Seasonal Water Quality Assessment Using Remote Sensing in

Al Rafisah Dam, United Arab Emirates

Afra Bint Abdulla Alserkal

, Amel Ahmed Alblooshi

and Rami Al-Ruzouq

Department of Applied Physics and Astronomy, University of Sharjah, Sharjah, U.A.E.

Civil and Environmental Engineering Department, University of Sharjah, Sharjah 27272, U.A.E.

Keywords: Remote Sensing, Water Quality, Turbidity, Chlorophyll-a, Total Suspended Matter, Colour Dissolved

Organic Matter, Sentinel-2.

Abstract: Water bodies differ in their chemical, biological and physical properties. These properties determine their

quality, and sequentially, their applications. Conventional surface water quality analysis involves timely,

costly, and intensive field and laboratory work. Remote sensing coupled with a geographic information

management system (GIS) can offer an alternative to estimating water quality in remote or inaccessible

locations. The main objectives of this study are to estimate chlorophyll-a, colour dissolved organic matter

(CDOM), total suspended matter (TSM) and turbidity using remote sensing methods and to display them in

temporal distribution maps. In this research, quantitative methodology was used to calculate the four water

quality parameters using Sentinel-2 images of the Al Rafisah Dam in Sharjah, United Arab Emirates during

the months of February, April, August and December of 2021. The Case 2 Regional Coast Colour (C2RCC)

processor in the Sentinel Application Platform (SNAP) developed the equations and performed the

calculations for chl-a, CDOM and TSM. ArcGIS Pro software was used for estimating turbidity with the

normalized difference turbidity index (NDTI), as well as creating the spatio-temporal distribution maps.

Overall comparative evaluation of the concentration patterns showed that the parameters selected for the study

are interrelated, yet may vary depending on seasonal variations and human activities. Water quality research

using remote sensing and GIS plays an important role in encouraging researchers to conduct more studies in

unattainable sites or understudied areas such as the Al Rafisah Dam.

1 INTRODUCTION

Water bodies differ in their chemical, biological and

physical properties. These properties determine their

quality and their applications. Optical parameters of

water quality include chlorophyl-a, colour dissolved

organic matter, total suspended matter, and turbidity,

which can affect water clarity, colour or algal content.

Salinity, dissolved oxygen (DO), temperature, etc., on

the other hand are non-optical parameters that do not

necessarily affect the appearance (KC et al., 2019).

The main targets of this research are to (1) use

appropriate satellite data to monitor water quality

parameters of the study area, (2) to identify water

quality parameters to be studied as per the study site

and available data, and (3) to evaluate the results of

the parameters based on the seasonal variations.

Contamination of water bodies is rampant due to a

combination of natural and human-induced factors.

Monitoring water quality is critical to the continual

existence of flourishing life and healthy environments,

especially because water is largely utilised for

drinking, recreation and agriculture (Khan et al., 2021).

Since the emergence of remote sensing, there have

been various applications and usages of satellite-

retrieved images. A topic that is being increasingly

researched nowadays is the use of remote sensing in

assessing the quality of water. Satellite earth

observation data may be utilised to overcome the

constraints of traditional water quality

methodologies. Conventional surface water quality

analysis involves timely, costly, and intensive field

and laboratory work. Remote sensing coupled with

GIS offers an alternative method to the estimation of

water quality parameters in remote locations or where

field samples have not been gathered.

Remote sensing is beneficial for long term

investigations. Several studies have found that

charting and observing water quality can help

112

Alserkal, A., Alblooshi, A. and Al-Ruzouq, R.

Seasonal Water Quality Assessment Using Remote Sensing in Al Raﬁsah Dam, United Arab Emirates.

DOI: 10.5220/0012563900003696

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 10th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2024), pages 112-119

ISBN: 978-989-758-694-1; ISSN: 2184-500X

improve regional and largescale assessments

(Abdullah et al., 2017; Acharya et al., 2019, Mucheye

et al.,2022; Suet al., 2008).

Seleem et al. (2022) adopted the Case 2 Regional

Coast Colour (C2CRR) atmospheric correction

method to monitor the physical features of various

water parameters such as chl-a, TSM and aquatic

reflectance. Satellite data was used to investigate

seasonal variance by comparing water quality over

the dry and wet seasons.

Furthermore, a new era of spaceborne

hyperspectral imaging has just begun with the recent

availability of data from PRISMA launched by ASI.

