water

Article
Phycocyanin Monitoring in Some Spanish Water Bodies with
Sentinel-2 Imagery
Rebeca Pérez-González 1 , Xavier Sòria-Perpinyà 2 , Juan Miguel Soria 1 , Jesús Delegido 2 ,
Patricia Urrego 2 , María D. Sendra 1 , Antonio Ruíz-Verdú 2 , Eduardo Vicente 1, * and José Moreno 2

1 Cavanilles Institute of Biodiversity and Evolutionary Biology (ICBiBE), Universitat de València,

46980 Valencia, Spain; repegon@alumni.uv.es (R.P.-G.); juan.soria@uv.es (J.M.S.);
maria.d.sendra@uv.es (M.D.S.)
2 Image Processing Laboratory (IPL), Universitat de València, 46980 Valencia, Spain;
javier.soria-perpina@uv.es (X.S.-P.); jesus.delegido@uv.es (J.D.); patricia.urrego@uv.es (P.U.);
antonio.ruiz@uv.es (A.R.-V.); jose.moreno@uv.es (J.M.)
* Correspondence: eduardo.vicente@uv.es

Abstract: Remote sensing is an appropriate tool for water management. It allows the study of some of
the main sources of pollution, such as cyanobacterial harmful algal blooms. These species are increas-
ing due to eutrophication and the adverse effects of climate change. This leads to water quality loss,
which has a major impact on the environment, including human water supplies, which consequently
require more expensive purification processes. The application of satellite remote sensing images
as bio-optical tools is an effective way to monitor and control phycocyanin concentrations, which
indicate the presence of cyanobacteria. For this study, 90 geo-referenced phycocyanin measurements



were performed in situ, using a Turner C3 Submersible Fluorometer and a laboratory spectroflu-


orometer, both calibrated with phycocyanin standard, in water bodies of the Iberian Peninsula.
Citation: Pérez-González, R.;
These samples were synchronized with Sentinel-2 satellite orbit. The images were processed using
Sòria-Perpinyà, X.; Soria, J.M.;
Sentinel Application Program software and corrected with the Case 2 Regional Coast color-extended
Delegido, J.; Urrego, P.; Sendra, M.D.;
Ruíz-Verdú, A.; Vicente, E.; Moreno, J.
atmospheric correction tool. To produce algorithms that would help to obtain the phycocyanin
Phycocyanin Monitoring in Some concentration from the reflectance measured by the multispectral instrument sensor of the satellite,
Spanish Water Bodies with Sentinel-2 the following band combinations were tested, among others: band 665 nm, band 705 nm, and band
Imagery. Water 2021, 13, 2866. 740 nm. The samples were equally divided: half were used for the algorithm’s calibration, and the
https://doi.org/10.3390/w13202866 other half for its validation. With the best adjustment, the algorithm was made more robust and
accurate through a recalculation, obtaining a determination coefficient of 0.7, a Root Mean Square
Academic Editor: Thomas Meixner Error of 8.1 µg L−1 , and a Relative Root Mean Square Error of 19%. In several reservoirs, we observed
alarming phycocyanin concentrations that may trigger many environmental health problems, as
Received: 15 September 2021
established by the World Health Organization. Remote sensing provides a rapid monitoring method
Accepted: 7 October 2021
for the temporal and spatial distribution of these cyanobacteria blooms to ensure good preventive
Published: 14 October 2021
management and control, in order to improve the environmental quality of inland waters.

Publisher’s Note: MDPI stays neutral

Keywords: remote sensing; Sentinel-2; phycocyanin; cyanobacterial harmful blooms; cyanotoxins
with regard to jurisdictional claims in
published maps and institutional affil-
iations.

1. Introduction
For populations and ecosystems, water is the most important and necessary natural re-
Copyright: © 2021 by the authors.
source. The massive increase in population in the last 70 years has led to increased demand
Licensee MDPI, Basel, Switzerland.
for water and pressure on aquatic ecosystems, which in turn has led to the construction of
This article is an open access article
reservoirs to ensure water and energy supplies [1]. The number of hydraulic infrastructures
distributed under the terms and combined with sparse ecological monitoring plans has triggered a series of problems that
conditions of the Creative Commons are difficult to manage. One of these problems is the increase of cyanobacteria blooms [2].
Attribution (CC BY) license (https:// In many reservoirs, they exceed the limits set by the World Health Organization [3], which
creativecommons.org/licenses/by/ means an increase in toxicity and a risk to the environment and human health. All this
4.0/). is accompanied by an increase in eutrophication, the loss or increase of sediment input,

Water 2021, 13, 2866. https://doi.org/10.3390/w13202866 https://www.mdpi.com/journal/water

