ReviewRemote sensing of cyanobacterial blooms in inland waters: present knowledge and future challenges
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.
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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.