Elsevier

Science Bulletin

Volume 64, Issue 20, 30 October 2019, Pages 1540-1556
Science Bulletin

Review
Remote sensing of cyanobacterial blooms in inland waters: present knowledge and future challenges

https://doi.org/10.1016/j.scib.2019.07.002Get rights and content

Abstract

Timely monitoring, detection and quantification of cyanobacterial blooms are especially important for controlling public health risks and understanding aquatic ecosystem dynamics. Due to the advantages of simultaneous data acquisition over large geographical areas and high temporal coverage, remote sensing strongly facilitates cyanobacterial bloom monitoring in inland waters. We provide a comprehensive review regarding cyanobacterial bloom remote sensing in inland waters including cyanobacterial optical characteristics, operational remote sensing algorithms of chlorophyll, phycocyanin and cyanobacterial bloom areas, and satellite imaging applications. We conclude that there have many significant progresses in the remote sensing algorithm of cyanobacterial pigments over the past 30 years. The band ratio algorithms in the red and near-infrared (NIR) spectral regions have great potential for the remote estimation of chlorophyll a in eutrophic and hypereutrophic inland waters, and the floating algae index (FAI) is the most widely used spectral index for detecting dense cyanobacterial blooms. Landsat, MODIS (Moderate Resolution Imaging Spectroradiometer) and MERIS (MEdium Resolution Imaging Spectrometer) are the most widely used products for monitoring the spatial and temporal dynamics of cyanobacteria in inland waters due to the appropriate temporal, spatial and spectral resolutions. Future work should primarily focus on the development of universal algorithms, remote retrievals of cyanobacterial blooms in oligotrophic waters, and the algorithm applicability to mapping phycocyanin at a large spatial-temporal scale. The applications of satellite images will greatly improve our understanding of the driving mechanism of cyanobacterial blooms by combining numerical and ecosystem dynamics models.

Introduction

On the Earth, inland waters are greatly important because they have numerous critical functions in the environment, despite covering only a relatively small area of the planet’s surface [1]. Available inland water resources are emerging as a limiting factor in both quantity and quality for human development and ecological stability [2]. Inland waters provide critical and diverse habitats for a large amount of species and ecosystem services, which is indispensable for supporting biodiversity maintenance [2]. In addition, inland waters influence the climate system, as shown in general circulation models, and these waters form the essential components of the global hydrological, carbon and nutrient cycles [3], [4], [5]. However, with increasing human activities and climatic changes, inland waters have experienced unprecedented threats from the synergistic effects of multiple, co-occurring environmental stresses, including nutrient enrichment, inorganic and organic pollution, and global warming [6], [7], [8], [9], [10].

One of the severely disastrous consequences of these threats is the globally increasing frequency of cyanobacterial blooms in inland waters [11], [12], [13]. Mounting evidences show that cyanobacterial blooms have increased at a global scale in recent decades, and these blooms are highly likely to expand further owing to ongoing eutrophication, rising CO2 concentration levels, and global warming in the future [14], [15], [16], [17], [18]. Cyanobacterial blooms can cause a series of serious environmental problems for inland waters and can severely stress the ecological structures, functions and aesthetics of aquatic ecosystems [19], [20]. Specifically, blooms can decrease water clarity and therefore suppress submerged aquatic vegetation growth and populations [21], [22]. The microbial degradation of cyanobacterial blooms may induce hypoxia resulting in the deaths of fish and benthic invertebrates [11], [23]. Furthermore, cyanobacteria can produce a variety of toxins that result in liver, digestive, and neurological diseases when ingested by humans, fish and birds [24], [25], [26]. In summary, cyanobacterial blooms can pose a major threat to the use of aquatic ecosystems for drinking and irrigation water, fishing and recreational purposes. Obviously, timely monitoring, detection and quantification of cyanobacterial blooms are especially important for controlling public health risks and understanding aquatic ecosystem dynamics.

Routine methods for analyzing cyanobacterial biomass have been well documented through field sampling and laboratory analyses [19], [27], [28]. However, the traditional method is ill suited for monitoring a large number of inland waters at regional or national scales because cyanobacterial blooms generally exhibit strong variability. The traditional method is highly laborious, time-consuming, expensive, and it is practically impossible to obtain an overview of the spatial information on cyanobacterial blooms, which stops its application to the timely monitoring of cyanobacterial blooms at a large scale. Thus, there is a clear need for new approaches to facilitate the development of reliable and cost-effective monitoring programs for cyanobacterial blooms at local, regional, national, and global scales. Due to the advantages of simultaneous data acquisition over large geographical areas and with high temporal coverage, remote sensing strongly facilitates the monitoring of cyanobacterial blooms in inland waters [29], [30], [31], [32], [33].

