Remote sensing for mapping algal blooms in freshwater lakes: a review

Abstract

A large number of freshwater lakes around the world show recurring harmful algal blooms, particularly cyanobacterial blooms, that affect public health and ecosystem integrity. Prediction, early detection, and monitoring of algal blooms are inevitable for the mitigation and management of their negative impacts on the environment and human beings. Remote sensing provides an effective tool for detecting and spatiotemporal monitoring of these events. Various remote sensing platforms, such as ground-based, spaceborne, airborne, and UAV-based, have been used for mounting sensors for data acquisition and real-time monitoring of algal blooms in a cost-effective manner. This paper presents an updated review of various remote sensing platforms, data types, and algorithms for detecting and monitoring algal blooms in freshwater lakes. Recent studies on remote sensing using sophisticated sensors mounted on UAV platforms have revolutionized the detection and monitoring of water quality. Image processing algorithms based on Artificial Intelligence (AI) have been improved recently and predicting algal blooms based on such methods will have a key role in mitigating the negative impacts of eutrophication in the future.

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Environmental Science and Pollution Research
https://doi.org/10.1007/s11356-023-25230-2
REVIEW ARTICLE
Remote sensing formapping algal blooms infreshwater lakes:
areview
SilviaBeatrizAlvesRolim1· BijeeshKozhikkodanVeettil2,3· AntonioPedroVieiro4· AnitaBaldisseraKessler5·
ClóvisGonzatti4
Received: 22 August 2022 / Accepted: 5 January 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023
Abstract
A large number of freshwater lakes around the world show recurring harmful algal blooms, particularly cyanobacterial
blooms, that affect public health and ecosystem integrity. Prediction, early detection, and monitoring of algal blooms are
inevitable for the mitigation and management of their negative impacts on the environment and human beings. Remote sens-
ing provides an effective tool for detecting and spatiotemporal monitoring of these events. Various remote sensing platforms,
such as ground-based, spaceborne, airborne, and UAV-based, have been used for mounting sensors for data acquisition and
real-time monitoring of algal blooms in a cost-effective manner. This paper presents an updated review of various remote
sensing platforms, data types, and algorithms for detecting and monitoring algal blooms in freshwater lakes. Recent studies on
remote sensing using sophisticated sensors mounted on UAV platforms have revolutionized the detection and monitoring of
water quality. Image processing algorithms based on Artificial Intelligence (AI) have been improved recently and predicting
algal blooms based on such methods will have a key role in mitigating the negative impacts of eutrophication in the future.
Keywords Algal blooms· Electromagnetic spectrum· Remote sensing· Phytoplankton· Spatiotemporal bloom mapping
Introduction
Phytoplankton or suspended algae plays a key role in the
food webs in freshwater ecosystems as these organisms are
the main source of organic matter (Paerl etal. 2001) and
phytoplankton productivity is highly dependent on nutrient
supply into freshwater resources. However, an increased sup-
ply of nutrients, such as fertilizers, or pollutants, may lead
to an enhanced primary production as a result of eutrophi-
cation. Eutrophication often causes uncontrolled growth of
algae (also known as algal blooms) which can be observed
by means of surface color changes, foul smell/taste, death of
aquatic animals due to toxins produced, such as fishes, and
alterations in the food web (Paerl etal. 2001) in open waters.
Toxins produced may cause health effects on human beings,
particularly in children (Weirich and Miller 2014). Among
various genera comprising the bloom-forming phytoplank-
ton, the blue-green algae (also known as cyanobacteria) are
the most notorious ones. Many of the bloom-forming species
are capable of surviving under extreme environmental condi-
tions, such as high temperatures, pH, and nutrient deficiency.
(Paerl etal 2001). Moreover, cyanobacteria have the abil-
ity to survive and act as a better competitor for light under
Responsible Editor: Philippe Garrigues
* Bijeesh Kozhikkodan Veettil
bkozhikkodanveettil@vlu.edu.vn
1 Programa de Pós-Graduação Em Sensoriamento Remoto,
Universidade Federal doRio Grande Do Sul (UFRGS),
RioGrandedoSul, PortoAlegre, Brazil
2 Laboratory ofEcology andEnvironmental Management,
Science andTechnology Advanced Institute, Van Lang
University, HoChiMinhCity, Vietnam
3 Faculty ofApplied Technology, School ofEngineering
andTechnology, Van Lang University, HoChiMinhCity,
Vietnam
4 Departamento de Mineralogia e Petrologia, Instituto de
Geociências, Universidade Federal doRio Grande Do Sul
(UFRGS), RioGrandedoSul, PortoAlegre, Brazil
5 Departamento de Geodésia, Instituto de Geociências,
Universidade Federal doRio Grande Do Sul (UFRGS),
RioGrandedoSul, PortoAlegre, Brazil

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low transparent environments. In addition to the warming
scenarios in marine and freshwater ecosystems due to cli-
mate change, the impacts of harmful algal blooms (HABs)
on these ecosystems are also intensifying in recent decades
(Gobler 2020; Griffith and Gobler 2020). It is found that
eutrophic habitats experiencing thermal extremes, low dis-
solved oxygen, and low pH presented recurring algal blooms
due to climate change (Griffith and Gobler 2020). In other
words, climate change and algal blooms impact freshwater
ecosystems in a combined mode, in many cases (Griffith and
Gobler 2020; Trainer etal. 2020).
Proper detection and monitoring methods of algal blooms,
including the species and toxins, are essential for the protec-
tion of aquatic lives, shellfish safety, drinking water quality,
and public health (Zhang and Zhang 2015). High concen-
trations of nutrients from agricultural and urban runoff can
cause algal blooms and hence proper monitoring of nutrient
concentrations is also important in the study of algal blooms
(Klemas 2012). In fact, a number of reliable and fast tech-
niques for detecting algal blooms have been developed in
recent years for this purpose (e.g., Hill etal. 2020). These
methods range from in-situ quick screening protocols for
the monitoring of algal blooms to the mass spectrometric
analysis of trace levels of various algal toxins and structural
elucidation; solid-phase adsorption toxin tracking (SPATT),
liquid chromatography-mass spectrometry (LC–MS) chro-
matography, bioassays, and ELISA-based methods, real-time
quantitative polymerase chain reaction (qPCR) have been
used in recent years (Zhang and Zhang 2015). New chemical
and biological sensors, in-situ and remote sensing of algal
blooms have been developed for early warning and rou-
tine monitoring of freshwater resources (Zhang and Zhang
2015). Time series analysis of algal blooms is believed to
be helpful in understanding freshwater ecosystem responses
to climate variability, such as El Niño -Southern Oscillation
(Trainer etal. 2020). Based on the global mapping, princi-
pally using satellite remote sensing, it has been reported
recently that there was an increase in lacustrine algal blooms
globally over the past decade (Hou etal. 2022).
