Lấy nhiệt độ bề mặt biển từ dữ liệu MODIS trong vùng biển ven bờ
Retrieval of Sea Surface Temperature from MODIS Data
in Coastal Waters
Rosa Maria Cavalli
National
Research Council (CNR), Research Institute for Geo-Hydrological Protection
(IRPI) via della Madonna Alta 126, 06128 Perugia, Italy;
rosa.maria.cavalli@irpi.cnr.it; Tel.: +39-075-501-422
Received: 31 August 2017;
Accepted: 28 October 2017; Published: 16 November 2017
Abstract: Accurate
measurements of sea surface temperature retrieved from remote images is a
fundamental need for monitoring ocean and coastal waters. This study proposes a
method for retrieving accurate measurements of SST in coastal waters. The
method involves the estimation of effect of total suspended particulate matter (SPM) concentration on the value of sea surface emissivity (SSE) and the inclusion of
this effect in SSE value that is put into SST calculation. Data collected in three Italian coastal waters were
exploited to obtain SSTskin and
SSE values and to analyze SPM effects on SSE value. The method was tested on
MODIS images. Satellite measurements of SST obtained with current operational
algorithm, which does not require SSE value as explicit input, were compared
with in situ values of SSTskin
and RMSD is equal to 1.13 K. Moreover,
SST data were retrieved with an algorithm for
retrieving SST measurements from MODIS data, which allows the inclusion of SSE
value with SPM effect. These data were compared with in situ values of SSTskin, and RMSD is equal to
0.68 K.
Keywords: coastal water; sea surface emissivity; sea surface temperature; total suspended particulate matter
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1.
Introduction
Coastal waters are very important for human
populations because we derive a lot of benefits from these habitats: food (e.g., most caught fish come from the coastal
waters and adjacent
upwelling areas), renewable and nonrenewable resources (e.g.,
hydrocarbons and extracted sand and gravel), and services such as
transportation, waste disposal, and recreation. In an assessment of world’s
ecosystems, the largest value in the whole ecosystem was assigned to the
coastal waters [1]. On the other hand, these valuable
areas have become very sensitive to impact from human activities. Human threats
to the coastal areas fall into four categories: effects of contaminants,
eutrophication, habitat loss, and overexploitation of fisheries resources [2]. Therefore, monitoring water quality, pollution assessment, and remediation are the most pressing requirements for ensuring sustainability of these valuable and
vulnerable habitats [3–5].
Sea surface temperature (SST) measurements retrieved
from remote images are used to analyze these valuable and vulnerable
habitats, e.g., environmental conditions of benthic marine organisms [6,7], ground water
discharges [8], interactions between residual
circulation, tidal mixing and fresh influence [9],
karstic springs [10], river plumes [11],
thermal plume contamination [12–14],
upwelling phenomena [15], and water quality [16]. Nevertheless, error, defined
as the difference between some idealized “true value” and the measured
value [17], in SST measurements is highlighted in different coastal regions by several studies,
e.g., China [13], Western Australia [7], South Africa
[18], and the US [7].
This error can be as large as 6 ◦C [18].
Another confirmation of the importance of accurate satellite
measurements of SST is the series of
infrared radiometers that were launched
after the first Advanced Very High
Resolution Radiometer (AVHRR) [19]. Among
these, two Moderate
Resolution Imaging Spectroradiometers (MODIS)
of NASA’s
Earth Observation System (EOS) constellation were designed for accurate measurements of SST: the first one on the Terra satellite
was launched on 18 December
1999, and the second one on the Aqua satellite was launched on 4 May 2002 [20]. These instruments
continue to produce an available “collection” of SST measurements. Collection specifically represents a revision
of the instrument calibration model
and the algorithm for SST retrieving [21]. Previous studies emphasize that the error in SST measurements can occur for
many reasons and that each adjustment to reduce the error in SST measurements is
important [22–27]. Each step of data acquisition and data processing is prone to additional error sources,
such as atmospheric correction errors, e.g.,
[28,29], cloud
contamination, e.g., [25,27], representativeness errors, e.g., [25,26], sampling
errors, e.g., [22,23,26], and surface
emissivity, e.g., [30,31]. The succession
of the “collections” clearly
demonstrates the importance
of providing accurate
measurements and of exploiting each adjustment that can reduce the error [19–27,32,33]. The operational
algorithm for retrieving SST from MODIS images is a derivative of the split window technique, which corrects the atmospheric absorption of radiation between sea surface and satellite with brightness temperature differences at a few adjacent infrared bands [21,25,27,32,34]. Therefore,
algorithm coefficients also include the impact of differences in column water vapor and SSE values. The split window algorithm for retrieving SST from MODIS images
which was proposed by Niclos et al. [35] incorporates separate terms for column water vapor and SSE value. Sobrino
et al. [28] already
showed that including
column water vapor in the split-window algorithm improves SST accuracy. Niclos et al. [35] considered that SST accuracy is improved by including column
water vapor value and SSE value in
the operational algorithm because
the variation in SSE values
is comparable to the variation in emissivity value
of other land surfaces
[35]. Some authors
[30,36–40] proposed
models for calculating SSE values. As shown by these
models, SSE value is a function of sediment and salinity
concentrations and zenith
observation angles. Moreover, sea
surface roughness, which is a function of sea surface wind speed, affects SSE value. Other authors [31,41–44] obtained SSE value from experimental data in order to improve the
knowledge of SSE behavior and to develop
and validate models.
A reference work for all these
studies is the paper
written by Masuda
et al. [30]. Based on Cox and Munch [45], the authors highlighted that the greatest
effect of surface
wind on emissivity is observed with surface wind speed greater than 15 m/s and zenith
observation angle greater than 50◦ [30]. All these
papers were mainly focused on open sea waters, whereas only a few studies
[46–50] were concentrated on SSE behavior in coastal waters. The previous
papers highlight that
SSE value is affected by changes in refractive index, which can
also be due to variation in concentration of total suspended particulate matter (SPM)
[30,31,36–44,46,50]. Coastal waters are characterized by greater concentrations of SPM than open sea waters. This characteristic is due to human activities and the runoff of rivers,
and it is so important
that its contribution to the optical properties was defined as “dominant” [51]. Therefore, Wen-Yao et al. [46] and Wei et al. [49] specifically retrieved
SSE behaviors with respect
to SPM concentrations from measurements of thermal radiometers at 8–14 µm in laboratory. They agreed that SSE value decreases with increase
in SPM concentrations that were included in the water
samples [46,49]: the decrease
is tiny for small concentrations and significant for large concentrations. However, the authors did not analyze SSE
behaviors with respect to SPM concentration from 0 to 100 mg/L (i.e., the first addition of sediment is
100 mg/L). Yao et al. [46] highlighted that SSE value
decreases with the first
addition of sediment
(i.e., 100 mg/L),
remains at the
same value up to 10,000 mg/L, and then falls again.
Besides great concentration of SPM, coastal waters
are also characterized by greater variations in SPM composition, salinity, and
sea surface wind speed than open sea waters [52]. The effects of SPM composition and salinity on SSE
values was, respectively, analyzed
in the laboratory by Salisbury [47] and Newman et al. [42]. SSE behaviors with respect to sea surface wind speed was
calculated by Masuda et al. [30], Masuda [36], and Watts et
al. [39]. SSE behaviors with respect to these variables
were evaluated in stable environment where variation in each variable
was under strict control [30,36,39,42,46,47,49]. Coastal
waters cannot be defined as a stable
environment [52].
This study develops and tests a method for retrieving
accurate measurements of SST in the coastal waters. This method is based on the inclusion of
column water vapor value and the effect
of SPM concentration on SSE value. This effect was
estimated from data collected in coastal waters. SSE behavior with respect
to SPM concentration confirms that SSE values
decrease with increase
in SPM concentration [46,49]. SSTskin measurements, which were
obtained from in situ data, were compared with SST measurements retrieved from
MODIS data with and without the inclusion of effect of SPM concentration. The
comparison shows that the inclusion of these effects minimizes the error in SST
measurements retrieved from remote images.
2. Materials
A cruise was performed to characterize waters
of the Manfredonia Gulf, the Taranto
Gulf, and the area close to Lesina Lagoon during
the summer of 2011 [53]. The Manfredonia Gulf
is situated in the western part of the southern Adriatic Sea (Figure 1). Urban and agricultural activities in this area are considered potential threats to coastal marine ecosystem [54].
