Remote Sensing of Environment 107 (2007) 521 – 532
www.elsevier.com/locate/rse
Extending the MODIS 1 km ocean colour atmospheric correction to the
MODIS 500 m bands and 500 m chlorophyll-a estimation towards
coastal and estuarine monitoring
J.D. Shutler ⁎, P.E. Land, T.J. Smyth, S.B. Groom
Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth, PL1 3DH UK
Received 2 June 2006; received in revised form 5 October 2006; accepted 7 October 2006
Abstract
National and regional obligations to control and maintain water quality have led to an increase in coastal and estuarine monitoring. A
potentially valuable tool is high temporal and spatial resolution satellite ocean colour data. NASA's MODIS-Terra and -Aqua can capture data at
both 250 m and 500 m spatial resolutions and the existence of two sensors provides the possibility for multiple daily passes over a scene.
However, no robust atmospheric correction method currently exists for these data, rendering them unusable for quantitative monitoring
applications. Therefore, this paper presents an automatic and dynamic atmospheric correction approach allowing the determination of ocean
colour. The algorithm is based around the standard MODIS 1 km atmospheric correction, includes cloud masking and is applicable to all of the
visible 500 m bands. Comparison of the 500 m ocean colour data with the standard 1 km data shows good agreement and these results are further
supported by in situ data comparisons. In addition, a novel method to produce 500 m chlorophyll-a estimates is presented. Comparisons of the
500 m estimates with the standard MODIS OC3M algorithm and to in situ data from a near-coast validation site are given.
Crown Copyright © 2006 Published by Elsevier Inc. All rights reserved.
Keywords: Medium resolution MODIS; Chlorophyll; 500-m MODIS; Remote sensing; Atmospheric correction; Ocean colour
1. Introduction
The U.K. Environment Agency has recently published its
findings on the health of the U.K. coastal and estuarine
environments, emphasising the need for greater monitoring
and management of these ecosystems (Andrews et al., 2005).
Similarly, the U.S. Environment Protection Agency's Environmental Monitoring and Assessment Program has listed hundreds
of coastal sites where baseline data are needed to enable future
assessment and monitoring. The collection of in situ data to
monitor these environments is expensive. Furthermore, their
highly dynamic nature can reduce the effectiveness of field
campaigns.
Near-real time remotely-sensed data from orbiting platforms
can provide a cost effective and convenient source of
monitoring data and a range of studies have demonstrated
⁎ Corresponding author.
E-mail address: [email protected] (J.D. Shutler).
capabilities for monitoring sediment concentrations (e.g.
Binding et al., 2003; Bowers et al., 2005), phytoplankton (e.g.
Groom et al., 2005) and primary productivity (e.g. Smyth et al.,
2005). However, most ocean colour sensors only capture data at
a spatial resolution of ≃1 km reducing their effectiveness for
coastal and estuarine applications. Higher spatial resolution
orbiting sensors (e.g. Enhanced Thematic Mapper Plus ETM+)
lack the sensitivity needed for ocean colour, have longer revisit
times and data can be expensive. The European Space Agency
environmental monitoring satellite (ENVISAT) carries the
MEdium Resolution Imaging Spectrometer (MERIS) sensor
which can provide 300 m spatial resolution data. However,
these data are not currently available in near-real time. This,
combined with the 2–3 day revisit time, make these data less
desirable for monitoring applications. NASA's MODerate
resolution Imaging Spectrometer (MODIS) sensors aboard the
Aqua and Terra platforms provide free access to data in nearreal time. The direct broadcast capability of these platforms
enables reception of data directly and the existence of two
0034-4257/$ - see front matter. Crown Copyright © 2006 Published by Elsevier Inc. All rights reserved.
doi:10.1016/j.rse.2006.10.004
522
J.D. Shutler et al. / Remote Sensing of Environment 107 (2007) 521–532
Table 1
The MODIS bands of interest in this study
Primary use
Band Resolution Bandwidth Centre wavelength
at nadir (m) (nm)
λ (nm)
Land, cloud and aerosol 1
boundaries
2
Land, cloud and aerosol 3
properties
4
Ocean colour,
8
phytoplankton and
9
biogeochemistry
10
11
12
13
14
15
16
250
250
500
500
1000
1000
1000
1000
1000
1000
1000
1000
1000
620–670
841–876
459–479
545–565
405–420
438–448
483–493
526–536
546–556
662–672
673–683
743–753
862–877
645
859
469
555
413
443
488
531
551
667
678
748
869
sensors provides the possibility of multiple daily passes over
one area. The MODIS sensors capture data in 36 spectral bands
between 405 nm and 14.385 μm, with spatial resolutions at
nadir of 250 m (2 bands), 500 m (5 bands) and 1 km (29 bands):
see Table 1. The majority of the bands covering the visible
wavelength have a spatial resolution at nadir of 1 km. However,
two of the 500 m bands and one 250 m band have centre
wavelengths in the visible spectrum, making it possible to use
them for ocean colour analyses, while the increased spatial
resolution of these bands suggests their application to coastal
studies (Esaias et al., 1998; Miller & McKee, 2004). The
MODIS 250 m and 500 m bands have similar performance
(radiometric sensitivity and noise equivalent radiance) to that of
the Coastal Zone Color Scanner (CZCS) (Esaias et al., 1998)
and in some cases exceed it. They are also 4–5 times more
radiometrically sensitive than Landsat-7 and ETM+ data (Hu et
al., 2004). Previous studies to determine the potential of these
MODIS bands for ocean colour analyses have concluded the
need for robust and automatic atmospheric correction (Hu et al.,
2004; Miller & McKee, 2004), and the development of specific
biological algorithms (Hu et al., 2004). The spectral range of the
MODIS band 1 at 250 m is 620–670 nm and is suited to the
measurement of suspended sediment concentrations (Miller &
McKee, 2004), whereas bands 3 and 4 at 500 m have spectral
ranges of 459–479 nm and 545–565 nm respectively, making
them suitable for chlorophyll-a (hereafter referred to as chl-a)
estimates. Typically, oceanic chl-a algorithms use ratios of
spectral bands centred around 443–510 nm to those at 551–
560 nm as these wavelengths can be used to characterise chl-a
spectral slope in regions of high, medium and low chl-a
absorption (O'Reilly et al., 1998). To develop a chl-a algorithm
based on band ratios, in situ are normally required, either to
empirically determine its coefficients or to enable its
validation. The lack of an extensive in situ data set at
469 nm (MODIS band 3 at 500 m) makes it impossible to
determine a robust polynomial relationship between chl-a
concentration and a ratio using 469 nm. Alternatively, a
reflectance model (e.g. Morel & Maritorena, 2001) could be
used to simulate a chl-a band ratio algorithm. However, a chl-a
estimation algorithm based around the response at 469 nm is
more likely to be affected by coloured dissolved organic matter
(CDOM), which can be more prevalent in coastal waters (e.g.