Satellite remote sensing offers a means for observing
algal blooms with unprecedented frequency and spatial cov-
erage (Binding etal. 2020). Marine algal blooms at global
levels are monitored using data from ocean color sensors
onboard satellites (e.g., Stumpf etal. 2003) in recent dec-
ades, where estimated spectral reflectance due to chloro-
phyll-a is an effective way for monitoring phytoplankton (Li
etal. 2018). In the case of cyanobacterial blooms in freshwa-
ter lakes, spectral reflectance due to Phycocyanin is an effec-
tive proxy using remote sensing (Klemas 2012). Most of the
studies investigating remote sensing of algal blooms used
multispectral sensors on satellites because of their ability
to distinguish color changes (Stumpf and Tomlinson 2005;
Klemas 2012) and the use of hyperspectral data (spaceborne/
airborne/UAV/in-situ) have been used to improve this ability.
In addition to multispectral data, hyperspectral sensors with
spectral bands fine-tuned for specific pigment analysis allow
the detection and analysis of species-specific algal blooms
because algal accessory pigments produce unique spectral
signatures (Klemas 2012). In this review article, we investi-
gated various remote sensing techniques used for detecting,
mapping, and monitoring algal blooms in freshwater lakes
recently. We also reviewed the advantages and limitations
of different platforms (spaceborne, airborne, UAV) and data
types (optical, radar, LiDAR) in mapping these events.
Monitoring algal blooms andits
eco‑environmental signicance
Early detection and broad monitoring of algal blooms
are important for the effective management of freshwater
resources and to reduce the negative environmental effects
of toxins produced by the bloom-causing microorganisms/
phytoplankton (Binding etal. 2020). Harmful algal blooms
are one of the serious threats to freshwater biodiversity in
the Anthropocene and need to be studied further, particu-
larly about the early detection of their occurrence (Amorim
and Moura 2021). Different methods have been developed
by researchers for the prediction, real-time detection, and
spatiotemporal monitoring of algal blooms in freshwater
lakes. Their prediction can provide an insight on the future
spatial–temporal dynamics of algal bloom and help the envi-
ronmental managers and decision-makers to design policies
and projects on preventing algal blooms and mitigation of
negative impacts on freshwater ecosystems. This is mainly
because, in general, they cause lowering of water quality,
plankton biodiversity, and ecosystem functioning (Amorim
and Moura 2021).
Predicting algal blooms in freshwater lakes can be done
based on variations on chlorophyll-a concentrations (Khan
etal. 2021)or other water quality variables, such as phos-
phorus (Cho etal. 2014; Bui etal. 2017). In general, for
better predicting algal blooms, a number of factors, such
as nutrient dynamics, local hydrology, groundwater chem-
istry, climatic perturbations, watershed geomorphology,
biogeochemistry, food-web control, and algal competi-
tion, need to be investigated (Brookfield etal. 2021). For
example, Bobbin and Recknagel (1984) developed genetic
algorithms for predicting algal blooms in freshwater lakes
based on water quality time series analysis (input variables
include chlorophyll-a, water temperature, turbidity, phos-
phates, nitrates, pH, dissolved oxygen, etc.). Barica (1984)
developed three empirical models for predicting algal
blooms, winter oxygen depletion, and freeze-out effect in
eutrophic lakes of North America. Bui etal. (2017) used
an artificial neural network (ANN) method for predicting

Environmental Science and Pollution Research
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cyanobacterial blooms (RMSE: 0.108) in Dau Tieng Reser-
voir in Vietnam, where eight environmental parameters (pH,
dissolved oxygen, temperature, total dissolved solids, total
nitrogen, total phosphorous, biochemical oxygen demand,
and chemical oxygen demand) were used as the input and
the defined outputs were cell density of cyanobacteria genera
(Anabaena, Microcystis, and Oscillatoria) with microcystin
concentrations. Similar ANN-based studies can be found in
Wei etal. (2001), Cho etal. (2014), and Coad etal. (2014),
and many more; few additional variables, such as electrical
conductivity, have been considered in these studies. Most
of the above-mentioned environmental parameters can also
be estimated using remote sensing (e.g., Veettil and Bian-
chini 2014; Veettil and Quang 2018). For instance, Cao
etal. (2022) used a convolutional neural network (CNN)
method for predicting cyanobacterial blooms in Taihu Lake
in China using MODIS imagery and meteorological data
during the bloom outbreaks between 2000 and 2019. A
detailed updated review on machine learning methods for
predicting algal blooms can be found in Cruz etal. (2021).
Other methods to predict freshwater algal blooms include
coupled eco-hydrodynamic model (Duquesne etal. 2021),
Bayesian model averaging (Hamilton etal. 2009), antecedent
environmental condition analysis (Xia etal. 2020), etc. Pre-
dicting algal blooms in freshwater resources has a significant
role in present-day conditions due to the ongoing long-term
climate change, including rising temperatures and changing
rainfall patterns, and the long evolutionary history of vari-
ous cyanobacteria genera and, hence, such studies can have
ecological and biogeochemical significance, in addition to
management implications (Paul 2008).
Monitoring and spatial modeling of algal blooms can be
done traditionally based on field measurements and sam-
pling followed by laboratory analysis of samples. Recent
studies, such as Ortiz and Wilkinson (2021), have estimated
spatial variability of algal bloom development in freshwa-
ter lakes based on multiple lake water sampling and insitu
analysis of several parameters, such as chlorophyll-a, phyco-
cyanin, dissolved oxygen, pH, and temperature. Such studies
have given importance to the spatial heterogeneity of algal
pigments, which is also having a key role in remote sensing
of algal blooms, in monitoring, and spatial modeling. Algal
bloom monitoring can be done in four phases—pre-bloom
phase, growth phase, bloom phase, and decay phase (Pet-
tersson and Pozdnyakov 2013). The concentration of algae
during the pre-bloom phase is usually very low and indirect
measurement of algal blooms can be done based on varia-
tions in water quality variables, such as total phosphorous
or BOD. The growth phase demonstrates a gradual increase
in algal cells which is dependent on various environmen-
tal conditions, such as light availability, temperature, and
inorganic nutrients (Pettersson and Pozdnyakov 2013). The
bloom phase denotes the period in which the maximum
concentration of algal cells occurs. Bloom phase is the most
visible phase of algal blooms and the intensity can be meas-
ured by insitu laboratory analysis or remote sensing meth-
ods (principally based on spectra properties of pigments).
The fourth phase is the decay phase when the cell growth
rate ceases and death and decay of cells occur, accompanied
by organic decomposition which produces fowl smelling
gases and toxic substances. Quantification of toxins, bio-
mass, and mucilage associated with harmful algal blooms
during bloom and decay phases needs to be quantified for
ecological management. Several insitu methods, such as
Fluorescent insitu Hybridization (FISH) or molecular meth-
ods such as Polymerase Chain reaction (PCR) have been
applied, so far, for genus-level detection of bloom-producing
genus (Hatfield etal. 2019).
