 3153–3159
Fig. 4 shows the log-transformed data marked for the dominant
PSC, with the numbers of each PSC in each domain listed in the
boxes with values for lower and higher limits for the nano–micro
boundary limits. For the higher limit, the number of micro in the
micro-domain are reduced (from 49 to 28) and the number of nano
reduced (from 29 to 3), clearly a higher fraction of microplankton. In
the nano-domain, the lower limit makes nano the more dominant
PSC (64 nano to 29 micro) and the higher limit makes the domain
more mixed (83 nano to 50 micro). The Chla to S relationships for
the three size domains are:
Spico ¼ 0:00016 log10 ½Chla þ 0:00029 r 2 ¼ 0:46 ; ChlaV0:025mg m3 ;
Snano ¼ 0:00049 log10 ½Chla
þ 0:0044 r 2 ¼ 0:32 ; Chla 0:25 toV1:8mg m3 ;
Smicro ¼ 0:0011 log10 ½Chla þ 0:00036 r2 ¼ 0:32 ; ChlaNto 1:8 mg m3
Fig. 3. Observed relationship (from NOMAD) between aph(443) and the spectral slope S,
marked with the phytoplankton size classes and nominal boundaries for pico at 0.023
and nano–micro at 0.046/0.069 m− 1 (lower/higher limits); upper curve, nanoplankton
data displaced by +0.0004, pico-eukaryotes displaced by +0.0003; lower curve,
prokaryote data displaced by − 0.0004. Boxes indicate the number of each size class in
each domain for lower (L) and higher (H) nano–micro boundary limits.
using the modified DPA. Picoplankton are most abundant at low aph
(443) ≤ 0.023 ± 0.002, nanoplankton most abundant in mid-range and
microplankton most abundant at aph(443) N 0.069. The partition
between the picoplankton domain and larger PSCs is relatively
sharp, but the partition between nano and microplankton classes
overlaps in the aph(443) range 0.046–0.069 m− 1. The lower curve
shows the prokaryote data displaced for clarity. The upper curve has
the nanoplankton dominated data displaced, showing the overlap
with the pico and microplankton domains across the nominal
boundaries at 0.023 and 0.069 m− 1. We designated all flagellates
(nanoplankton) with Chla b0.25 mg m− 3 as pico-eukaryotes; these
are marked as squares in Fig. 3. Microplankton are dominant at high
aph but co-exist with nanoplankton across the nano–micro domains,
albeit overestimated (10–25%), by the co-occurrence of Fuc (MP for
micro) in nanoplankton. The numbers of each PSC in each domain are
listed in the boxes in Fig. 3, with values for both lower and higher
limits for the nano–micro boundary. In the micro-domain with the
higher limit, the number of micro are reduced (from 54 to 28) and
the number of nano reduced (from 39 to 12), overall a higher fraction
of microplankton. In the nano-domain, the lower limit makes nano
more dominant (40 nano and 18 micro) and the higher limit makes
the domain more mixed (67 nano and 44 micro). The relationship
can be partitioned into 3 domains for each of the 3 dominant PSCs,
according to the value of S or aph(443):
The partitioning on the basis of Chla is better in number terms
(fewer micro in the nano-domain), possibly because the integral
pigment dataset is less ‘spiky’ than the merged aph and Chla dataset
and it is the pigment data that are used to determine the dominant
PSC. The Chla regressions have lower r2 than the aph(443) data,
possibly due to lower precision of the pigment data and no autocorrelation; the data for S from aph and the size classification from
pigments are the same for both analyses, so the increased variance
must arise from the Chla data. These Chla-relationships could be used
to partition ocean colour data (SeaWiFS, MODIS, MERIS) into PSCs (as
per Aiken et al. 2007), but the standard error for prediction would be
larger than for aph.