Water 2021, 13, 2866 2 of 22

and an increase in invasive species, leading to major environmental problems, which are
exacerbated mainly by the effects of climate change on the Earth [4].
Since the adoption of the Water Framework Directive (WFD) [5], the European Union
has tightened regulations in order to improve the management of inland waters. Better
management of water resources and water quality in water bodies is promoted through
the implementation of the proposals set out in the WFD. The aim is to achieve a common
strategy for the whole of Europe in relation to hydrological planning, including reservoirs,
taking into account aspects such as wastewater treatment, public awareness of water use,
and the regulation of water supply systems. One of the most important aspects in relation
to the WFD is the importance of the ecological status of water bodies along with their
chemical status, a concept that has been established as an ecological potential for heavily
modified water bodies such as reservoirs. Cyanobacteria play a fundamental role in the
assessment of the ecological status of water bodies, as their proportions, especially toxic
ones, are used together with other variables, such as chlorophyll-a concentration (Chl-a),
transparency, or total phosphorus, to assess the ecological status of water bodies [6].
There are more than 1200 reservoirs in Spain. It is necessary to assess the risks
present in these reservoirs and the resulting consequences for the environment and for
human beings, since their waters are mainly used for the irrigation of agricultural land and
the supply of drinking water, as all major Spanish cities are supplied by surface waters
regulated by one or more reservoirs, according to Executive Order No. 817/2015. The water
from reservoirs must be treated in drinking water treatment plants before consumption
to avoid problems with the security of supply. However, the increase in nutrient levels is
too high for lentic waters. Remote sensing plays a very important role in all the studies
that need to be carried out to improve the management of water supplies because satellite
sensors can be used to observe and estimate the concentrations of various variables, such
as Chl-a, suspended solids, water transparency, and phycocyanin (PC), to identify and
assess risks and existing problems.
Cyanobacteria are microscopic prokaryotic organisms that contain photosynthetic
pigments typical of eukaryotic algae and plants, such as Chl-a and phycobilins, which give
them their typical blue-green color [6]. They are estimated to have originated 3.6 billion
years ago and to have contributed to the formation of the present atmosphere through
oxygenic photosynthesis. Their physiology has evolved little since their emergence. They
form a phylum of bacteria with unique ecological characteristics, which makes their classi-
fication difficult in many cases. Two different taxonomic classification methods are used:
botanical classification and microbiological nomenclature [7]. They are organisms with a
high tolerance of sudden environmental changes, which is important for the functioning of
aquatic ecosystems [8].
Cyanobacteria are primary producers as they serve as food for zooplankton and
some bacteria, thus maintaining the trophic webs of aquatic ecosystems [9]. They occur in
different types of habitats, both terrestrial and aquatic, although they are more abundant
and significant in aquatic environments. In aquatic environments, they are a fundamental
component of food webs, including in marine, brackish and freshwater environments,
where blooms or mass blooms may occur [10]. In terrestrial environments, they are found
in soils, deserts, tree bark, and symbionts.
Cyanobacteria play an important role in various biogeochemical cycles, such as carbon
and nitrogen cycles, with elements being fixed by phytoplankton. In the case of carbon,
fixation by phytoplankton leads to a decrease in CO2 concentration at the sea surface, acts
as a CO2 sink, and leads to an increase in ocean pH [8]. The ability to fix carbon depends
largely on the availability of macronutrients and the changes that can occur in the Redfield
Ratio C:N:P (106:16:1) [11]. When phytoplankton consume too much carbon, as occurs
during the development of phytoplankton blooms, the C:N ratio changes and can lead
to the temporary release and accumulation of carbon-rich dissolved organic matter [12].
The ratio and availability of these nutrients can be altered by the massive artificial input of
nutrients, nitrogen and phosphorus into water bodies, resulting in their eutrophication.
Water 2021, 13, 2866 3 of 22

Some species of cyanobacteria contain heterocysts, through which they are able to fix
atmospheric N more efficiently. Therefore, when eutrophy occurs due to excess nutrients,
cyanobacteria are able to grow when there is excess phosphorus and nitrogen becomes
limited; eventually, they obtain it directly from atmosphere, which makes them colonizers
of phosphorous-rich environments [13].
Cyanobacteria require phosphorus and nitrogen dissolved in water to grow and
reproduce. In many cases, low concentrations of these nutrients control the growth of
cyanobacteria and all phytoplankton. However, human activities can cause excessive sup-
ply, triggering the uncontrolled growth of cyanobacteria and resulting in large blooms [14].
These blooms turn the water turbid with an emerald green hue that limits light pene-
tration, thus reducing the photosynthetic capacity of the species found in deeper areas
and therefore generating oxygen shortages that can turn the affected areas hypoxic or
anoxic [10]. Another major problem in the occurrence of blooms is cyanotoxins. Many taxa
of cyanobacteria present secondary metabolites, which generate a wide range of toxins,
known as cyanotoxins [15].
Phycocyanin, the majority pigment of cyanobacteria, is a phycobiliprotein pigment
that is able to capture light. Along with allophycocyanin and phycoerythrin, it is considered
an accessory pigment of chlorophyll [16]; it is water-soluble so it cannot exist inside
membranes, unlike carotenoids. PC is blue in color, capable of absorbing orange and red
light at a wavelength of about 620 nm, and emits fluorescence at about 650 nm. It should
be noted that allophycocyanin absorbs and emits a longer wavelength than PC, 650 nm
and 660 nm, respectively [17].
The assessment of the impact of cyanobacteria on drinking waters can be performed
by measuring PC, since the fluorescence detection of PC pigment in freshwaters is a
good tool to determine cyanobacterial biomass, or the cyanobacterial blooms mentioned
above [18]. In fresh waters, the only microorganisms that produce significant amounts of
PC are cyanobacteria and their derivative, allophycocyanin, which is of great ecological
importance as it indicates cyanobacterial blooms. In many cases, the concentration of
chlorophyll-a is used to obtain the number of cyanobacteria, but it is not an ideal measure
because it is present in a large number of phytoplankton groups [19]. The World Health
Organization (WHO) established guidelines for drinking water quality that have been up-
dated over the years [3]. These guidelines establish the standards for drinking water, which
not only require water management and treatment, but also prior planning and continuous
monitoring. The guidelines do not necessarily have to be the same for all the places on the
planet, since each country or region adapts them according to its environmental conditions
and political and economic conditions. That is why there is no consensus, and each country
adopts the measures it considers appropriate within the framework and recommendations
pre-established by the WHO, agreed in 1999 in the so-called Stockholm Framework, for
future guidelines for drinking water, wastewater, and recreational water. These incorporate
risk assessment, risk management options, and exposure control elements in a single
framework containing quality objectives.
There are different pathogens in water, usually microbial, that can influence water
quality and are treated in order to make water safe for human consumption. To eliminate
these pathogenic microorganisms, disinfection is necessary. This is usually performed with
chemical substances, mainly chlorine and fluorine compounds, although it should be noted
that the WHO established that disinfection with chlorine does not exempt the water from
being contaminated, since chlorine is not able to eliminate all the harmful microorganisms
that may be present, such as protozoa (Cryptosporidium) and some viruses, and does not
eliminate cyanotoxins.
Water quality can be monitored by remote sensing, a tool that allows us to obtain data
from the Earth’s surface from sensors carried by satellites. The continuous electromagnetic
interaction between the Earth’s surface and the sensor generates a series of data that must
be subsequently processed to obtain information that is of interest and interpretable. Since
the beginning of the European Copernicus program, in 2014, environmental changes have
Water 2021, 13, 2866 4 of 22