Darecki & Stramski, 2004), whereas an algorithm that uses
longer wavelengths (e.g. 488 nm) will be less affected by
CDOM. Therefore, the 500 m chl-a estimation approach
detailed here uses a previously determined polynomial
algorithm (Carder et al., 1999) which uses the response at
488 nm as the ratio's numerator. The polynomial was proposed
by Carder et al. (1999) as a global algorithm and is defined as
log10[chl-a] = 0.3147 − 2.859R + 2.007R2 − 1.730R3; R ¼ log10 RRrsð488Þ
.
rs ð551Þ
This algorithm is used in place of a band switching algorithm (e.g.
the SeaWiFS OC4v4 (O'Reilly et al., 2000) or the MODIS OC3M
(O'Reilly et al., 2000)) due to the limited number of available
500 m bands.
In this paper a robust method of atmospherically correcting
and cloud clearing the MODIS 500 m data is presented. In
addition, a simple approach to simulate 500 m normalised water
leaving radiance (LWN(λ)) at 488 nm and 551 nm is shown. This
approach also allows for internal consistency checks between
500 m
the new LWN
(λ) data and that of the standard 1 km MODIS
1 km
data, LWN (λ). These data are then used to produce 500 m chl-a
estimates using the empirical algorithm of Carder et al. (1999).
All of the algorithms are characterised by comparison with the
standard 1 km ocean colour data. Preliminary validations of
both the LWN(λ) and chl-a data are achieved using in situ data
from a well understood near-coast validation station in the
English Channel (Southward et al., 2005).
2. Methods
2.1. Overview
The atmospheric correction approach detailed here is based
on the standard MODIS 1 km algorithm. The parameters
required for this algorithm are first calculated for the 1 km data.
They are then spatially and spectrally interpolated to allow their
application to the MODIS 500 m bands. This allows the
500 m
generation of LWN
(469) (i.e. the water leaving radiance at
500 m
469 nm at a spatial resolution of 500 m) and LWN
(555) data.
500 m
500 m
Estimated or interpolated LWNi (488) and LWNi (551) data sets
are then produced from these using an interpolation technique
(where the i indicates the spectral and spatial interpolation).
These estimated data sets then form the input to an empirical
case 1 waters chl-a expression (Carder et al., 1999) to allow the
generation of chl-a estimates at 500 m. The atmospheric
correction method is equally applicable to MODIS band 1 at
250 m resolution, allowing further investigations towards
estimating suspended sediment concentrations (Miller &
McKee, 2004).
2.2. Atmospheric correction
Atmospheric correction of the MODIS optical 250 m and
500 m data, if not performed in conjunction with the 1 km
(lower resolution) ocean colour bands, must use a rather simple
method due to the restricted spectral information at the 500 m
wavelengths. Previous work by Hu et al. (2004) used three
J.D. Shutler et al. / Remote Sensing of Environment 107 (2007) 521–532
methods to correct these data for the affects of the atmosphere.
The first approximated an atmospheric correction by using an
empirical relationship determined between in situ data and the
top of atmosphere radiance. However, the collection of these in
situ data can be both costly and time consuming and does not
allow for spatial variations in the atmospheric correction. Their
other two approaches assume white aerosols (wavelength
independent aerosol path reflectance, a good approximation
for marine aerosols), zero near infrared (NIR) water leaving
radiance at 859 nm, and calculate atmospheric transmission as
an exponential function of Rayleigh optical depth. The
assumption of white aerosols could have a significant effect
in coastal regions where the actual aerosols may contain
significant continental or anthropogenic components. Alternatively, Miller and McKee (2004) used the dark pixel approach of
Gordon and Morel (1983) to correct MODIS band 1 250 m data.
This assumes that the aerosol is of known type and fixed
concentration over the entire scene. The darkest pixel in the
scene in a single near infrared band is subtracted to correct for
aerosol path radiance. This leaves the result vulnerable to noise
(the darkest pixel could be that with the greatest negative noise
deviation and is usually a dark ‘outlier’ pixel), and takes no
account of atmospheric transmission. In contrast, the standard
NASA 1 km ocean colour algorithm (Gordon & Wang, 1994)
(implemented in the NASA SeaWiFS Data Analysis System
(SeaDAS) http://oceancolor.gsfc.nasa.gov/seadas/) retrieves the
aerosol properties, including aerosol optical depth and diffuse
transmittances on a pixel by pixel basis, using a comparison of
the spectral variation between the two MODIS NIR bands and a
suite of aerosol models. By default it also iteratively estimates
the NIR water leaving radiance from the visible spectrum
(Arnone et al., 1998), overcoming the assumption of zero LWN
in the NIR. This is applicable in case 1 waters and weakly turbid
coastal waters where Arnone's assumed relationship between
the visible and NIR LWN is likely to be valid. Therefore, the
standard 1 km correction allows all aerosol quantities to vary
across the image and corrects for atmospheric transmission and
non-zero NIR water leaving radiance. The basis of the
atmospheric correction method presented here is to apply the
atmospheric properties found by the standard 1 km correction,
suitably interpolated, to the 500 m bands. The same algorithm is
then used on the 500 m top of atmosphere (TOA) radiances as
on the 1 km TOA radiances, providing consistency between the
two data sets.
