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Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 1
Determining Seasonal Spatial and
Temporal Distribution of Chlorophyll-
a, and its Concentration in Kuwait
and the Arabian Gulf Using Satellite
and In-situ Data
Jasem A Albanai
As an indicator of phytoplankton density in an area, the concentration of chlorophyll-a (Chl-a) is an
important reflection of marine water quality. Remote sensing techniques have been developed to measure the
near-surface concentration of Chl-a in water by combining spectral bands and in situ data. This algorithm can
be applied to sensors of varying spatial, temporal and spectral resolutions. However, in this study, Chl-a level 2
and 3 products of SNPP – VIIRS spectrometer (Equation OC3) from NASA’s OceanColor suite were relied upon
to establish the spatial and temporal distribution of Chl-a concentration in the Arabian Gulf (also known as the
Persian Gulf) and the territorial waters of the State of Kuwait (located in the north-eastern part of the Arabian
Gulf) in 2012-2019. Ground truthing points (n = 192) matched with level 2 products have been used to build and
cross-validate an empirical model. The correlation was positive, with r^2 = 0.79 and validation RMSE = ± 0.64
mg/m-3. The derived algorithm was then applied to Chl-a level 3 seasonal products. Additionally, the Chl-a
concentration values in Kuwaiti waters were enhanced using the IDW algorithm to increase spatial resolution,
given its small size compared to the spatial resolution of level 3 Chl-a products. The model derived from IDW
was tested using the Mann Whitney test (Sig = 0.948 p > 0.01). However, the result showed that Chl-a
concentration was higher in the Kuwait Bay (average = 2.8 mg/m-3) than in Kuwaiti waters (average = 2.3 mg/m-
3), and higher in Kuwaiti waters (average = 2.1 mg/m-3) than in the Arabian Gulf (average = 0.7 mg/m-3). Coasts
have higher concentrations than open water. Generally, the Chl-a increases in winter and had a semi-regular
cycle during the years of study; this cycle is more regular in the Gulf than in Kuwait.
KEY WORDS
~ Geography
~ Biogeography
~ GIS
~ OceanColor
~ VIIRS
University of Oxford, School of Geography and the Environment, Oxford, United Kingdom
e-mail: albanay.com@gmail.com
doi: 10.7225/toms.v13.n02.016
Received: 10 Jul 2023 / Revised: 30 Jun 2024 / Accepted: 12 Sep 2024 / Published: 21 Oct 2024
This work is licensed under
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 2
1. INTRODUCTION
Sea and ocean water quality is determined based on its physical, chemical and biological properties,
such as sea temperature, salinity, dissolved oxygen, pH and Chl-a concentration, among others. One of the most
valuable approaches in studying water quality is the analysis of the temporal and spatial distribution of marine
variables, and the measurement of the changes in these patterns. Chl-a concentration is an indicator of
phytoplankton density (Muller-Karger et al., 2005) which can help monitor and assess marine resources and
water quality (Al-naimi et al., 2017). Remote sensing techniques have been widely used to obtain marine quality
parameters (Al-Rashidi et al., 2009; Cahyono et al., 2017; Huang et al., 2014; Mutlow et al., 1994; Thomas et al.,
2002). Chl-a data for large geographic areas can be obtained systematically through remote sensing
instruments, rather than through the use of field-point observations (Jensen, 2016). Statistical methods have
been used extensively in literature to describe and analyse the temporal and spatial distribution of marine water
quality parameters using remote sensing methods and field surveys (Williams et al., 2013; Yoder et al., 2001).
Several methods have been used to determine Chl-a concentrations from space. While some focus on stationary
variation, such as using the Empirical Orthogonal Function (Williams et al., 2013) which analyses the spatio-
temporal variation with a set of orthogonal functions, others focus on non-stationary variation methods
(Mendonça et al., 2010; Zhang et al., 2012) which highlight sudden changes due to abnormal events (Moradi
and Kabiri, 2015), more often than not controlled by hydrodynamics, bathymetry, stratification, mixing processes
and nutrient uptake (Glibert et al., 2002; Moradi and Kabiri, 2012). Therefore, both stationary and non-stationary
variations contribute to our understanding of the spatial and temporal distribution of Chl-a concentration (Moradi
and Kabiri, 2015).
Studies attempting to extract marine environment parameters in the Arabian Gulf from satellites are few
and far between, despite their global relevance and importance, mainly due to dust storms that obstruct the
operation of remote sensing instruments (Nezlin et al., 2010; Zhao and Ghedira, 2014), the lack of quality in-situ
measurements, complex sea-river environments (Al-naimi et al., 2017), and the limited spatial coverage of many
global remote sensing products over the marginal seas of the Arabian Gulf. However, the general water
circulation, bathymetry and hydrodynamics in the Arabian Gulf have been discussed and analysed by numerous
authors (Brewer and Dyrssen, 1985; Nezlin et al., 2010; Reynolds, 1993, 1993; Sheppard et al., 2010). Nezlin et
al. (2007) divided the Gulf into regions based on oceanographic properties and water circulation (Reynolds,
1993), estimating that the Chl-a concentration is much higher in the northern part of the Gulf. Lately, remote
sensing instruments have been widely used to obtain near-surface Chl-a concentrations, measured by moderate
spatial resolution satellites provided by NASA through the correlation of blue-to-green spectral range and in-
situ data (O’Reilly et al., 1998). As to the benefits of remote sensing methods, Reilly et al. (2000) showed that
remote sensing could be affected by bottom reflection and high turbidity. The NASA OceanColor products were
developed to measure Chl-a in open oceans, where water color mainly depends on Chl-a concentration (Reilly
et al., 2000). The Arabian Gulf is classified as a marginal water, influenced by the discharge of the Tigris and the
Euphrates, which means that Chl-a concentration is affected by the concentrations of both dissolved and
suspended matter (Siegel et al., 2005). In such circumstances, regional models for the Arabian Gulf give more
accurate results (Nezlin et al., 2007).
