Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data

Legleiter, Carl J.; King, Tyler V.; Carpenter, Kurt D.; Hall, Natalie; Mumford, Adam C.; Slonecker, Terry; Graham, Jennifer L.; Stengel, Victoria G.; Simon, N.S.; Rosen, Barry H.

doi:10.1016/j.rse.2022.113089

Cited by 38 publications

(21 citation statements)

References 72 publications

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“…Satellite sensor technology is currently (2023) undergoing rapid development that is enabling advanced spectral resolution, increased overpass frequency, imaging across a range of spatial scales, interoperability and harmonization of satellite sensors, and high performance cloud platforms that facilitate advancements in geoprocessing and spatial data science. 27,36,72 The chlorophyll-a concentration estimates described herein are limited to the in-situ matches between water-quality data and satellite imagery obtained during the study period. Because of the performance uncertainties described herein, chlorophyll-a concentrations estimated from satellite imagery would benefit from additional field observations for validation purposes.…”

Section: Discussion Of Findingsmentioning

confidence: 99%

“…Real-time earth observation requires robust approaches that are resilient to changes in dynamic environmental conditions including water-quality, phytoplankton communities, and atmospheric conditions. Satellite sensor technology is currently (2023) undergoing rapid development that is enabling advanced spectral resolution, increased overpass frequency, imaging across a range of spatial scales, interoperability and harmonization of satellite sensors, and high performance cloud platforms that facilitate advancements in geoprocessing and spatial data science 27 , 36 , 72 …”

Section: Discussion Of Findingsmentioning

confidence: 99%

“…Sensors with expanded spectral resolution that can detect phycocyanin (e.g., Sentinel 3) could be ingested into the real-time earth observation system to leverage sensor assets and capabilities across multiple satellite platforms 30 . Cyanobacteria genera reveal unique hyperspectral signatures, 57 and when applied to space-borne hyperspectral imagery, they have demonstrated potential to differentiate aquatic cyanobacteria genera 72 advancing detection beyond chlorophyll-a and phycocyanin proxy estimators of algae and cyanobacteria.…”

Section: Discussion Of Findingsmentioning

confidence: 99%

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Near real-time satellite detection and monitoring of aquatic algae and cyanobacteria: how a combination of chlorophyll-a indices and water-quality sampling was applied to north Texas reservoirs

Stengel,

Trevino,

King

et al. 2023

J. Appl. Rem. Sens.

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.Aquatic algae and cyanobacteria can impair water-quality and pose risks to human and animal health. Several metrics of in-situ water-quality, including chlorophyll-a, phycocyanin, turbidity, Secchi depth, phytoplankton taxonomy, and hyperspectral reflectance, were collected in coordination with Sentinel-2 satellite overpasses to ascertain water-quality conditions and calibrate satellite detection and estimation of chlorophyll-a concentration. The performance of multiple satellite chlorophyll-a detection indices was evaluated by comparing satellite imagery to field observations of chlorophyll-a concentrations. Seventeen chlorophyll-a spectral indices were implemented using the ACOLITE atmosphere correction; the top performing indices were selected for further evaluation using the Sen2Cor and MAIN atmosphere corrections. The Moses three-band spectral index delivered the strongest linear agreement with field measurements of chlorophyll-a concentration across all reservoir sampling sites (R2 = 0.70). Compared to open-water sites, the Moses three-band spectral index delivered better linear agreement with chlorophyll-a field measurements at inlet sites where there was a greater abundance of near surface aquatic chlorophyll-a concentrations, and the overall chlorophyll-a hyperspectral reflectance signal was stronger. Chlorophyll-a concentration estimates were implemented in a cloud-computation remote sensing platform designed for regional scale remote sensing analysis to map spatiotemporal patterns of aquatic chlorophyll-a across 10 study reservoirs located primarily in north Texas.

