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

Duan, Hongtao; Tao, Min; Loiselle, Steven; Zhao, Wei; Cao, Zhigang; Ma, Ronghua; Tang, Xiaoxian

doi:10.1016/j.watres.2017.06.022

Cited by 119 publications

(38 citation statements)

References 73 publications

(103 reference statements)

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“…Within inland water remote sensing, machine learning algorithms including artificial neural networks [182][183][184], genetic algorithms/programming [185,186], support vector machines [187], random forest/boosted regression trees [188], and empirical orthogonal functions [189,190] have all shown promise in accurately estimating water quality parameters across a variety of spatiotemporal scales. As with traditional empirical models, machine learning approaches are only applicable within the range and setting of data used to train a given model.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…The model ultimately resulted in over one million tons of algal scum being removed from a drinking water reservoir in China [27]. Other authors have similarly identified public threats to drinking water in Lake Mead (USA) [182] and Lake Chaohu in China [190]. Two specific studies stood out through their novel use of remote sensing to facilitate epidemiological studies.…”

Section: Pub Year Study Duration Study Scale Study Categorymentioning

confidence: 99%

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Research Trends in the Use of Remote Sensing for Inland Water Quality Science: Moving Towards Multidisciplinary Applications

Topp

Pavelsky

Jensen

et al. 2020

Water

206

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Remote sensing approaches to measuring inland water quality date back nearly 50 years to the beginning of the satellite era. Over this time span, hundreds of peer-reviewed publications have demonstrated promising remote sensing models to estimate biological, chemical, and physical properties of inland waterbodies. Until recently, most of these publications focused largely on algorithm development as opposed to implementation of those algorithms to address specific science questions. This slow evolution contrasts with terrestrial and oceanic remote sensing, where methods development in the 1970s led to publications focused on understanding spatially expansive, complex processes as early as the mid-1980s. This review explores the progression of inland water quality remote sensing from methodological development to scientific applications. We use bibliometric analysis to assess overall patterns in the field and subsequently examine 236 key papers to identify trends in research focus and scale. The results highlight an initial 30 year period where the majority of publications focused on model development and validation followed by a spike in publications, beginning in the early-2000s, applying remote sensing models to analyze spatiotemporal trends, drivers, and impacts of changing water quality on ecosystems and human populations. Recent and emerging resources, including improved data availability and enhanced processing platforms, are enabling researchers to address challenging science questions and model spatiotemporally explicit patterns in water quality. Examination of the literature shows that the past 10–15 years has brought about a focal shift within the field, where researchers are using improved computing resources, datasets, and operational remote sensing algorithms to better understand complex inland water systems. Future satellite missions promise to continue these improvements by providing observational continuity with spatial/spectral resolutions ideal for inland waters.

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Section: Machine Learning Modelsmentioning

confidence: 99%

Section: Pub Year Study Duration Study Scale Study Categorymentioning

confidence: 99%

Research Trends in the Use of Remote Sensing for Inland Water Quality Science: Moving Towards Multidisciplinary Applications

Topp

Pavelsky

Jensen

et al. 2020

Water

206

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“…To examine its wider applicability, the dual band ratio (681 nm/560 nm and 754 nm/560 nm) algorithm was applied to Lake Chaohu, which is the fifth largest freshwater lake in China and is a typical eutrophic lake [63,64]. A total of 23 synchronous satellite and ground data points for Lake Chaohu were obtained by following the synchronous satellite and ground data matching criteria.…”

Section: Applicability Of the Algorithm To Other Lakesmentioning

confidence: 99%

Determination of the Downwelling Diffuse Attenuation Coefficient of Lake Water with the Sentinel-3A OLCI