SeaDAS calculates LWN (λ) in each of the ocean colour
bands from the TOA radiance Lt(λ) and ancillary data using the
following relations (Gordon & Wang, 1994):
tLw ðkÞ ¼
ðLt ðkÞ−tLf ðkÞÞ
oz ðkÞt oz ðkÞ −Lr ðkÞ−LA ðkÞ−TLg ðkÞ
tsol
sen
LWN ðkÞ ¼
tLw ðkÞBRDF
tsol ðkÞtsen ðkÞl0 fsol
ð1Þ
ð2Þ
where the nomenclature follows that used by the SeaDAS
source code and symbol definitions can be found in Table 2.
SeaDAS allows all the variables in the above equations to be
523
Table 2
List of symbols used
Symbol
Description
Units
λ
t
T
LWN(λ)
Wavelength
Diffuse transmittance
Direct transmittance
Normalised water leaving radiance
nm
LWNi(λ)
Lt (λ)
Interpolated normalised water
leaving radiance
TOA radiance
tLf (λ)
Whitecap radiance at sensor
toz
sol (λ)
toz
sen (λ)
tsol (λ)
tsen (λ)
Lr (λ)
Inbound ozone diffuse transmittance
Outbound ozone diffuse transmittance
Solar to sensor diffuse transmittance
Surface to sensor diffuse transmittance
Rayleigh path radiance
LA(λ)
T Lg (λ)
The total aerosol radiance at the sensor
in the presence of Rayleigh scattering
TOA glint radiance
F0 (λ)
Annual mean extraterrestrial solar irradiance
mW cm− 2
sr− 1 μm− 1
mW cm− 2
sr− 1 μm− 1
mW cm− 2
sr− 1 μm− 1
mW cm− 2
sr− 1 μm− 1
mW cm− 2
sr− 1 μm− 1
mW cm − 2
sr− 1 μm− 1
mW cm− 2
sr− 1 μm− 1
mW cm− 2
μm− 1
BRDF
Bidirectional Reflection Distribution
Function correction used in SeaDAS (no units)
fsol
Earth–sun distance correction
μ0
Cosine of the solar zenith angle
Spectral absorption coefficient of ozone
DU− 1
aoz (λ)
Lu(0−,λ)
Sub-surface upwelling radiance
mW cm− 2
sr− 1 μm− 1
Es(λ)
Downwelling solar irradiance
mW cm− 2
μm− 1
ρ(λ,θ)
Fresnel reflectance of sea water
sr− 1
nw (λ)
Refractive index of sea water, respectively
x
Pixel indices
α1x km (λ1, λ2) Per-pixel 1 km linear factor
km
α500
(λ1, λ2) Per-pixel 500 m linear factor
x
P 1x km
1 km pixel
Aggregated 1 km pixel (determined
Q 1x km
from six 500 m pixels)
m
σ500
Per-pixel 500 m linear factor standard
x,n
deviation for an n × n mask
Rrs
Remote sensing reflectance
sr− 1
output to a level 2 (atmospherically corrected and geolocated
data) file, with the exception of the earth–sun distance
correction fsol, which can be calculated separately. The first
step is to process the 1 km ocean colour bands in SeaDAS,
outputting the correction parameters LA(λ), Lr(λ), T Lg(λ),
oz
oz
tLf (λ), tsol(λ), tsen(λ), tsol
(λ), tsen
(λ) and BRDF for the 1 km
bands centred at 413, 443, 488, 531, 551, 667, 748 and 869 nm.
Then for each 1 km pixel, these correction parameter values are
spectrally interpolated to the 500 m wavelengths. Each 500 m
pixel is checked for cloud and/or land contamination (described
in Section 2.3) and if a 500 m pixel is not masked, the atmospheric correction parameters are spatially interpolated from
the surrounding 1 km pixels. If any of these contain invalid
data, values are spatially interpolated from the nearest 5 × 5
grid of 1 km pixels. If no valid data exists within this grid, then
524
J.D. Shutler et al. / Remote Sensing of Environment 107 (2007) 521–532
Table 3
MODIS-Aqua data covering 45–51 N, 12–27 W of the north east Atlantic for all
months in 2004, used to i) determine the interpolation methods in the
atmospheric correction algorithm and ii) compare the 1 km and 500 m level 2
products
Dates
MODIS-Aqua granule time (UTC)
18 January 2004
07 February 2004
21 March 2004
12 April 2004
16 May 2004
15 June 2004
23 July 2004
31 August 2004
08 September 2004
02 October 2004
13 November 2004
03 December 2004
13:15
12:50
14:10
13:35
13:20
13:35
13:00
13:05
13:55
13:05
13:40
13:15
the 500 m pixel is not processed. Finally, Eqs. (1) and (2) are
500 m
applied to Lt500 m(λ) to obtain LWN
(λ) for each of the 500 m
bands.
The method of spectral interpolation used for a given
variable depends on its spectral behaviour, determined by
observing the spectrum of each variable in the 2004 data set
detailed in Table 3 (these data will be described in Section 2.6).
Transformations were then applied to the data to determine the
most linear fit for spectral interpolation. At each pixel
containing valid data, the proposed functional form was fitted
using cubic splines to the 1 km data through all ocean colour
wavelengths (413, 443, 488, 531, 551, 667, 748 and 869 nm).