been studied. Among these environmental changes, the study of water quality by remote
sensing has been highly developed, especially in the development of studies of the presence
of unicellular algae, the concentration of which is directly proportional to that of their most
frequent photosynthetic pigments, such as Chl-a, which is present in all photosynthetic
organisms, and PC, which is present in cyanobacteria [18].
Included in this program are the Sentinel missions, whose function is mainly based on
transforming data obtained by satellites, remotely, into information through processing and
analysis; the data are later integrated with other sources and subsequently validated [20].
Sentinel missions refer to each of the satellites sent into space over the years. Sentinel-2A
(S2A) in 2015, together with its companion, Sentinel-2B (S2B), which was launched in 2017,
are part of a high resolution, multispectral mission in polar orbit that allow the study and
monitoring of the Earth’s surface, which is mainly focused on monitoring natural disasters
and land use areas, as well as land surface isolation for territorial planning and forest
management [21]. It is also useful for small water bodies due to its spatial resolution.
The novelty of this study consists in observing the presence of PC using Sentinel-2 im-
agery, reviewing the most appropriate equations in published papers for image processing
in the reservoirs considered, and thus selecting the most suitable equation for a study of
the presence of PC in Spanish reservoirs that can be applied elsewhere. Thus, the objectives
of the present work are to apply remote sensing as a tool to quantify the concentration of
PC using satellite sensors as a proxy for the abundance of cyanobacteria for the monitoring,
control, and management of inland waters. To this end, specific fluorescence from PC will
be measured as a function of cyanobacterial biomass; a model relating in situ phycocyanin
concentrations, and the reflectance measured by the Sentinel-2 Multispectral Instrument
sensor (MSI) will be calibrated. Finally, the model for estimating PC concentration will be
validated to obtain thematic maps of concentrations and alert levels in some reservoirs as a
case of study.

2. Materials and Methods

2.1. Study Site
For this work, data were collected from 2016 to 2020 from 30 different reservoirs
in three basins of the Iberian Peninsula: Ebro, Jucar and Tagus (Spain). Their spatial
distribution is presented in Figure 1, including one reservoir in the Tagus basin, nine in the
Jucar and twenty in the Ebro, whose characteristics are described in Table A1. They stand
out because they are all have a capacity greater than 10 hm3 .

Figure 1. Map of watersheds of Spain showing the location of the reservoirs studied, the number of
which coincides with that used in Table A1 of the Appendix A.
Water 2021, 13, 2866 5 of 22

The need for reservoirs in Spain is due to the irregularity of rainfall. For this reason,
it is necessary to have special control over the reservoirs and rationalize the water they
supply to avoid future problems in periods of drought.

2.2. Sampling Methods

In situ sampling allows us to know the concentrations of solids, organic matter, Chl-a
and PC. For in situ sampling, we used a boat to surf the reservoir and go to the sampling
sites, which were usually in the same site field. Prior to sampling, it was necessary to
consult the weather forecast, since the satellite image is not affected by cloud cover and the
wind speed does not exceed 10 km h− 1 .
A GPS was used to reach the sampling point. Once there, the anchor and the buoy
were pulled to affix the boat and make the corresponding measurements. The transparency
was measured by means of the Secchi disk depth (SDD), submerging a disc of 20 cm of
diameter until it reaches a depth at which it is not visible. The in situ determination of
PC and Chl-a was performed using a Turner Design submersible C3 field fluorometer
calibrated with Spirulina Standard 40% purity (Sigma-Aldrich CAS 11016-15-2, Sant Louis,
MO, USA). This fluorimeter works by means of a sequence of excitation LEDs that ensure
that the data measurement is independent of the environmental lighting. A vertical profile
of measurements was produced descending to the depth of the SDD, recording every four
seconds the depth value and the fluorometric measurements.
In the Ebro, the integrated samples were taken until the SDD, using a ballasted PVC
tube, preserved in darkness and refrigerated. The PC concentrations were also analyzed in
the laboratory using the Hitachi FL-7000 spectrofluorometer so that the PC concentration
values were obtained by measuring the fluorescence intensity with respect to the PC
concentration, for which the Spirulina Standard was also used.
The PC adjustment (Figure 2) was PC = 0.3253 × RF, calculated from the PC standard
concentration and raw fluorescence (RF). From fitting the equation developed, the values
for each sample were obtained.

Figure 2. Calibration adjustment between raw fluorescence units (RF) and standard phycocyanin
concentration for Hitachi FL-7000 spectrofluorometer.

The Chl-a was measured in the laboratory using the spectrophotometric method. The
samples were filtered through 0.4–0.6 µm GF/F glass fiber filters, extracted using standard
methods [22] and calculated with Jeffrey and Humphrey methods [23].

2.3. Image Processing

The S2 satellite includes 13 bands of different spatial resolutions: 10 m, 20 m and 60 m
(Table 1). The S2 images were downloaded at the same time as the field data acquisition,
level L1C (without atmospheric correction), from two different servers, mainly from the
Water 2021, 13, 2866 6 of 22

ESA Open Access Hub server, which is a free ESA tool that allows the download of images
from different satellites for further processing. The images not available on the ESA server
were downloaded from Earth Explorer, a North American server of the U.S. Geological
Survey.

Table 1. Sentinel-2 spectral bands: Visible (VIS), Near-infrared (NIR) and Short-Infrared (SWIR),
according to ESA [21].

Wavelength (nm) Spatial

Band Objective
Central Wide Resolution (m)
B1 443 20 60 Aerosol Correction
Aerosol Correction, Blue Band
B2 490 65 10
VIS

Measure
B3 560 35 10 Green Band Measure
B4 665 30 10 Red Band Measure
B5 705 15 20 Red Edge 1 Band Measure
B6 740 15 20 Red Edge 2 Band Measure
B7 783 20 20 Red Edge 3 Band Measure
Water Vapor Correction, Near
NIR

B8 842 115 10
Infrared Band
Water Vapor Correction, Near
B8a 865 20 20
Infrared Band
Water Vapor Correction, Near
B9 945 20 60
Infrared Band
B10 1380 20 60 Cirrus Detection, Infrared Band
SWIR