Nezlin et al. (2010) derived the monthly Chl-a concentration, and analysed environmental factors
relevant for phytoplankton distribution. The study showed that the Chl-a concentration peaks in August and
October, and hits minimum in February and March. The field-based measurements of Chl-a show that the
concentration ranges from 0.01 to 10 mg/m-3 in the Arabian Gulf (Sheppard et al., 2010), and up to 55.4 to 4525
mg/m-3 in abnormal bloom conditions in the northwest of the Gulf (Al-Yamani et al., 2012). Moradi and Kabiri
(2015) studied the spatial and temporal variation of Chl-a in 2002-2013 using MODIS data. Their study showed
that the stationary level of Chl-a concentration is higher in coastal areas, while the temporal peaks can be noticed
in summer and winter in the north-western region. However, the accuracy of VIIRS and other merged satellites
sensors’ datasets in measuring Chl-a concentration were validated positively in the Arabian Gulf by Al-naimi et
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 3
al. (2017) through ground truthing points taken in the mid-west of the Gulf. Additionally, Zhao et al. (Zhao et al.,
2016) developed and tested the red tide index over the Arabian Gulf using the MODIS Aqua satellite sensor,
while Polikarpov et al. (2019) discussed the phytoplankton variability over the Arabian Gulf, also using the
MODIS Aqua satellite sensor.
As for the northern reaches of the Arabian Gulf, the territorial waters of Kuwait are in the west. Studies
looking at the spatial and temporal distribution of Chl-a concentration are limited to Alyamani et al. (2004) study.
In their comprehensive analysis of Kuwait’s marine environment, the authors show the spatial-temporal variation
of Chl-a in Kuwaiti waters based on field measurements from Kuwait Environmental Public Authority. The study
showed that the concentration of Chl-a decreases from north to south, with the concentration being much higher
in the north and in the Kuwait Bay during the winter and early spring. Given the small number of studies
conducted in Kuwaiti waters specifically, there is a need to clarify the spatial-temporal difference. Therefore, this
study aims to contribute to our understanding of the spatial-temporal Chl-a distribution based on empirical
spatial enhancements of VIIRS - SNPP data for Kuwaiti waters as a part of the Arabian Gulf. The distribution and
dynamics of Chl-a concentration in Kuwaiti waters is the key indicator for understanding phytoplankton density,
as well as Kuwait’s marine ecosystem environment, from both spatial and temporal perspective. Additionally,
VIIRS is seen as a MODIS successor in providing geophysical data with higher spatial resolution (Feldman,
2020b). The two sensors differ slightly in the green, blue and red spectral bands used in the final products of
NASA’s OceanColor (Feldman, 2020a). However, in several studies, VIIRS showed a lower average error than
MODIS in measuring Chl-a concentration, using an OC3 algorithm, across different regions of the world
(including the Gulf of Gabes, the northern South China Sea, the Red Sea, the California Current Bed and the
Arabian Gulf) (Al-naimi et al., 2017; Brewin et al., 2013; Hattab et al., 2013; Kahru et al., 2014; Shang et al.,
2014). Therefore, using VIIRS on the North Arabian Gulf will increase our understanding of the satellite response
in order to obtain measurements for both Chl-a concentration and phytoplankton density.
2. STUDY AREA
2.1. Arabian Gulf
The Arabian Gulf (also known as the Persian Gulf) is a shallow marginal sea of the Indian Ocean (Fig.
1), between the Arabian Peninsula and south-western Iran (J. A. Albanai, 2021a). The Gulf is located between
23.5° and 30.05° North and 47.5° and 56.4° East, approximately 56 km in length – 338 km in width, covering the
total surface area of 240,000 km2 (Alyamani et al., 2004; Pokavanich et al., 2014; Polikarpov et al., 2019). The
total water volume in the Gulf is estimated to be roughly 6000 km3 (Moradi and Kabiri, 2015). The Gulf is relatively
shallow, with the deepest point reaching just over 100m, and the average depth of about 35m. The main source
of fresh water comes from the Shatt al-Arab deltaic system (the delta of Tigris, Euphrates and Karun rivers) in
the northern end of the Gulf. The maximum discharge occurs in late spring and early summer (Alyamani et al.,
2004). The quantity of material discharged into the Gulf by the main northern rivers is estimated to be 1.1 * 10
of water and 4.8 * 10 tons of sediment annually (Reynolds, 1993). The Gulf is connected to the Arabian Sea
and the Indian Ocean by the Strait of Hormuz, allowing the slow circulation of water (Hunter, 1983). The mean
hydrodynamic circulation is counter-clockwise cyclonic, forced with the inflowing currents to the north of the
Gulf in summer, and weakened by the north-westerly winds along the Iranian coast in winter (Reynolds, 1993).
The Arabian Gulf has a subtropical hyper-arid climate and is surrounded by deserts, where precipitation levels
are relativity limited (Alyamani et al., 2004), and evaporation rate is estimated by numerous authors to be very
high (1.44 - 1.64 m year ) (Johns et al., 2003).