In general, algal blooms are considered as a severe eco-
logical disaster threatening aquatic systems throughout the
world (Shen etal. 2012). Continuous monitoring and devel-
oping early warning systems for harmful algal blooms are
important for the protection of public health, biodiversity
conservation (including wild and farmed animal species)
(Stumpf 2001). The majority of the current studies use the
measurement of toxic cells concentration in water for moni-
toring algal blooms and these methods are logistically dif-
ficult and labor-intensive (Stumpf 2001; Shen etal. 2012),
particularly for data collection and insitu analysis of sam-
ples. Compared to these labor-intensive field data collection
and insitu data analysis, remote sensing is considered as a
promising technique for studying algal blooms because of
its large-scale applicability, real-time data collection, and
long-term monitoring (Shen etal. 2012; Binding etal. 2020).
Remote sensing data so far used in studying algal blooms
comes from a wide variety of platforms and data types and
various image processing algorithms have been applied in
recent studies for its prediction, successful detection, and
monitoring in freshwater resources.
Remote sensing formonitoring algal blooms
Spaceborne and airborne measurements of spectral reflec-
tance are considered as an effective method for monitoring
phytoplankton by means of its proxy (such as chlorophyll-a
and Phycocyanin) (Klemas 2012). In addition to water sur-
face color changes, other information, such as variations in
backscattered microwave radiation (e.g., Wang etal. 2015)
or thermal data (e.g., Haddad 1982), were also used in
detecting algal blooms. In fact, Mueller (1979) and Strong
(1974) demonstrated the potential use of remote sensing in
mapping and monitoring phytoplankton algal blooms as
early as in the 1970s. Remote sensing offers cost-effective,
long-term observation of algal blooms with unprecedented
frequency and spatial coverage (Binding etal. 2020).

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Remote sensing platforms formonitoring
freshwater algal blooms
A number of remote sensing platforms, such as ground-
borne, spaceborne, and airborne (including aircrafts, bal-
loons, and UAV-based ones), are available for the successful
monitoring of algal blooms in freshwater lakes and reser-
voirs. Since the majority of inland lakes and reservoirs have
small surface areas, spatial resolution of data from space-
borne sensors can influence the mapping accuracy (Lekki
etal. 2019a). A combination of aerial photographs and
satellite imagery has been used for algal bloom mapping
since the early 1970s (e.g., Strong 1974). Remote sensing
is a technology that advances day by day, particularly in
terms of sensor developments and improvements in mount-
ing high-quality sensors in already existing platforms and
newly introduced platforms, such as UAVs and drones (Toth
and Jozkow 2016).
Ground‑borne andship/boat‑based platforms forremote
sensing ofalgal blooms
Sensors mounted on ground-borne platforms have been
used to acquire detailed information of the land surface
(ground-truth data) that can be used to validate the infor-
mation collected from airborne or spaceborne sensors.
Portable cameras, webcams, and spectroradiometers are
mounted on ground-based platforms. For example, Free
etal. (2022) used WISPstation (above-water autonomous
spectroradiometer) data for estimating chlorophyll-a con-
centration trends and dynamics in Lake Trasimeno in Italy.
Similar sensors mounted on WISPstation were used previ-
ously in multiple locations (Curonian Lagoon in Lithuania,
Lake Võrtsjärv in Estonia, and Lake Trasimeno in Italy) in
Europe by Free etal. (2021) for quantifying chlorophyll-a. In
addition, ground-based sensors have also been used recently
for estimating water quality variables (Sun etal. 2022) and
real-time monitoring of algal blooms (Wang etal. 2022).
Airborne platforms forremote sensing ofalgal blooms
Airborne platforms are used for acquiring high-resolution
data at a desired time and were the only remote sensing plat-
forms other than ground-based sensors until the emergence
of satellite imagery. Different airborne platforms used for
collecting remote sensing data are airplanes, balloons and
kites, and unmanned aerial vehicles (UAVs). Among these,
UAV technology has been developed even to carry LiDAR
sensors in recent years.
Airplanes andhelicopters Airplanes and helicopters on
which cameras and sensors mounted are traditionally used
to take aerial photographs and recently LiDAR data of land
surfaces. The spatial resolution of required dimensions can
be taken by controlling the altitude of the aircraft (higher
resolution imagery can be obtained from a lower altitude).
Recently, in addition to RGB aerial photography, multi-
spectral and radar imagery can be obtained from airplanes.
Hyperspectral data taken from airplanes are considered as
particularly important in the study of algal blooms by many
researchers (Lekki etal. 2019b). The Airborne visible/infra-
red imaging spectrometer (AVIRIS) hyperspectral imagery
taken by USGS is well known due to its wide applications,
including spectral discrimination of algal blooms (Hoogen-
boom etal. 1998). For example, Kudela etal. (2015) used
AVIRIS and other hyperspectral data for remote sensing
of cyanobacterial blooms in inland waters (Pinto Lake,
California).
Balloons andkites Sensors and cameras, including hyper-
spectral sensors, mounted on balloons and kites have been
used for aerial remote sensing observation of the land cover
in a number of studies (e.g., Chen and Vierling 2006). The
first use of aerial imagery taken by camera mounted on bal-
loons date back to 1858 (Glaser and Saliba 1972). Currently,
balloons and kites are rarely used in remote sensing stud-
ies as the flight is not stable (altitude and velocity) and the
course of flight is unpredictable. However, compared to the
data taken from airplanes, the cost of data acquisition using
kites and balloons is extremely lower (Klemas 2015).
Drones and unmanned aerial vehicles (UAVs) Drones and
UAVs are generally considered as a low-cost platform for
aerial data acquisition requirements without a pilot. Pre-
sent-day drones and UAVs are capable of acquiring various
types of data such as multispectral, hyperspectral, radar,
and LiDAR imagery and such data acquisition capabilities
can facilitate data requirements for remote sensing of algal
blooms. UAVs have been recently emerged as a powerful
tool for the detection and monitoring of algal blooms and
the greatest advantage of UAV is the acquisition of high-res-
olution imagery at lower cost (Kislik etal. 2018) and com-
pared to spaceborne data, UAV data quality is not affected
by the presence of cloud cover as image acquisition is done
from lower altitudes. However, in addition to low payload
capabilities and effective resolution dependence on flight
parameters, there exists a lack of standardized methods in
processing imagery acquired from UAVs and hence the
use of UAVs has not gained much attraction by research-
ers studying algal blooms until the end of 2010s (e.g., Lyu
etal. 2017). Shang etal. (2017) demonstrated the use of
UAV data for the quantitative assessment of phytoplankton
blooms for the first time. The capability of UAV platforms
to host hyperspectral sensors can be effective in detecting
multiple parameters of small-scale freshwater areas, such as
algal blooms and turbidity (e.g., Pölönen etal. 2014).