B11 1610 90 20 Infrared Band

B12 2190 180 20 Aerosol Correction, Infrared Band

Once downloaded, the images were processed with the Sentinel Application Platform
(SNAP, Brockmann Consult Gmbh, Hamburg, Germany), an application developed for
ESA. The SNAP application is a free resource offered to process and analyze satellite
images together with the tools of the Sentinel and other satellites. The software has
several atmospheric correction methods; the most developed for aquatic subjects is Case 2
Regional Coast Colour (C2RCC), adapted for S2 and used for Case-1 waters, which are not
as turbulent as marine waters. On the other hand, there is the Case-2 Extreme Cases (C2X)
process, a version developed for turbid waters, such as continental Case-2 waters [23].
These two atmospheric correction methods are based on reflectance and radiance data
obtained through simulations in radiative transfer models, which are responsible for
performing the inversion through neural networks [24]. In this case, the C2X processor
was selected for the atmospheric correction, which is the best process for inland waters due
to the amount of organic matter and phytoplankton they contain. In addition, SNAP has
a large number of built-in algorithms with different biophysical variables, which allows
the development of new algorithms [25], which in this case were used to evaluate aquatic
aspects related to PC.
The downloaded images were resampled to 20 m. This was because for further
processing, it was necessary for all the bands to have the same spatial resolution. Once
resampled, the area of interest was cropped to facilitate the atmospheric correction process.
This process also resulted in automatic products that presented information on water
quality through the values of maximum transparency, Chl-a concentration, and suspended
solids, among others. Once the atmospheric correction was performed, the values of the
pixel bands were extracted from a 3 × 3 window centered in the coordinates of each sample
point. For the nine values of the 13 bands, the mean and standard deviation were calculated;
the values with a difference greater or less than the standard deviation with respect to the
mean were eliminated. With the remaining values, a new mean and standard deviation
were calculated to obtain more adjusted data.
Water 2021, 13, 2866 7 of 22

The Band Maths tool was used to obtain the thematic maps of PC, a biophysical
variable not included in SNAP. This tool makes it possible to apply the equation from the
best model obtained once the calibration and validation process is complete.

2.4. Calibration
Once all the data were compiled, we proceeded to compile the database by gathering
all the available information from the PC and reflectance data. With all these data, we
calculated indices that related the different reflectance bands with the PC in order to
establish the one that best fitted our study (Table 2).

Table 2. Reference equations for the study and corresponding values. N: number of data; R2 : Pearson determination
coefficient; RMSE: Root mean square error.

Atmospheric
Reference Sensor Bands Relation N R2 RMSE Data Range
Correction
In situ
[25] S2-MSI R740−R665 29 0.70 4.82 0–23 RFU
Reflectance
In situ
[25] S3-OLCI R707/R679 9 0.86 1.45 0–23 RFU
Reflectance
[26] S2-MSI Sen2cor R740/R665 21 0.84 141 10–1287 mg/m3
[27] Dron - R709/R620 92 0.95 - 0.43–13.07 mg/m3
In situ   
[28] S3-OLCI 1
− 0.4
− 0.6
× R754 216 0.69 22.7 0.33–317.74 mg/m3
Reflectance R620 R560 R709
This Study S2-MSI C2X R705−R665 45 - - 0.23–364.7 mg/m3

Some of the indices used were based on previous studies. They were modified to
adapt to the available S2 bandwidth. A new index was also proposed.
Once the normality of the data was verified, the reflectances were correlated with the
PC concentrations and their logarithm, with the band ratio on the abscissa axis and the
PC concentration, or its logarithm, on the ordinate axis. To proceed with calibration and
validation, all the data from 2016 to 2020 were equally divided into two groups of data
with the same range. Finally, with the best ratio, the adjustment was performed using all
the data. This procedure helped to better fit the data and to establish valid correlations.
This procedure was performed for all the indices, and the determination coefficients
(R2 ) were obtained. The type of regression used varied according to the best fit to our
data; in some cases, potential or logarithmic regressions were used when a better fit was
observed.

2.5. Validation
The calibration of each index provided the equations, which were later used to estimate
the PC values to be validated. The validation process was performed for each index to find
the one that best fitted our specific case of study.
For the validation, an adjustment was made to relate the estimated PC with the
observed PC, obtaining the R2 value. In addition, different errors were calculated: the root
mean square error (RMSE) and the relative root mean square error (RRMSE). The statistical
processes were calculated with SigmaPlot 14 software.
It should also be noted that this was an iterative process, which allowed us to corrobo-
rate the best method for both calibration and validation. We performed a series of tests to
find the best algorithm to apply to the case in our study.
Once the validations were completed and we obtained the best retrieval algorithm for
PC, we proceeded to elaborate the thematic maps with the classification established by the
WHO with respect to PC (Table 3).
Water 2021, 13, 2866 8 of 22

Table 3. Classification according to WHO, with the modifications of density, biovolume, and
chlorophyll-a according to [29], and phycocyanin according to [18].

Drinking Density Biovolum Chlorophylla PC

Bath Water
Water (Cell mL−1 ) (mm3 L−1 ) (µg L−1 ) (µg L−1 )
Surveillance level 200 0.02 0.1 <0.1
Alert level I 2000 0.2 1.0 4
Guidance
20,000 2 10 30 ± 2
level I
Guidance
Alert level II 100,000 10 50 90 ± 2
level II

3. Results
After the treatment and processing, a total of 90 data along with high quality images
were used in the study. The table of descriptive statistics of the results (Table 4) includes
the descriptive statistics of the variables measured in the reservoirs. In some samples,
we observed PC values higher than the Chl-a concentration, as is typical of eutrophic
systems [26].

Table 4. Descriptive statistical values of chlorophyll-a (Chl-a), Secchi disk depth (SDD), suspended
solids (SS), and phycocyanin (PC).

Chl-a (µg L−1 ) SDD (m) SS (mg L−1 ) PC (µg L−1 )

Maximum 91.92 9.10 48.56 364.70
Minimum 0.61 0.35 0.30 0.23
Mean 9.82 3.36 6.07 37.56
St. Deviation 16.61 2.23 10.77 80.18

Descriptive statistics were also established, including a comparison between the

concentrations of PC and Chl-a in the studied reservoirs of the Jucar and Ebro watersheds
(Figure 3), where it has been verified by means of a t-test that the difference was not
statistically significant. Even so, the values of PC in the Jucar basin were much higher than
those of Ebro. The results suggested a difference between the Ebro and the Jucar, with
much higher concentrations in the reservoirs belonging to the Jucar basin than to those of
the Ebro, as well as a correlation between PC and Chl-a concentrations.

Figure 3. Comparison between PC and Chl-a values in Ebro and Jucar basins in µg L−1 .