The physiochemical properties of Gulf’s waters reflect the shallowness, high evaporation, limited
freshwater river runoff and low rainfall. Sea surface temperature reaches the maximum of roughly 36°C and the
minimum of 14°C (Albanai, 2022). The Arabian Gulf is considered to be one of the most saline basins in the
Earth. The salinity concentration ranges from 35-40 PSU, reaching 70 PSU in shallow south-eastern
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 4
embayments (Al-naimi et al., 2017; Polikarpov et al., 2019; Reynolds, 1993). The deeper parts of the Gulf have
an evident saline stratification, while the shallows are well mixed by both the wind and current systems. The
north-westerly winds and south-eastward coastal currents cause the upwelling along the eastern Iranian coasts
and downwelling along the western Arabian coasts (Reynolds, 1993). The recent study by Al-Yamani and Naqvi
(2019) showed an increase in salinity in the northern part of the Gulf due to reduced river runoff. The Gulf is one
of the main sources of aeolian dust deposits in the world (Husar et al., 1997). Belt dust storms intensify in the
summer, with the strong dust-laden north-western winds which ease in the winter (Nezlin et al., 2010; Zhao and
Ghedira, 2014). There is a strong correlation between the biological productivity in the Arabian Gulf and the
geographical distribution of nutrients (Polikarpov et al., 2019). The limited upwelling conditions in the Gulf result
in a limited nutrient concentration in most offshore Gulf waters (Johns et al., 2003); by contrast, some coastal
river-discharge-influenced areas have much higher nutrient concentrations (Polikarpov et al., 2016). However,
a significant increase in nutrients has been identified (J. A. Albanai, 2021b), chiefly attributable to the sewage
discharge from the urban coastal areas along Gulf’s coasts. Recently, the anthropogenic stresses have been
cited as one of the biggest challenges facing the marine ecological environment of the Arabian Gulf (Al-Yamani
and Naqvi, 2019). Nezlin et al. (2007) have divided the Gulf into regional sub-basins depending on properties,
and a more recent study by ((Polikarpov et al. 2019) sought to modify this partition (Fig. 1). The Arabian Gulf is
of great economic and international importance due to the transportation of oil through the Strait of Hormuz
(Kvenvolden and Cooper, 2003), in addition to the Gulf’s highly productive ecosystems (Polikarpov et al., 2019).
2.2. Kuwait
Kuwait is a state situated in the north-western corner of the Arabian Gulf, surrounded by Saudi Arabia
to the south and west, and Iraq to the north. Kuwait is part of the Arabian desert and is considered one of the
warmest regions in the world. Summer temperatures go above 50°C, and drop to about 7°C in the winter
(Albanai, 2021). The flat topography of Kuwait plays a role in the drought; the elevation rises from sea level on
the eastern coasts to the highest point in the south-east of the country, reaching up to 280 meters above sea
level (Albanai, 2019). Kuwaiti waters are considered to be a part of the submerged northern estuarine flat of the
Arabian Gulf (Fig. 1), which is mainly affected by the sea-river environment of Shatt al-Arab (Alyamani et al.,
2004). The length of the State of Kuwait’s coastline is more than 700 km (Misak et al., 2003). This length has, in
recent years, increased due to various development projects and the construction of artificial beaches. Kuwait
has nine islands (Albanai, 2021d, 2021c). Physiographically, these islands can be classified into two groups:
coastal and offshore (Alyamani et al., 2004). The coastal group consists of six islands located in the north of the
country’s territorial waters. These are Warba, Bubyan, Miskan, Failaka, Awhah and Umm Al-Namil. A narrow
channel called Khor Al-Sabbiya separates Bubyan and Warba from the mainland of the State of Kuwait. The
remaining the islands of Kubber, Qaruh and Amm Al-Maradim are located in the country’s southern waters
(Albanai et al., 2022b).
Kuwait's territorial waters, having the surface of about 8,000 square kilometres (Albanai et al., 2022a),
are divided into three main sub-regions: Kuwait Bay, the northern waters, and the southern waters. Kuwait Bay
is located in the middle of the country’s coastline. The bay is shallow, with the average depth of about five
meters; a slow counterclockwise circulation appears in Kuwait Bay throughout much of the year while, in winter,
the circulation is significantly reversed due to the predominant south-easterly (Alyamani et al., 2004). Kuwait
Bay has other different sub-circulation patterns, such as the net clockwise drift in the west, and the counter-
clockwise pattern in the east (Dames and Moore, 1983). As for the northern region, it is characterized by a
narrow channel surrounding Warba and Bubyan islands, which makes the currents the main hydrodynamic
factor in the region (El-Baz and Al-Sarawi, 2000). Maximum current speeds were observed in Khor Al-Subbiya
(1.2 meters ) (Alyamani et al., 2004). Northern waters are a part of the submerged northern estuarine flat,
while southern waters are more similar to those of the open Arabian Gulf (Albanai, 2020). Here, maximum depth
increases to approx. 30 meters, which makes the average depth of Kuwaiti waters about 20 meters (Alyamani
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 5
et al., 2004). Kuwait’s territorial waters have the average sea surface temperature of 23.8°C and salinity of around
40 PSU (Alyamani et al., 2004).
The study area was chosen for many reasons: 1) it has high concentrations of Chl-a compared to other
seas in the world, 2) the study area is exposed to strong human influence, which provides an opportunity to
examine the potential connections between human factors and the natural environment, 3) the availability and
access to field data for the study area which are important for remote sensing data validation.
3. METHODOLOGY
3.1. In-situ data
The field data used have been obtained from the Kuwait Environmental Public Authority (KEPA)
(epa.org.kw). KEPA has been keeping a data archive on Kuwait’s marine environment since 1983. In 2016, KEPA
established a marine monitoring system using 15 buoys spread across Kuwaiti territorial waters (Fig. 1). Three
buoys are located in the northern waters (S1 - S3), five in the Kuwait Bay (S4 – S8), while the rest can be found
in the southern waters (S9 – S15). The buoy coordinates are available on KEPA website. The archive comprises
data on multiple water quality parameters, such as sea surface temperature, salinity, conductivity and turbidity,
dissolved oxygen and pH. The Chl-a was measured by the Turner CYCLOPS-7 (Model SEN-CHA-XAN-06)
sensor. Instruments are calibrated annually, and periodically, 15-30 days after the field visit. Calibration is
conducted in keeping with the NIST standard. Readings are taken every 10 minutes, at the depth of 0.5-1 m
from the surface. The instrument has occasionally malfunctioned, causing some gaps in readings.