Fifteen measurement locations
situated at distance of about 4 km from the coastline and between
bathymetric lines of 10 m and 15 m were selected for describing these waters
(Figure 1). Sampling of these locations were carried
out during four days, and principal locations were monitored several times: in
total, 39 water columns were analyzed. Each water column highlighted unique features, even though it was examined
in the same position during different days. The waters of the
Manfredonia Gulf were described with 39 different
cruise locations.
 |
Figure 1. Measurement
locations of the Manfredonia Gulf. Study
area location (black box) in the top right.
The Taranto Gulf,
which is located in the Ionian Sea (Figure 2),
represents an example of coastal marine ecosystem where
biological balances have
been altered by industrial development, i.e., iron and steel factories, petroleum
refineries, and shipyards [55]. Because their impact
on environment is great, the Taranto province
was officially classified as an “Area of High Environmental Risk” [56] and later was also included in the 14 “Sites of National
Interest” that need to be remediated [57].
Seven measurement locations situated at different distance from the coastline
(i.e., from 2 to 12 km) and at different
depths (i.e., from 23 to 303 m) were chosen
to analyze these
waters (Figure 2). All these
locations were monitored three times during four days for a total of 21 water
columns. Each water column highlighted unique features, even though it was
monitored in the same position during different days. The Taranto Gulf was described with 21
different locations.
Figure 2. Measurement locations of the Taranto Gulf. Study area location (black
box) in the top left.
Waters close
to Lesina Lagoon are situated along the western part of the southern Adriatic
Sea (Figure 3). The lagoon is characterized by shallow
water, i.e., from 0.75 to 1.5 m, and
a limited sea-lagoon exchange. Human intervention influences environment
quality and determines the main factors of impact such as accumulation of
nutrients, introduction of opportunistic species, protection of sea-lagoon exchange, and commercial activities
of fishing and aquaculture [58]. Six measurement
locations situated at a distance of about 10 km from the coastline and around a
bathymetric line of 20 m were selected for describing the waters close to Lesina Lagoon (Figure
3). Survey of these waters
was performed during one day.
 |
Figure 3. Measurement
locations of coastal waters close to Lesina Lagoon. Study area location (black
box) in the top left.
The position of all cruise observations was chosen in accordance with Mueller et al. [52] protocol and knowledge of these areas of study.
Waters of
the Manfredonia Gulf, the Taranto Gulf,
and the area close to Lesina Lagoon were analyzed during an oceanographic cruise
[53] by means of collection and analysis of
water samples, measurement of sea temperatures, calculation of salinity
concentrations, and acquisition of thermal infrared radiances from the sea surface
and sky. All in situ measurements were carried out from 5:40 to
17:30 UTC (Table 1).
Table 1. Date and
time of the surveys and mean values of SSTskin and SSTsubskin
estimated
using Webster et al. [59] and Fairall et al. [60] models, respectively.
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Coastal
Waters of the Area Close to Lesina Lagoon
|
Start time (UTC) End
time (UTC) Number of locations Mean of SSTSkin
07 August 2011 7:30 16:00 6 300.12 300.14
Coastal
Waters of the Manfredonia Gulf
Date Start time (UTC) End
time (UTC) Number of Locations Mean of SSTSkin
08 August 2011 7:01 15:20 6 301.25 301.27
09 August 2011 6:30 15:00 9 301.15 301.26
12 August 2011 7:50 16:10 10 299.79 299.99
24 August 2011 5:40 17:30 14 301.86 302.05
Coastal
Waters of the Taranto Gulf
Date Start time (UTC) End
time (UTC) Number of Locations Mean of SSTSkin
13 August 2011 11:00 15:10 5 299.46 299.59
14 August 2011 7:05 14:30 7 300.25 300.34
15 August 2011 7:00 14:00 7 299.81 299.99
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16 August 2011 10:00 14:00 2 299.95 300.02
In accordance with protocols laid down by Mueller et al. [61] and Pegau et al. [62], water samples
were analyzed in the laboratory for calculating SPM concentrations. SPM concentrations were retrieved
from superficial water samples. In accordance with Mueller et al. [52] protocol, each water column was classified as coastal
water because SPM concentration of each one is more than 0.5 mg/L (Table 2).
Table 2. Values of mean and standard
deviation (σ) of total suspended
particulate matter (SPM) and salinity concentrations and sea surface emissivity
(SSE) values with and without SPM effect, i.e., SSE (SPM ƒ= 0) and SSE (SPM = 0)
respectively.
 |
Coastal Waters of
SPM (mg/L) Salinity (g/L) SSE
(SPM ƒ= 0) SSE
(SPM = 0) Mean σ Mean σ Mean σ Mean σ
the Manfredonia Gulf 5.07 2.36 38.30 0.11 0.975 0.003 0.981 0.003
the Taranto Gulf 2.15 0.60 38.30 0.04 0.975 0.001 0.978 0.001
|
area close to Lesina
Lagoon 1.50 0.41 37.86 0.08 0.981 0.001 0.984 0.001
Sea temperature measurements of each location were
acquired with three multi-parametric platforms: SeaBird Electronics SBE 911-plus Conductivity-Temperature-Depth (CTD),
ELFO, which is equipped with Falmouth C-T sensor to measure sea temperature [63] and
TFLAP, which acquires sea temperature with MicroTSG
(MicroThermosalinograph) SBE 45 sensor [64].
Data were processed in accordance with UNESCO standards [65].
Thermal infrared radiances were obtained with an
infrared camera: an FLIR Systems
FLIR B series 360. FLIR records
brightness temperature at wavelengths from 7.5 to 13 µm
and has a sensitivity of 0.05 K at 30 ◦C and an accuracy
of ±2%. The calibrations were carried out before
and after the campaign to understand the stability of
the instrumentation. In order to estimate SSE value, the previous studies [25–52] and the user’s manual
ThermalCAM Reseacher Professional [66]
provide a useful procedure for detecting thermal infrared radiances. This
procedure was thoroughly applied for each acquisition. (i)
Radiance was measured, under specific conditions of weather (i.e.,
clear-sky
and sea surface wind speed less than 5 m/s) from the deck of ship over sea portion
where the multi-parametric platform
was dived. (ii) The radiometer was alternately pointed
downward to view the sea and upward to view the sky at
required zenith angle θ equal to 45◦ and
at required azimuth angle φ equal to 90◦ or 180◦, where φ was calculated with respect to sun’s azimuth
and ship’s heading should point the sun, i.e., azimuth
angle equal to 0◦. In order to verify
the view angle,
the radiometer equipped with a goniometer was mounted on a fixed
position. (iii)
Each pair of radiance measurements from sea and sky was
simultaneously acquired with measurements of sea temperature; atmosphere
temperature and relative
humidity and sea surface wind speed were measured from each location.
The MODIS on board the Aqua satellite acquired nine
images during the oceanographic cruise. The MODIS data were obtained from NASA’s Distributed Active Archive Centers.
In accordance with the previous
papers [25–52,66], each location selected
from MODIS images
has a zenith observed angle smaller
than 50◦, and the greatest zenith
observed angle is about 50◦ (i.e., the observations of the
coastal water of the Manfredonia acquired on 14 August 2011).
3. Estimation
of Sea Surface Skin Temperature Value from in Situ Data
Infrared radiometers (i.e., in situ and satellite) acquire the brightness temperature at surface
skin layer of the water column (SSTskin), which is thin (about 500 µm), whereas sensors
mounted on buoys, profiles, and ships measure
sea temperature at any depth beneath the skin (SSTdept) [67]. The vertical temperature structure of the
upper ocean such as coastal waters is variable; therefore, the quality of SST
observations depends on the vertical position of the measurement within the
water column and on the time of the day at which the measurements were obtained [68,69]. Consequently, some authors
developed models for estimating diurnal
and nocturnal warming
at a specific depth [70].
Since three multi-parametric platforms measure SSTdepth, their data were
exploited to estimate SSTskin values
using the empirical parametric model for retrieving diurnal SSTskin measurements proposed by Webster
et al. [59]. This algorithm was selected because
it was extensively compared with in situ measurements under
light-to-moderate wind conditions [70–73]. It has the following form:
∆T = SSTskin − SSTdepth = f + a(PS) + b(P) + c[ln(u)] + d(PS) ln u + e(u) (1)
where PS is
the daily peak surface solar radiation in Wm−2; P is
the daily mean precipitation rate in mmh−1; u is sea surface wind speed in m/s; and a, b, c, d,
e, and f are the coefficients
provided by Webster et al. [59] that are a function of sea surface
wind speed. The authors
highlighted that ∆T value
values cannot exceed 3 K [59].