On the other hand, the monitoring and alert level values, classified according to the
WHO [3] for all the reservoirs studied, are presented in Table A2.
Water 2021, 13, 2866 9 of 22

3.1. Calibration
As described in the methodology section, six models were calibrated. The iterative
fitting process was performed with five band ratios compiled from previous studies,
together with another self-developed ratio. Considering the coefficient of determination,
the best ratio was the R705/R665 using raw data (Figure 4). The band ratio was based on
the algorithms described by [25,27].

Figure 4. Correlation between the R705/R665 ratio and PC observed in the reservoir.

Table 5 shows the results of the calibration for the six different band relations. A
potential or linear fit was performed, depending on which worked best with their respective
coefficients of determination using the PC concentration values on the one hand and Ln on
the other. As can be seen, the R2 of [28] was the best relationship; the problem was that this
relationship estimated a large number of negative values in the validation.

Table 5. Calibration results: fit equations and coefficients of determination.

Band Ratio PC R2 PC Log. R2

[25] −295.15 (R740 − R665) + 36.165 0.0007 −47.348 ( R740
 − R665) + 1.9091 0.05
[25,27] 25.792 (R705/R665)3.4595 0.70 R705
3.1454 R665 − 0.2847 0.69
 
[26] 387.84 (R740/R665)2.3932 0.67 5.9764 R740
R665 + 0.758
0.64
     
1 0.4 0.6 1 0.4 0.6
669.65 R620 − R560 − R709 × R754 + 11.071 R620 − R560 − R709 × R754 +
[28] 0.74 0.59
14.808  1.9056
R705−R665 8595.3 ( R705 − R665) + 38.796 0.64 141.82 R705
R665 − 2.3022
0.51

3.2. Validation
The best results in the validation process were obtained by the algorithm calculated
with the band ratio R705/R665 (Figure 5), because it offered the best level of errors without
estimating negative values. The results of all the validations tested are given in Table 6.
Water 2021, 13, 2866 10 of 22

Figure 5. (a) Validation of R705/R665 algorithm (the dotted line is 1:1 line) and (b) R705/R665 algorithm recalculated with
all the data.

Table 6. Validations results.

RMSE RRMSE
Band Ratio Equation R2
(µg L−1 ) (%)
R740−R665 y = −47.348 x + 1.9091 0.02 13.42 31.22
R740/R665 y = 387.84 x2.3932 0.61 9.92 23.08
R705/R665 y = 24.665 x3.4607 0.71 8.13 18.91
 R705−R665  y = 8595.3 x + 38.796 0.72 7.21 16.78
1 0.4 0.6 y = 669.65 x +14.808 0.66 7.82 18.19
R620 − R560 − R709 × R754

The results demonstrated that the best R2 was obtained with the R705–R665 ratio
(R2 = 0.72), although it estimated negative values. Therefore, the best results were obtained
with the R705/R665 ratio, applying the equation y = 24.665 x3.4607 with an R2 of 0.71, an
RMSE of 8.13 mg/m3 and an RRMSE of 18.91%, taking into account all the characteristics
of the study and the modification of bands with respect to the initial band ratio estab-
lished by [27] using a hyperspectral sensor. Finally, Figure 6b shows the model with the
R705/R665 ratio using all the data of the study.

3.3. Thematic Maps

After downloading, the required images were processed with SNAP software, resam-
pled, and cropped. The RGB false color image of the study area was obtained using bands
2, 3 and 4 (Figure 6a). Afterwards, the atmospheric correction was performed (Figure 6b)
by applying the C2X correction.
At the same time, the automatic SNAP products, such as Chl-a and SS, were obtained,
to which a color palette was applied and superimposed over the RGB image (Figure 6c,d).
The PC was calculated using SNAP Band Maths tool, which allowed mathematical opera-
tions to be performed with the bands. The equation in Figure 5b was applied and the gray
image was obtained (Figure 6e), the color palette was applied and then superimposed on
the initial RGB image (Figure 6f).
Water 2021, 13, 2866 11 of 22

Figure 6. Example of thematic maps processing. The image of the lower Ebro in summer 2018: Mequinenza and Ribarroja
reservoirs. (a) RGB map; (b) RGB image with C2X atmospheric correction; (c) map of total suspended matter automatic
product; (d) map of Chl-a automatic product; (e) map of PC in gray scale applying the R705/R665 algorithm; (f) PC (µg/L)
in its corresponding scale.

We selected some images of the reservoirs for consideration in our case of study. The
thematic maps of PC concentration and WHO classification are presented in Appendix A,
Figures A1–A3. We can view the classification of alert and guideline levels according to the
concentrations of PC present in some reservoirs. Some of them exceed the concentration of
100 µg L− 1 , and could lead to serious water quality problems. The water bodies with greater
concentrations of PC are the Albufera of Valencia lagoon, Bellus reservoir, and Valdecañas
reservoir. Albufera of Valencia is a highly eutrophicated lagoon and, therefore, its PC
Water 2021, 13, 2866 12 of 22

concentrations are higher than in other reservoirs; according to [26], PC concentrations of

1200 µg L− 1 are reached at certain times of the year, which represents a significant risk
related to the proliferation of potential toxic cyanobacteria. In that study, the R740/R665
band ratio was considered the best fitted to the PC trend, and therefore the algorithm
derived from this ratio was used in their PC estimation, which obtained a very tight
relation between the in situ measurements and the estimated ones. This was applied to
Valdecañas reservoir, a reservoir that has been extensively studied over the years due to its
concentrations of PC and its associated risk of toxic cyanobacteria. Already in 2004, the
toxicity in Tagus reservoirs, including Valdecañas, was really worrying due to microcystins
caused by the ineffective treatment of wastewater in Madrid County.