The continuous form of these functions for all λ enables the
response at the 500 m wavelengths (469, 555 nm) to be
determined. Using the 2004 data set (Table 3) the following
relationships were determined:
i) The Rayleigh and aerosol path reflectances are expected
to be smooth functions of λ, hence Rayleigh path radiance Lr and the additional path radiance due to the
presence of aerosols LA are divided by the mean extraterrestrial solar irradiance F0(λ) before interpolating. An
exponential variation was found to be the closest approxLr ðkÞ
LA ðkÞ
imation to the
behaviour
spectral
of
both F0 ðkÞ and F0 ðkÞ .
Lr ðkÞ
LA ðkÞ
Hence log10 F0 ðkÞ and log10 F0 ðkÞ are interpolated as
functions of λ.
ii) The atmospherically transmitted sun glint radiance TLg(λ)
is a function of the direct atmospheric transmittance T,
F0(λ) and the surface reflectance ρ. Of these, F0(λ) is a
known function of λ while ρ varies weakly with λ.
Hence, the quantity TLg(λ) is expected to behave in the
same manner as T. It was found empirically that this
could be approximated as an exponential function of λ.
iii) Under clear sky conditions, the diffuse transmittances
tsol (λ) and tsen(λ) at the solar and sensor geometries can be
approximated as exponential functions of total optical
depth, and hence (approximately) of path reflectance, so
−log10 (t(λ)) should exhibit the exponential behaviour i)
described above. This means that log10 (−log10 (t(λ))) can
be approximated as a linear function of λ. In practice this
was clearly nonlinear, and it was found empirically that the
most linear fit was between log10 (−log10 (tsol(λ))) and
log10 (λ), and log10(−log10 (tsen(λ))) and log10( fs).
oz
oz
iv) The ozone transmittances tsol
(λ) and tsen
(λ) are expected
to vary as exp(− k aoz( fs)), where aoz(λ) is the spectral
absorption coefficient of ozone and k is a spectrally
neutral factor proportional to the amount of ozone. Using
tabulated values of aoz(λ), good linearity was found
oz
between − log10(tsol
(λ)) and aoz(λ), where aoz(λ) is large.
As aoz(λ) tends to zero (λ ≤ 440 nm) the quality of the
fit deteriorates due to rounding errors. However, in
this region the effect of aoz(λ) is negligible so the
exact interpolation method is unimportant. Similarly,
oz
good linearity was found between −log10 (tsen
(λ)) and
aoz ( fs).
v) The atmospherically attenuated foam reflectance tLf (λ) is
expected to be proportional to the atmospherically
transmitted TOA solar irradiance t(λ)F0(λ), assuming
that the whitecap reflectance is spectrally neutral. The
oz
diffuse transmittance t(λ) is equal to tsol(λ) tsen(λ) tsol
(λ)
tLf ðkÞ
oz
tsen(λ), hence ðtðkÞF0 ðkÞÞ can be interpolated as a function
of λ.
vi) The default SeaDAS BRDF correction only corrects for
Fresnel reflection from a flat surface. This has a weak
wavelength dependence and is interpolated as a function
of λ.
Verifying the internal consistency of the above approach is
made difficult by the lack of comparable 1 km values at 469 nm
and 555 nm. It is assumed that the error at 469 nm will be
greater than the error at 555 nm, since the correction parameters
of the former are spectrally interpolated from 443 nm to 488 nm
(minimum 19 nm difference) and the latter from 551 nm to
667 nm (minimum 4 nm difference). An overestimate of the
spectral interpolation error at 469 nm can be found by
performing a more difficult spectral extrapolation, from the
1 km bands at 443 nm and 531 nm to estimate the parameters at
488 nm (minimum 44 nm difference). These results can then be
compared with the actual known result at 488 nm. This upper
limit was determined for each pixel, for all data in Table 3, for
each of the spectrally interpolated parameters and the results are
presented in terms of an rms in the retrieved LWN.
2.3. Cloud clearing
Cloud clearing using only the 1 km data can negate the
improvement in spatial coverage made possible by the 500 m
bands (e.g. around coastlines). The standard 1 km cloud
clearing algorithm in SeaDAS masks cloud using the near
infrared albedo calculated from MODIS band 16 at 870 nm.
Therefore, to mask the 500 m data the near infrared albedo is
calculated using MODIS band 2 at 859 nm (MODIS band 2 is
also used within the standard 1 km cloud clearing (Ackerman
et al., 1998)). A 500 m pixel is masked if the albedo of any of
the 250 m pixels with which it overlaps exceeds 0.027, which is
the threshold used in the SeaDAS 1 km cloud clearing.
J.D. Shutler et al. / Remote Sensing of Environment 107 (2007) 521–532
525
500 m
of each 500 m linear
determining the standard deviation σx,n
500 m
factor for an n × n mask centred on that pixel. If σx,n
is
greater than a threshold (i.e. a large variation within a small
region) then the pixel can be flagged as in-error, providing a
per-pixel quality flag for the linearity assumption. Similarly, a
500 m
500 m
simulated LWNi
(551) data set is estimated using LWN
(555)
1 km
and LWN (551). Conversion between normalised water leaving
radiance and remote sensing reflectance (Rrs(λ)) is achieved
ðkÞ
via Rrs ðkÞ ¼ LFWN
. Estimates of chl-a concentration at 500 m
0 ðkÞ
can then be produced by using the empirical algorithm derived
by Carder et al. (1999) detailed in section 1.
2.5. Field samples
Fig. 1. MODIS 500 m and 1 km pixel alignment.