∆T values were estimated with SSTdepth values and sea surface
wind speeds monitored during the cruise and with the daily peak surface solar radiations, which were obtained
from aerosol robotic network (AERONET) data. Therefore, 198 measurements of SSTdepth were analyzed to retrieve SSTskin values of 66 observations, and mean values of these results are shown in
Table 1.
In order to validate estimated values of SSTskin, simplified method proposed by Fairall et al. [60]
was selected because it was also extensively tested [70,73]. This algorithm calculates a value of SST (i.e., SSTsubskin) that is assumed
to be independent of the depth. A previous study
highlighted that this value can highlight a little
difference with respect to SSTskin value [70] because “the model assumes linear profiles of temperature
and surface-stress-induced current in this warm layer” [60].
SSTsubskin values were evaluated using the
following equation [70,74]:
. z − δ .v
T(z) = SSTsubskin −
DT −
δ
[SSTsubskin −
T(DT ] (2)
where T(z)
is temperature profile in the warm layer;
z is the depth; δ is the depth the skin
layer; DT is the depth of the warm layer; v is an
empirical parameter which is equal to
1 [70–74]. Therefore, 198 measurements of sea temperature were
exploited to evaluate SSTsubskin values of 66 observations and mean values of these results are shown in
Table 1.
The retrieved values
of SSTsubskin are slightly greater than SSTskin values in accordance with Kawai and Wada [70]. Root mean square deviation (RMSD) between SSTsubskin and SSTskin
values is equal to 0.12 K.
SSTskin
values were exploited to retrieve SSE values from brightness temperature
data which were acquired with in situ radiometer and to validate
the results of the proposed
method for retrieving SST from MODIS data (Figure 4).
 |
4. Estimation
of SSE Value from in Situ Data
As above mentioned, SSTskin data allowed to retrieve SSE values from
brightness temperature data that were acquired with in situ radiometer. Estimation of SSE values was performed
by ThermalCAM QuikReport version
1.1., which employs
the general formula
used to all FLIR systems
thermographic equipment [66]. This formula is based
on the assumption that an instrument receives the radiation from the object
itself and from the atmosphere surrounding the object. The received radiation
is given by
Wtot = ετWobj + (1 −
ε)τWre f l + (1 − τ)Watm (3)
where ετWobj is the
emission from the object, which has a temperature equal to Tobj; ε is
the emissivity of the object;
τ is
the transmittance of
the atmosphere; (1
−
ε)τWre f l
is the reflected emission
from surrounding sources, which have the temperature equal to Trefl; (1
−
τ)Watm emission from atmosphere, which has the
temperature equal to Tatm.
In accordance with the user’s manual, each pair of radiance measurements acquired from
sea surface and sky was processed together with the simultaneous SSTskin value, the relative humidity, and the atmosphere temperature.
Each surface water was characterized by at least five sets of these variables.
Each resultant value of SSE was compared with the others of the same station,
and the values characterized by standard deviation smaller than 0.001
were taken into
consideration. The mean of all these values was identified
as the value of that station. The coastal waters of the Manfredonia Gulf, the Taranto Gulf, and the area close to Lesina
Lagoon were described by 201 values of SSE for 39 observed locations, by 112
values of SSE for 21 observed locations, and by 28 values of SSE for six observed
locations, respectively.
5. Retrieval
of SSE Values
SSE behavior with respect to SPM concentration in the
coastal waters of the Manfredonia Gulf, the Taranto
Gulf, and the area close to Lesina
Lagoon was derived
from in situ data. The relationship
between SSE and SPM in these coastal waters is well defined (Figure 5), and the following functions for adequately representing
the data were found using optimal least squares fit (R2 coefficients are equal to 0.865, 0.785, and
0.901, respectively) as follows:
|
7.5−13µm =
−0.0011 SPM + 0.981 (4)
|
7.5−13µm =
−0.0012 SPM + 0.978 (5)
|
7.5−13µm =
−0.0013 SPM + 0.984 (6)
where SPM is
the concentration of total suspended particulate matter in mg/L.
The relationship between SSE values and salinity
concentrations and the relationship between SSE values and sea surface wind
speeds of these coastal waters cannot be adequately represented.
As above mentioned, the radiometer utilized consists
of a single band in the range 7.5–13 µm, whereas MODIS bands 31 and 32
are extended from 10.78 to 11.28 µm and from 11.77 to 12.27 µm, respectively. Therefore, it is necessary to
transform Equations (4)–(6) into algorithms for
calculating
SSE values with
SPM effect in MODIS bands 31 and 32.
For this purpose,
it is important to confirm
that the Equations
(4)–(6) evaluate SPM effect on SSE
value. Since previous papers proposed
models for estimating SSE values without
SPM effect and with
the effects of salinity concentration and surface wind speed zenith observation
angle, e.g., [30,75],
the decrease in SSE value associated with SPM concentration of each station was
estimated with the Equations (4)–(6) and was added
to SSE value derived from in situ radiance. All resultant values
were compared with emissivity from 8 to 13 µm
calculated with Masuda et al. [30] model (i.e., emissivity
was evaluated with
zenith observation angles equal to 40◦ and 50◦, with wind speeds equal to 4 m/s
and with salinity
concentration from 37 to 39 g/L, Figure
4). SSE values
tabulated by Masuda
et al. [30] were selected because these values
were only obtained with the inclusion of dissolved salt effect in the
emissivity of the pure water and were confirmed by several authors [31,37–40,46,47]. The results of the
comparison attest that SSE value of each station estimated without SPM effect
is emissivity of sea water that is characterized by salinity of that station
and by SPM concentration equal to 0 mg/L, SSE7.5–13 µm (SPM = 0). Therefore, this comparison proves
that SSE variation, which is evaluated with Equations (4)–(6), is mainly due to change
of SPM. Thus,
0.981, 0.978 and 0.984 are the average
values of SSE7.5–13 µm (SPM = 0) of the coastal
waters of the Manfredonia Gulf, the Taranto Gulf, and the area
close to Lesina Lagoon, respectively (Figure 6, Table 2).
 |
Figure 6. SSE values
tabulated by Masuda et al. [30] and average values of the
stations obtained without SPM effect versus salinity concentration.
In conclusion, the Equations
(4)–(6) were rewritten into the following forms:
. SSE (SPM = 0) .
SSEManfredonia Gulf λ
λ = −0.0011 SPM
SSE7.5−13µm
(SPM = 0)
+ SSEλ (SPM = 0) (7)
. SSE (SPM = 0) .
SSETaranto Gulf λ
λ = −0.0012SPM
SSE7.5−13µm
(SPM = 0)
+ SSEλ (SPM = 0) (8)
SSEarea close to Lesina Lagoon
. SSEλ(SPM = 0) .
λ = −0.0013 SPM
where
λ is the spectral region.
SSE7.5−13µm
(SPM = 0)
+ SSEλ (SPM = 0) (9)
In order to obtain SSE
in MODIS bands 31 and 32 with effect of SPM concentration (SSEMODIS_band31 (SPM ƒ= 0) and SSEMODIS_band32 (SPM ƒ= 0)), it is
necessary to estimate SSE in these regions
without this effect (SSEMODIS_band31 (SPM =
0) and SSEMODIS_band32 (SPM = 0)), since SPM
concentrations are known (Equations (7)–(9)).
MODIS acquisitions over all stations
on 14 August 2011 were performed with zenith observation angles larger than 50◦, and
SSE values tabulated with these angles by Masuda et al. [30]
were not confirmed by some authors [31,37–39,43]. Therefore, SSE (SPM = 0) values
in MODIS bands
31 and 32 were evaluated with the following equations proposed by Niclos and Caselles [75]:
. . cU+d ..b31
SSEMODIS_band
31(θ, U) =
SSEMODIS_band31 (0◦) cos θ (10)
. . cU+d ..b32
SSEMODIS_band32 (θ, U) =
SSEMODIS_band
32(0◦) cos θ (11)
where θ is zenith observation angle; U is sea surface wind speed in m/s; SSEMODIS_band31 (0◦) and SSEMODIS_band32 (0◦) are SSE values in MODIS bands 31 and 32,
which were acquired with zenith observation
angle equal to 0◦; c and d are
constant coefficients (i.e.,
−0.037 ±
0.003 s/m and 2.36 ±
0.03); and b31 is
equal to 0.0342; b32 is equal to 0.0508.