Niroumand-Jadidi et al. (2020) makes use of

PRISMA level 2D imagery that were tested for their

ability to retrieve typical water quality measures such

as TSM, chl-a and CDOM. In their findings, they

show that PRISMA level 2D imagery has a strong

potential for mapping water quality indicators.

Another study made use of Sentinel-3/2 and

Landsat-8 to retrieve chl-a, TSM, CDOM in various

water types in Egypt. The study validated the

usefulness of the visible and near-infrared (NIR)

bands in predicting chlorophyll-a in waters. Using the

C2RCC, the concentration of chl-a in the visible and

NIR wavelengths revealed the amount of algae in the

region (Masoud, 2022).

A paper by Ouma et al. (2020) also used Sentinel-

2 to determine the same water quality parameters for

the Chebara Dam in Kenya. They concluded that the

satellite is capable of monitoring inland waterways

and reservoirs for water quality.

Many studies have shown that the remote sensing

approach can be used to estimate and predict the

levels of turbidity, DO, chl-a, and heavy metals in

water using Machine Learning (ML) Algorithms.

These include the supervised processes; support

vector machine and artificial neural networks, and

unsupervised processes; principal component

analysis. Ma et al. (2021) investigated turbidity of

lakes by using Sentinel-2 satellite images with ML.

Water quality mapping utilising GIS and remote

sensing is critical in today's environment. It is an

effective instrument for environmental monitoring,

resource management, public health, and scientific

study. These technologies help to make educated

decisions, conserve ecosystems, provide clean

drinking water, respond to emergencies, and build

successful policies by delivering reliable, real-time

data on water quality. In an era when water resources

are being threatened by pollution, climate change, and

rising demands due to growing populations, the

capacity to map and analyse water quality geogra-

phically and temporally is critical for the long-term

management and protection of this crucial resource.

Figure 1: Study Area

Seasonal Water Quality Assessment Using Remote Sensing in Al Raﬁsah Dam, United Arab Emirates

113

The main objectives of this study are (1) to

estimate chlorophyll-a, colour dissolved organic

matter (CDOM), total suspended matter (TSM) and

turbidity from Sentinel-2 images of Al-Rafisah Dam

using ArcGIS Pro and Sentinel Application Platform

and (2) to display them in spatio-temporal distribution

maps for evaluation and comparison.

Water quality distribution maps will show the

spatial distribution and temporal results of chl-a,

CDOM, TSM and turbidity values in different months

of 2021. These parameters were chosen due to their

applicability and extensive use in remote sensing

applications (Bangira et al., 2024 ; Virdis et al., 2022).

2 STUDY AREA

The study area selected for the project is the Al

Rafisah Dam in Sharjah, United Arab Emirates,

shown in Figure 1. It is located along Sharjah-Khor

Fakkan highway between Al-Hajar Mountains that

runs down to the city of Khor Fakkan on the east coast

of Sharjah with coordinates 25° 21′ 0″ N, 56° 18′ 0″

E. It has an area of 10684 m2. Its elevation is roughly

at around 197 meter or 649 feet.

Al Rafisah Dam is an area rich in local flora and

fauna, and attracts many visitors during trips within

the country. Visitors of the dam can take part in a

number of touristic activities. Surrounding the site are

several eateries, archaeological spots, and a hiking

trail. Aside from offering spectacular views, the

waterbody is used for boating activities and kayaking.

This paper focuses on assessing water quality in

Al Rafisah Dam because it is an unprecedented

subject matter that has not yet been investigated.

3 DATA USED

Given that several previous studies relied on Sentinel-

2 satellite imagery to monitor and estimate water

quality parameters, it was chosen as the primary data

source for this research. Copernicus Open Access

Hub was used to download Sentinel-2 Level 1C

imagery containing the Al Rafisah Dam (Table 1).

The criteria used to locate images of the dam included

S2MSI1LC as product type and 5% of cloud coverage

within the year 2021. Four dates were used to

compare the results between the months of February,

April, August and December.

4 METHODOLOGY

In this research, quantitative methodology involving

deep learning equations and numerical indices were

used to evaluate the chlorophly-a, CDOM, TSM and

turbidity of Sentinel-2 images in Al Rafisah Dam.

The programs utilized were ArcGIS pro version 3.1.0

and Sentinel Application Platform (SNAP). The Case

2 Regional Coast Colour (C2RCC) processor in the

Sentinel Application Platform (SNAP) developed the

equations and performed the calculations for chl-a,

CDOM and TSM. ArcGIS Pro software was used for

estimating turbidity with the normalized difference

turbidity index (NDTI), as well as creating the spatio-

temporal distribution maps.