2.4. 500 m chlorophyll-a estimation
The chl-a algorithm is dependent upon the following
assumptions. Firstly, the remote sensing reflectance (or normalised water leaving radiance) at two spectrally close wavelengths
will be linearly related. The parameters of this linear
relationship will be dependent on the constituents of the water
(itself dependent on season, rainfall and mixing) and will be
valid for a localised region, dependent on the pigment
concentrations remaining relatively consistent for that region
(this assumption will be tested within the algorithm). For
example, within the SeaBAM data set (O'Reilly et al., 2000)
data were translated between 565 nm and 555 nm using a
relationship determined from coincident data measured at these
two wavelengths. Secondly, for simplicity it is assumed that
each pixel has an ideal point spread function (PSF) and contains
the same contribution of noise. For an ideal PSF only
contributions from within each pixel determine its value and
no interaction between surrounding pixels occurs (this assumption will be revisited in the discussion). Fig. 1 illustrates the
pixel alignments for the MODIS array between the different
pixel resolutions. The linear relationship between the 469 nm
and 488 nm bands ensures that the intra-relationships within
500 m
each group of six LWN
(469) pixels are likely to be the same as
500 m
those for the equivalent group of LWNi
(488) pixels, differing
only in their magnitude (as per the linear relationship). From
Fig. 1, an expression to determine the aggregated 1 km pixel
Qx1 km from the six 500 m pixels which are spatially aligned with
it can be defined as Px1 km = α x1 km(λ1, λ2)Qx1 km, where Q1x km ¼
fx
cx
dx
1 ax
and αx1 km(λ1, λ2) is a scalar
4 2 þ bx þ 2 þ 2 þ e x þ 2
quantity to translate the pixel between the response at λ1 to the
response at λ2. If the 1 km pixel result Px1 km is known (e.g.
1 km
LWN
(488)), then the corresponding six 500 m pixels from
500 m
the LWN
(469) data set can be used to determine a modelled
1 km
LWNi
(469). These values can then be used to determine
αx1 km (469, 488) for each 1 km pixel. Next, sub-sampling
the αx1 km (469, 488) data set for all pixels using bilinear
interpolation produces a 500 m equivalent linear factor data
set, αx500 m (469, 488). Applying these 500 m linear factors
500 m
500 m
to the LWN
(469) data produces a simulated LWN
(488)
data set. The validity of the linearity assumption (between
469 nm and 488 nm) for each 500 m pixel can be verified by
In situ data were collected from a regularly sampled site in
the western English Channel (Southward et al., 2005). On nine
different days (n = 9 in situ data points) optical measurements
were taken using a multi-spectral radiometer profiler. The
radiometers attached to the profiling rig were engineered by SEI
Satlantic: downwelling irradiance and upwelling radiance were
measured at 413, 443, 490, 510, 559, and 619 nm. The
instrument was profiled at 30 cm s− 1 with a data acquisition rate
of 10 Hz and only the upcast was used for data regressions. The
raw data were binned into 1 m intervals, and the top 2 m of data
were neglected. No correction was made for instrument self
shading. LWN (λ) can then be calculated (Gordon & Clark, 1981)
from the optical data using:
LWN ðkÞ ¼ Lu ð0− ; kÞ
F0 ðkÞ
Es ðkÞ
1−qðk; hÞ
n2w ðkÞ
ðmW cm−2 sr−1 lm−1 Þ
ð3Þ
where Lu(0−,λ) is the sub-surface upwelling radiance calculated
from the optical profiler measurements and Es(λ) is the downwelling solar irradiance measured by the deckcell. Lu(0−,λ) is
determined using a linear regression of loge(Lu(z, λ)) against
depth (z) to extrapolate to the sea surface; the values of Es (λ) are
smoothed values of the deckcell data using a moving “window” of
32 data points, or 10 seconds. F0(λ) is the mean extraterrestrial
solar irradiance (Neckel & Labs, 1984) and ρ(λ,θ) and nw (λ) are
the Fresnel reflectance and refractive index of sea water,
respectively.
Water samples (n = 27) were taken to analyse pigment
concentrations using high performance liquid chromatography (HPLC) with a diode array detection system (Mantoura
& Llewellyn, 1983). Water samples were filtered through
25 mm GF/F filters and stored in liquid nitrogen. Pigments
were extracted with the aid of sonification in 90% acetone,
clarified using centrifugation and analysed following the
procedure outlined in Barlow et al. (1997). Pigments were
separated using a 3 μm Hypersil MOS2 C8 column on a
Thermo Separations product HPLC, detected by absorbance at
440 nm and identified by retention time and on-line diode array
spectroscopy.
All of these in situ data form part of the ESA/EU project
Regional Validation of MERIS Products (REVAMP) data set, so
526
J.D. Shutler et al. / Remote Sensing of Environment 107 (2007) 521–532
Table 4
Matchup data set detailing on which dates HPLC samples and optical profiles
were collected
Dates
In situ measurements
Matchups
N
17 March 2003
07 April 2003
09 April 2003
14 May 2003
20 June 2003
09 July 2003
10 July 2003
22 April 2003
05 June 2003
12 June 2003
17 September 2003
HPLC
HPLC
HPLC
HPLC, optical profiles
HPLC, optical profiles
HPLC
HPLC, optical profiles
optical profiles
HPLC, optical profiles
HPLC, optical profiles
HPLC
1
1
1
1
1
1
1
1
1
1
1
Quality
Strict
Strict
Strict
Strict
Strict
Strict
Strict
Relaxed
Relaxed
Relaxed
Relaxed
MODISAqua
granule
time
(UTC)
11:08
13:00
12:50
13:20
13:40
12:30
13:15
12:20
12:45
12:50
13:35
MERIS and SeaWiFS data collection and calibration protocols
were strictly followed (Mueller et al., 1995; Tilstone et al.,
2004).
2.6. MODIS data and matchups
MODIS-Aqua level 1B and geolocation granules (collection
004) were downloaded via the NASA GES DAAC web interface.