SSEMODIS_band31 (0◦) and SSEMODIS_band32 (0◦) values were obtained
by Newman et al. [42] model. The authors investigated SSE
behaviour with respect to the salinity concentration using in situ data and
their results in MODIS bands 31 and 32 are confirmed by the SSE values of the
most adopted models [30,76]. Therefore,
SSEMODIS_band31 (0◦) and SSEMODIS_band32 (0◦) values
are equal to 0.9922 and 0.9888, respectively.
SSEMODIS_band31
(SPM = 0) and SSEMODIS_band32
(SPM = 0) values of all stations were obtained with zenith observation
angles retrieved from MODIS data and with sea surface wind speeds measured during the cruise. In order to confirm
that these values are emissivity of each surface
water characterized by its salinity and by SPM concentration equal to 0 mg/L, values
estimated with Niclos and Caselles [75]
equations were compared with SSE in 11 µm and 12 µm
(i.e., MODIS bands 31 and
32) tabulated by Masuda et al. [30] (i.e., emissivity was obtained with zenith observation
angle equal
to the angle
of each analyzed
image, with wind speed equal
to 4 m/s, and with salinity concentration equal to 38.26 g/L, i.e.,
average salinity, which was measured
in situ, Figure 4). RMSD values between SSEMODIS_band31 (SPM = 0) and SSEMODIS_band32 (SPM = 0) values
evaluated with Niclos and Caselles [75]
equations and emissivity values calculated by Masuda et al. [30] are equal to 0.008 and 0.009, respectively. In accordance with the previous
papers that did not confirm
SSE values tabulated with angles larger than 50◦ by Masuda et al. [30,31,37–39,43], RMSD values of MODIS
bands 31 and 32 acquired on 14 August
2011 are the largest, i.e.,
0.020 and 0.017,
respectively.
6. Retrieval
of SST Measurements from MODIS Data
In order to test the method, locations
monitored within ±2 h with respect
to MODIS overpasses were selected, i.e., 56
locations (Table 3).
The values of SSTskin that
were obtained with the model proposed by Webster
et al. [59] were compared with nearest pixels to
ship locations obtained by MODIS Aqua Global
Level 3 Mapped Thermal SST products at 4.63 km spatial resolution, which were provided by PO.DAAC FTP-site
[77]. Values of RMSD, bias, and standard deviation
(σ) are shown in
Table 3.
Table 3. Comparisons
(i.e., root mean square deviation (RMSD), bias and standard deviation, σ) between
SSTskin data and SST
measurements which were obtained by Moderate Resolution Imaging
Spectroradiometers (MODIS) Aqua Global Level 3 Mapped Thermal SST products.
 |
Coastal Waters
of the Area Close to Lesina Lagoon
Date Start Time W (g/cm2) Number of Locations Number of Locations Obtained
from MODIS Level 3
7
August 2011
12:40
UTC 1.563 6 6
SST (K)
MODIS Level 3
Bias 1.43
σ 0.44
RMSD 1.49
Coastal Waters of the Manfredonia Gulf
Date Start Time W (g/cm2) Number of locations Number of Locations Obtained
from MODIS Level 3
8
August 2011
11:45 UTC 5.246 5 2
9
August 2011
12:25
UTC 10.655 7 1
12 August 2011
11:20
UTC 0.743 8 8
24 August 2011
11:40
UTC 2.103 11 7
Coastal Waters of Taranto
Gulf
Date Start Time W (g/cm2) Number of Locations Number of Locations Obtained
from MODIS Level 3
13
August 2011
12:00
UTC 1.370 5 5
14
August 2011
12:45 UTC 1.517 6 1
15
August 2011
11:50 UTC 0.743 6 7
16
August 2011
12:30 UTC 1.197 2 1
SST (K)
MODIS Level 3
Bias 1.12
σ 0.64
RMSD 1.21
Bias 1.36
σ -
RMSD -
Bias 1.11
σ 0.39
RMSD 1.17
Bias 0.20
σ 0.11
RMSD 1.26
SST (K)
MODIS Level 3
Bias 1.41
σ 0.31
RMSD 1.31
Bias 1.33
σ -
RMSD -
Bias 0.98
σ 0.43
RMSD 1.06
Bias 0.44
σ -
RMSD -
The current operational procedure for deriving SST
from MODIS data [21,24]
is a regression to buoys data, which has not a value of SSE as an explicit
term, whereas the split-window algorithm developed by Niclos et al. [35] includes SSE value. Therefore, this method was selected
because it allows putting SSE value estimated with SPM effect into retrieval of
SST measurements. MODIS images were exploit to retrieve SST measurements using
the following equation [35]:
SST
= TMODIS_band31 + [a1(secθ − 1) + a2].TMODIS_band31 − TMODIS_band32
.+
2+
+[b1(secθ − 1) + b2].TMODIS_band31 − TMODIS_band32
.
|
|
SSEMODIS_band31
+SSEMODIS_band32 .
|
1 − 2 0 1 2 −
(12)
|
−.β0 + β1w
+ β2w2..SSEMODIS_band31 − SSEMODIS_band .
where TMODIS_bandi is brightness temperature at
satellite level in K; θ is
zenith observation angle; w is total
atmospheric water vapor content in g/cm2; SSEMODIS_bandi is sea surface emissivity in MODIS
band; a1,
a2,
b1,
b2,
c1,
c2,
α0,
α1,
α2,
β0,
β1,
β2 are
constant coefficients provided by Niclos et al. [35].
Brightness
temperatures in MODIS bands 31 and 32 and zenith observation angle were derived
from MODIS data; it was therefore necessary to
calculate three input data: SSEMODIS_band31 (SPM ƒ= 0), SSEMODIS_band32 (SPM ƒ= 0) and total atmospheric water vapor content (Figure
4).
SSEMODIS_band31 (SPM
ƒ= 0) and SSEMODIS_band32
(SPM ƒ= 0) values of each
location were evaluated from in situ concentrations of SPM with
the Equations (7)–(9)
and with the
method which was
proposed by Wen-Yao et al. [46].
Total atmospheric water vapor content was retrieved
from MODIS data using the following algorithm proposed by Sobrino et al. [29]:
w = 0.0192WMODIS_band17 + 0.453WMODIS_band18 + 0.355WMODIS_band19 (13)
with
|
W =
26.314 −
54.434
LMODIS_band17
MODIS_band2
+ 28.449
. LMODIS_band17 .2
LMODIS_band2
(14)
|
W =
5.012 −
23.017
LMODIS_band18
MODIS_band2
|
W =
9.446 −
26.887
LMODIS_band19
MODIS_band2
+ 27.884
+ 19.914
. LMODIS_band18 .2
LMODIS_band2
. LMODIS_band19 .2
LMODIS_band2
(15)
(16)
and where w is total atmospheric water vapor
content in g/cm2 and LMODIS_bandi is the radiance in W m−2 sr−1 µm−1. Table 4 shows the results of each
MODIS image. The results were
compared with the values of precipitable water that were obtained from AERONET data (Figure 4). The best fit logarithmic curve between total
atmospheric water vapor content and precipitable water values was identified in accordance with
Mavromatakis et al. [78], and its R2 is equal to 0.717.
Therefore, SST measurements at nearest pixels
to ship locations were obtained
with and without the inclusion of SPM effects in
SSE values which were used as input into Niclos et al. [35]
algorithm (Figures 4 and 7). In order to analyze the capability of SPM effect to
minimize error in SST measurements, the included effects were obtained with Equations (7)–(9)
and with the
model proposed by Wen-Yao et al. [46]. The resultant data were compared
with SSTskin
values obtained with the model proposed by Webster et al. [59] (Table 4).
 |
Table 4. Total atmospheric water vapor content values and comparisons (i.e., RMSD, bias and standard deviation, σ) between
SSTskin data and SST
measurements at nearest pixels to ship locations which were retrieved from
MODIS data using Niclos et al. [35] algorithm with and without
the inclusion of SPM effects in SSE values. The included effects were evaluated
with Equations (7)–(9) and with the method proposed by Wen-Yao et al. [46].