4. Discussion
4.1. Satellite Sensors and Spectral Resolution
The different indices on which the study was based were adjusted to S2 bands, since
the sensor used was not the same in all the previous studies considered. In the case of the
work of Kwon et al. [27], the possibility of using S2 or Landsat-8 sensors was mentioned, but
they defended the idea that the spectral resolution of these two sensors was too rough. This
may affect the performance of algal pigment detection, since with a bandwidth between 60
and 80 nm, it may become difficult to discriminate between absorption features [30]. This is
why they proposed the use of drone-type sensors, since they offer a finer spectral resolution
compared to satellites. The study conducted by Kwon et al. [27] was carried out in three
relatively close areas of the same river, in a short period of time (26 July to 25 October 2018);
it is likely that using a smaller range of values would lead to better results in this case,
where a coefficient of determination of 0.95 from a total of 92 analyzed data was obtained.
On the other hand, in the case of the study by Beck et al. [25], the possibility of finding PC
and Chl-a reflectance algorithms that are adaptable to a variety of satellite sensors using
simulated data is presented, in order to maximize their use and counteract the results
obtained in inland water masses. The results obtained by Beck et al. [25], conducted in an
area of 8.9 km2 , were varied as they observed that, in the same body of water, some sensors
performed better than others.
This methodology is complex, since it involves a much longer and more complicated
data collection and processing procedure, and the results may not be conclusive due to
the wide variety of sensors used. The validated and best-performing band ratios were
R740-R665 and R707/R679, with determination coefficients of 0.7 and 0.86, respectively.
Both relationships were applied in this study, and it was seen how the second relationship
could be validated with an R2 of 0.702; on the other hand, the first relationship did not
provide statistically significant results (R2 = 0.0007).
A study by Liu et al. [28], performed with Sentinel-3 (S3) and MERIS (Medium
Resolution Imaging Spectrometer) also presented considerable difficulties. The bands
also had to be adapted for use in the MERIS sensor, since they involved a more complex
mathematical development when calculating the number of bands necessary to establish a
good algorithm. In our study, a similar procedure was used, separating the data into two
equal groups to make calibrations and validations, although Liu et al.’s procedure was
performed randomly when selecting the data and therefore did not have the same range.
Another of the studies on which the algorithm development of this work was based,
by Sòria-Perpinyà el al. [26], established a relationship with S2 using in situ measurements
and a ratio of R740/R665. This relationship provided good results when applied to our
data with an R2 of 0.67, but not the best, compared to the other algorithms used.
Many studies use equal groups to establish algorithms, some of which are mentioned
above [21]. In order to develop good algorithms that adjust better to the intended results, it
is necessary to obtain an error that fits well with the parameters that are considered valid
in these types of studies.
The use of S3 is quite recurrent in these types of studies, since its superior spectral
resolution allows a much more accurate estimation and, therefore, the development of
Water 2021, 13, 2866 13 of 22

algorithms that are more faithful to reality. Even so, the use of S2 is still common in inland
waters because of its superior spatial resolution, but the position and bandwidth of S2 is
not optimal for detecting some specific features, such as peaks or depressions caused by
water reflectance [31].

4.2. Problems of Cyanobacteria

Global human population growth has led to an increase in demand and pressure on
continental aquatic ecosystems. To deal with this effect, management and environmental
monitoring tools are being developed by the European Union and government agencies
to reduce the degradation of ecosystems. One example is the WFD, which is a key tool at
the European level for the protection and management of water resources throughout the
continent. All of this makes it important to carry out work such as the study presented
here, in which, using a long list of data taken from reservoirs, models can be developed
that can be used to monitor water bodies and create warning systems [32].
We worked with a wide range of data, which shows the variability of the water bodies
sampled. The data in this study were collected from 2016 to 2020, and we are still carrying
out fieldwork in order to increase their number and thus create a broader and more robust
database.
In Table 4, we can observe the maximum and minimum values of the data range noted
for PC, with a maximum of 364.7 µg L− 1 and a minimum of 0.23 µg L− 1 . On the other hand,
as can be seen in Figure 3, where pigments are compared between basins, the widest range
for the concentration of PC was obtained in the Jucar basin. It should be noted that for our
data, there was a direct, proportional relationship between Chl-a and PC, since the greater
the Chl-a, the greater the PC, and vice versa [1], as is the case with suspended solids, since
a greater number of Chl-a and PC in reservoirs means that there is more phytoplankton
mass and, therefore, the amount of suspended organic matter will be higher. This is linked
to eutrophication processes and, therefore, to a decrease in the availability of oxygen in
water, generating hypoxic environments in which organisms that are unable to survive
die and generate more organic matter, thus increasing its concentration [33]. Chl-a is the
major pigment present in all plants and algae, and cyanobacteria, PC, and phycoerythrin
are accessory pigments. It is important to note that these accessory pigments are capable of
absorbing light at different wavelengths than Chl-a, hence their importance [34].
As mentioned earlier, PC is a proxy for the concentration of cyanobacteria, whose
proliferation can lead to toxicity in water. Therefore, it can be used as an effective tool for
the monitoring and surveillance of reservoir water quality. This fact has led the WHO [3]
to establish a range of concentrations, using tables to apply levels (Table 3). These tables
have been modified over the years by different studies to establish the differences between
densities and bioaccumulations, as well as Chl-a and PC concentrations, the object of study
in this work.
There is a relationship between land use and water quality. Although there are gener-
ally strict regulations regarding wastewater treatment, what are not so well developed are
the self-purification systems of the reservoirs, which favors all the physicochemical and
biological processes that reduce the contribution of organic matter and nutrients [35,36].
The WHO and WFD guidelines are very important management tools, since they establish
the limit of values for water quality, especially those that affect human consumption, what-
ever the type (agricultural, domestic, drinking, etc.). In recent years, similar regulations,
such as Directive 200/60/EC of the European Parliament and Council [5], have gained in
importance due to the increased interest in environmental welfare, although, for example,
in the case of this directive, Spain did not meet the limits set in 2015, and requested an
extension until 2027.
Cyanobacteria are of great environmental importance, since they are CO2 sinks, but
not all of them are beneficial, since there are potentially toxic species and because alterations
of aquatic ecosystems can occur [37], which can lead to serious problems in water quality
and environmental health. These affect both ecosystems and human beings. For this reason,
Water 2021, 13, 2866 14 of 22

a continuous monitoring of their concentrations tis necessary in order to determine when