These were then processed using SeaDAS 4.8 (update 4) to
generate the 1 km level 2 data and the atmospheric correction
parameters. Atmospheric correction and generation of the 500 m
chl-a estimates takes 1 hour (for a 5 minute granule) on a single
2 GHz desktop machine. Comparisons between the 1 km and
500 m data were conducted using a 12-month set of AquaMODIS granules (Table 3). These data cover a region of open
ocean (45–51° N, 12–27° W) in the north east Atlantic
traditionally considered as case 1. Lee and Hu (2006) classified
waters globally from SeaWiFS using the ratio of RRrsrs ð412Þ
ð443Þ and the
absolute value of Rrs(555); application of their (albeit arbitrary)
limits classified this region as non-case 1 for all seasons except
spring. Nevertheless, the depth of the water (N2000 m) and
significant distance from the coast suggests minimal influence
on the optical properties of riverine suspended particulates or
CDOM. Analysing data from every month allows the detection of
any temporal trends and ensures that a wide range of water
conditions are analysed. Regressions between the 1 km and 500 m
data were determined using all water pixels from all of the
granules in Table 3, (producing more than 32 million potential
matchups), using the latitude and longitude of each pixel to
determine a match. Matches were only retained if i) satellite
viewing angle ≤60°; ii) sun zenith angle ≤75°; iii) no level 2
500 m
flags were raised for the 1 km data and iv) σx,3
b 0.1 for the
500 m data.
All MODIS-Aqua granules corresponding to the collected in
situ data were obtained and processed to allow comparison.
Only satellite matchups sampled within 2 hours of the collection of the in situ were retained to minimise differences due to
temporal variations. Satellite data were extracted from the
average of a 3 × 3 mask centred on the validation site and were
only retained if criteria i) to iv) above were met. Reduced major
axis regressions have been used for all in situ comparisons as
this allows errors in both axes. Following these criteria the dates
listed in Table 4 were retained for comparison.
3. Results and discussion
3.1. Estimation of errors in the atmospheric correction
interpolations
To evaluate the interpolation methods detailed in Section 2.2,
the difference was calculated between each 1 km pixel
1 km
(488)) as determined by SeaDAS and an equivalent
(LWN
1 km
LWN (488) value obtained by interpolating the atmospheric correction parameters to 488 nm from the surrounding bands at 443 nm and 531 nm. This process was first
applied to the aerosol path reflectance with all other
parameters being determined from SeaDAS, producing an
1 km
estimate of LWN
(488). Next the Rayleigh path reflectances were the only interpolated parameters, and lastly all
atmospheric parameters were interpolated. Fig. 2 shows the
month by month root mean square of differences (rms) due to
aerosol path reflectance interpolation, Rayleigh path reflectance interpolation and the interpolation of all the atmospheric
parameters. As described in Section 2.2, these results are likely
to be an overestimate of the error in the interpolation methods
as they have been determined by spectrally interpolating the
atmospheric parameters between 443 nm and 531 nm at 1 km
to estimate values at 488 nm (a minimum spectral difference of
44 nm). Whereas, in practice the interpolation needed for the
500 m band at 469 nm will be achieved by using the results at
443 nm and 488 nm (a minimum difference of 19 nm). The
lack of 1 km data at 469 nm means it is not possible to perform
this analysis at the actual 500 m wavelength of 469 nm. The
errors due to interpolating the atmospheric correction parameters to the 500 m band at 555 nm are expected to be
considerably lower due to its spectral proximity to the 1 km
band at 551 nm. Fig. 2 shows that the errors increase in the
winter months, with an annual average rms of 0.06 mW cm −2
1 km
Fig. 2. The interpolation rms in LWNi
(488) for interpolation of
Rayleigh path reflectances, the aerosol path reflectances and
parameters in Section 3.1 are interpolated. All pixels for each
month (scene) listed in Table 3 were analysed showing seasonal
Units are mW cm− 2 sr− 1 μm− 1.
solely the
when all
individual
variations.
J.D. Shutler et al. / Remote Sensing of Environment 107 (2007) 521–532
527
Fig. 3. Density scatter plots between MODIS 1 km and 500 m data for all pixels in the data listed in Table 3 with regression lines. Units are mW cm− 2 sr− 1 μm− 1 for the
1 km
500 m
1 km
500 m
1 km
500 m
1 km
500 m
(488) versus LWN
(469); b) LWN
(551) versus LWN
(555); c) LWN
(488) versus LWNi
(488) and d) LWN
(551) versus LWNi
(551).
LWN data; a) LWN
sr − 1 μm− 1 , which as explained should be seen as a considerable
500 m
overestimate of the interpolation error at LWN
(469). Using
1 km
1 km
the mean values of LWN (488) and LWN (551) for each month
1 km
in Table 3 and assuming that LWN
(551) is unperturbed by
noise (as we are assuming that the noise within the 488 nm
data will dominate), then a ± 0.06 mW cm− 2 sr− 1 μm− 1 error
500 m
in LWN
(488) results in a annual average chl-a error of 33%.
These techniques do not take into account any errors that may
exist in the sensor calibration.
500 m
1 km
(488) and LWN
(488) data as
agreement between the LWNi
shown in Fig. 3(c). Fig. 3(b) shows the result of regressing the
1 km
500 m
LWN
(551) data against LWN
(555) data producing a highly
2
correlated result (r = 0.906; slope = 0.985; constant = 0.340;
rms = 0.070; n= 1527094 in mW cm− 2 sr− 1 μm− 1). This
illustrates the accuracy of the 500 m atmospheric correction
3.2. Characterisation
1 km
Fig. 3(a) shows the results of regressing all LWN
(488)
500 m
data against all LWN (469) data for all MODIS-Aqua passes
in Table 3 and Fig. 3(c) shows the equivalent results for
1 km
500 m
LWN
(488) against the simulated LWNi
(488). The need to
determine the linear spectral interpolation between 469 nm and
488 nm on a per-pixel basis is very apparent from Fig. 3(a)
as a split distribution is visible. A scene-wide fixed linear
relationship would be insufficient to spectrally translate these
data, as αx1 km(469, 488) (and hence αx500 m(469, 488)) will vary
spatially dependent on the water constituents. There is good
Fig. 4. The LWN seasonal variations of rms (between the 1 km and 500 m data)
for all pixels for each individual month (scene) listed in Table 3; units are mW
cm− 2 sr− 1 μm− 1.