Remote sensing technology has been widely used to investigate the biogeochemical constituents of inland waters, including total suspended matter (TSM) [34], [35], chromophoric dissolved organic matter (CDOM) [36], particulate organic carbon (POC) [37], nutrients [38], [39], trophic state index [40], [41], submerged aquatic vegetation [21], [42] and algae-associated indexes (such as chlorophyll-a, phycocyanin, cyanobacterial dominance, and algal bloom area) [30], [43], [44], [45], [46], [47]. A bibliometric analysis shows that “hyperspectral”, “ocean color”, and “chlorophyll-a” are the three most commonly used keywords in SCI-indexed papers published between 1900 and 2018 regarding water color remote sensing and thus indicates that chlorophyll-a (Chla) is one of the hotspots and cores of this field (Fig. 1). With the development of satellite instruments and available algorithms, remote sensing is evolving towards the routine use of cyanobacterial bloom monitoring [48], [49], [50], [51]. Presently, most of the remote sensing methods developed for quantification of cyanobacterial biomass rely on algorithms aiming at Chla and phycocyanin (PC) concentrations [43], [44], [52]. There are two types of characteristic pigment associated with cyanobacteria in inland waters [53], [54], [55].

For further application of remote sensing techniques to cyanobacterial bloom monitoring and research, an overview of the available state-of-the-art methods is demonstrated in this paper, the challenges and future directions are outlined based on recent publications, and the objective is to obtain a deeper insight into the problem and derive a basis for further improvements in this domain. The present review focuses on the optical properties of the cyanobacterial community, the algorithm development and validation for cyanobacterial bloom remote sensing, and the applications of multi-satellite data to cyanobacterial monitoring. This work partially complements the review of accomplishments in studies regarding inland water remote sensing [51], [56], [57], [58], [59].

  • (1)

    We conduct a comprehensive review of the optical properties of cyanobacterial communities covering various types of inland waters, including the absorption, specific absorption and remote sensing reflectance. This part provides the intrinsic physical basis for the development of cyanobacterial remote sensing algorithms and an explanation for challenges in constructing universal algorithms for inland waters.

  • (2)

    We perform a comparative analysis of bio-optical, semi-analytical and empirical algorithms specifically used to detect and quantify cyanobacterial pigments and blooms in various types of inland waters based on in situ measured remote sensing reflectance or multispectral satellite imagery. The advantages and limitations of these algorithms are discussed.

  • (3)

    We discuss the application of MODIS, MERIS, GOCI, and Landsat data to the monitoring and mapping of cyanobacterial blooms over short and long-term periods and in local and regional water bodies. The roles of remote sensing techniques in lake management are addressed. We highlight the significant implications of satellite-derived cyanobacterial bloom dynamics at high temporal and spatial resolutions in addressing the impacts of climatic warming and eutrophication on cyanobacterial blooms.

  • (4)

    We conclude with the present challenges in algorithm development and remote sensing applications to the environmental management of inland waters and aquatic ecosystem research. Future research directions are proposed regarding the development of more accurate and transferable algorithms, the extension and application of remote sensing data and techniques in the research field of cyanobacterial blooms.

Section snippets

Overview of remote sensing of cyanobacterial blooms

Over the past several decades, cyanobacterial blooms remote sensing has made great progress, which is evidenced by a rapid increase in peer-reviewed publications on this study topic [56]. The ubiquitous phytoplankton pigment Chla is generally considered an important indicator of cyanobacterial biomass, which can quickly respond to environmental changes [31]. This pigment exhibits a unique spectral characteristic with noticeable peaks in the blue (nearly at 440 nm) and red wavelengths (at nearly

Optical properties of the cyanobacterial community

Optical properties can be classified into two categories: inherent optical properties (IOPs) and apparent optical properties (AOPs). IOPs vary only with the composition and concentration of the medium or constituents and are independent of the ambient light field, such as absorption and scattering coefficients. AOPs not only depend on the composition and concentration of the medium or constituents but also on the ambient light field structure, such as remote sensing reflectance (Rrs) and

Algorithms for cyanobacteria remote sensing

Algorithm development of cyanobacterial pigments has greatly progressed over the past 30 years. There is a wide variety of algorithms that can remotely quantify cyanobacterial biomass and blooms through Chla or PC. The algorithms can be classified simply into three types: empirical, semi-empirical, and analytical approaches [32], [122]. Empirical and semi-empirical approaches are usually developed with statistical relationships between Rrs(λ) and cyanobacterial pigments (Chla or PC). The