Figure 1. The Arabian Gulf (with sub-basin regions): the upper coastal Northern Shatt Al-Arab zone (UN), the
northern open waters (N), the southern open waters (S), upper coastal western Saudi Arabian zone (UW), and
the lower shallow waters between Qatar and UAE, including Bahrain’s coasts (LW). On the right are the
locations of Kuwaiti waters and Environmental Public Authority marine buoys. According to Polikarpov et al.
(2019) with modifications
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The Chl-a data as measured by the 15 buoys at 10:00 – 11:00 AM (hourly average) throughout 2017
have been used in the study as ground truthing points (GTPs). Additionally, turbidity data (measured by AML
oceanographic sensor model XCH-TRB-A3000-02W) were used to understand the optical properties in different
studied parts of Kuwait’s waters, and a bathymetric layer obtained from the admiralty chart of Kuwaiti territorial
waters, referred to the Ministry of Defence and the Ministry of Communications archived by KEPA, was used to
show the bathymetry of Kuwait territorial waters.
3.2. Satellite data
The data used have been downloaded from the OceanColor website (oceancolor.gsfc.nasa.gov). NASA
OceanColor is a platform supported by the Ocean Biology Processing Group (OBPG) at NASA's Goddard Space
Flight Center. Since 1996, NASA has supported the scientific community with different products that relate to
the marine environment and water quality, using satellite data. The products are divided into levels. Generally,
level 1 and 2 data contain full-resolution, time-referenced and radiometrically- and geometrically-calibrated data,
as well as derived geophysical variables such as sea surface temperature and Chl-a concentration. On the other
hand, level 3 data contain time-derived geophysical variables over a specific period (monthly, annually etc.). The
OceanColor website provides data for several sensors, such as SeaWiFS, Aquarius, MODIS and VIIRS. The
Visible and Infrared Imager/Radiometer Suite (VIIRS) is a spectrometer carried by the Suomi National Polar-
orbiting Partnership (S-NPP) launched in October 2011, which is being orbited on the Joint Polar Satellite
System (JPSS). VIIRS is a moderate-resolution spectrometer with 22 spectral bands, ranging from 412 μm to
12 μm; 16 bands have the spatial resolution of 750m at their lowest, while five image resolution bands have the
spatial resolution of 375m, and there is one day-night band (DNB) (Feldman, 2020b; Gallegos et al., 2015). The
spectrometer obtained the geophysical variables of near-surface concentration of Chl-a in mg/m-3 using an
empirical correlation between the blue to green bands ratios of spectral reflections depending on two to four
bands, ranging between 440 and 570 μm, and in-situ measurements (Feldman, 2020a). The band ratio algorithm
developed by O’Reilly et al. (1998) was merged with a prior water index algorithm developed by Hu et al. (2012)
to produce the final product provided by NASA. Hu et al. (2012) clearly show that the improvement is limited to
relativity clear water. The algorithm applied slightly differs from that published in their paper because the
transition between the water color index (CI) and the band rationing algorithm (OCx) now occurs at 0.15 < CI <
0.2 mg/m-3 to ensure smooth transition (Feldman, 2020a). The current Chl-a product is based on the following
algorithms:
= () +
( ) (() ()) (1)
where (λblue, λgreen and λred) are spectral bands in the wavelengths closest to 443, 555 and 670 nm
respectively, CI is the color index.
()= +
( (()
()) (2)
where the numerator, (λblue) is the highest of several input values and coefficients, a0-a4, are sensor-
specific (0.2424 and -1.2280 for MODIS, and 0.2228 and -0.7768 for VIIRS (OC3), respectively. Chl-a is the
output of Chl-a concentration in mg/m-3.
The water color index algorithm is used for Chl-a concentration below 0.15 mg/m- 3, while the band
rationing algorithm is used for concentrations above 0.2 mg/m-3. Both algorithms are combined for
concentrations between these values, using the weighted method (Feldman, 2020a).
In this study, 45 level 2 VIIRS (SNPP) Chl-a concentration products were downloaded from the
OceanColor website for the Arabian Gulf region. The data cover all twelve months of 2017. The pixel size of
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 7
level 2 data is 1 square km, and temporal resolution is daily. On the other side, 64 seasonal level 3 VIIRS (SNPP)
Chl-a concentration products for the Arabian Gulf were ordered and used. The data cover eight years from 2012
to 2019 with one product covering each season (4 products for each year). This means that one product
constitutes geophysical mean values of about 30 daily images. So, the level 3 data used in this study refer to
more than 2,929 analysed Chl-a daily level 2 products. The spatial resolution of level 3 products is 4 and 9
square km. The 4 km product was used. The reason for choosing the 2017 level 2 products are calibration and
verification using KEPA GTPs, while level 3 data were chosen due to spatial and temporal differences and
coverage.
3.3. Prediction and validation
Although the Chl-a products of VIIRS - SNPP are modelled using in-situ data (Feldman, 2020b), and
many studies have demonstrated the accuracy of these data (Al-naimi et al., 2017; Kahru et al., 2014), the
products must be validated locally. The correlation between satellite and near-surface Chl-a concentration may
be affected by several local properties, broadly speaking controlled by the geographical and oceanographic
conditions (Jensen, 2016). Therefore, GTPs (n = 192) observed by 15 stations covering all Kuwaiti waters were
used to build and validate a new empirical local model. Linear regression analysis was used to build the model
that reflects the correlation between GTPs and satellite level 2 Chl-a products (OC3). The following matrix was
used to compute the regression algorithm:
.
.
.
=
+
+
.
.
.
+
+
.
.
.
(3)
where and are population Chl-a intercept and population slope coefficient. is the Chl-a (OC3)
product at specific points and is random error. is the predicted output of Chl-a values.