528
J.D. Shutler et al. / Remote Sensing of Environment 107 (2007) 521–532
Fig. 5. Comparison results between the in situ measured and 500 m MODIS data. Units are mW cm−super 2 sr−super 1 μm−super 1 for the LWN data and mg m− 3 for chl-a;
in situ
500 m
in situ
500 m
a) LWN
(559) versus LWN
(555) and b) LWN
(489) versus LWNi
(488) and c) in situ chl-a versus 500 m chl-a. chl-a rms values are log10[chl-a] and vertical error bars
indicate the standard deviation within a 3 × 3 pixel mask centred on the validation site. All data labelled ‘strict’ satisfied criteria i–iv in Section 2.F, those labelled
m
‘relaxed’ satisfied σ500
b 0.5.
x,3
500 m
approach for the LWN
(555) data when compared to the
1 km
standard MODIS 1 km data at LWN
(551). If the bandwidths
and sensitivity of the 555 nm and 551 nm bands were identical then a ≃ 5% difference would be expected between the
1 km
500 m
LWN
(551) and LWN
(555) data, due to the difference in centre
wavelengths (Gerald Moore, personal communication, 2005).
However, the bandwidths and sensitivities of these two bands
are different and Fig. 3(b) exhibits a 2.0% deviation. Fig. 3(d)
1 km
shows the results of regressing the LWN
(551) data against the
500 m
estimated LWNi (551) showing good agreement. Fig. 4 shows
the month by month variations in rms for LWN and LWNi. It is
important to note that the two MODIS 500 m bands have a
wider bandwidth of 10 nm in comparison to the typical 5 nm
of the MODIS 1 km bands. This may result in confusion
between spectral characteristics. Notwithstanding this, all of
500 m
500 m
these data (LWN
, and LWNi
) compare well with their 1 km
500 m
counterparts. The lower rms values for the LWNi
(488) with
500 m
respect to LWN (555) (Fig. 4) despite the lower radiometric
sensitivity of the 469 nm channel (from which the 488 nm data
are derived) are due to the linear extrapolation algorithm.
500 m
(488) data are effectively bounded by the use of
The LWNi
1 km
the LWN (488) data within the algorithm. A similar characteris500 m
tic is exhibited by the LWNi
(551) data.
3.3. Validation
Fig. 5 shows the in situ matchup results for the 500 m data
500 m
500 m
(LWNi
(488), LWN
(555) and chl-a) with the mean values and
± 1 standard deviation error bars. These compare well and
exhibit the same trends as the 1 km equivalent results shown in
Fig. 6. Despite the lower sensitivity of the MODIS 500 m bands,
500 m
Fig. 5(a) shows that the LWN
(555) data compare well with the
in situ derived LWN(559) with an rms = 0.15 mW cm− 2 sr− 1
μm− 1, n = 3 (labelled strict). Indeed, they are very close to those
1 km
of the LWN
(551) data with an rms = 0.14 mW cm− 2 sr− 1 μm− 1,
n = 3 (Fig. 6(a)). This result is consistent with a recent study that
in situ
1 km
in situ
1 km
Fig. 6. Equivalent to Fig. 6 but for 1 km MODIS data; a) LWN
(559) versus LWN
(551) and b) LWN
(489) versus LWN
(488) and c) in situ chl-a versus OC3M.
J.D. Shutler et al. / Remote Sensing of Environment 107 (2007) 521–532
529
Fig. 7. Example mapped chl-a estimates (in mg m− 3), LWN and LWNi (mW cm− 2 sr− 1 μm− 1) data of the Celtic Sea and western English Channel showing the position of
1 km
500 m
the in situ data collection site (L4); on 11 July 2005 13:38 UTC a) the standard LWN
(488); b) the standard 1 km OC3M chl-a; c) the LWNi
(488) equivalent and d) the
equivalent novel chl-a (500 m) result.
reported an rms of 0.15 mW cm− 2 sr− 1 μm− 1, n = 215 for
1 km
MODIS LWN
(551) when compared with in situ data from
a near-coast validation site in the northern Adriatic Sea
(Zibordi et al., 2006). Unfortunately, in situ data at 469 nm
were unavailable for this validation. Figs. 5(b) and 6(b) show
the similarity between the 1 km and 500 m 488 nm data. The
500 m chl-a results in Fig. 5(c) (rms = 0.21 log10 [chl-a]mg m− 3;
n = 7) compare well with the OC3M 1 km results in Fig. 6(c)
(rms = 0.24 log10 [chl-a]mg m− 3; n = 7). Relaxing the matchup
500 m
criterion by quality filtering solely using σx,3
b 0.5, produces
the results labelled as relaxed in Figs. 5 and 6. These in situ
matchup results further illustrate the similar performance of
these 1 km and 500 m data.