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Section: Discussion Of Findingsmentioning

confidence: 99%

Section: Discussion Of Findingsmentioning

confidence: 99%

Section: Discussion Of Findingsmentioning

confidence: 99%

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Near real-time satellite detection and monitoring of aquatic algae and cyanobacteria: how a combination of chlorophyll-a indices and water-quality sampling was applied to north Texas reservoirs

Stengel,

Trevino,

King

et al. 2023

J. Appl. Rem. Sens.

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“…Recently, Legleiter et al (2022) combined spectral imaging from the laboratory with satellite data to resolve bloom‐forming cyanobacteria. They obtained reflectance spectra of different cyanobacteria species in the laboratory with a spectral imager attached to a microscope and used those as spectral endmembers to resolve to taxa level using data from satellite images.…”

Section: Discussionmentioning

confidence: 99%

Resolving phytoplankton pigments from spectral images using convolutional neural networks

Salmi,

Pölönen,

Beckmann

et al. 2023

Limnology & Ocean Methods

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Motivated by the need for rapid and robust monitoring of phytoplankton in inland waters, this article introduces a protocol based on a mobile spectral imager for assessing phytoplankton pigments from water samples. The protocol includes (1) sample concentrating; (2) spectral imaging; and (3) convolutional neural networks (CNNs) to resolve concentrations of chlorophyll a (Chl a), carotenoids, and phycocyanin. The protocol was demonstrated with samples from 20 lakes across Scotland, with special emphasis on Loch Leven where blooms of cyanobacteria are frequent. In parallel, samples were prepared for reference observations of Chl a and carotenoids by high‐performance liquid chromatography and of phycocyanin by spectrophotometry. Robustness of the CNNs were investigated by excluding each lake from model trainings one at a time and using the excluded data as independent test data. For Loch Leven, median absolute percentage difference (MAPD) was 15% for Chl a and 36% for carotenoids. MAPD in estimated phycocyanin concentration was high (102%); however, the system was able to indicate the possibility of a cyanobacteria bloom. In the leave‐one‐out tests with the other lakes, MAPD was 26% for Chl a, 27% for carotenoids, and 75% for phycocyanin. The higher error for phycocyanin was likely due to variation in the data distribution and reference observations. It was concluded that this protocol could support phytoplankton monitoring by using Chl a and carotenoids as proxies for biomass. Greater focus on the distribution and volume of the training data would improve the phycocyanin estimates.

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“…Satellite-derived information on the land cover type, temperature, humidity, spectral data, and other environmental variables can be used in the surveillance of algal blooms in fresh, brackish, and salt water [ 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 ]. Formal surveillance programs are already in place within the United States, such as the Center for Disease Control’s (CDC) One Health Harmful Algal Bloom System (OHHABS) and the Environment Protection Agency’s (EPA) Cyanobacteria Assessment Network (CyAN).…”

Section: Discussionmentioning

confidence: 99%

Reporting of Freshwater Cyanobacterial Poisoning in Terrestrial Wildlife: A Systematic Map

Ash

Patterson

2022

Animals

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Global warming and over-enrichment of freshwater systems have led to an increase in harmful cyanobacterial blooms (cyanoHABs), affecting human and animal health. The aim of this systematic map was to detail the current literature surrounding cyanotoxin poisonings in terrestrial wildlife and identify possible improvements to reports of morbidity and mortality from cyanotoxins. A systematic search was conducted using the electronic databases Scopus and Web of Science, yielding 5059 published studies identifying 45 separate case reports of wildlife poisonings from North America, Africa, Europe, and Asia. Currently, no gold standard for the diagnosis of cyanotoxin intoxication exists for wildlife, and we present suggested guidelines here. These involved immunoassays and analytical chemistry techniques to identify the toxin involved, PCR to identify the cyanobacterial species involved, and evidence of ingestion or exposure to cyanotoxins in the animals affected. Of the 45 cases, our recommended methods concurred with 48.9% of cases. Most often, cases were investigated after a mortality event had already occurred, and where mitigation was implemented, only three cases were successful in their efforts. Notably, only one case of invasive cyanobacteria was recorded in this review despite invasive species being known to occur throughout the globe; this could explain the underreporting of invasive cyanobacteria. This systematic map highlights the perceived absence of robust detection, surveillance, and diagnosis of cyanotoxin poisoning in wildlife. It may be true that wildlife is less susceptible to these poisoning events; however, the true rates of poisoning are likely much more than is reported in the literature.

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Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data

Cited by 38 publications

References 72 publications

Near real-time satellite detection and monitoring of aquatic algae and cyanobacteria: how a combination of chlorophyll-a indices and water-quality sampling was applied to north Texas reservoirs

Near real-time satellite detection and monitoring of aquatic algae and cyanobacteria: how a combination of chlorophyll-a indices and water-quality sampling was applied to north Texas reservoirs

Resolving phytoplankton pigments from spectral images using convolutional neural networks

Reporting of Freshwater Cyanobacterial Poisoning in Terrestrial Wildlife: A Systematic Map

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