A window of 3*3 pixels was built around each GTP to extract the satellite data for matching. This
technique was used in previous studies (Albanai, 2019; D’Alimonte and Zibordi, 2003). The satellite data that
have the standard deviation of more than 3 mg/m-3 were omitted from the analysis to improve matching accuracy.
The GTPs were divided into two groups: GTPs (n = 113) monitored in three seasons (spring, summer and
autumn) were used to find the correlation and build the model, while GTPs (n = 79) observed in winter were
used to validate the predicted model (through a seasonal cross-validation process). The correlation was positive
where = 0.792 (Fig. 2 and Fig. 3). The credibility of the extracted model was increased by calculating the
correlation index (d). The index had previously been proposed by Willmott (Willmott, 1981) for geographical
model evaluation. The index can measure predication model error on the 0-1 scale, where 1 indicates perfect
correlation, while 0 means no correlation at all. However, due to squared differences, the index is sensitive to
outliers. The (d) index can be calculated as follows:
= ()
(|
|)(|
|)
(4)
where and are the observation and forecast values, respectively.
The accuracy of the empirical regression model was examined through the root mean square error
(RMSE) and the mean absolute error (MAE). These two indices are used to determine the correlation between
two variables quantitatively, and were used in several similar studies (Al-Yamani and Naqvi, 2019; Marrari et al.,
2006; Zhang et al., 2006). The RMSE and MAE are calculated as follows:
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 8
= ±
(5)
=
(6)
where is the value of the derived empirical model, and the value of GTPs.
Figure 2. The correlation between GTPs and satellite data of VIIRS – SNPP level 2 products was estimated as
positive through regression analysis
Figure 3. The residual plot shows that the points are randomly dispersed around the horizontal axis,
confirming the appropriateness of the linear regression model for the data
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 9
3.4. Raster analysis and spatial enchantment
The level 3 Chl-a concentration VIIRS – SNPP products have been geometrically corrected to the WGS
84 coordinate reference system in the BEAM – DIMAP format using the SeaDAS 7.5.3 software. This format can
be used in the ArcGIS Pro software for further analysis. Following that, the regression algorithm was applied to
seasonal products. The Arabian Gulf data were directly analysed by extracting the spatial mean and standard
deviation for each seasonal plate. The spatial mean is the average of all the pixels in the study area, where the
standard deviation of the spatial mean describes the deviation of Chl-a concentration values from the average.
The seasonal mean values for the Arabian Gulf were used to show how the average changed over the study
period. Moreover, the geophysical values of the north-west Arabian Gulf for each plate which covers Kuwaiti
territorial waters have been clipped for analyses. The spatial resolution of level 3 product data was 4 square km,
which means that the data were limited to mapping large-scale areas, such as Kuwaiti waters (8,000 square km).
On the other hand, although the data used have acceptable spatial coverage, they falter in narrow areas, such
as the creeks and embayments found in Kuwait’s northern marine environment around Bubyan and Warba
islands, as well as some parts of the Kuwait Bay. This issue was resolved by extracting the values of each pixel
on the North West Arabian Gulf to build an inverse distance weighted (IDW) model. The IDW is a method of
estimating an unknown point value from several surrounding known point values. The best results for this model
are obtained when the distribution of control points (samples) is of high density and has a wide spatial spread
over the study area concerned, in order to simulate all existing spatial differences; otherwise, the results of the
model may be affected (Watson and Philip, 1985). The IDW method has higher accuracy in the spatial
enhancement of raster model with conditions of high variation coefficient values, strong anisotropy and spatial
structure (Chaplot et al., 2006). These conditions were relatively identical to those in the model used.
Additionally, Musashi et al. (2018) showed that this model was more accurate than other derivative models. The
IDW model was created based on 2,000 extracted values from the north-western Arabian Gulf, and has facilitated
maximum spatial resolution increase, as determined by the function in the ArcGIS Pro 2.3 toolbox (spatial analyst
– interpolation tools) based on the number of points entered (Fig. 4). The following algorithm was used to
calculate the inverse distance weighted (IDW) model:
=
+
1
(7)
where z () is the output value, is the value of known control points, hij is the separation distance between
interpolated value and control point value, ß is the weighting power, and n is the total number of control points
(samples) values.
Figure 4. Increasing the spatial resolution by applying the inverse distance weighted (IDW) model to the
geophysical values of Chl-a concentration products in the north-west Arabian Gulf
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 10
Following that, Kuwaiti waters were divided into north waters, the Kuwait Bay, and south waters (Fig. 5).
The division was based on the similarities and differences of marine properties mentioned in the study area
description. Mean and SD of each region were computed using zonal statistics. Additionally, Kuwait’s territorial
waters were divided into three areas based on their depth to show the correlation between near-surface Chl-a
and the depth of Kuwaiti waters. The mean value and SD for each class were calculated as well.
Figure 5. The sub-regions of Kuwaiti waters: the north region, the Kuwait Bay and the south region
Through 57 spatial mean GTPs, the IDW seasonal model was examined using the Mann Whitney test.
The Mann Whitney test is a statistical hypothesis test used to compare two populations based on their
medians/means. This test can be used when the data do not follow a normal distribution, in addition to other
non-parametric testing conditions (Coleman, 2015), noticeable in Chl-a concentration histograms. The following
formulae were used to conduct the Mann Whitney test:
= (,)
where:
= + (+ 1)
2
= + ()
(8)
where η1 and η2 are sample sizes, R1 and R2 the sums of observations rank from sample 1 and 2 populations,
respectively.
Fig. 6. shows the methodology summary, from downloading the data to the spatial analysis through pre-
processing and GTPs.