3.2. Analyses of SeaWiFS data
Fig. 5 shows the global maps of PSCs (micro, nano and picoplankton) derived from SeaWiFS data in 2004, using aph(443) thresholds at
≤0.023 and N0.069 m− 1. Microplankton were dominant in coastal
upwelling zones (e.g. Benguela) and temperate seasonally stratified
regions (e.g. North Atlantic spring and summer). Nanoplankton were
dominant at mid-latitudes after the spring bloom and in equatorial
Spico ¼ 0:00027 log10 aph ð443Þ
2
þ 0:00064 r ¼ 0:89 ; aph ð443ÞV0:023m1
Snano ¼ 0:00076 log10 aph ð443Þ
2
þ 0:0014 r ¼ 0:88 ; aph ð443ÞN0:023 toV0:069m1
Smicro ¼ 0:0023 log10 aph ð443Þ
þ 0:0032 r 2 ¼ 0:93 ; aph ð443ÞN0:069m1
The relationship between Chla (mg m− 3) and S (from NOMAD) is
also a linear function, though the fraction of variance explained is less
than for aph(443) and S, with no auto-correlation:
S ¼ 0:00021 Chla þ 0:00017 r 2 ¼ 0:667
Fig. 4. Observed relationship (from NOMAD) between Chla and the spectral slope S,
marked with the dominant phytoplankton size classes and nominal boundaries for pico
at 0.25 and nano–micro at 0.95/1.8 mg m− 3 (lower/higher limits); upper curve,
nanoplankton data displaced by +0.0006; lower curve, prokaryote data displaced by
−0.0005. Boxes indicate the number of each size class in each domain for lower (L) and
higher (H) nano–micro boundary limits.
T. Hirata et al. / Remote Sensing of Environment 112 (2008) 3153–3159
3157
Fig. 5. Global distribution of PSCs from SeaWiFS for 2004: a) Jan; b) Mar; c) May; d) July; e) Sep; f) Nov. Red, microplankton dominant; green, nanoplankton dominant; blue,
picoplankton dominant.
regions. Picoplankton were dominant in equatorial zones, tropical and
sub-tropical ocean gyres. Table 2 shows the monthly occurrence of
pico, nano and microplankton in the global ocean, Indian, North and
South Atlantic, North and South Pacific oceans, from Jan to Dec 2004.
The occurrence of microplankton in the global oceans was 1.9–2.8%
(annual mean 2.3%), while that of picoplankton ranged from 77.7 to
80.5% (mean 79.1%). Nanoplankton occurrence was 17.3–19.6 % (mean
18.5%). The N. and S. Atlantic and the N. and S. Pacific oceans showed
seasonal cycles with both micro and nanoplankton increasing in spring
and summer in each hemisphere, and picoplankton decreasing. The S.
Pacific had the largest fraction of picoplankton (annual mean 83.4%)
and the least microplankton (0.6%). The Indian Ocean shows seasonal
features associated with the SW monsoon in Sep and NE monsoon in
Jan and Feb. The mean occurrence for each size class have been
converted to mean fraction of Chla (lower panel of Table 2), using mean
factors (mg m− 3) calculated from NOMAD: pico (b0.25) = 0.13; nano
(0.25–1.8) = 0.72; micro (N1.8) = 3.31 mg m− 3. These factors are
comparable to values derived from AMT data (Aiken et al., in press).