 |
Coastal Waters of the Area Close to Lesina Lagoon
SST (K)
Retrieved by [35]
Date Start Time W (g/cm2) Number of Locations
with SSE (SPM = 0)
with SSE (SPM ƒ=
0) Using Equations
(7)–(9)
7 August 2011
12:40 UTC 1.563 6
Bias −0.49 −0.40 −0.49
σ 0.48 0.50 0.48
RMSD 0.66 0.60 0.66
Coastal
Waters of the Manfredonia Gulf
SST (K)
Retrieved by [35]
Date Start Time W (g/cm2) Number of Locations
with SSE (SPM = 0)
with SSE (SPM ƒ=
0) Using Equations
(7)–(9)
8 August 2011
11:45 UTC 5.246 5
9 August 2011
12:25 UTC 10.655 7
12 August 2011
11:20 UTC 0.743 8
24 August 2011
11:40 UTC 2.103 11
Bias −0.80 −0.65 −0.72
σ 0.48 0.43 0.43
RMSD 0.91 0.76 0.82
Bias −0.73 −0.67 −0.72
σ 1.09 1.09 1.09
RMSD 1.26 1.23 1.26
Bias −0.81 −0.50 −0.79
σ 0.52 0.46 0.52
RMSD 0.95 0.66 0.93
Bias −0.71 −0.29 −0.69
σ 0.31 0.31 0.31
RMSD 0.77 0.42 0.75
Coastal
Waters of Taranto Gulf
Date Start Time W (g/cm2) Number of Locations
with SSE (SPM = 0)
with SSE (SPM ƒ=
0) Using Equations
(7)–(9)
13 August 2011
12:00 UTC 1.370 5
14 August 2011
12:45 UTC 1.517 6
15 August 2011
11:50 UTC 0.743 6
16 August 2011
12:30 UTC 1.197 2
Bias −1.03 −0.89 −1.03
σ 0.12 0.15 0.12
RMSD 1.04 0.89 1.03
Bias −0.54 −0.42 −0.54
σ 0.25 0.24 0.25
RMSD 0.59 0.48 0.59
Bias −0.73 −0.59 −0.72
σ 0.31 0.31 0.32
RMSD 0.78 0.65 0.78
Bias −0.52 −0.41 −0.51
σ 0.07 0.08 0.07
RMSD 0.52 0.42 0.52
7.
Sensitivity Analysis
Sensitivity analysis was aimed at assessing the error in SST measurements in coastal waters
due to the omission of SPM effect from the estimation of SSE value. The
error is the difference between SST obtained with and without the inclusion of
SPM effect in SSE value. These two SSE values are specifically put into Niclos
et al. [35] algorithm for retrieving SST from MODIS
data using different total atmospheric water vapor content. The relative influence
of SPM concentration and total atmospheric
water vapor content on the error in SST measurements was calculated, and the
zenith observation angle was set equal to 45◦ because
its effect on SST measurements can be considered negligible.
SPM effect was derived from the increase in SPM concentration
from 0 to 10 mg/L because this range was
monitored in these coastal waters. SSE values were obtained from this range of
concentrations with the Equations (7)–(9).
Total atmospheric water vapor
content was varied from
Figure 8 shows the behavior of the error in SST measurements with respect to the error due to the
omission of SPM effect from the estimation of SSE value.
(b)
Figure 8. The error in
SST measurements due to the omission of SPM effect from the estimation of SSE value:
(a) the values obtained
with total atmospheric water vapor contents
(w) equal to 0.1 g/cm2 is contained in the first panel; (b) the values obtained with w equal to 10 g/cm2 is contained in the second panel.
8. Discussion
and Conclusions
The paper aims to propose a method for retrieving
accurate measurements of SST (Figure 4)
and to demonstrate that the inclusion of the effect of SPM concentration in SSE
value, which is put into the algorithms, minimizes the error in SST
measurements, especially in coastal waters. For this purpose, an oceanographic
cruise was performed to survey the coastal waters of the Manfredonia Gulf, the Taranto
Gulf, and the area close
to Lesina Lagoon,
and 66 observations of water
column were performed. Data
collected in situ allowed for the estimation of SSTskin and SSE values, the analysis of SSE behavior with respect to SPM
concentration, and the validation of the results of the proposed method. Data
acquired during the cruise by MODIS on board Aqua satellite was exploited to
test the method.
SSTskin values were estimated
with the empirical parametric model for retrieving diurnal measurements of SSTskin proposed by Webster et al. [59]. Moreover, SSTsubskin values were
obtained with the simplified method for retrieving diurnal measurements
of SSTsubskin
proposed by Fairall
et al. [60] in order
to evaluate the SSTskin values. These
algorithms were chosen
because they were extensively tested and were successfully applied
[70,72,73]. Therefore, 198 measurements of sea
temperature were exploited to retrieved 66 values of SSTskin and SSTsubskin.
In order to validate the
results, SSTsubskin
data were compared with SSTskin values.
In accordance with [70], SSTsubskin values are slightly greater than SSTskin values (i.e., RMSD is equal to 0.12 K).
In accordance with the procedure
for detecting thermal
infrared radiances [25–52,66], SSE values
from 7.5 to 13 µm
were retrieved from at least five sets of variables: radiance measurements acquired from sea surface and sky (i.e.,
first and second variables), the relative humidity and atmosphere temperature
data collected in situ (i.e., third and fourth variables), and validated values
of SSTskin obtained by [59] (i.e., fifth
variable). Therefore, 66 values of SSE were averaged out from 341 estimated
values. The standard deviation values were smaller than 0.001. In order to
analyze SSE behaviors, these values of SSE were compared with SPM and salinity
concentrations and with sea surface wind speeds monitored in the same location.
Only SSE behavior with respect to SPM concentration is well defined.
In summary, the effect of SPM concentration on SSE
value from 7.5 to 13 µm can be evaluated from in situ
concentrations with the developed algorithms (i.e., Equations (7)–(9)), which
adequately
represent SSE behaviors
with respect to SPM concentrations of the Manfredonia Gulf, the Taranto Gulf, and the area close to Lesina Lagoon (R2 coefficients are equal to 0.865, 0.785, and 0.901, respectively). SSE behaviors with respect to SPM concentrations of these three
coastal waters are slightly different
(Figure 5) because
SSE value is affected by feature variability of the adjacent
river basins and
In order to validate 66 values of SSE from 7.5 to 13 µm, these values without SPM effect were compared with SSE values calculated by [30], and these values are comparable (Figure
6). SSE values for MODIS bands 31 and 32 were
evaluated with Niclos and Caselles [75]
equations. In order to validate these values,
the data were compared with SSE values
which were calculated by [30] (RMSD values
are equal to 0.008 for SSEMODIS_band31 and 0.009 for SSEMODIS_band32).
SSTskin measurements
monitored within ±2 h with respect to MODIS overpasses were
selected
to test the method, i.e., 56 values. These values were
compared with SST data provided by MODIS level 3 products. RMSD is equal to 1.13
K (Table 3). Moreover, SST values were retrieved from
MODIS data using Niclos et al. [35] algorithm, which allows for including SSE values with SPM effect. Total atmospheric water vapor content
values, which are required by [35], were retrieved
from MODIS data using algorithm proposed
by Sobrino et al. [29].
The results were validated with AERONET data (R2 is equal to 0.717). In order to analyze the
capability of SPM effect to minimize the error in SST retrieval, SSE values were evaluated with two models
for retrieving SPM effect: developed algorithms (i.e., Equations (7)–(9)) and the model proposed by [46]. Therefore, 56 measurements of SSTskin were compared with SST values obtained with
the inclusion of these two data set using Niclos et al. [35]
algorithm. Total values of RMSD are
equal to 0.62 K and 0.84 K, respectively (Table
4 and Figure 9).
 |
Figure 9. RMSD values of these coastal waters between SSTskin data and SST measurements
which were obtained by MODIS Aqua Global Level 3 Mapped Thermal SST products.
RMSD values of these coastal waters between SSTskin data and SST measurements retrieved from MODIS data
using Niclos et al. [35] algorithm with and without the inclusion of SPM
effects in SSE values.
In all stations monitored within ±2 h with
respect to MODIS overpasses, SST retrieved from MODIS images with this
inclusion using Niclos et al. [35] algorithm exhibits a
reduction in error. The decrease with respect to MODIS level 3
products is up to 2.67 K. It should be noted that MODIS level 3 products are characterized by 4.63 km spatial resolution; only a partial number of stations,
i.e., 40 locations over 56, (Table 3) was derived from these products, and standard MODIS SST
algorithms do not perform well in coastal
situations because the atmospheric correction algorithms are
optimized for oceanic conditions [21].
Sensitivity analysis was performed to analyze the behavior of the error
in SST measurements in the coastal waters with respect
to the error in SPM concentration (i.e., the error in SST measurements if the
SPM concentration is assumed to be zero). SST measurements were derived from
MODIS data using Niclos et al. [35] algorithm. The analysis took into consideration the
increases in SPM concentration from 0 to 10 mg/L and total atmospheric water
vapor content from 0.1 to 10 g/cm2.