Applications of satellite data to monitor cyanobacteria dynamics

Great progress has been made in water color algorithms as well as the products, technology and maturity of satellite sensors, which have demonstrated confidence in remotely sensed data with potential applications to water environmental management [32], [58], [59], [158]. A variety of satellite data, such as Landsat, MODIS, MERIS, Sentinel OLCI, GOCI, Himawari-8 AHI, and NPP VIIRS, has been utilized to retrieve water quality in several oceans, coastal and inland waters [34], [63], [68], [125],

Future challenges

Presently, we have gained many important findings and progresses; however, some critical challenges need to be resolved. First, the accuracy of Chla estimation is severely limited by the high variations in aph*(λ) across various inland waters. The variability interferes with the relationships between aph(λ) and Chla, indicating that the use of algorithms based on cyanobacterial absorption for remote sensing of low Chla is challenging. Therefore, more effort should be focused on how to remove

Conflict of interest

The authors declare that they have no conflict of interest.

Acknowledgments

This work was supported by the National Science and Technology Major Project of China (2017ZX07203001), the National Natural Science Foundation of China (41771472 and 41621002), the Youth Innovation Promotion Association of Chinese Academy of Sciences (2017365), the Key Research Program of Frontier Sciences of Chinese Academy of Sciences (QYZDB-SSW-DQC016), and the Strategic Priority Research Program of Chinese Academy of Sciences (XDA19070301).

Author contributions

Kun Shi and Botian Zhou charted the figures and tables. Kun Shi, Yunlin Zhang, Boqiang Qin and Botian Zhou collected and analyzed the literatures. Kun Shi wrote the original draft. Yunlin Zhang and Boqiang Qin reviewed and edited the draft.

Kun Shi is an associate professor in Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences. His research interests include water optics, water color remote sensing, ecological effects of changes in lake environment.

References (184)

  • R.P. Stumpf et al.

    Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria

    Harmful Algae

    (2016)
  • H.J. Gons et al.

    MERIS satellite chlorophyll mapping of oligotrophic and eutrophic waters in the Laurentian Great Lakes

    Remote Sens Environ

    (2008)
  • B. Nechad et al.

    Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters

    Remote Sens Environ

    (2010)
  • T. Kutser et al.

    Mapping lake CDOM by satellite remote sensing

    Remote Sens Environ

    (2005)
  • S.L. Wang et al.

    Trophic state assessment of global inland waters using a MODIS-derived forel-ule index

    Remote Sens Environ

    (2018)
  • Z.D. Wen et al.

    Quantifying the trophic status of lakes using total light absorption of optically active components

    Environ Pollut

    (2019)
  • X.X. Han et al.

    Wetland changes of China’s largest freshwater lake and their linkage with the three gorges dam

    Remote Sens Environ

    (2018)
  • S. Mishra et al.

    Quantifying cyanobacterial phycocyanin concentration in turbid productive waters: a quasi-analytical approach

    Remote Sens Environ

    (2013)
  • K. Shi et al.

    Remote estimation of cyanobacteria-dominance in inland waters

    Water Res

    (2015)
  • D. Odermatt et al.

    MERIS observations of phytoplankton blooms in a stratified eutrophic lake

    Remote Sens Environ

    (2012)
  • B.A. Schaeffer et al.

    Mobile device application for monitoring cyanobacteria harmful algal blooms using Sentinel-3 satellite ocean and land colour instruments

    Environ Modell Softwa

    (2018)
  • Y. Oyama et al.

    Monitoring levels of cyanobacterial blooms using the visual cyanobacteria index (VCI) and floating algae index (FAI)

    Int J Appl Earth Obs

    (2015)
  • S.C.J. Palmer et al.

    Remote sensing of inland waters: challenges, progress and future directions

    Remote Sens Environ

    (2015)
  • L.H. Li et al.

    A semi-analytical algorithm for remote estimation of phycocyanin in inland waters

    Sci Total Environ

    (2012)
  • S.G.H. Simis et al.

    Influence of phytoplankton pigment composition on remote sensing of cyanobacterial biomass

    Remote Sens Environ

    (2007)
  • D. Blondeau-Patissier et al.

    A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans

    Prog Oceanogr

    (2014)
  • Y. Yan et al.