Table 2
Dominance of PSCs (M, N, P) by sea surface area (%) from SeaWiFS data 2004; cloud-covered or other invalid pixels are ignored in the calculation
Global
Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Mean
Chla
M
2.3
2.1
2.0
2.0
2.9
2.8
2.4
2.6
2.9
2.4
1.9
1.9
2.3
22.2
Indian Ocean
N
19.0
17.9
17.6
18.7
18.6
17.3
17.1
18.9
19.5
18.9
19.1
19.6
18.5
45.3
P
78.6
79.9
80.4
79.3
78.5
79.8
80.5
78.5
77.6
78.7
79.0
78.5
79.1
32.6
M
1.5
1.2
0.7
0.5
0.5
0.5
0.6
0.7
1.7
0.8
0.7
0.8
0.9
9.2
N
21.0
17.0
11.7
11.7
15.3
17.8
19.9
21.1
21.9
22.0
24.4
22.9
18.9
52.9
N. Atlantic
P
77.5
81.9
87.6
87.8
84.1
81.6
79.5
78.2
76.3
77.2
74.9
76.3
80.2
37.9
M
2.8
3.2
3.8
5.0
8.1
8.1
5.7
5.2
4.8
4.1
3.3
2.3
4.7
36.6
N
13.3
14.8
20.9
27.5
27.0
22.6
19.9
18.7
19.2
15.5
12.4
10.8
18.5
37.3
S. Atlantic
P
83.8
82.0
75.3
67.6
64.9
69.3
74.4
76.2
76.0
80.4
84.3
87.0
76.7
26.1
M
5.5
4.3
3.3
1.8
1.8
2.3
2.7
2.0
1.5
1.9
2.6
4.4
2.8
21.5
N
30.5
27.5
27.2
26.0
26.5
23.0
22.5
27.1
29.8
36.1
33.2
30.0
28.3
55.6
N. Pacific
P
64.0
68.1
69.5
72.1
71.7
74.8
74.8
70.9
68.7
62.0
64.2
65.6
68.9
22.8
M
2.4
2.5
2.7
3.2
4.6
4.4
3.7
4.0
4.7
3.3
2.4
2.0
3.3
29.1
S. Pacific
N
16.6
18.9
19.4
21.6
20.9
19.0
17.5
19.0
19.5
16.6
14.1
13.7
18.1
41.0
P
81.1
78.6
77.7
74.7
73.4
75.6
78.3
76.2
74.7
78.8
82.9
84.0
78.0
29.8
M
1.1
0.8
0.7
0.5
0.4
0.5
0.5
0.5
0.4
0.4
0.6
0.8
0.6
7.3
N
17.6
15.2
15.3
15.6
14.0
12.9
13.9
5.3
16.0
15.0
17.0
21.3
15.8
49.0
P
80.9
83.5
83.7
83.7
85.2
86.6
85.6
84.1
83.6
84.3
82.2
77.6
83.4
43.7
The bottom rows show the mean annual occurrences of each size class by area (%) and mean annual Chla-biomass (%) using conversion factors derived from NOMAD: see text.
3158
T. Hirata et al. / Remote Sensing of Environment 112 (2008) 3153–3159
3.3. Preliminary verification of PSC aph-model
PSCs derived by the aph-model from SeaWiFS data are compared
with PSCs derived by DPA from pigment data acquired on AMT cruises
(Aiken et al., in press) as a preliminary verification, illustrating the
difficulties and the need for a comprehensive global validation. This
comparison is not a true independent validation, since DPA was used
to develop the aph-model and PSCs derived from pigments are
indicative and not definitive. Table 3 shows results from AMT-07 (26
stations), UK to Uruguay (19 Sept to 16 Oct 1998) and contemporary
SeaWiFS data (analysed for aph(443), Smyth et al., 2006) along the
cruise track (47° N to 37° S); 3 stations had no valid SeaWiFS Chla and
are suspect. The diagnoses showed good agreement (verification
score: match = 2; near match = 1; no match = 0; suspect = S). In the gyres
out of 18 stations (16 valid) all picoplankton, 15 scored 2 and 1 scored 1
about 94% success; in mesotrophic and eutrophic zones out of 8
stations (7 valid), 4 scored 2, 1 scored 1 and 3 scored 0, about 56%
success. Comparison of PSC diagnoses for other AMT cruises, with
contemporary SeaWiFS data showed practically 100% agreement for
picoplankton in the gyres and ~50% elsewhere. The good results for
pico can be explained by the low heterogeneity in the gyres; the
poorer results elsewhere arise from the increased mesoscale variability that renders 8-day mean values of aph or Chla unsuitable for
validation in dynamic zones. SeaWiFS protocols require in situ data
acquisition within ± 1 h of overpass but this is unacceptably long in
dynamic zones; Aiken et al. (2007) suggested that ± 10 min was
needed in the Benguela upwelling ecosystem. Regional validation of in
situ and satellite-derived PSCs with contemporary imagery is needed
also. A preliminary analysis of Benguela data (Fishwick et al., 2006)
using aph(443) data, derived PSCs comparable to the Chla analysis
(Aiken et al., 2007) despite the extreme heterogeneity of the area.