Sensitivity analysis shows that error as large as 0.69 K in SST measurements is
associated with an error in SPM concentration equal to 10 mg/L and with total
atmospheric water vapor content equal to 0.1 g/cm2 and error as large as 0.25 K in SST measurements
is associated with an error in SPM concentration equal to 10 mg/L and with
total atmospheric water vapor content equal to 10 g/cm2. The analysis highlights that the increase
in total atmospheric water vapor content
decreases the error
[28,29].
In summary, the
analysis confirms that SSE values decrease with the increase of the SPM
concentrations, and this decrease is tiny [46,49]. Moreover, the
results of the developed method highlight that the error in SST measurements in
these coastal waters decreases with the inclusion of SPM effect in the estimation of SSE value,
which is used as input
into the retrieval of SST from MODIS
data. Certainly, an achieved map is
never the territory [81,82],
and therefore, a model cannot fully represent
the variability and the complexity of the territory. However,
the results attest
to the accuracy of the procedure to acquire and analyze the in situ data and the accuracy
of the developed algorithms
for estimating the effect of SPM concentration on SSE values
in MODIS bands
31 and 32.
In conclusion, this paper demonstrates that the inclusion
of the effect of SPM concentration in SSE
value, which is put into the algorithms for retrieving SST from remote data, minimizes
the error in SST
measurements in coastal
waters. It is shown that an estimation of SPM effect on SSE value provides
a useful adjustment for minimizing this
error.
Future work should
aim to improve spatial variability of SST measurements in coastal waters:
SST measurements calculated with SPM effect will be estimated at
monitored locations and in the whole remote image. For this purpose, the best
method for retrieving SPM concentrations of these coastal waters from remote data will be developed, and the uncertainties will carefully be analyzed. Therefore, SPM concentration and total
atmospheric water vapor content will be retrieved from MODIS data, and these products will be included
in the algorithm for retrieving SST measurements of coastal waters from MODIS data.
Acknowledgments:
This
research was supported by the Italian National Research Council. The author
thanks the Principal Investigators and their staff for establishing and
maintaining the six AERONET sites used in this investigation. The author would
like to thank many professors for their encouraging judgment, their valuable
comments and suggestions, and their
useful corrections which
improved the quality
of this manuscript. The author is
particularly grateful to Stuart Newman.
Conflicts of
Interest: The
author declares no conflict of interest.
References
1.
Costanza, R.; de Groot, R.; Sutton, P.; van der Ploeg,
S.; Anderson, S.J.;
Kubiszewski, I.; Farber,
S.; Turner, R.K. Changes in the global value of ecosystem services. Glob. Environ. Chang. 2014, 26, 152–158. [CrossRef]
2.
Crain, C.M.; Halpern, B.S.; Beck, M.W.; Kappel, C.V. Understanding and managing human threats to the coastal marine environment. Ann. N. Y. Acad. Sci. 2009, 1162, 39–62. [CrossRef] [PubMed]
3.
Ahuja, S. Monitoring
Water Quality: Pollution Assessment, Analysis, and Remediation; Elsevier: Waltham, MA, USA, 2013; 379p.
4.
Sala, O.E.; Chapin, F.S.;
Armesto, J.J.; Berlow, E.;
Bloomfield, J.; Dirzo, R.; Leemans, R. Global biodiversity scenarios for the
year 2100. Science 2000, 287, 1770–1774. [CrossRef]
5.
USCOP (US Commission on Ocean Policy). An Ocean Blueprint for the 21st Century:
Final Report of the US Commission on Ocean Policy; US Commission on Ocean Policy:
Washington, DC, USA, 2004. Available
online: https://oceanconservancy.org/wp-content/uploads/2015/11/000_ocean_full_report-1.pdf
(accessed
on 31 July 2017).
6.
Blanchette, C.A.; Miner Melis, C.; Raimondi, P.T.; Lohse, D.; Heady, K.E.; Broitman, B.R. Biogeographical patterns of rocky
intertidal communities along the Pacific coast of North America. J. Biogeogr. 2008, 35, 1593–1607. [CrossRef]
7.
Smale, D.A.; Wernberg,
T. Satellite-derived SST data
as a proxy for water temperature in nearshore
benthic ecology.
Mar.
Ecol. Prog. Ser. 2009, 387, 27–37. [CrossRef]
8.
McCaul, M.; Barland,
J.; Cleary, J.; Cahalane, C.; McCarthy,
T.; Diamond, D. Combining
Remote Temperature Sensing
with in-Situ Sensing to Track Marine/Freshwater
Mixing Dynamics. Sensors 2016, 16, 1402. [CrossRef]
[PubMed]
9.
Thomas, A.; Byrne,
D.; Weatherbee, R. Coastal sea surface temperature variability from Landsat
infrared data.
10.
Fusilli, L.; Palombo,
A.; Cavalli, R.M.;
Pignatti, S. Airborne
thermal data for detecting karst
water resources in the Kotor Bay. In Proceedings of the 33rd
International Symposium on Remote Sensing of Environment (ISRSE
2009), Stresa, Italy, 4–8 May 2009;
pp. 356–359.
11.
De Boer, G.J.; Pietrzak,
J.D.; Winterwerp, J.C. SST observations of upwelling induced
by tidal straining
in the Rhine
ROFI. Cont. Shelf Res. 2009, 29, 263–277. [CrossRef]
12.
Ahn, Y.H.; Shanmugam, P.; Lee, J.H.; Kang,
Y.Q. Application of satellite infrared data for mapping of thermal
plume contamination in coastal ecosystem of Korea. Mar. Environ. Res. 2006, 61, 186–201. [CrossRef] [PubMed]
13.
Tang, D.; Kester,
D.R.; Wang, Z.; Lian, J.; Kawamura,
H. AVHRR satellite remote sensing
and shipboard measurements of the thermal plume
from the Daya Bay. nuclear power
station. China. Remote Sen. Environ.
2003, 84, 506–515. [CrossRef]
14.
Xing, Q.; Chen, C.Q.; Shi, P. Method of integrating Landsat-5 and Landsat-7 data to retrieve
sea surface temperature in coastal waters on the basis of local empirical
algorithm. Ocean Sci. J. 2006, 41, 97–104. [CrossRef]
15.
Azzaro, F.; Cavalli, R.M.; Decembrini, F.; Pignatti,
S.; Santella, C. Biochemical and dynamical characteristics of the Messina Straits
water by means of hyperspectral data. In Proceedings of the Second
International Asia-Pacific Symposium on Remote Sensing of the Atmosphere,
Environment, and Space, Sendai, Japan, 23 January
2001; pp. 240–249. [CrossRef]
16.
Diofantos, G.H.; Marinos, G.H.; Kyriacos, T.; Agapiou, A. Integration of
micro-sensor technology and remote sensing for monitoring coastal water quality
in a municipal beach and other areas in Cyprus. In Proceedings of the SPIE Remote
Sensing for Agriculture, Ecosystems, and Hydrology,
Berlin, Germany, 18 September 2009.
17.
BIPM; IEC; IFCC; ILAC; ISO; IUPAC; IUPAP; OIML. Evaluation of Measurement Data—Guide to the
Expression of Uncertainty in Measurement. International Organization for
Standardization (ISO), 2008. Available online:
http://www.bipm.org/en/publications/guides/gum.html (accessed on 31 July 2017).
18.
Smit, A.J.; Roberts,
M.; Anderson, R.J.;
Dufois, F.; Dudley,
S.F.; Bornman, T.G.; Bolton, J.J. A Coastal
Seawater Temperature Dataset for Biogeographical Studies:
Large Biases between
In Situ and Remotely-Sensed Data Sets
around the Coast of South Africa. PLoS
ONE 2013, 8, e81944. [CrossRef]
[PubMed]
19.
Harries, J.E.; Llewellyn-Jones, D.T.; Minnett, P.J.; Saunders, R.W.; Zavody, A.M.; Wadhams, P.; Taylor, P.K.; Houghton, J.T. Observations of sea-surface
temperature for climate research. Philos.
Trans. R. Soc. Lond. A Math. Phys. Eng. Sci. 1983, 309, 381–395. [CrossRef]
20.
Esaias, W.E.; Abbott,
M.R.; Barton, I.; Brown, O.B.; Campbell, J.W.;
Carder, K.L.; Clark, D.K.;
Evans, R.H.; Hoge, F.E.; Gordon, H.R.;
et al. An overview of MODIS capabilities for ocean science
observations. IEEE Trans. Geosci.
Remote Sens. 1998, 36, 1250–1265. [CrossRef]
21.