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Figure 6. Summary of the methodology used. Data collection, pre-processing, analysis and assessment steps
4. RESULTS AND DISCUSSION
4.1. Accuracy assessment
The validation results of the empirical level 2 Chl-a concentration model derived from SNPP – VIIRS
spectrometer using GTPs (number = 79) monitored in the winter of 2017 and taken by 15 marine buoys showed
a significant correlation. The statistical indices confirmed the strong correlation, where the overall RMSE and
MAE were = ± 0.841 and 0.638 mg/m-3, respectively. Thus, the empirical model has improved the accuracy by
0.329 and 0.256 mg/m-3 where the RMSE and MAE were = ± 1.17 and 0.841 mg/m-3 prior to the application of
the regression algorithm. Additionally, a wider perspective was obtained by dividing GTPs into two different
ranks according to their geographical location and concentration levels. Geographically, they were divided into
two regions, with the Kuwait Bay and the northern waters being considered a single region; these showed RMSE
and MAE of ± 1.11 and 0.89 mg/m-3. The northern waters were considered together with the Kuwait Bay due to
their relative lack of GTPs. By contrast, the southern region showed an RMSE and MAE of ± 0.53 and 0.44 mg/m-
3 (Fig. 7). The GTPs were also divided into two classes (< 2 and > 2). Lower concentration values were more
accurate (RMSE = ± 0.552 and MAE = ± 0.468 mg/m-3) than the higher concentration values (RMSE = ± 1.09
and MAE = ± 0.84 mg/m-3) (Fig. 8).
The regression algorithm was applied to the IDW model derived from level 3 Chl-a concentration data.
The correlation between the seasonal averages of the IDW model and the seasonal averages of GTPs (n = 57)
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 12
recorded by 15 marine buoys in 2017 was tested using the Mann Whitney test, where the null hypothesis was
rejected (sig = 0.948, p < 0.01 - 99% confidence). Overall, the empirical model established lower average Chl-
a concentration values in the study period and area, meaning that primary Chl-a products of SNPP -VIIRS had
been overestimated (Table. 1 and Fig. 9).
Figure 7. The correlation between GTPs (n = 35) and the empirical model in the Kuwait Bay and the northern
waters (a), and the correlation between GTPs (n = 44) and the empirical model in the southern waters (b)
Figure 8. The correlation between GTPs (n = 43) and the empirical model with concentration values lower than
2 mg/m-3 (a), and the correlation between GTPs (n = 36) and the empirical model with concentration values
above 2 mg/m-3 (b)
By value By location Overall
< 2 > 2 Kuwait Bay & North South region
RMSE (±) 0.552 1.09 1.113 0.533 0.841
MAE (±) 0.468 0.84 0.89 0.437 0.639
Table 1. Accuracy assessment by value, location and overall in Kuwaiti waters
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 13
Figure 9. Chl-a average fluctuations in 2012-2019 using the empirical model data from this study and VIIRS –
SNPP original product data for both Kuwait and the Arabian Gulf
Empirical model validation results were more accurate for low-concentration water monitoring, and their
accuracy decreased in high-concentration waters. This pattern is inevitably affected by depth. Accordingly, the
accuracy was measured geographically where the average depth is known. The results confirmed that the
accuracy improves in deep water (southern waters of Kuwait), and decreases in shallow and turbid waters
(Kuwait Bay and northern waters) (Table 2). When the extracted Chl-a concentration was reviewed using
different equations and the NASA satellite sensors for other seas and different bays around the world, with
different GTP numbers (30-114), the accuracy in literature varied from 0.18 to 0.64 mg/m-3 (Brewin et al., 2013;
Hattab et al., 2013; Kahru et al., 2014; Shang et al., 2014). The accuracy was estimated at 0.23 mg/m-3 in the
middle of the Arabian Gulf using 29 GTPs (Al-naimi et al., 2017), and thus the accuracy of the empirical model
developed in this study falls within a reasonable range, especially in a complex environment such as the north-
western Arabian Gulf.
Despite the advantages of remote sensing data and solutions, this method does have some limitations.
One of these limitations is that the accuracy of the obtained Chl-a concentration results is affected by water
depth and high turbidity (Reilly et al., 2000). This is perhaps one of the most important characteristics of the
Arabian Gulf, especially of its north-western region (Alyamani et al., 2004). Therefore, developing a new and
validated local model is essential (Alyamani et al., 2004). However, the northern part of Kuwaiti waters is
considered one of those areas where Chl-a models rarely obtain sensor spatial resolution and coverage data,
among other things due to weather factors, such as dust bands (Nezlin et al., 2010; Zhao and Ghedira, 2014).
Accordingly, the developed spatial interpolation model for this region does have some limitations, especially
with insufficient GTPs available to calibrate the results.
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 14
South region
North region &
Kuwait Bay
mean SD
mean SD
Spring 4.14 7.39
5.45 7.19
Summer 2.65 7.37
6.23 7.25
Fall 5.57 7.37
6.46 7.32
Winter 2.69 7.33
6.13 7.32
Overall 3.76 7.36
6.07 7.27
Table 2. The turbidity seasonal averages and standard deviation (NTU) obtained from KEPA buoys in 2017.
The Kuwait Bay and the northern waters are characterized by higher turbidity than the southern waters
throughout the year.
4.2. Spatial-temporal variation
Based on the sub-division of the Arabian Gulf (Polikarpov et al., 2019) (Fig. 1), and given our knowledge
of water circulation and Gulf bathymetry (Polikarpov et al., 2019), the analysis of seasonal averages in 2012-
2019 using the SNPP - VIIRS spectrometer showed that the coastal areas have higher Chl-a concentrations
compared to those in the open waters. As for coasts, the Iranian eastern coast has a lower concentration than
western coasts. However, the northern Shatt al-Arab coastal zone had the highest concentration of Chl-a in
winter and late autumn, especially its western parts, where the Kuwaiti waters are located (Fig. 10 and Fig. 11).