4. Discussion
Recent basin-scale observations from the Atlantic Meridional
Transect (AMT) provide validation of the satellite-derived PSC
distributions a priori. Notably, Zubkov et al. (1998, 2000), Heywood
et al. (2006), Tarran et al. (2006) showed that phytoplankton in the
tropical and sub-tropical gyres in the Atlantic Ocean were predominantly picoplankton (prokaryotes and pico-eukaryotes), quantitatively determined by flow-cytometry for cell counts and C-biomass
equivalents. As shown by Aiken et al. (in press), the transition out of
the sub-tropical gyres into the temperate provinces was sharp in
terms of Chla (N0.25 mg m− 3) with phytoplankton types switching
from picoplankton to nano-flagellates, confirmed by phytoplankton
species counts. Microscope phytoplankton species counts for AMT-01
to -08 cruises have been converted to C-biomass for larger
phytoplankton (nano-flagellates, diatoms, dinoflagellates), but counts
for picoplankton (prokaryotes, pico-eukaryotes) are qualitative, due to
inherent sampling and storage problems. Merging Flow Cytometry
(some but not all cruises) and microscopic counts over the whole size
range requires careful inter-calibration and is not complete.
The classification of the dominant PSC (N0.45 MP/DP ratio) does
not mean that only a single PSC is present, and usually a mixture
occurs. Ambiguity may arise from errors in DPA and uncertainty for
the threshold values of aph(443) in the regions of overlap and coexistence of the nano and micro PSCs. Additionally, Fuc (MP for micro)
occurs in most nanoplankton and results in an overestimation of the
micro class, with most impact in the nano-domain (Chla 0.25–1.8). The
changes of the Hex/Fuc ratio may offer a method (mixing model) for
estimating the fraction of nano and micro PSCs in this very mixed
region. For a crude model, the value of the Hex/Fuc ratio for picoeukaryotes (zero microplankton) is the lower end-point for ‘pure’
flagellates and the ratio for Chla N 3.0 mg m− 3 is the upper end-point
for ‘pure’ microplankton. Refinement of this model is needed and true
validation (against phytoplankton species counts) would be more
demanding than for the present analyses. Similar approaches could be
used to partition the pico class into pico-eukaryotes and prokaryotes
and the latter into Synechococcus spp. and Prochlorococcus spp. DPA is
an approximate method but there is scope for enhancing the analyses
by combining with backscatter characteristics of different phytoplankton groups (e.g. Aiken et al., 2007).
Table 3
Comparison of PSCs from pigments for AMT-07 (Sept–Oct 1998) and contemporary SeaWiFS 8-day composite data using aph-model
St.
Lat.
Long.
SDY
Chla
Micro
Nano or [PE]
Prok
Pico
PSC
DayDif
ChlaDif
SPSC
Score
A702
A704
A706
A709
A711
A712
A715
A717
A719
A721
A724
A726
A728
A731
A733
A735
A737
A739
A742
A744
A746
A748
A750
A753
A756
A757
46.61
41.63
38.78
36.31
32.55
30.06
25.82
22.02
17.69
14.59
14.39
12.18
08.08
04.28
00.41
−03.99
−08.04
−11.92
− 15.94
− 19.60
− 23.49
− 26.48
− 29.60
− 32.74
−35.77
−37.73
−10.80
−10.80
−13.60
− 17.49
−16.95
−19.94
−19.98
−19.99
−20.00
−17.76
− 17.73
− 21.32
−22.20
−23.75
−25.32
−27.09
−28.75
−30.32
−32.01
−34.94
−36.90
−39.90
−43.23
−46.66
−49.65
−51.96
259
260
265
266
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
0.28
0.28
0.08
0.05
0.04
0.08
0.08
0.21
1.24
0.56
0.26
1.06
0.22
0.15
0.16
0.11
0.06
0.06
0.03
0.06
0.04
0.13
0.16
0.07
0.34
0.27
23.8
19.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
8.1
11.5
26.8
8.8
11.6
0.0
0.0
0.0
8.6
0.0
17.3
13.6
16.7
23.6
15.1
48.9
16.1
47.1
30.6
[53.2]