Kilpatrick, K.A.; Podestá,
G.; Walsh, S.; Williams, E.; Halliwell, V.; Szczodrak, M.; Brown, O.B.; Minnett, P.J.;
Evans, R. A decade of sea surface temperature from MODIS. Remote Sens. Environ. 2015, 165, 27–41. [CrossRef]
22.
Liu, Y.; Minnett,
P.J. Sampling errors in
satellite-derived infrared sea-surface temperatures. Part I: Global and
regional MODIS fields. Remote Sens.
Environ. 2016, 177, 48–64. [CrossRef]
23.
Liu, Y.; Chin, T.M.; Minnett,
P.J. Sampling errors in satellite-derived infrared
sea-surface temperatures. Part II:
Sensitivity and parameterization. Remote Sens. Environ. 2017, 198, 297–309. [CrossRef]
24.
Kilpatrick, K.A.; Podesta, G.P.; Evans, R. Overview of the NOAA/NASA advanced very high
resolution radiometer Pathfinder algorithm for sea surface temperature and
associated matchup database. J. Geophys. Res. Oceans 2001,
106, 9179–9197. [CrossRef]
25.
Brown, O.B.; Minnett, P.J.; Evans, R.; Kearns, E.; Kilpatrick, K.; Kumar, A.; Sikorski, R.; Závody, A. MODIS
Infrared Sea Surface Temperature Algorithm
Algorithm Theoretical Basis Document; Version
2.0; University of Miami: Coral Gables, FL,
USA, 1999; 91p.
26.
Kennedy, J.J. A review of uncertainty in in situ measurements and data sets of sea surface temperature.
27.
Minnett, P.J.; Brown, O.B.;
Evans, R.H.; Key, E.L.; Kearns,
E.J.; Kilpatrick, K.; Kumar, A.; Maillet, K.A.; Szczodrak, G. Sea-surface temperature measurements from the Moderate-Resolution Imaging Spectroradiometer
(MODIS) on Aqua and Terra. In Proceedings
of the 2004 IEEE International
Geoscience and Remote Sensing Symposium (IGARSS ’04), Anchorage, AK, USA, 20–24 September 2004; Volume 7, pp. 4576–4579. [CrossRef]
28.
Sobrino, J.A.; Li, Z.L.; Stoll, M.P. Impact of the atmospheric
transmittance and total water vapor content in the algorithms for estimating
satellite sea surface temperature. IEEE Trans. Geosci. Remote Sens. 1993, 31, 946–952. [CrossRef]
29.
Sobrino, J.A.; El Kharraz, J.; Li, Z.L. Surface temperature and water vapour retrieval from MODIS data. Int. J.
Remote Sens. 2003,
24, 5161–5182. [CrossRef]
30.
Masuda, K.; Takashima, T.; Takayama, Y. Emissivity of pure and sea waters
for the model
sea surface in the
infrared window regions. Remote Sens. Environ. 1988,
24, 313–329. [CrossRef]
31.
Konda, M.; Imasato,
N.; Nishi, K.; Toda, T. Measurement of the sea surface emissivity. J. Oceanogr. 1994,
50, 17–30. [CrossRef]
32.
Kilpatrick, K.A. Climate Algorithm Theoretical Basis Document (C-ATBD): Pathfinder SST. CDRP-ATBD-0099 v2; 2013. Available online: http://www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/Sea_Surface_Temperature_ Pathfinder/AlgorithmDescription.pdf (accessed on 25 August
2017).
33.
Kilpatrick, K.; Podesta, G.; Walsh, S.; Evans, R.; Minnett, P.
Implementation of Version 6 AQUA and TERRA SST Processing; White Paper; University of Miami:
Coral Gables, FL, USA, 2014.
34.
McMillin, L.M. Estimation of sea surface temperatures
from two infrared window measurements with different
absorption. J. Geophys. Res. 1975, 80, 5113–5117. [CrossRef]
35.
Niclòs, R.; Caselles, V.; Coll, C.; Valor, E.
Determination of sea surface temperature at large observation angles using an
angular and emissivity-dependent split-window equation. Remote Sens. Environ. 2007,
111, 107–121.
[CrossRef]
36.
Masuda, K. Influence of wind direction on the infrared
sea surface emissivity model including multiple reflection
effect. Meteorol. Geophys. 2012, 63, 1–13. [CrossRef]
37.
Niclòs, R.; Valor, E.; Caselles, V.; Coll, C.; Sánchez,
J.M. In situ angular measurements of thermal infrared
sea surface emissivity—Validation of models. Remote Sens. Environ. 2005,
94, 83–93. [CrossRef]
38.
Niclòs, R.; Caselles, V.; Valor, E.; Coll, C.; Sánchez, J.M. A simple
equation for determing seasurface emissivity
in the 3–15 µm region. Int. J. Remote Sens. 2009,
30.
[CrossRef]
39.
Watts, P.D.; Allen, M.R.; Nightingale, T.J.
Wind speed effects on sea
surface emission and reflection for the along track
scanning radiometer. J. Atmos. Ocean. Technol. 1996, 13, 126–141. [CrossRef]
40.
Wu, X.; Smith, W.L. Emissivity of rough sea surface for 8–13 µm: Modeling and verification. Appl. Opt. 1997,
41.
Fiedler, L.; Bakan,
S. Interferometric measurements of sea surface
temperature and emissivity. Dtsch. Hydrogr.
Z. 1997,
49, 357–365. [CrossRef]
42.
Newman, S.M.; Smith, J.A.; Glew, M.D.; Rogers, S.M.; Taylor,
J.P. Temperature and salinity
dependence of sea surface emissivity in the thermal
infrared. Q. J. R. Meteorol. Soc. 2005, 131, 2539–2557. [CrossRef]
43.
Niclòs, R.; Caselles, V.; Coll,
C.; Valor, E.; Rubto,
E. Autonomous Measurements of Sea Surface
Temperature Using In Situ Thermal Infrared
Data. J. Atmos. Ocean. Technol. 2004, 21, 683–692. [CrossRef]
44.
Smith, W.L.; Knuteson, R.O.; Revercomb, H.E.; Feltz, W.; Howell,
H.B.; Menzel, W.P.; Nalli, N.R.; Brown, O.;
Brown, J.; Minnett, P.; et al.
Observations of the infrared radiative properties of the ocean-implications for
the measurement of sea surface temperature via satellite remote sensing. Bull. Am. Meteorol. Soc. 1996, 77, 41–51. [CrossRef]
45.
Cox, C.; Munk, W. Measurement
of the roughness of the sea surface from photographs of the sun’s glitter.
46.
Wen-Yao, L.; Field, R.T.; Gantt, R.G.; Klemas, V. Measurement of the surface
emissivity of turbid waters.
47.
Salisbury, J.W. Emissivity of terrestrial materials in the 8–14 µm atmospheric window.
Remote
Sens. Environ.
48.
Park, J.H.; Na, S.I. SST and SS changes during
Saemangeum seawall construction using Landsat TM and ETM
imagery. Proc. SPIE 2010, 7831.
[CrossRef]
49.
Wei, J.A.; Wang, D.; Gong, F.; He, X.; Bai, Y. The Influence
of Increasing Water Turbidity on Sea
Surface Emissivity. IEEE Trans. Geosci. Remote
Sens. 2017, 55, 3501–3515. [CrossRef]
50.
Zhao, Y.S. Principles and Methods of Remote Sensing
Application; Science Press: Beijing, China, 2003.
51.
Morel, A. Optical modelling of the upper ocean in relation to its
biogenous matter content (case 1 waters).
52.
Mueller, J.L.; Austin, R.W.; Morel, A.; Fargion, G.S.; McClain, C.R. Ocean Optics Protocols for Satellite Ocean
Color Sensor Validation. Volume I:
Introduction. Background and Conventions; Revision 4, NASA Tech. Memo. 2003-21621; NASA Goddard Space Flight Center: Greenbelt, MD, USA, 2003; pp. 1–56.
53.
Cavalli, R.M.; Betti, M.; Campanelli, A.; Di Cicco, A.; Guglietta, D.; Penna, P.; Piermattei, V. A methodology
to assess the accuracy with which remote
data characterize a specific surface,
as a Function of Full Width at Half
Maximum (FWHM): Application to three Italian
coastal waters. Sensors 2014, 14, 1155–1183. [CrossRef] [PubMed]
54.