In this area, the Kuwait Bay has the highest concentration of Chl-a as, in the southern waters, the deep-water
characteristics begin to become clear. The northern waters also have high Chl-a concentration compared to the
southern waters, (Fig. 12 and Fig. 13). The physical, topographical and anthropogenic factors contribute to the
high level of Chl-a in the north-western zone: low depths contribute to the influx of nutrients from the deep rich
layers (Polikarpov et al., 2019), the discharge of the rivers in the northern Arabian Gulf, the general water
circulation in the Gulf that carries nutrients to the north-western coasts of the Arabian Gulf (Polikarpov et al.,
2019), and the human stresses on the coastal areas (Polikarpov et al., 2019). However, they point out that an
overestimation by remote sensing instruments can happen in this area due to high turbidity (see Table 2) and
bottom reflection.
As for seasonal fluctuations, Chl-a concentration in the Arabian Gulf in 2012-2019 was at its lowest in
spring, with the quarterly average of 0.57 mg/m-3. The concentration increases in summer with the 0.64 mg/m-3
average. The concentration continues to increase in early autumn, with the average hitting 0.87 mg/m-3, and
remaining fairly steady in winter. In this season, the high concentration is noticeable in the north-western region,
and on the western coasts, as well as at the entrance to the Arabian Gulf in the Strait of Hormuz, where the
concentration increases significantly until it reaches around 6 mg/m-3 (Table 3). The sub-regions of Kuwait’s
waters follow a seasonal pattern comparable to the general seasonal cycle in the Arabian Gulf waters. The
seasonal peak is reached in autumn and winter, while the concentration reaches a low in the summer. However,
the annual average concentration of Chl-a in the Kuwait Bay increases significantly, with 1.86 mg/m-3 in the
south, and 0.57 mg/m-3 in the north. Fig. 14 illustrates seasonal differences, while Table 4 looks at both the
averages and the standard deviation variation. The drop in Chl-a concentrations in autumn is often due to
nutrients being depleted by the phytoplankton bloom in winter (Al-naimi et al., 2017). Nezlin et al. (2007) clearly
show that the seasonal cycle of Chl-a concentration in tropical and subtropical oceans is typical because
phytoplankton growth is affected by nutrient depletion resulting from pycnocline build-up, in addition to the
effects of thermal stratification in the water column, which limits vertical mixing and nutrients float to the surface
(Doney, 2006). This study result is consistent with other seasonal fluctuation studies (Al-naimi et al., 2017; Moradi
and Kabiri, 2015; Nezlin et al., 2007).
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 15
Figure 10. The spatial distribution of Chl-a concentration seasonal averages in the Arabian Gulf in 2012-2019.
Chl-a concentration increases in winter in the Strait of Hormuz and in the northern waters of the Arabian Gulf
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 16
Figure 11. The statistical distribution of Chl-a concentration seasonal averages in the Arabian Gulf in 2012-
2019. Chl-a concentration fluctuations increase in summer and spring, and are more gradual in winter and
autumn.
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 17
Figure 12. The spatial distribution of Chl-a concentration seasonal averages in Kuwaiti waters and the north-
western Arabian Gulf in 2012-2019. Chl-a concentration increases in winter in the Kuwait Bay and the northern
waters of the Arabian Gulf.
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 18
Figure 13. The statistical distribution of Chl-a concentration seasonal averages in Kuwaiti waters and the north-
western Arabian Gulf in 2012-2019. The peak of the seasonal curves can be seen at low concentrations. A
clear second smaller peak of Chl-a concentration can be observed at 1.8 - 2 mg/m-3 in summer and spring.
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 19
Kuwait
Arabian Gulf
mean SD
mean SD
Spring 1.88 0.38
0.57 0.38
Summer 1.81 0.43
0.64 0.5
Fall 2.41 0.35
0.87 0.47
Winter 2.38 0.56
0.87 0.48
Overall 2.12 0.43
0.74 0.46
Table 3. Seasonal means and standard deviations for the Arabian Gulf and Kuwaiti territorial waters
Kuwait Bay
South region
North region
mean SD
mean SD
mean SD
Spring 2.3 0.48
1.11 0.48
2.22 0.18
Summer 2.64 0.65
0.93 0.52
1.86 0.12
Fall 3.21 0.38
1.55 0.37
2.47 0.31
Winter 3.21 0.78
1.39 0.45
2.54 0.44
Overall 2.84 0.57
1.24 0.46
2.27 0.26
Table 4. Seasonal means and standard deviation for the sub-regions of Kuwaiti territorial waters in 2012-2019
Figure 14. Seasonal averages for Kuwaiti territorial waters in 2012-2019. The seasonal pattern of Chl-a
concentration appears to be erratic in Kuwaiti waters.
The Chl-a concentration trend in the Arabian Gulf and Kuwaiti waters in 2012-2019 shows that the
average concentration fluctuates in semi-regular seasonal cycles, mostly increasing in winter and late autumn.
However, the Chl-a concentration average fluctuated between approximately 0.5 and 1 mg/m-3 in the study
period, 2012-2019. The highest average concentration was recorded in the winter of 2018, when it exceeded 1
mg/m-3 (Fig. 15). Chl-a concentration in Kuwaiti waters shows that the seasonal cycles are less regular and more
severe than those in the Gulf as a whole. Kuwaiti waters are situated in the Shatt al-Arab region, which has a
higher concentration overall, and a more complex cycle affected by the complex river-sea system. However,
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 20
the highest peak was observed in the winter of 2018 and autumn of 2019. The results indicate that the average
Chl-a concentration in Kuwaiti waters is increasing (Fig. 16). Fig. 17 shows the seasonal moving average of Chl-
a concentrations in sub-regions of Kuwaiti waters in 2012-2019.
Kuwaiti waters are characterized by quarterly concentration fluctuations similar to those in the Gulf as a
whole. The recorded high average concentrations in the north-western region of the Arabian Gulf, and especially
in the Kuwait Bay, where the values are significantly higher than in the northern and southern regions, are
consistent with the study of spatial fluctuations in Kuwait’s Chl-a concentration by Alyamani et al. (2004).