The low number of high quality in situ matchups illustrates
the difficulty of performing validation exercises in U.K.
waters. For example, an operational moored buoy system
(Pinkerton et al., 2003) located in the English Channel during
1997 and 1998 produced just 15 high quality SeaWiFS
matchups. However, the validation of algorithms in these
waters is still important to enable the characterisation and study
of these environments. To allow matchups and thus illustrate
that the 500 m data behave in a similar manner to their
equivalent 1 km data the temporal matchup criteria were set to
± 180 min. The use of an averaged value determined from a
3 × 3 pixel mask has helped reduce differences due to time and
location. The use of in situ measurements will invariably
introduce uncertainties into the validation of these remotelysensed data. Conversely, this supports the need to utilise
remote sensing to monitor these environments as it illustrates
the difficulty with collecting and interpreting in situ data. Due
to the highly dynamic nature of coastal waters any temporal
variations between in situ data collection and the satellite
overpass can affect subsequent comparisons. For example,
assuming that the average tidal speed for the validation site is
0.4 ms− 1 (as measured by Southward et al., 2005), a 20 min
temporal difference between the satellite overpass and in situ
data collection could result in a potential difference of up to
480 m (in any direction) between the water body sampled in
situ and that captured at the same position by the sensor. The in
situ data used here were collected for MERIS validation and so
were timed to coincide with the MERIS overpass time which is
different to that of MODIS-Aqua.
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J.D. Shutler et al. / Remote Sensing of Environment 107 (2007) 521–532
1 km
500 m
Fig. 8. Example LWN data (in mW cm− 2 sr− 1 μm− 1) on 17 March 2003 12:45 UTC; a) LWN
(551) displayed at the same spatial scale as b) the LWN
(555) data and
c) the 1 km aerosol optical thickness at 551 nm.
3.4. Point spread function, adjacency and example data
This study has assumed that the PSF function for each 1 km
and 500 m pixel is ideal, but in practice this is not the case; the
MODIS-Aqua PSF was not determined prior to launch but can
be modelled using a 2D Gaussian function (Meister et al., 2005;
Qiu et al., 2000; Townshend et al., 2002). However, the
uncertainty in the modelled PSF precludes its use for correcting
the observations due to stray light contamination (Meister et al.,
2005). NASA is currently addressing these issues towards
deriving the MODIS-Aqua PSF from prelaunch and in-orbit
measurements; unfortunately these data are currently unavailable (NASA ocean colour team, personal communication via
the ocean colour forum, 08 April 2005).
Where the distribution of signal within a scene has
discontinuities, for example along the coast, interactions
between a potentially dark pixel (e.g. ocean) which is adjacent
to a bright pixel (e.g. land) can cause problems, as photons
reflected by the bright area may be scattered into the sensor.
These effects will be inversely proportional to the distance
between the dark and bright pixels, e.g. the effects will reduce as
the distance from the coast increases. This adjacency is further
complicated by the orbit direction of the sensor in relation to the
coast. These issues are known, although not well understood
with only a small number of studies having been carried out
(e.g. Santer & Schmechtig, 2000). The analysis and study of
adjacency is beyond the scope of this paper, although, it is
important that the reader is aware of it.
Fig. 7 illustrates the improved spatial resolution of the 500 m
data for LWN, LWNi and chl-a estimates for the Celtic Sea on 11
July 2005 13:38 UTC. A suspected coccolithophore bloom is
visible in the LWN data south of the English coast (appearing red
and orange in Fig. 7(a) and (c)). Furthermore, a pale blue plume
can be seen extending westwards from the English coast. Both
the 1 km and 500 m data show the plume. However, the hooked
end to the plume (possibly due to the existence of an eddy) is
clearly visible in the 500 m data, whereas this feature is not as
discernible in the 1 km data. Fig. 8 shows suspended particulate
matter from the rivers Dart, Teign and Exe distributed along the
English coast with the Kingsbridge estuary in the centre of the
scene on 17 March 2003 12:45 UTC. The 1 km scene, Fig. 8(a),
shows the suspended particulate matter distributed along the
coast and around the Kingsbridge estuary with higher concentrations to the east of the scene. The increased resolution of the
500 m scene shows that the suspended matter forms a plume
which is detached from the coastline, suggesting that it is not
associated with the Kingsbridge estuary. Finally Fig. 8(c) shows
the equivalent 1 km aerosol optical thickness at 551 nm which
shows very little correlation with the observed plume in the LWN
data, suggesting that it is not a result of any atmospheric features.
4. Conclusions
The monitoring of coastal and estuarine environments is
becoming increasingly important due to the increase in
anthropogenic pressures and the possible effects of climate
change. Near-real time remotely-sensed data from orbiting
platforms can provide a cost effective and convenient source of
data for monitoring these environments. This paper presents a
method to automatically atmospherically correct the MODIS
J.D. Shutler et al. / Remote Sensing of Environment 107 (2007) 521–532
bands 3 and 4 which capture optical data at a spatial resolution of 500 m and enables these data to be used for monitoring purposes. The approach provides a dynamic pixel by
pixel atmospheric correction that allows the aerosol properties to
vary across the scene. A method to determine chlorophyll-a
concentration, from MODIS 500 m data has also been
presented. The outputs from the algorithms have been compared
against those of the standard 1 km MODIS atmospheric
correction algorithm, and a small in situ data set. The results
illustrate that these algorithms have the potential to increase the
ocean colour database and should enable the development of
further biological algorithms. Further work is needed to extend
the 500 m atmospheric correction method to case 2 waters and
approaches similar to those developed for MERIS (Moore et al.,
1999) and SeaWiFS (Lavender et al., 2005; Ruddick et al.,
2000) may provide a solution.
Acknowledgements
The authors gratefully acknowledge partial funding through
the Natural Environment Research Council Remote Sensing
and Data Analysis Service (NERC RSDAS), the EC project
Association of Physical and Biological processes acting on
Recruitment and post-Recruitment of Anchovy (ANREC)
(QLRT-2001-01216) and the EU project Data Integration
System for Marine Pollution and Water Quality (DISMAR)
(IST-2001-37657). The authors also thank Gavin Tilstone and
Victor Martinez Vicente for kindly providing the in situ data set.
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