Fiesoletti, F.; Specchiulli,
A.; Spagnoli, F.; Zappalà, G. A new
near time monitoring network in the Gulf of Manfredonia-Southern Adriatic
Sea. In European Operational Oceanography: Present and Future, Proceedings of the 4th International Conference on EuroGOOS, Brest, France,
6–9 June 2005; European Commission Research Directorate-General:
Brussels, Belgium, 2005; pp. 782–792.
55.
Meftah, M.B.; De Serio, F.; Mossa, M.; Petrillo, A.F.;
Pollio, A. Numerical results of the pollutant spreading offshore Taranto (Italy). In Proceedings of the
33rd IAHR Congress: Water Engineering
for a Sustainable Environment, Vancouver, BC, Canada, 9–14 August 2009.
56.
Law n. 349 (1986). Istituzione del Ministero
Dell’ambiente e Norme in Materia di Danno Ambientale. Gazzetta Ufficiale della
Repubblica Italiana del 15 luglio 1986, n. 162, Supplemento Ordinario n. 59.
Available online: http://www.minambiente.it/sites/default/files/legge_08_07_1986_349.pdf (accessed on
6 November 2017).
57.
Law n. 426 (1998).
Nuovi interventi in campo ambientale.
Gazzetta Ufficiale della Repubblica Italiana del 14 Dicembre 1998, n. 291, Serie Generale.
Available online: http://www.agentifisici.isprambiente.it/
(accessed on 6 November 2017).
58.
Roselli, L.; Fabbrocini, A.; Manzo, C.; D’Adamo, R.
Hydrological heterogeneity. nutrient dynamics and water quality of a non-tidal lentic
eco system (Lesina
Lagoon. Italy). Estuar. Coast.
Shelf Sci. 2009, 84, 539–552. [CrossRef]
59.
Webster, P.J.; Clayson, C.A.;
Curry, J.A. Clouds, radiation, and the diurnal
cycle of sea surface temperature in the tropical western Pacific. J. Clim. 1996, 9, 1712–1730. [CrossRef]
60.
Fairall, C.W.; Bradley, E.F.;
Hare, J.E.; Grachev, A.A.;
Edson, J.B. Bulk parameterization of air–sea fluxes: Updates
and verification for the COARE algorithm. J.
Clim. 2003, 16, 571–591. [CrossRef]
61.
Mueller, J.L.; McClain, G.; Bidigare, R.; Trees, C.; Balch, W.; Dore, J.; Drapeau, D.; Karl, D.; Van, L. Ocean Optics Protocols for Satellite Ocean
Color Sensor Validation. Revision 5. Volume V: Biogeochemical and Bio-Optical
Measurements and Data Analysis Protocols; NASA Tech. Memo. 2003-21621; NASA Goddard Space Flight Center: Greenbelt, MD, USA, 2003; pp. 1–36.
62.
Pegau, S.; Zaneveld, J.R.V.;
Mitchell, B.G.; Mueller, J.L.;
Kahru, M.; Wieland, J.; Stramska,
M. Ocean Optics Protocols For Satellite Ocean Color Sensor Validation. Revision 4. Volume IV:
Inherent Optical Properties: Instruments. Characterizations. Field Measurements and Data Analysis
Protocols; NASA Tech. Memo.
2003-211621; NASA Goddard Space Flight Center: Greenbelt, MD, USA, 2003; pp. 1–76.
63.
Bonamano, S.; Piermattei, V.; Marcelli, M.; Peviani, M. Comparison Between Physical
Variables Acquired by a New Multiparametric Platform, ELFO, and Data Calculated
by a Three-Dimensional Hydrodynamic Model in Different Weather Conditions at Tiber River mouth (Latium coast, Italy).
EGU General Assembly
Conference Abstracts, May 2010; Volume 12, p. 5226. Available online:
http://meetingorganizer.copernicus.
org/EGU2010/EGU2010-5226.pdf
(accessed
on 31 July 2017).
64.
Marcelli, M.; Piermattei, V.; Madonia, A.; Lacava, T.; Mainardi,
U. T-FLaP advances: Instrumental and operative implementation.
J. Oper.
Oceanogr. 2016, 9, s185–s192. [CrossRef]
65.
Crease, J.; Dauphinee, T.; Grose, P.L.; Lewis,
E.L.; Fofonoff, N.P.; Plakhin, E.A.; Striggow, K.;
Zenk, W. The
Acquisition. Calibration and Analysis of CTD Data; UNESCO Technical Papers
in Marine Sciences, 54; UNESCO: Paris, France, 1988;
pp. 1–105.
66.
User’s
Manual Thermal CAM Reseacher Professional—Professional Edition; Version 2.9; FLIR Systems: Limbiate (MI), Italy, 2009.
67.
Mueller, J.L.; Morel, A.; Frouin, R.; Davis, C.;
Arnone, R.; Carder, K.; Lee, Z.P.; Steward, R.G.; Hooker, S.; Holben,
B.; et al. Ocean Optics Protocols For
Satellite Ocean Color Sensor Validation. Revision 4. Volume III: Radiometric Measurements and Data Analysis
Protocols; NASA Tech. Memo. 2003-21621; NASA Goddard Space Flight
Center: Greenbelt, MD, USA, 2003; pp. 1–84.
68.
Donlon, C.; Rayner,
N.; Robinson, I.; Poulter, D.J.S.;
Casey, K.S.; Vazquez-Cuervo, J.; May, D. The global ocean data assimilation experiment
high-resolution sea surface temperature pilot project. Bull. Am. Meteorol. Soc. 2007, 88, 1197–1213. [CrossRef]
69.
Donlon, C.J.; Minnett, P.J.; Gentemann, C.; Nightingale, T.J.; Barton, I.J.; Ward, B.; Murray, M.J.
Toward improved validation of
satellite sea surface skin temperature measurements for climate research. J. Clim. 2002, 15, 353–369. [CrossRef]
70.
Donlon, C.J.; Keogh, S.J.; Baldwin, D.J.; Robinson, I.S.;
Ridley, I.; Sheasby, T.; Barton,
I.J.; Bradley, E.F.; Nightingale, T.J.; Emery, W. Solid-State Radiometer Measurements of Sea Surface
Skin Temperature. J. Atmos. Ocean. Technol. 1998, 15, 775–787. [CrossRef]
71.
Kawai, Y.; Wada, A. Diurnal
sea surface temperature variation and its impact on the atmosphere and ocean: A review. J. Oceanogr. 2007, 63, 721–744. [CrossRef]
72.
Fairall, C.W.; Bradley, E.F.;
Godfrey, J.S.; Wick, G.A.;
Edson, J.B.; Young, G.S. Cool-skin
and warm-layer effects on sea surface temperature. J. Geophys. Res. Oceans 1996, 101, 1295–1308. [CrossRef]
73.
Gentemann, C.L.; Minnett, P.J.; Ward, B. Profiles of ocean surface heating (POSH): A new
model of upper ocean diurnal warming. J. Geophys. Res. Oceans 2009, 114, C07017. [CrossRef]
74.
Zeng, X.; Beljaars, A. A prognostic scheme of sea surface skin temperature for modeling and data assimilation.
75.
Niclòs, R.; Caselles, V. Angular variation of the sea surface emissivity. In Recent Research Development in Thermal Remote Sensing; Research Signpost: Kerela,
Indian, 2005; pp. 37–65.
76.
Friedman, D. Infrared characteristics of ocean water (1.5–15 µ). Appl.
Opt. 1969, 8, 2073–2078. [CrossRef]
77.
PO.DAAC MODIS Level 3 Data User Guide, MODIS Dataset, Version 2014.0.
Available online: ftp://podaac-
ftp.jpl.nasa.gov/allData/modis/L3/docs/UserGuide_MODIS_L3_v2014.0.pdf (accessed on 1 February
2017).
78.
Mavromatakis, F.; Gueymard,
C.A.; Franghiadakis, Y. Improved
total atmospheric water vapour amount determination from near-infrared filter
measurements with sun photometers. Atmos.
Chem. Phys. 2007, 7, 4613–4623. [CrossRef]
79.
Polemio, M.; Dragone, V.; Limoni, P.P. Monitoring
and methods to analyse the groundwater quality degradation risk in coastal
karstic aquifers (Apulia, Southern Italy). Environ.
Geol. 2009, 58, 299–312. [CrossRef]
80.
Simeoni, U. I litorali
tra Manfredonia e Barletta (Basso Adriatico): Dissesti, sedimenti, problematiche ambientali.
82.
Milella, M. Esplorare le Frontiere verso
una Interculturalitá Formativa; Edizione Ateneo: Perugia,
Italy, 2007.
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