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Spaceborne platforms forremote sensing ofalgal blooms
Spaceborne platforms, such as satellite or space shuttle,
have a higher initial cost but relatively have lower cost per
unit coverage area and can obtain imagery of the entire
planet without any additional cost (Toth and Jozkow
2016). Spaceborne platforms have revolutionized remote
sensing of land surface since the launch of the first Land-
sat in 1972, followed by SPOT series in 1986. The main
advantages of spaceborne remote sensing are large cov-
erage area compared to airborne platforms, high tempo-
ral resolution, use of radiometrically calibrated sensors,
and low image acquisition cost per unit coverage area.
Examples of widely used spaceborne platforms for remote
sensing of algal blooms are Landsat series, Sentinel-2 and
Sentinel-3, SPOT series, IRS series, and CBERS series.
It is worth noting that the red-edge band of Sentinel-2
and the orange band of Sentinel-3 sensors are a revolution
in the detection of algal blooms. High spatial resolution
data provided by commercial spaceborne platforms, such
as WorldView series, are now available for remote sens-
ing applications of algal blooms at a higher cost. Rela-
tively lower resolution data spaceborne platforms, such as
MODIS ocean-color satellite data, were also for long-term
mapping and time series analysis of algal blooms (e.g.,
Sayers etal. 2019a).
Data types
Remote sensing of algal blooms is based on the variations
in water color due to the pigments produced by algae which
can be measured or detected using optical sensors mounted
in an aircraft, satellite, or a UAV (Binding etal. 2020). A
non-exhaustive list of currently available satellite sensors,
including multispectral, hyperspectral, and Radar (Synthetic
Aperture Radar—SAR) sensors, for observing algal blooms
is given in Table1. As mentioned previously, spatial resolu-
tion of data from spaceborne sensors can influence the map-
ping accuracy (Lekki etal. 2019a). In addition, the spectral
resolution, particularly within the visible and near-infrared
domain of the electromagnetic spectrum, is extremely use-
ful in the species-specific analysis of algal blooms. Land-
sat-8 OLI, Sentinel-2A and 2B, and Sentinel-3A and 3B
data have been used in a large number of studies recently
(e.g., Clark etal. 2021; Castro etal. 2021; Xu etal. 2021;
Viero 2022). It has to be noted that inconsistencies in reso-
lution (spatial, spectral, radiometric, and temporal) among
data acquired from different sensors may require additional
data preprocessing while conducting a time series analy-
sis of algal blooms in a specific study area (Binding etal.
2020). For example, time series analysis of algal blooms
using Landsat series (TM, ETM + , and OLI) and Sentinel-2
data may require additional preprocessing to compensate
differences in spectral and spatial resolution. A short review
of data types, including optical (multispectral and hyper-
spectral), LiDAR, and radar data, used in remote sensing
applications of freshwater algal blooms can be found in Shen
etal. (2012).
Optical data
Optical imagery, which is acquired from airborne, space-
borne, or from UAVs, is the most used remotely sensed data
used for freshwater algal bloom studies (e.g., Bangyi etal.
2013; Veettil and Bianchini 2014; Chang etal. 2014; Kudela
etal. 2015; Veettil and Quang 2018). This is because one
of the most frequently used parameters measured is color
changes in the open water during algal blooms, which can
be best estimated using optical imagery. Optical data can be
either multispectral (e.g., Sentinel-2 MSI, Landsat8 OLI), or
hyperspectral (e.g., AVIRIS, EO-1 Hyperion).
Multispectral sensors Multispectral data have been widely
used in freshwater algal bloom studies, particularly due to
its easiness and cost-effectiveness to mount on airborne and
spaceborne platforms compared to hyperspectral sensors. The
most widely used multispectral data series for algal bloom
remote sensing studies is the Landsat (TM, ETM + , OLI)
series (e.g., Chang etal. 2004; Veettil and Bianchini 2014;
Veettil and Quang 2018; Alarcon etal. 2018; Li etal. 2018;
Xu etal. 2021). In addition to cyanobacterial blooms, mul-
tispectral reflectance has been used for the optical detection
of other algal blooms (e.g., Bangyi etal. 2013) because the
spectral reflectance properties of the phytoplankton are the
key variable in detecting the bloom. For example, Bangyi
etal. (2013) used a ground-based spectroradiometer (Field-
Spec) for detecting Prorocentrum donghaiense blooms in
China (Changjiang River Estuary). Ali etal. (2013) used
Dubaisat-1 multispectral data with a 5m spatial resolution
for estimating water quality variables, including chlorophyll-
a due to algal growth, in Dubai. Multisource multispectral
data, such as from different spaceborne sensors or from spa-
ceborne and UAV-mounted sensors, have also been used in
a number of recent algal bloom remote sensing studies. For
example, Castro etal. (2021) used a multi-sensor, multi-scale
approach for period monitoring of a freshwater reservoir in
Spain using spaceborne multispectral data (Sentinel-2 MSI
and Landsat-8 OLI) as well as UAV (Octocopter Atyges FV8)
multispectral (Rededge Micasense) imagery. An example of
mapping freshwater algal blooms in southern Brazil from
Landsat-8 data is given in Fig.1.
Hyperspectral sensors Hyperspectral imagers have signifi-
cant capability for detecting algal blooms and other water
quality variables, such as nitrates and turbidity. One of the

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significant shortcomings of multispectral data in algal bloom
remote sensing is the failure in discriminating cyanobacte-
ria genera (Kutser 2009) because the toxins and pigments
produced by different genera have very little spectral reflec-
tance differences. Hyperspectral imagery has been used for
detailed (e.g., species level) detection and monitoring of
harmful algal blooms in a number of studies (e.g., Chang
etal. 2014; Kwon etal. 2020). Hyperspectral data is found
to provide a considerable increase in the accuracy of algal
bloom detection when the concentration of algae is smaller
in freshwater resources (Chang etal. 2014; Kudela etal.
2015). Spaceborne hyperspectral data is not widely used at
the moment (e.g., PRISMA, EnMAP); the majority of the
available hyperspectral data comes from airborne platforms
(e.g., Hyperspectral Imager for the Coastal Ocean – HICO;
Advanced Visible/Infrared Imaging Spectrometer – AVIRIS)
and more sophisticated hyperspectral sensors are being
developed to be used on UAV platform (Kislik etal. 2018;
Kwon etal. 2020). For example, O’Shea etal. (2021) used
PRISMA data for estimating the biomass and phycocyanin
concentration of cyanobacteria by applying semi-analytical
algorithms. Hyperspectral Plankton Aerosol and Cloud
Ecosystem (PACE) sensor are planned to be launched in
2024 which is believed to revolutionize algal bloom studies.