[27.5]
[44.0]
[42.5]
[36.8]
[68.3]
86.5
59.1
40.6
63.7
[32.1]
[30.8]
[34.8]
[23.4]
[32.3]
[31.9]
[31.9]
[29.8]
[17.8]
[28.3]
[29.9]
[30.5]
44.4
79.1
29.0
49.4
46.7
72.4
55.9
57.4
63.1
31.6
13.4
32.7
47.7
9.4
59.0
57.4
65.1
76.5
67.6
59.4
68.0
52.8
68.5
54.9
46.4
54.2
6.6
4.6
29.0
49.4
100
100
100
100
100
100
13.4
32.7
47.7
9.4
91.1
88.2
100
100
100
91.3
100
82.6
86.3
83.2
76.3
84.7
6.6
4.6
N
P
P
P
P
P
P
P
N
N
P
N
P
P
P
P
P
P
P
P
P
P
P
P
M
N
−2
−1
−4
−3
−1
0
+1
+2
+3
−4
−3
−2
−1
0
+1
+2
+3
−4
−3
−2
−1
0
+1
+2
+3
+4
−0.05
0.03
−0.02
−0.03
S
0.0
−0.02
S
S
0.21
0.03
0.65
0.08
0.01
0.01
0.00
−0.04
0.02
0.00
−0.01
−0.07
−0.02
0.07
−0.04
−0.01
−0.09
N
P
P
P
N
P
P
N
P
P
P
P
P
P
P
P
P
P
P
P
P
N
P
P
N
N
2
2
2
2
1
2
2
0
0
0
2
0
2
2
2
2
2
2
2
2
2
0
2
2
1
2
Station No.; position; serial day year (SDY); in situ Chla (mg m− 3); DPA (% micro, nano, prok, pico); nano = PE if Chla b0.25; dominant PSC(M = micro, N = nano, P = pico = prok + PE);
DayDif = SDY — (day 5 of SeaWiFS 8 day composite); ChlaDif = Chla — SeaWiFS Chla (mg m− 3); S = SeaWiFS suspect; SPSC (= SeaWiFS PSC); Score: match = 2, near match = 1, no
match = 0.
T. Hirata et al. / Remote Sensing of Environment 112 (2008) 3153–3159
NOMAD comprises a global data set of phytoplankton bio-optical
properties. Other data sets from the English Channel in 2001 (Aiken
et al., 2004) and other years (2003–2007), the Atlantic Ocean (Barlow
et al., 2004), N. Atlantic (Moore et al., 2005) and Benguela (Fishwick
et al., 2006) overlie NOMAD the S vs. aph(443) function precisely,
concurring with NOMAD analyses and providing assurance of the aphmodel for diverse regional ecosystems. We demonstrate that the
absolute magnitude of aph(443) provides a practical method for the
analysis of ocean colour data because: 1) satellite ocean colour
provides aph(λ) at discrete wavelength bands in the blue–green
spectral region (443, 490, 510 nm) that can be retrieved with
precision; 2) the model uses only one variable aph(443), so the
number of error sources are minimal; 3) implementation is simple so
large data sets (e.g. complete SeaWiFS, MODIS, MERIS or merged data)
can be processed quickly, which is a priority activity, following
comprehensive validation of the model. This study has shown that
phytoplankton size classes can be derived from remotely sensed ocean
colour data by an absorption model or a Chla model. From the remote
sensing perspective, the aph-model is preferred, as aph(443) is
derivable analytically from normalised water-leaving radiance, a
direct physical observable of natural waters, whereas Chla is a
biogeochemical variable, usually approximated by an empirical
band-ratio algorithm that minimises the ‘bias’ over the full range of
observations, but does not minimise the standard error of prediction.
PSCs, C-biomass, pigments, productivity or other variables can be
derived from aph. Marra et al. (2007) model production from aph(λ),
showing that production for different PSCs could be derived from aph
(443).
Acknowledgments
The authors acknowledge NASA SeaWiFS project team for satellite
data and all contributors to the NOMAD data set for in situ bio-optical
data. We commend NASA for the excellence of NOMAD and
encourage continued enlargement and improvement of the dataset.
This work is funded by Natural Environment Research Council, UK,
through the Centre for observations of Air–Sea Interactions and
fluXes (CASIX: publication number 52) and Plymouth Marine
laboratory and was supported by AMT and MarQUEST projects
(AMT publication number 164).
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