more exhaustive analysis of the possible presence of toxins in water is required.
There is a clear difference between the reservoirs in the Ebro Basin and those of the
Jucar, although in neither case are they at a surveillance level, which is the lowest of the
levels established by the WHO and features the fewest monitoring and requirements. In
the case of the Ebro, the reservoirs present levels of PC that are around Alert level I; some
are already at Alert level II, although these are few. The maximum PC concentrations are
34.05 µg L− 1 . By comparison, the maximum PC in Jucar reservoirs is 364.7 µg L− 1 , which
exceeds all the established limits and could pose a serious problem for the management
and treatment of these waters.
The pollution of inland water masses is a serious environmental problem that is
becoming more and more accentuated, although there are reservoirs that are not negatively
affected, as in the case of those of the Ebro. In the area of the Jucar basin, the effects are
beginning to be perceived in a more notorious and serious manner. These phenomena
are aggravated by climatic conditions and urban planning, which encompasses both what
has already been built and future forecasts for the construction of new residential and
industrial areas near the reservoirs. Valdecañas in the Tagus, Bellus and Beniarres in the
Jucar basin, and El Val in the Ebro basin are clear examples of this.
Cyanobacterial blooms in reservoirs whose purpose is to supply drinking water can
cause serious problems for water quality, from altering its taste and odor to compromising
its use as a water supply for human consumption [38]. This necessitates the presence
of control and management tools both to prevent it from happening and to mitigate its
negative effects, such as eutrophication or toxin production [39]. Over the years, Chl-a
concentrations and cyanobacterial cell density have been used to estimate the magnitude
of cyanobacterial blooms, although Chl-a is not only present in cyanobacteria but is found
in all eukaryotic algae [14]. PC is a parameter closely related to cyanobacterial blooms,
which makes it possible to establish criteria, such as the modified WHO criteria (Table 3),
to determine PC limit values and, therefore, to obtain a much simpler and more effective
parameter for measuring blooms, since, by using the optical properties of PC, it can be
used as a spectral response standard to determine the abundance of cyanobacteria [39].
The use of remote sensing as a tool for the detection of cyanobacterial blooms through
the detection of PC is also important because of their spectral characteristics. In another
study [40], Chl-a and PC concentrations were estimated from hyperspectral images of the
Airborne Imaging for Applications Spectrometer (AISA) in a mesoeutrophic reservoir with
in situ measurements of both pigments and reflectances. After the subsequent processing
of collected data, relationships between both parameters were established to develop
algorithms, obtaining a better relationship of PC with the band at 628 nm (R2 = 0.8). With
these results [40], it was possible to establish that the relationship between reflectance at
628 nm and PC concentration provides an approximation of cyanobacteria concentrations
in waters, and that hyperspectral images are therefore a useful tool for authorities to
determine water quality.
Studies on the estimation of PC have turned towards remote sensing techniques in
recent years. These have evolved from in situ pigment measurement techniques, through
laboratory experiments to the quantification of the optical properties of pigments in situ, in
the field, and directly from satellite data [41,42]. This work follows this line, offering a new
tool for determining PC at a wide range of concentrations, i.e., for bodies of water with
very different reflectance spectrums.

5. Conclusions
Remote sensing was shown to be a helpful tool for the quantification of PC in water
masses and, therefore, a useful tool for the monitoring and surveillance of inland waters.
This was demonstrated by the algorithm found for the estimation of this variable from the
reflectances of the R705 and R665 bands measured by S2-MSI.
Water 2021, 13, 2866 15 of 22

The best correlation of PC was observed with reflectances obtained with the C2X
atmospheric correction, and using the R705/R665 ratio, for which an RMSE = 8.1 µg/L and
an RRMSE = 18.9% were obtained.
Monitoring by satellite sensors has featured in many studies, and this work further
reinforces remote sensing as a key tool, in addition to supporting the use of S2, which,
despite not having as powerful a spectral resolution as other satellites, such as S3, provides
better spatial resolution and robust algorithms with statistically significant correlations
across all its products. S3 could serve as a tool to validate algorithms developed with S2
and, therefore, to obtain better results.
The elaboration of thematic maps provides information as to the presence of PC levels,
especially the spatial heterogeneity of their distribution, which cannot be seen with point
control sampling. It also allows the detection and monitoring of bloom periods, their
appearance and subsequent disappearance.
This type of work is important for several reasons. One is that the use of remote
sensing tools facilitates its elaboration, as well as reducing costs in terms of resources
and allowing monitoring on a global scale, thus reaching areas that are difficult to access.
On the other hand, this work shows the importance of monitoring water quality, as the
concentration of phytoplankton was monitored for many years in this study. However,
a significant increase was observed in cyanobacteria, which has become more important
in the last fifteen years and will be even more important in the future in a climate change
scenario.

Author Contributions: Conceptualization, J.M. and J.D., R.P.-G., J.M.S., X.S.-P. and A.R.-V. designed
the methodology for this work. Field and laboratory works: X.S.-P., M.D.S., P.U. and E.V., R.P.-G.
analyzed the data and wrote the paper. J.D., E.V. and J.M.S. supervised the manuscript. Funding
acquisition, J.M., J.D. and E.V. Writing—review and editing, all authors. All authors have read and
agreed to the published version of the manuscript.
Funding: This research was partially funded by the European Union—ERDF and the Ministry
of Science and Innovation and the State Research Agency of Spain under project RTI2018-098651-
BC51 (FLEXL3L4—Advanced Products L3 and L4 for the FLEX-S3 mission) and partially funded
by the GENERALITAT VALENCIANA postdoc research grant (X.S.-P) APOSTD/2020/134, project
SEQUARMON (Sentinel quality reservoirs monitoring).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Images are available at ESA Copernicus Hub and Eathexplorer web of
United States Geological Survey.
Acknowledgments: The authors would like to thank the field personnel for their collaboration in
the sampling work.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.
Water 2021, 13, 2866 16 of 22

Appendix A

Table A1. Some hydrologic characteristics of the studied reservoirs.