Recently, Legleiter etal. (2022) used DESIS hyperspectral
data deployed in the Multi-User System for Earth Sensing
(MUSES) on the International Space Station for species-
level discrimination of cyanobacterial blooms by apply-
ing spectral mixture analysis. It is interesting to note that,
recently, Johansen etal. (2019) provided the most effective
Table 1 Currently operating satellite sensors used for mapping and monitoring algal blooms in recent studies
Satellite Sensor Spatial resolution Spectral resolution Temporal
resolution
Data availability
period
References
Landsat-8 Operational Land Imager
(OLI)
30m 11 + pan 16days 2013–present Alarcon etal.
(2018)
AQUA (EOS
PM-1)/
TERRA (EOS
AM-1)
Moderate Resolution Imaging
Spectroradiometer (MODIS)
250m–1km 13 Daily 1999/2002–present Avouris and Ortiz
(2019)
Sentinel-2A,2B Multispectral Instrument
(MSI)
10m–60m 9 5days 2015/2017–present Cao etal. (2021);
Lobo etal. (2021)
Sentinel-
3A/3B/3C
Ocean and Land Color Instru-
ment (OLCI);
SLSTR (Sea and Land Surface
Temperature Radiometer);
SRAL (SAR Altimeter);
DORIS (Doppler Orbitography
and Radiopositioning Inte-
grated by Satellite);
MWR (Microwave Radiom-
eter)
300m 17 5days 2016/2018–present Ogashawara (2019)
Dubaisat-1 DubaiSat-1 Medium Aperture
Camera (DMAC)
5m 4 + pan 3–5days 2009–present Ali etal. (2013)
Dubaisat-2 High Resolution Advanced
Imaging System (HiRAIS)
4m 4 + pan 8days 2013–present Ben-Romdhane
etal. (2018)
WorldVew-2 Multispectral Sensor (MSS) 1.84m 8 + pan 1.1–
3.7days
2009–present Gray etal. (2021)
WorldVew-3 MSS, CAVIS, and
SWIR sensors
1.24–3.2 8 + pan + 12
CAVIS bands
< 1day 2014–present Gray etal. (2021)
GaoFen-1 Active Pixel Sensor (APS) star
sensor
3.2m 4 5days 2014–present Hang etal. (2022)
RapidEye Multispectral Imager (MSI) 5m 5 1day 2008–present Mishra etal.
(2019)
PlanetScope Dove-C, Dove-R, SuperDove 3m 4 (Dove)/8 (Super-
Dove)
1day 2016–present Niroumond-Jadidi
and Bovolo
(2021)
PRISMA HYC (Hyperspectral Camera)
module and the PAN (Pan-
chromatic Camera) module
30m;
5m in panchro-
matic
239 bands in vis-
ible, infrared,
and shortwave
infrared spectra
29days 2019–present O’Shea etal.
(2021)

Environmental Science and Pollution Research
1 3
combination of hyperspectral bands of a satellite sensor spe-
cifically designed for algal bloom studies (Harmful Algal
Bloom Satellite-1 or HABSat-1) which have spectral chan-
nels close to the CASI hyperspectral airborne imager.
Fusion between multispectral and hyperspectral data A few
studies used a fusion between multispectral and hyperspec-
tral data for a better monitoring of algal blooms in lakes. For
example, Chang etal. (2014) used such a fusion (integrated
data fusion and mining or IDFM techniques) between hyper-
spectral MERIS data and multispectral Landsat and MODIS
data for monitoring microcystin bloom in Lake Erie in North
America. The same study (Chang etal. 2014) observed that
hyperspectral data increases the accuracy of algal monitor-
ing and prediction, particularly when the algal concentration
is small.
LiDAR
The degree to which phytoplankton cells respond to light
in terms of buoyancy regulation differs between genera and
species and these differences can be used to detect species-
specific algal blooms in freshwater lakes and reservoirs
(Moore etal. 2019). Laser remote sensing has been used in
recent years for remote sensing studies on freshwater algal
blooms (e.g., Grishin etal. 2016), including estimating the
freshwater distributions of cyanobacterial blooms in fresh-
water lakes (Moore etal. 2019). For example, Grishin etal.
(2016) used a Raman Laser mounted on a boat for moni-
toring algal blooms based on the quantification of chloro-
phyll fluorescence in Gorky Reservoir in Russia. The active
LIDAR can penetrate the water column that can be helpful
in providing a detailed picture of the particle distribution
for the whole water column, including the identification of
cyanobacterial bloom-producing genera such as Microcys-
tis and Planktothrix (Moore etal. 2019). Airborne active
LiDAR equipment is simple and cost-effective compared to
airborne and spaceborne passive remote sensing systems and
can be used under all weather conditions (Han etal. 2011).
Radar
Radar imagery, particularly Synthetic Aperture Radar (SAR)
has been used to compensate for the limitations of optical
imagery due to cloud cover, particularly above waterbodies
such as oceans and lakes as radar waves can penetrate clouds.
A number of studies observed that algal scum can cause dark
regions in SAR imagery, which can be used as an effective
Fig. 1 Algal bloom occurred in
Lagoa dos Barros (Rio Grande
do Sul, Brazil) in March 2020.
Landsat-8 and Sentinel-2
imager acquired during the
bloom and Chlorophyll-a esti-
mated from Landsat-8 data

Environmental Science and Pollution Research
1 3
remote sensing tool for monitoring cyanobacterial scum in
inland lakes, due to the reduction in microwave backscatter
caused by a lipopeptide biosurfactant (Wang etal. 2015, 2017).
However, low microwave backscatter may also be associated
with other factors such as low wind speeds, resulting in inter-
ference when monitoring algal blooms solely using SAR data
(Wang etal. 2015). Some studies used a combination of optical
and radar imagery for long-term monitoring of algal blooms in
lakes successfully (e.g., He etal. 2018; De Santi etal. 2019).
It is worth noting that, in addition to cyanobacterial blooms,
SAR data have been used for detecting aquatic plants, such as
macrophytes (e.g., Niculescu etal. 2020).
Image processing algorithms formapping algal
blooms fromremotely sensed data
Numerous image processing algorithms have been applied
to different image types (optical, LiDAR, radar, etc.) for
detecting and monitoring algal blooms in freshwater lakes.
Optical images have been applied with numerous methods
for estimating chlorophyll-a or phycocyanin concentrations
(Binding etal. 2019). For example, chlorophyll-a retrieval
based on band arithmetic algorithms make use of the pig-
ment’s primary (442nm) and secondary (665nm) absorp-
tion maxima (Bricaud etal. 1995; Odermatt etal. 2012).
Even though a number of algal bloom detection algorithms
with different mathematical/regression models or neural
network simulations were developed, the method in bloom
mapping is driven by expected variations in the shape and/
or magnitude of the spectral water-leaving radiance or
reflectance signal detected by the satellite sensor that can
be empirically or analytically related to a specific measure,
such as chlorophyll-a or phycocyanin concentrations and
cyanobacterial biomass/bio-volume, of the bloom (Bind-
ing etal. 2019, 2020). Broadly, optical image processing
algorithms can be classified as either empirical (data-driven)
approaches or physics-based solutions (Binding etal. 2019).