SNAP was used for processing and estimating

chl-a, CDOM, and TSM values. To start

preprocessing of the images, the resample operator

converted the spatial resolution of all satellite bands

to a single pixel size (10m). The geometric subset

operator was used to create a smaller image, and

therefore require less computational resources for

processing.

Next, atmospheric correction was applied; which

is crucial in order to be able to separate different

optically active constituents in water such as

chlorophyll-a, TSM and CDOM. The retrieval of the

optically active constituents is done in the form of

C2RCC S2MSI processor, an algorithm based on a

Table 1: Satellite and sensor data.

Satellite and Sensor Product Name Product Level Sensing Date

Sentinel 2 MSI

S2B_MSIL1C_20210217T064929_N0500_R020_T40R

DP_20230603T172352.SAFE

Level 1C February 17 2021

Sentinel 2 MSI

S2A_MSIL1C_20210423T064621_N0500_R020_T40R

DP_20230602T234425.SAFE

Level 1C April 23 2021

Sentinel 2 MSI

S2A_MSIL1C_20210821T064631_N0500_R020_T40R

DP_20230216T134509.SAFE

Level 1C Aug 21 2021

Sentinel 2 MSI

S2B_MSIL1C_20211224T065309_N0500_R020_T40R

DP_20221226T173342.SAFE

Level 1C December 24 2021

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114

deep learning, neural networks approach. This

processor required Sentinel-2 level 1C as source

input.

With this, Sentinel-2 satellite imagery was

downloaded and processed by geometric, radiometric

and atmospheric correction within the

aforementioned softwares. The complete

methodology framework is illustrated in Figure 2.

4.1 Watershed

The pre-processing of the SRTM DEM (30m) from

USGS was completed using the following ArcGIS

Pro tools; fill, flow direction, flow accumulation,

stream order, stream segmentation, catchment

delineation, catchment polygon processing, snap pour

point and slope. All of these processes were applied

to the reconditioned DEM to increase the accuracy of

Al Rafisah watershed delineation utilizing stream

networks.

4.2 Water Quality

Sentinel 2 level 1C data was downloaded from

European state agency website with 5% cloud

coverage for four months in 2021; February, April,

August and December and input to SNAP.

Processing parameters such as the salinity was set

to 0.0001, since it is freshwater body, and the

elevation was set to 197m as per the study area. Other

parameters such as atmospheric ozone and air

pressure at sea level will be set in our brush script

specifically for each images as they changed in time.

Finally, three-output data sets were selected; which

are output normalized water leaving reflectance,

output irradiance attenuation coefficients and output

uncertainties. The following are the default equations

within the C2RCC processor that computed chl-a and

TSM (Doerffer, 2019):

Chl-a [mg m-3] = 21 x a_pig(443)

1.04

(1)

TSM = 1.7 x b_part(443) (2)

The water leaving reflectance resulting from the

C2RCC operator was also used to calculate chl-a

indices with two empirical models; the band ratio of

NIR over red and the maximum chlorophyll index

(MCI), where the peak is at around NIR 705nm

(Ansper & Alikas, 2019):

MCI = R705 – R665- 0.53 *(R740 – R665) (3)

In-situ data was not collected and no existing field

measurements were available for this study.

Therefore, validating results and calibrating based on

line regression for empirical models to extract the

chlorophyll concentration could not be done.

The results were reprojected and exported as tiff

format. Finally, these files were imported to ArcGIS

Pro to create the final distribution map layouts for chl-

a, CDOM and TSM.

Figure 2: Methodology framework.

Seasonal Water Quality Assessment Using Remote Sensing in Al Raﬁsah Dam, United Arab Emirates

115

Figure 3: Watersheds and drainage networks map.

In ArcGIS Pro, Sentinel-2 level 1C data was

atmospherically corrected for the four months in

2021. Then, McFeeters (1996) normalized difference

water (NDWI) index was calculated to detect the

surface water bodies. Normalized difference turbidity

index (NDTI) was used to estimate the turbidity in

water bodies (Sankaran et al., 2023). The equation for

each are as follows:

NDWI= (Green-NIR/Green+NIR) (4)

NDTI= (Red-Green/Green+Red) (5)

Next, binary raster classification was created for

NDWI raster using the (greater_than) function to

differentiate between water and non-water pixels.