et al. 2017

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Abstract:The Ocean and Land Color Imager (OLCI) on the Sentinel-3A satellite, which was launched by the European Space Agency in 2016, is a new-generation water color sensor with a spatial resolution of 300 m and 21 bands in the range of 400-1020 nm. The OLCI is important to the expansion of remote sensing monitoring of inland waters using water color satellite data. In this study, we developed a dual band ratio algorithm for the downwelling diffuse attenuation coefficient at 490 nm (K d (490)) for the waters of Lake Taihu, a large shallow lake in China, based on data measured during seven surveys conducted between 2008 and 2017 in combination with Sentinel-3A-OLCI data. The results show that: (1) Compared to the available K d (490) estimation algorithms, the dual band ratio (681 nm/560 nm and 754 nm/560 nm) algorithm developed in this study had a higher estimation accuracy (N = 26, coefficient of determination (R 2 ) = 0.81, root-mean-square error (RMSE) = 0.99 m −1 and mean absolute percentage error (MAPE) = 19.55%) and validation accuracy (N = 14, R 2 = 0.83, RMSE = 1.06 m −1 and MAPE = 27.30%), making it more suitable for turbid inland waters; (2) A comparison of the OLCI K d (490) product and a similar Moderate Resolution Imaging Spectroradiometer (MODIS) product reveals a high consistency between the OLCI and MODIS products in terms of the spatial distribution of K d (490). However, the OLCI product has a smoother spatial distribution and finer textural characteristics than the MODIS product and contains notably higher-quality data; (3) The K d (490) values for Lake Taihu exhibit notable spatial and temporal variations. K d (490) is higher in seasons with relatively high wind speeds and in open waters that are prone to wind-and wave-induced sediment resuspension. Finally, the Sentinel-3A-OLCI has a higher spatial resolution and is equipped with a relatively wide dynamic range of spectral bands suitable for inland waters. The Sentinel-3B satellite will be launched soon and, together with the Sentinel-3A satellite, will form a two-satellite network with the ability to make observations twice every three days. This satellite network will have a wider range of application and play an important role in the monitoring of inland waters with complex optical properties.

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“…Cultures were stored for a maximum of 2 wk, and if they were not used in that time, they were discarded. Cyanobacterial cultures were used at a density of 100 to 200 µg/L chlorophyll a , which is within the reported range for dense cyanobacterial blooms worldwide (Yuan et al ; Sayers et al ; Duan et al ). Microcystis aeruginosa (UTEX) was purchased through the Culture Collection of Algae at The University of Texas at Austin, M. aeruginosa (GLERL) was provided by the Great Lakes Environmental Research Lab, and Anabaena flos‐aquae , Aphanizomenon flos‐aquae , Dolichospermum lemmermannii , Gloeotrichia echinulata , M. aeruginosa (BQ11‐02, ZUR‐HINDAK, and LSC‐13‐02), M. wesenbergii , and Planktothrix suspensa were provided by Environment and Climate Change Canada.…”

Section: Methodsmentioning

confidence: 92%

Cyanobacteria reduce motility of quagga mussel (Dreissena rostriformis bugensis) sperm

Boegehold

Alame

Johnson

et al. 2019

Enviro Toxic and Chemistry

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The temporal expansion of harmful algal blooms, primarily associated with cyanobacteria, may impact aquatic organisms at vulnerable life-history stages. Broadcast spawning species release gametes into the water column for external fertilization, directly exposing sperm to potential aquatic stressors. To determine if cyanobacteria can disrupt reproduction in freshwater broadcast spawners, we evaluated sublethal effects of cyanobacteria exposure on quagga mussel (Dreissena rostriformis bugensis) sperm. In laboratory studies, sperm were collected after inducing mussels to spawn using serotonin and exposed to 11 cultures of cyanobacteria including Anabaena flos-aquae, Aphanizomenon flos-aquae, Dolichospermum lemmermannii, Gloeotrichia echinulata, 5 cultures of Microcystis aeruginosa, M. wesenbergii, and Planktothrix suspensa. Sperm motility, using endpoints of cumulative distance traveled and mean velocity, was calculated for a minimum of 10 individual sperm using a novel optical biotracking assay method. The distance and velocity at which sperm traveled decreased when exposed to Aphanizomenon flos-aquae and 2 M. aeruginosa cultures. Our findings indicate that cyanobacteria impede the motility of quagga mussel sperm, which can potentially result in reproductive impairments to mussels and potentially other broadcast spawning species. Environ Toxicol Chem 2019;38:368-374. C 2018 SETAC

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

Cited by 119 publications

References 73 publications

Research Trends in the Use of Remote Sensing for Inland Water Quality Science: Moving Towards Multidisciplinary Applications

Research Trends in the Use of Remote Sensing for Inland Water Quality Science: Moving Towards Multidisciplinary Applications

Determination of the Downwelling Diffuse Attenuation Coefficient of Lake Water with the Sentinel-3A OLCI

Cyanobacteria reduce motility of quagga mussel (Dreissena rostriformis bugensis) sperm

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