Regression methods and neural network simulations fall into
the former category, whereas the latter includes band ratio
methods and radiative transfer models.
Optical image processing methods formonitoring algal
blooms
Optical data, such as Landsat series, has been used for pre-
dicting algal blooms in freshwater lakes during the last few
decades, mainly based on the optical properties of blooms.
For example, Chang etal. (2004) used Landsat TM data
for predicting algal bloom in Techi Reservoir in Taiwan;
the study established statistical relationship (forward step-
wise regression method) between radiance and dinoflagel-
late densities and the authors obtained 74.07% accuracy in
bloom prediction. It is recommended to use high spectral
and spatial resolution data for higher accuracy in freshwater
bloom prediction (Chang etal. 2004). Chang etal. (2014)
used a combination of multispectral (Landsat TM, MODIS)
and hyperspectral (MERIS) imagery for developing an inte-
grated data fusion and mining (IDFM) technique by apply-
ing data mining methods to quantify their performance for
providing near real-time monitoring of the spatiotemporal
distributions and prediction of microcystins in Lake Erie in
North America. Preprocessing of optical data, particularly
for spaceborne imagery, including atmospheric correction
and land adjacency correction, is necessary for improved
algal bloom monitoring accuracy (Sagan etal. 2020).
Reflectance band ratio and spectral index methods using
optical imagery have been used for a number of remote sens-
ing applications, including detecting algal blooms (e.g.,
Bresciani etal. 2011; Cao etal. 2021; Choe etal. 2021;
Lobo etal. 2021). Bresciani etal. (2011) used MERIS data
for estimating the lake phytoplankton population in Lake
Idro in Italy using band ratios (620nm/560nm) and for com-
parisons with insitu data. Cao etal. (2021) applied a spectral
index (algal bloom detection index – ABDI) for algal bloom
detection in China using Sentinel-2 MSI imagery; the result-
ant algal bloom maps were consistent with those identified
from visual interpretation maps after applying a suitable
threshold to ABDI. ABDI was calculated as:
where RGreen, RRed, RRE2, and RNIRn represent the reflectance
at Green, Red, Red Edge-2, and NIRn bands, respectively, and
λRed (665nm), λRE2 (740nm), and λNIRn (865nm) represent
the central wavelengths of Red, Red Edge-2 and NIRn bands,
respectively (Cao etal. 2021). In fact, algorithms using the red
and near-infrared (R-NIR) portion of the spectrum perform well
in coastal and inland turbid eutrophic waters (Gilerson etal.
2010). Spectral index methods have been applied to multispec-
tral data from UAVs as well. For example, Choe etal. (2021)
applied a normalized difference chlorophyll index (NDCI = [RRE
– RRed]/[RRE + RRed]) for monitoring river algal blooms in South
Korea. NDCI was originally introduced by Mishra and Mishra
(2012) for remote estimation of chlorophyll-a concentration in
turbid productive waters using MERIS data. It is to be noted
that atmospheric correction algorithms are required when using
band ratios or spectral indices if spaceborne optical data, such as
Sentinel-2 data, is used (Lobo etal. 2021). It must be noted that
chlorophyll retrieval algorithms based band ratios may not per-
form well in optically complex waters due to: (a) the interference
(1)
ABDI
=
[
RRed −RRED −
(
RNIRn −RRed
)
×
(
λRE2 −λ
Red
)
∕
(
λNIRn −λ
Red
)]
−
[
RRed −0.5 ×RGreen
]

Environmental Science and Pollution Research
1 3
on the optical properties of water due to suspended minerals and
dissolved organic matter, and (b) additional errors and uncertain-
ties due to incorrect atmospheric correction algorithms due to
regional factors in optically complex waters (Zeng and Binding
2019; Binding etal. 2020).
Color-producing agents (CPA) in algal blooms can be
successfully determined using varimax-rotated, principal
component analysis (VPCA) spectral decomposition of
derivative reflectance spectra, which can be obtained from
insitu and remote sensing data (e.g., Avouris and Ortiz
2019). Ground-based remote sensing data (e.g., field spec-
troradiometer) can be used to identify the signals from spa-
ceborne data. For example, Avouris and Ortiz (2019) used
MODIS imagery and water samples, and spectrometer for
identifying CPA patterns and its spatiotemporal variations in
Lake Erie in North America. However, these approaches are
yet to be fully validated, often require hyperspectral resolu-
tion, and are computationally demanding such that prompt
access to operational products may be challenging (Binding
etal. 2019, 2020).
A number of studies used regression models and empiri-
cal data analysis for mapping algal blooms in freshwater
resources, mostly based on estimating chlorophyll-a con-
centrations (e.g., Binding etal. 2019, 2020). Alba etal.
(2020) applied regression model for estimating chlorophyll-a
concentrations from Sentinel-2 imagery for sentinel 2 algal
composition analysis; chlorophyll-a was estimated using an
empirical model and a linear regression between the remote
sensing imagery and insitu data. In addition, the authors
(Alba etal. 2020) used different combinations of Sentinel-2
bands to perform bi-variate (logarithms, indexes, and ratios)
analysis and Pearson correlation for chlorophyll-a estimation
and a ratio model was created and validated using multi-date
data. A slightly similar method was used by Alarcon etal.
(2018) using Landsat-8 OLI data; chlorophyll-a concentra-
tion and algae abundance data were measured using simulta-
neous data from the field and remote sensing that were used
to build semi-empirical models and a linear model from blue
(0.450 to 0.515µm) and NIR (0.845 to 0.885µm) presented
the best results with a determination coefficient of 0.89.
Semi-analytical algorithms, such as Quasi-analytical
algorithms (QAA), Generalized Stacked Constraints Mod-
els (GSCM), and Generalized Ocean Color Inversion model
(GIOP), have been widely used for remote sensing of algal
blooms using multispectral and hyperspectral data (e.g.,
Ali etal. 2014; Watanabe etal. 2018; Rotta etal. 2021;
Dev etal. 2022), particularly in optically complex waters.
Semi-analytical algorithms based in reflectance from remote
sensing data, which are simplified solutions to the radiative
transfer equation, are used to model the underwater light
behavior based on physical principles represented by spec-
tral measurements of specific absorption and backscatter-
ing coefficients and light geometry factors (Watanabe etal.
2018). For example, Rotta etal. (2021) used QAA, which is
originally applied by Lee etal. (2002), for retrieving chloro-
phyll-a concentration from Landsat OLI data across the Tiete
River Cascade System in Sao Paulo (Brazil) successfully.
Recently, Dev etal. (2022) used SAA for quantifying both
chlorophyll-a and phycocyanin in Lake Kinneret (Israel)
from hyperspectral (HICO) data. Zheng and DiGiacomo
(2017) applied the GSCM method, which is conceptually
consistent with QAA, for detecting phytoplankton diatom
fraction based on the spectral shape of the satellite-derived
algal light absorption coefficient. One of the advantages of
semi-analytical methods is that one algorithm parameterized
to quantify chlorophyll-a for a freshwater lake/reservoir can
be applied to other lakes/reservoirs with similar bio-optical
characteristics (Watanabe etal. 2018). In addition, semi-
analytical methods can be used for long-term trophic status
monitoring (Rotta etal. 2021).