After that, a water body mask was created to extract

the boundaries of the Al Rafisah waterbody. Finally,

the NDTI raster was clipped with NDWI to extract

the intersect part between both of them to have the

turbidity product.

Final map layouts were produced to evaluate and

compare the water quality results.

5 RESULTS AND DISCUSSION

Sentinel-2 MSI was suitable as the data source for this

study considering its resolution and the area size of

Al Rafisah Dam.

5.1 Watershed

The watersheds and drainage networks map is shown

in Figure 3. The results of the watershed delineation

show that the dam location is perfect because high

and moderate streams end in the dam location, where

the water can be collected.

5.2 Water Quality

Chl-a concentrations predicted from Sentinel 2

satellite images were done by two approaches. The

first approach was from the C2RCC processing.

Concentrations ranged from 0.0003 mg/m3 up to

29.5157 mg/m3. Figure 4 shows that February and

December displayed higher maximum values, while

minimum values for all months were between 0.0003

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116

mg/m

to 0.0007 mg/m

. In terms of the average

concentrations, they were highest during the summer

months of April and August. The lowest

concentration was in February and the highest in

April at 2.6936 mg/m

and 11.8809 mg/m

respectively. Maximum values can be seen towards

the edges of the waterbody for all Chl-a distribution

maps, as well as towards the dam in April. Higher

temperatures during summer can create increased

production and appearance of phytoplankton and Chl-

a containing organisms.

The second approach used empirical models; first

of which is the Band Ratios Model where Red Band

4 and NIR Band 5 are used (Figure 5). Mean values

resulting from this method ranged from 0.8003 to

0.8202. MCI was the other empirical model and

utilized Band 4 (Red), Band 5 (NIR) and Band 6

(NIR) (Figure 6). Mean values from MCI calculation

ranged from 0.0033 to 0.0049. Average

concentrations for these approaches followed similar

Figure 4: Chl-a distribution maps using C2RCC for (A)

February, (B) April, (C) August, and (D) December.

Figure 5: Chl-a distribution maps using band ratios for (A)

February, (B) April, (C) August, and (D) December.

trend with higher values in April and December and

lower values in February and August. Like the

C2RCC process, the highest average for both

empirical models was found to be for the month of

April.

The predicted concentrations of CDOM among all

months ranged from 0.0001 m

-1

to 0.763 m

-1

(Figure

7). The concentration pattern corresponds to that of

Chl-a. Higher average values were displayed for the

summer months. Similarly, the winter months

received the lowest values. The lowest mean

concentration was in December and the highest mean

in April at 0.1684 m

-1

and 0.5893 m

-1

, respectively.

The concentrations for TSM retrieved from the

satellite images ranged from 0.0101 g/m

to 37.6331

g/m

(Figure 8). February and December observed

higher maximum values. The highest average

concentrations were found in the cooler months of

February, April, and December which were over 5

g/m

. The highest average concentration was once

Figure 6: Chl-a distribution maps using MCI for (A)

February, (B) April, (C) August, and (D) December.

Figure 7: CDOM distribution maps for (A) February, (B)

April, (C) August, and (D) December.

Seasonal Water Quality Assessment Using Remote Sensing in Al Raﬁsah Dam, United Arab Emirates

117

Figure 8: TSM distribution maps for (A) February, (B)

April, (C) August, and (D) December.

again in April at 5.7653 g/m

, whereas the drier,

hotter month of August had the lowest average value

at 3.9537 g/m

This trend could be a result of rainfall and tourist

activity at the dam during these cooler months. The

TSM maps display that maximum values are

collected in the middle of the waterbody away from

the edges.

The turbidity results shown in Figure 9 were

obtained from NDTI calculations on ArcGIS Pro.

Bands 3 (Green) and 4 (Red) from the satellite images

were utilized. Higher NDTI values closer to 1 indicate

turbid waters where reflectance is greater in the red

band. Lower values nearer to -1 indicate clearer

water. Turbidity values among all months ranged

from -0.1151 to 0.0422. The lowest mean value was

in February and the highest in August at -0.0848 and

-0.0366 m

-1

, respectively. The higher values were

located in the southern part of the dam, as well as

several spots towards the north, for all months. All

mean NDTI values were just under 0.

According to Garg et al. (2017), values from

around 0 to 0.2 are commonly categorized as

moderate turbidity. Clear waters display values from

0 to -0.2, and highly turbid waters greater than 0.25.