    Phycocyanin concentration retrieval in inland waters: a comparative review of the remote sensing techniques and algorithms

    J Great Lakes Res

    (2018)
  • D. Odermatt et al.

    Review of constituent retrieval in optically deep and complex waters from satellite imagery

    Remote Sens Environ

    (2012)
  • R.J.W. Brewin et al.

    Regional ocean-colour chlorophyll algorithms for the Red Sea

    Remote Sens Environ

    (2015)
  • R.M. Kudela et al.

    Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters

    Remote Sens Environ

    (2015)
  • K. Randolph et al.

    Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll a and phycocyanin

    Remote Sens Environ

    (2008)
  • R.K. Vincent et al.

    Phycocyanin detection from Landsat TM data for mapping cyanobacterial blooms in Lake Erie

    Remote Sens Environ

    (2004)
  • A. Ruiz-Verdú et al.

    An evaluation of algorithms for the remote sensing of cyanobacterial biomass

    Remote Sens Environ

    (2008)
  • S.C.J. Palmer et al.

    Satellite remote sensing of phytoplankton phenology in lake balaton using 10 years of MERIS observations

    Remote Sens Environ

    (2015)
  • Y. Oyama et al.

    Distinguishing surface cyanobacterial blooms and aquatic macrophytes using landsat/TM and ETM + shortwave infrared bands

    Remote Sens Environ

    (2015)
  • B. Zhou et al.

    Distinguishing two phenotypes of blooms using the normalised difference peak-valley index (NDPI) and cyano-chlorophyta index (CCI)

    Sci Total Environ

    (2018)
  • H.T. Duan et al.

    MODIS observations of cyanobacterial risks in a eutrophic lake: implications for long-term safety evaluation in drinking-water source

    Water Res

    (2017)
  • M.W. Matthews et al.

    Remote sensing of cyanobacteria-dominant algal blooms and water quality parameters in Zeekoevlei, a small hypertrophic lake, using MERIS

    Remote Sens Environ

    (2010)
  • L.H. Li et al.

    Remote sensing of freshwater cyanobacteria: an extended iop inversion model of inland waters (IIMIW) for partitioning absorption coefficient and estimating phycocyanin

    Remote Sens Environ

    (2015)
  • K. Xue et al.

    Variability of light absorption properties in optically complex inland waters of Lake Chaohu, China

    J Great Lakes Res

    (2017)
  • L. Lorenzoni et al.

    Characterization of phytoplankton variability in the cariaco basin using spectral absorption, taxonomic and pigment data

    Remote Sens Environ

    (2015)
  • A. Nguy-Robertson et al.

    Determination of absorption coefficients for chlorophyll a, phycocyanin, mineral matter and cdom from three central Indiana reservoirs

    J Great Lakes Res

    (2013)
  • A. Simon et al.

    Estimation of the spectral diffuse attenuation coefficient of downwelling irradiance in inland and coastal waters from hyperspectral remote sensing data: validation with experimental data

    Int J Appl Earth Obs

    (2016)
  • C.E. Binding et al.

    Spectral absorption properties of dissolved and particulate matter in Lake Erie

    Remote Sens Environ

    (2008)
  • H.T. Duan et al.

    Evaluation of remote sensing algorithms for cyanobacterial pigment retrievals during spring bloom formation in several lakes of East China

    Remote Sens Environ

    (2012)
  • J. Seppälä et al.

    Ship-of-opportunity based phycocyanin fluorescence monitoring of the filamentous cyanobacteria bloom dynamics in the Baltic Sea

    Estuar Coast Sci

    (2007)
  • J.P. Cannizzaro et al.

    A novel technique for detection of the toxic dinoflagellate, Karenia brevis, in the gulf of Mexico from remotely sensed ocean color data

    Cont Shelf Res

    (2008)
  • M.W. Matthews et al.

    Improved algorithm for routine monitoring of cyanobacteria and eutrophication in inland and near-coastal waters

    Remote Sens Environ

    (2015)
  • J.F. Pekel et al.

    High-resolution mapping of global surface water and its long-term changes

    Nature

    (2016)
  • J. Corman

    Cleaner Chinese lakes

    Nat Geosci

    (2017)
  • Cited by (107)

    View all citing articles on Scopus

    Kun Shi is an associate professor in Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences. His research interests include water optics, water color remote sensing, ecological effects of changes in lake environment.

    Yunlin Zhang is a professor in Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences. His study interests include lake optics and water color remote sensing, lakes thermodynamics, and chromophoric dissolved organic matter (CDOM) biogeochemistry cycle.

    View full text