A number of recent studies have used machine learning
algorithms for estimating chlorophyll-a concentration in
freshwater lakes from Landsat and Sentinel-2/3 data (Smith
etal. 2021; O’Shea etal. 2021; Pahlevan etal. 2022) with
accuracy comparable to insitu analysis. Machine learning,
which is a set of statistical methods that can automatically
learn from the input data to develop a model to detect and
estimate parameters, has been used to overcome a few limi-
tations of semi-analytical and quasi-analytical chlorophyll-
a and phycocyanin retrieval algorithms by combining the
information from multiple optical features (Sagan etal.
2020; Topp etal. 2020). For example, O’Shea etal. (2021)
used hyperspectral data (HICO and PRISMA) for estimating
cyanobacterial biomass by applying Mixture Density Net-
works (MDN) machine learning model. The authors (O’Shea
etal. 2021) foresee the applicability of MDN methods for
future hyperspectral missions such as PACE. Pahlevan etal.
(2022) applied machine learning algorithms (MDN) for the
same purpose using Landsat-8, Sentinel-2, and Sentinel-3
multispectral data while Smith etal. (2021) used only Land-
sat-8 data for estimating near-surface chlorophyll-a concen-
tration by applying MDN. Sagan etal. (2020) argued that
machine learning methods provided the best overall accu-
racy in detecting water quality variables (including chloro-
phyll-a) compared to other methods such as band ratios and
semi-analytical methods.
LiDAR image processing methods formonitoring algal
blooms
LiDAR data has been processed using empirical algorithms to
estimate chlorophyll-a and other pigments for mapping algal
blooms in a number of studies (e.g., Molkov etal. 2018). In
order to detect a wide range of variability of optically active
components in water, splitting the original records of LIDAR
signals into small intervals is required. LiDAR is the only

Environmental Science and Pollution Research
1 3
remote sensing technique that can profile the upper water
column from above the surface and is able to detect vertical
profiles of optical backscattering from particles, including the
cells of bloom-producing microorganisms (Churnside 2014).
In a study by Palmer etal. (2013), multispectral Ultravio-
let Fluorescence LiDAR (UFL) was used to estimate water
quality parameters, including chlorophyll-a produced during
algal blooms in Lake Balaton in Hungary. Here, the fluores-
cence emission signals across UFL bands for each insitu and
field measurements were used to develop empirical relation-
ships to estimate water quality parameters and, based on UFL
emission spectra, authors could differentiate different species
(Cylindrospermopsis raciborskii and Scenedesmus armatus)
biomass concentrations (Palmer etal. 2013).
Grishin etal. (2016) used a ship-based compact Raman
LiDAR for mapping an algal bloom in the Gorky freshwa-
ter reservoir on the Volga River in central Russia. Elastic
and Raman scattering as well as chlorophyll fluorescence
were quantified, mapped, and compared with data acquired
by a Salinity, Temperature, and Depth probe (STD probe)
equipped with a blue-green algae sensor, and the authors
obtained an acceptable correlation between the two data
types (LiDAR and STD).
The vertical distributions of the cyanobacteria genera are
found to be related to light intensity in the water column
(Moore etal. 2019). Moore etal. (2019) processed LiDAR
data in several steps to estimate vertical distributions of
cyanobacteria genera in Lake Erie: initially, data segments
were identified where the aircraft flight is straight and level.
Then convert the raw digitization levels for these segments
to photo-cathode current values by means of the measured
system response. From the LiDAR return, the surface can
be identified and, based on the time difference between the
surface and sample, depth of east subsurface ca be estimated.
A linear regression to the logarithm of the return over a
specific depth range (2–4m) was used to estimate the expo-
nential attenuation of the signal in water and data correction
was applied to remove the attenuation effects before the data
were multiplied by the square of aircraft altitude the data at
the final stage so that the data taken at different altitudes can
be compared directly (Moore etal. 2019).
Radar image processing methods formonitoring algal
blooms
Since SAR image acquisition is not influenced by weather,
such data is often used for monitoring freshwater resources
(Wang etal. 2015, 2017; He et al. 2018). As mentioned
previously, dark regions in SAR imagery can be caused by
either cyanobacterial blooms (scum) or due to no or low-
speed winds and the dark regions in SAR can be extracted
based on backscattering, shape, and texture characteris-
tics. For example, Wang etal. (2015) used a support vector
machine—recursive feature elimination (SVM-RFE) feature
selection method for reducing feature vector dimensional-
ity; classification accuracy was evaluated using a confusion
matrix and an overall accuracy of 67.74% has been achieved
by the authors. The accuracy of algal bloom mapping from
SAR depends highly on the elimination of dark regions in
backscattered radiation from surfaces with low-speed winds
(Wang etal. 2015, 2017). Furthermore, Wang etal. (2017)
mentioned that scum-free areas with large amounts of algal
biomass in the water column may affect the radar backscat-
ter, and further field validation and modeling of the electro-
magnetic scattering maybe needed in such cases. He etal.
(2018) used multi-polarization radar remote sensing data
format; algal bloom extraction is performed by Wishart
supervised classification. Radar remote sensing has not been
used in its full potential in studying freshwater algal blooms.
Future challenges ofremote sensing ofalgal
blooms infreshwater lakes
Currently, a large number of different data types and algo-
rithms are available to the user for mapping and monitoring
algal blooms in freshwater lakes, particularly based on the
reflectance properties of pigments produced by algal blooms
(Castro etal. 2021). Detection of phycocyanin, whose spec-
tral reflectance is used for the remote sensing-based map-
ping of cyanobacterial blooms, is a challenging task due
to its relatively lower contribution to absorption compared
to chlorophyll-a and there is a significant overlap in their
absorption properties (Binding etal. 2020). Challenges in
remote sensing of alga blooms primarily arise from con-
straints in spectral, spatial, and radiometric resolution. For
real-time and long-term monitoring of algal blooms, the
temporal resolution also plays a key role.
Despite the fact that a number of studies used spaceborne
multispectral data for bloom detection and monitoring, the
limited number and broad bandwidth of available spectral
bands, combined with the low radiometric sensitivity of his-
torical sensors causes many challenges. For example, red
band (630–690nm) can discriminate chlorophyll-a whose
absorption is at 670nm and fluorescence at 685nm whereas
no specific spectral bands exist for phycocyanin detection
whose absorption is at 620nm (Stumpf etal. 2016; Binding
etal. 2020). Moreover, medium-resolution spaceborne opti-
cal data, such as Landsat and Sentinel-2, and low-resolution
data, such as SeaWiFS and MODIS, have relatively lower
temporal resolution which is relatively inferior in monitoring
highly dynamic algal blooms (Sayers etal. 2016), in addi-
tion to the requirement of accurate atmospheric correction
algorithms (Castro etal. 2021).