Turbidity increases with existence of suspended

and dissolved substances in fluids, thereby reducing

the clarity. The amalgamation of chl-a, TSM and

CDOM could be a cause for moderate turbidity in the

dam.

In their study, Ouma, Noor and Herbert (2020)

found that maps for chl-a concentrations and turbidity

values in the Chebara Dam followed similar patterns.

However, the concentrations for chl-a and TSM were

higher than those found in the Al Rafisah Dam. Using

Sentinel-2A and empirical models, concentrations for

Figure 9: Turbidity distribution maps for for (A) February,

(B) April, (C) August, and (D) December.

chl-a ranged from 11 to 222 mg/m

, while TSM

ranged from 35 to 574 g/m

A study on a Portuguese Estuary observed higher

concentrations for the same four parameters during

warmer months of 2020 compared to the winter

months (Sent et al., 2021). These results were

analogous to the average concentrations found at the

dam.

6 CONCLUSION

The principal aim of this paper was to use appropriate

remote sensing methods to monitor selected water

quality parameters in the Al Rafisah Dam over

several months in 2021. Specific targets were to

identify which water quality parameters were to be

studied. Additionally, to evaluate and compare the

results based on the distribution maps that were

created for each parameter and month. Sentinel-2

MSI was the chosen satellite and sensor due to its

high spatial resolution and wide use in water quality

monitoring. Chl-a, CDOM, TSM, and turbidity were

also selected for the study due to their wide use in

similar studies. The main objectives were achieved by

estimating values and displaying distribution maps

for the four water quality parameters.

Results showed that April had the highest average

values for chl-a, CDOM, and TSM. August received

the highest average for turbidity. Overall comparative

evaluation of the concentration patterns showed that

the parameters selected for the study are interrelated,

yet may vary due to environmental and human

influences.

This paper offered an unprecedented study on the

water quality of the Al Rafisah Dam. Despite this, the

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118

study observed some limitations that can be addressed

in future works. It would be worthwhile to obtain

ground truth from in-situ data for calibration and

result validation. With these field measurements,

models for estimating chl-a, CDOM, TSM, and

turbidity can be tailored based on the requirements

and objectives of the study. Additionally, future

research could focus on a different selection of

parameters or on comparing various satellite sensors

as the data source. Water quality research using

remote sensing and GIS plays an important role in

encouraging researchers to conduct more studies in

unexplored or unattainable locations.

REFERENCES

Acharya, T. D., Subedi, A., Huang, H., & Lee, D. H. (2019).

Application of water indices in surface water change

detection using Landsat imagery in Nepal. Sensors and

Materials, 31(5).

Abdullah, H. S. A., Mahdi, M. S. M., & Ibrahim, H. M. I.

(2016). Water quality assessment models for Dokan

Lake using Landsat 8 OLI satellite images. Journal of

Zankoy Sulaimani, 19(3-4), 25–42. https://www.

researchgate.net/publication/322978901_Water_Qualit

y_Assessment_Models_for_Dokan_Lake_Using_Land

sat_8_OLI_Satellite_Images

Ansper, A. & Alikas, K. (2019) Retrieval of chlorophyll a

from Sentinel-2 MSI data for the European Union water

framework directive reporting purposes. Remote

Sensing, 11(1), 64. doi:10.3390/rs11010064.

Bangira, T., Matongera, T. N., Mabhaudhi, T., & Mutanga,

O. (2024). Remote sensing-based water quality

monitoring in African reservoirs, potential and

limitations of sensors and algorithms: A systematic

review. Physics and Chemistry of the Earth, 134,

103536. https://doi.org/10.1016/j.pce.2023.103536

Doerffer, R. (2015). Algorithm Theoretical Bases

Document (ATBD) for L2 processing of MERIS data

of case 2 waters, 4th reprocessing. Retrieved from

https://c2rcc.org/wp-content/uploads/2022/05/C2RCC

_MERIS_ATBD_4Reproc_20150319.pdf

KC, A., Chalise, A., Parajuli, D., Dhital, N., Shrestha, S., &

Kandel, T. (2019). Surface water quality assessment

using remote sensing, gis and artificial intelligence.

Technical Journal 1(1), 113-122.

Khan, R. M., Salehi, B., Mahdianpari, M., &

Mohammadimanesh, F. (2021). Water Quality

Monitoring Over Finger Lakes Region Using Sentinel-

2 Imagery On Google Earth Engine Cloud Computing

Platform, ISPRS Annals of the Photogrammetry.