Position Max Depth Volume Elevation Res. Time

Name Climate
Lat. Lon. (m) (×106 m3 ) (m.a.s.l.) (Year)
1 Alarcón 39.69 −2.17 71 1118 806 2.15 Csa
2 Albufera 39.34 −0.35 2 23 1 0.15 Csa
3 Barasona 42.14 0.33 66 85 448 0.24 Cfa
4 Bellus 38.93 −0.47 34 69 144 0.63 Csa
5 Benageber 39.73 −1.09 90 221 450 0.35 Csb
6 Beniarres 38.80 −0.35 53 27 318 1.22 Csa
7 Canelles 42.03 0.65 150 201 506 1.00 Cfb
8 Contreras 39.62 −1.53 129 384 669 1.48 Csa
Cueva
9 40.97 −0.69 65 22 580 0.65 Bsk
Foradada
10 Ebro 42.97 −4.07 34 540 838 1.55 Cfb
Estanca de
11 41.06 −0.18 15 7 342 0.14 BSk
Alcañiz
12 Flix 41.23 0.53 26 11 41 0.01 BSk
13 Gallipuen 40.87 −0.41 36 4 694 0.71 Cfb
14 La Loteta 41.82 −1.32 34 100 288 3.51 BSk
Maria
15 40.02 −0.16 38 18 100 5.96 Csa
Cristina
16 Mezalocha 41.42 −1.07 45 4 473 1.17 Cfa
17 Moneva 41.17 −0.83 45 8 615 0.95 Cfb
18 Oliana 42.12 1.30 102 84 519 0.08 Cfa
19 Regajo 39.89 −0.52 23 6 407 0.14 Csa
20 Rialb 41.97 1.23 99 402 430 0.36 Cfa
21 Ribarroja 41.33 0.36 60 207 70 0.03 Csb
22 Sitjar 40.01 −0.23 58 49 160 0.37 Csa
23 Sobrón 42.76 −3.15 39 20 511 0.06 Cfb
24 La Sotonera 42.11 −0.68 31 189 417 0.58 Cfa
25 Terradets 42.05 0.88 47 33 372 0.04 Cfa
26 Tous 39.13 −0.65 110 378 135 0.28 Csa
27 Tranquera 41.24 −1.78 81 84 684 0.68 BSk
28 Urrunaga 42.98 −2.65 31 72 547 0.31 Csb
29 Utchesa 41.50 0.53 5 4 147 0.31 BSk
30 Valdecañas 39.82 −5.42 98 1446 315 0.36 Csb
Water 2021, 13, 2866 17 of 22

Table A2. Concentrations of Phycocyanin (PC) and Chlorophyll-a (Chl-a) in reservoirs during sampling year.

Reservoir and Year PC (µg/L) Chl a (µg/L) Reservoir and Year PC (µg/L) Chl a (µg/L)
Alarcon 2020 5.30 1.75 Las Torcas 2017 2.98 1.72
Alarcon 2020 5.60 1.80 Lechago 2019 4.30 3.01
Alarcon 2020 3.75 1.94 Mansilla 2016 1.54 2.37
Alarcon 2020 4.75 1.80 Mansilla 2017 1.61 2.96
Alarcon 2020 5.77 1.80 Mezalocha 2017 34.08 7.65
Alarcon 2020 5.97 1.10 Mezalocha 2018 5.96 2.62
Albufera 2020 188.14 30.14 Moneva 2019 6.89 11.86
Albufera 2020 269.55 81.34 Monteagudo 2018 8.70 1.59
Albufera 2020 329.05 91.92 Oliana 2017 1.28 3.38
Albufera 2020 364.70 90.93 Oliana 2019 2.76 6.50
Alloz 2017 3.10 1.34 Oliana 2019 9.04 2.58
Barasona 2018 2.57 1.67 Regajo 2017 11.14 8.97
Bellus 2017 185.43 61.39 Regajo 2018 4.71 5.57
Bellus 2018 307.93 49.09 Regajo 2018 4.94 4.58
Bellus 2018 318.50 51.59 Regajo 2018 6.23 4.63
Bellus 2018 307.72 41.54 Rialb 2018 5.19 2.89
Bellus 2020 63.15 24.59 Rialb 2018 17.18 20.07
Bellus 2020 70.67 25.83 Rialb 2019 5.38 4.29
Bellus 2020 129.52 30.06 Ribarroja 2017 0.64 13.17
Bellus 2021 125.61 29.95 Santolea 2016 4.01 1.11
Bellus 2021 145.27 29.87 Sitjar 2017 3.49 0.61
Benageber 2020 8.68 2.50 Sitjar 2017 3.71 0.72
Benageber 2017 9.06 5.77 Sobron 2017 15.69 6.89
Benageber 2017 10.53 5.47 Sobron 2019 11.60 10.31
Benageber 2018 8.01 4.91 La Sotonera 2016 2.99 0.68
Benageber 2018 7.26 4.85 La Sotonera 2016 1.29 2.25
Benageber 2018 8.19 4.91 La Sotonera 2016 1.58 2.33
Benageber 2020 7,05 2.12 La Sotonera 2018 15.52 3.44
Benageber 2020 8.80 2.44 La Sotonera 2019 7.33 2.51
Benageber 2020 10.53 2.96 Terradets 2018 24.76 1.16
Benageber 2020 7.37 2.38 Tous 2017 1.70 1.85
Benageber 2020 8.73 2.68 Tous 2017 4.01 0.69
Beniarres 2017 21.70 8.50 Tous 2017 4.61 0.64
Beniarres 2017 19.02 17.17 Urrunaga 2018 16.79 3.21
Canelles 2016 0.84 1.16 Utchesa-Seca 2019 16.08 9.91
Canelles 2016 1.62 2.86 Valdecañas 2020 22.70 1.78
Canelles 2020 1.93 1.41 Valdecañas 2020 22.54 1.48
Cazalegas 2020 230.98 6.93 Yesa 2017 2.23 3.46
Contreras 2018 8.37 2.46
Cueva Foradada
3.70 3.43
2017
Cueva Foradada
13.23 14.22
2018
Ebro 2019 4.38 2.34
Estanca de Alcañiz
11.43 3.24
2018
Eugui 2017 2.93 5.95
Flix 2018 14.29 2.28
Galipuen 2019 27.28 3.41
Itoiz 2017 2.36 2.52
La Loteta 2019 9.10 1.83
La Peña 2017 4.21 1.77
La Tranquera 2016 10.94 5.95
La Tranquera 2017 12.04 8.45
La Tranquera 2018 17.35 11.75
Water 2021, 13, 2866 18 of 22

Figure A1. Thematic maps of PC (µg L− 1 ) in some reservoirs, showing the alert level in a color scale according to Table 3.
For each site, the date of the image and the watersheds are indicated.
Water 2021, 13, 2866 19 of 22

Figure A2. Thematic maps of PC (µg L− 1 ) in some reservoirs, showing the alert level in a color scale according to Table 3.
For each site, the date of the image and the watersheds are indicated.
Water 2021, 13, 2866 20 of 22

Figure A3. Thematic maps of PC (µg L− 1 ) in some reservoirs, showing the alert level in a color scale according to Table 3.
For each site, the date of the image and the watersheds are indicated.

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