Binding etal. (2020) discussed four key challenges
in remote sensing of algal blooms in freshwater lakes,

Environmental Science and Pollution Research
1 3
particularly using optical imagery: product validation, esti-
mating algal bloom toxicity, vertical variability of algal
blooms, and variable optical properties. Product validation
is highly challenging because the spatial distribution of algal
blooms may vary from a small region to a large basin, where
difficult to collect samples for validation from the field, and
spatial resolution of spaceborne optical data has limitations
in this matter. Spatial resolution constraints can be reduced
using high-resolution UAV data to some extent. Algal bloom
toxicity measurement is mostly impossible to estimate from
remote sensing data since no direct detection is possible from
the optical reflectance properties of cyanotoxins (microcys-
tins) and existing methods rely on proxy-based approaches
(pigment concentration for example), where the results can
be problematic because the toxin production by cyanobacte-
rial cells may vary from species to species and even from one
event to the next (Stumpf etal. 2016; Binding etal. 2020).
Moreover, these methods still need to be standardized for
evaluating the reproducibility of parameterizations among
the lakes (Stumpf etal. 2016). Vertical variability of algal
blooms, due to the ability of cyanobacterial cells to move
vertically in the water column for exploiting optical light
and nutrient conditions (Moreno-Ostos etal. 2009), are
relatively difficult to retrieve using optical spaceborne data.
Since the vertically distributed of cyanobacteria has a sig-
nificant contribution to the overall biomass of phytoplankton
and the vertical distribution of bloom-producing cells may
spatially vary, any erroneous quantitative measurement of
algal blooms solely based on the surface extent of the bloom
can reduce the accuracy (Binding etal. 2020). As mentioned
previously, even though relatively expensive than optical
data acquisition, additional information of the vertical dis-
tribution of cyanobacterial blooms can be obtained using
LiDAR data (Moore etal. 2019). Inherent optical proper-
ties of different materials (dissolved or suspended) always
contribute to the overall surface reflectance of freshwater
resources, which causes difficulty in detecting algal blooms
from spectral reflectance (Moore etal. 2019; Sayers etal.
2019b; Binding etal. 2020).
The differences in the degree to which phytoplankton
cells of different genera respond to light in terms of buoy-
ancy regulation have been detected using LiDAR in a num-
ber of recent studies (e.g., Moore etal. 2019). However,
there are limitations of the penetration of LiDAR in deeper
lakes with sub-surface cyanobacterial populations (Moore
etal. 2019), even though LiDAR provides better penetra-
tion compared to optical data. In addition, algorithms used
in LiDAR image processing is relatively complex com-
pared to those used for processing multispectral data.
As mentioned previously, Radar remote sensing has not yet
been used to its full potential in algal bloom studies, espe-
cially in freshwater lakes. SAR data can be an effective tool for
cyanobacterial bloom mapping when weather patterns do not
allow the use of optical data; future studies using multi-polar-
ization, multi-band SAR data can be helpful in obtaining more
accurate information on algal blooms in such areas (Wang etal.
2015, 2017). Current challenges, such as flat surface water with
low-speed wind, need to be considered while using SAR data
for inland lakes (Wang etal. 2015). Wang etal. (2017) sug-
gested the use of developing a suitable electromagnetic scat-
tering model and recognition algorithm with multi-frequency
and multi-polarization SAR for mapping algal blooms in inland
lakes. A number of recent studies have proven the accuracy if
algal bloom remote sensing a combination of radar and opti-
cal data (e.g., Bresciani etal. 2014; He etal. 2018; De Santi
etal. 2019), which can be useful when long-term monitoring
is required. Moreover, a number of spaceborne radar sensors
capable of high resolution will be launched in the near future,
which will revolutionize the accurate mapping of algal blooms
from microwave data (He etal. 2018). It has to be noted that,
using currently available techniques, algal bloom mapping
solely based on SAR data is challenging without additional
information or optical imagery of the same area (De Santi etal.
2019). Moreover, inherent optical properties may affect the use
of microwave imagery too due to the differences in backscat-
tered radiation from the surfaces dominated by different spe-
cies (Wang etal. 2015).
Multi-sensor and multi-platform data have been used in
a number of recent studies for better performance of algal
bloom detection using remote sensing (e.g., Bresciani etal.
2014; Castro etal. 2021; Xu etal. 2021). Recently used
methods, including semi-analytical and machine learning
algorithms, have improved the retrieval of algal pigments in
optically deep and complex waters from remotely sensed data
(Odermatt etal. 2012; Sagan etal. 2020; Topp etal. 2020).
Conclusions
Despite its limitation in spatial, spectral, and temporal
coverages, remote sensing is accepted as a valuable tool
in monitoring algal blooms of algal blooms by researchers
and environmental managers worldwide. Compared to field
sampling and insitu analysis, which cannot offer a real-time
monitoring system, remote sensing methods are capable of
providing nearly real-time updates on the appearance and
expansion of blooms. Integration of multi-sensor data type
is often helpful in mitigating the limitations, such as the
effects of weather conditions on data and image frequency,
of a specific remote sensing data type. In addition, data
quality and sensor capabilities are being improved in recent
years. Radar and LiDAR remote sensing of freshwater algal
blooms will be more sophisticated in the future with the aid
of multi-frequency, multi-polarization data as well as its use
combined with optical data to improve the accuracy with
additional information. More advanced LiDAR sensors are

Environmental Science and Pollution Research
1 3
being mounted on UAV platforms that would be helpful in
cost-effective data acquisition in the future.
Author contribution BKV designed the study. BKV, SBAR, AV, ABK,
and CG contributed equally to the manuscript.
Funding This study was supported by Companhia Riograndense de
Saneamento – CORSAN, Porto Alegre, Brazil, and the Institute of
Geosciences, Universidade Federal do Rio Grande do Sul -UFRGS,
Porto Alegre, Brazil.
Data availability All data used in this study are available from public
domain resources.
Declarations
Ethical approval Hereby, we consciously assure that for the manu-
script/insert title/the following is fulfilled: (1) this material is the
authors’ own original work, which has not been previously published
elsewhere. (2) The paper is not currently being considered for publica-
tion elsewhere. (3) The paper reflects the authors’ own research and
analysis in a truthful and complete manner. (4) The paper properly
credits the meaningful contributions of co-authors and co-researchers.
(5) The results are appropriately placed in the context of prior and
existing research. (6) All sources used are properly disclosed (correct
citation). Literally copying of text must be indicated as such by using
quotation marks and giving proper references. (7) All authors have
been personally and actively involved in substantial work leading to the
paper and will take public responsibility for its content.
Consent to participate Not applicable.
Consent for publication Not applicable.
Competing interests The authors declare no competing interests.
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