Remote Sensing and Spatial Information Sciences, 3,

279 – 283. http://dx.doi.org/10.5194/isprs-annals-V-3-

2021-279-2021

Garg, V., Kumar, S., Aggarwal, S. P., Kumar, V., Dhote, P.

R., Thakur, P. K., Nikam, B. R., Sambare, R. S.,

Siddiqui, A., Muduli, P. R., & Rastogi, G. (2017).

Spectral similarity approach for mapping turbidity of an

inland waterbody. Journal of Hydrology, 550, 527-537.

https://doi.org/10.1016/j.jhydrol.2017.05.039

Ma, Y., Song, K., Wen, Z., Liu, G., Shang, Y., Lyu, L., Du,

J., Yang, Q., Li, S., Tao, H., & Hou, J. (2021). Remote

sensing of turbidity for lakes in northeast China using

sentinel-2 images with machine learning algorithms.

IEEE Journal of Selected Topics in Applied Earth

Observations and Remote Sensing, 14, 9132–9146.

https://doi.org/10.1109/jstars.2021.3109292

Masoud. A. A. (2022). On the retrieval of the water quality

parameters fromsentinel-3/2 and landsat-8 oli in the

Nile Delta’s coastal and inland waters. Water 14, 593.

Mucheye, T., Haro, S., Papasyrou, S., & Cabllero, I. (2022).

Water quality and water hyacinth monitoring with the

Sentinel-2A/B satellites in Lake Tana (Ethiopia).

Remote Sensing, 14, 4921

Niroumand-Jadidi, M., Bovolo, F., & Bruzzone, L. (2020)

Water Quality Retrieval from PRISMA hyperspectral

images: First experience in a turbid lake and

comparison with Sentinel-2. Remote Sensing, 12, 3984.

http://dx.doi.org/10.3390/rs12233984

Ouma, Y. O, Noor. K, & Hebert, K. (2020). Modelling

reservoir chlorophyll-a, tss, and turbidity using

sentinel-2A MSI and Landsat-8 OLI satellite sensors

with empirical multivariate regression.

Journal of

Sensors. https://www.researchgate.net/publication/344

332045_Modelling_Reservoir_Chlorophyll-a_TSS_an

d_Turbidity_Using_Sentinel-2A_MSI_and_Landsat-8

_OLI_Satellite_Sensors_with_Empirical_Multivariate

_Regression

Sankaran, R., Al-Khayat, J. A., Aravinth, J., Chatting, M.

E., Sadooni, F. N., Al-Kuwari, H. A. (2023). Retrieval

of suspended sediment concentration (SSC) in the

Arabian Gulf water of arid region by Sentinel-2 data.

Science of the Total Environment, 904.

https://doi.org/10.1016/j.scitotenv.2023.166875

Seleem, T., Bafi, D., & Parchardidis, I. (2022). Water

quality monitoring using Landsat 8 and Sentinel-2

satellite data (2014–2020) in Timsah Lake, Ismailia,

Suez Canal region (Egypt). Journal of the Indian

Society of Remote Sensing. https://doi.org/10.1007/

s12524-022-01613-9

Sent, G., Biguino, B., Favareto, L., Cruz, J., Sa, C.,

Dogliotti, A. I., Palma, C., Brotas, V., & Brito, A. C.

(2021). Deriving water quality parameters using

Sentinel-2 imagery: A case study in the Sado Estuary,

Portugal. Remote Sensing, 13(5) 1043.

https://doi.org/10.3390/rs13051043

Su, Y.-F., Liou, J.-J., Hou, J.-C., Hung, W.-C., Hsu, S.-M.,

Lien, Y.-T., Su, M.-D., Cheng, K.-S., & Wang, Y.-F.

(2008). A multivariate model for coastal water quality

mapping using satellite remote sensing images.

SENSORS, 8(10), 6321–6339. https://doi.org/10.3390/

s8106321

Virdis, S. G. P., Xue, W., Winijkul, E., Nitivattananon, V.,

& Punpukdee, P. (2022). Remote sensing of tropical

riverine water quality using Sentinel-2 MSI and field

observations. Ecological Indicators, 144, 109472.

https://doi.org/10.1016/j.ecolind.2022.109472

Seasonal Water Quality Assessment Using Remote Sensing in Al Raﬁsah Dam, United Arab Emirates

119