Additionally, the long-term results showed regular seasonal fluctuations at Arabian Gulf level, with peaks in
certain years, as well as less regular fluctuations in Kuwaiti waters, where remarkable leaps were seen, especially
in the Kuwait Bay.
Figure 15. The seasonal moving average trend and pattern of Chl-a concentration in the Arabian Gulf in 2012-
2019. A semi-regular cycle can be observed in the study period.
Figure 16. The seasonal moving average of Chl-a concentration in Kuwait’s territorial waters in 2012- 2019.
Two peaks can be observed in 2016 and 2018.
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 21
Figure 17. The seasonal moving average of Chl-a concentration in sub-regions of Kuwaiti waters in 2012-2019.
The pattern in the Kuwait Bay is more extreme than in the northern and southern waters.
4.3. Chl-a vs. bathymetry
The spatial distribution of Chl-a concentration derived from the empirical model of SNPP – VIIRS level
3 data was studied over three bathymetric classes extracted and analysed by the spatial interpolation model
derived from depth GTPs in Kuwaiti waters. The results confirmed that Chl-a concentration is affected by depth,
with mean values decreasing with increasing depth. At lower depths (less than 10 meters), the average Chl-a
concentration over an eight-year period (2012-2019) was 2.34 mg/m-3. However, the concentration decreases
gradually to 1.5 mg/m-3 in the depth range of 10-20 meters, and continues to drop until it reaches 0.88 mg/m-3
at 20 meters and below. The Chl-a concentration level decreases steadily as depth increases at 10-meter
intervals. The standard deviation decreases by about 0.2, with an increase for every ten meters in depth. This
correlation applies to all seasons, with similar degrees of decline with increasing depth (Table 5).
Although many studies have indicated that the concentration of nutrients increases until the depth of
1,000 meters in seas throughout the world (Hayase and Shinozuka, 1995; Sunda, 2012), and the mixing process
can lift the nutrients from the rich near-bottom to the near-surface layer (Wirasatriya et al., 2018). The limited
upwelling process in the Arabian Gulf causes limited nutrient concentration offshore and in the northern open
waters of the Arabian Gulf (Johns et al., 2003). This may explain the lower Chl-a concentration in Kuwait’s
southern waters. By contrast, many sources feed the submerged northern estuarine flat where the Kuwait Bay
is situated. The standard deviation of the Chl-a concentration increases slowly with decreasing depth; this
suggests that Chl-a concentration is more homogenous in deeper areas, such as Kuwait’s southern waters, than
in the shallow areas such as the Kuwait Bay, which is a shallow semi-enclosed water, with river freshwater
discharge bringing the nutrients from the land. Further anthropogenic stresses and the presence of sewage in
the waters of the Bay (Al-Yamani and Naqvi, 2019) may be other reasons behind higher chlorophyll-a
concentration and its relatively wide spatial variation. Fig. 18 is a 3D bathymetric model of near-surface Chl-a
concentration, which clearly shows the spatial correlation between depth and Chl-a concentration in the north-
western Arabian Gulf (Kuwait’s waters). The submerged northern estuarine flat has a higher average seasonal
concentration compared to other Kuwaiti waters.
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 22
Figure 18. 3D models show the seasonal averages of Chl-a concentration in 2012-2019 in the north-eastern
Arabian Gulf bathymetry (Kuwaiti waters). The concentration increases as depth decreases (e.g. in the
northern part).
< 10
10 to 20
> 20
mean SD
mean SD
mean SD
Spring 2.32 0.65
1.61 0.66
0.7 0.25
Summer 1.97 0.52
1.2 0.5
0.46 0.3
Fall 2.44 0.59
1.65 0.41
1.29 0.24
Winter 2.64 0.98
1.53 0.43
1.05 0.27
Overall 2.34 0.69
1.5 0.5
0.88 0.27
Table 5. Seasonal Chl-a concentration decreases as water depth increases. Three depth ranges show
fluctuating concentration values by averages and standard deviation (data 2012 – 2019).
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 23
5. CONCLUSION
The subject of this study is the seasonal spatial and temporal distribution of near-surface Chl-a
concentration in the Arabian Gulf and Kuwaiti waters in 2012-2019, explained using SNPP – VIIRS spectrometer
data. The study included the use of a new empirical model based on GTPs observed in 2017 in the Arabian Gulf,
specifically in the north-west, where Kuwaiti waters are located. Additionally, the relationship between Chl-a
concentration and the depth of Kuwaiti waters was studied, where the spatial resolution of the model was
improved to clearly show the spatial dimension in this specific area. The results showed that Chl-a concentration
is higher near the coasts and in the north-western region of the Arabian Gulf (Kuwaiti waters), which area was
spatially enhanced, as well as that Chl-a concentration increases in the Kuwait Bay, decreasing gradually
towards the south. Seasonally, Chl-a concentration has a cycle that peaks in winter and early autumn in both
Kuwaiti waters and the wider Arabian Gulf. However, the concentration averages trend was more stable in the
Arabian Gulf compared to Kuwaiti waters in the study period. Additionally, the Chl-a concentration was observed
to decrease at a stable rate as depth increases. The empirical model accuracy was tested through GTPs, and
showed an overall RMSE and MAE of 0.841 and 0.638 mg/m-3, respectively. Remote sensing techniques provide
a valuable insight into the marine environment, including measuring Chl-a concentration and phytoplankton
density in seas and oceans by spatial modelling. The improved experimental model (equation) presented in this
study could be used in further research from this study area to monitor future Chl-a concentrations.
CONFLICT OF INTEREST
Author declares that he has no conflict of Interest.
Trans. marit. sci. 2024; 02 ~ Albanai: Determining Seasonal Spatial and Temporal… 24
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