scholarly journals Early Warning of Harmful Algal Bloom Risk Using Satellite Ocean Color and Lagrangian Particle Trajectories

2021 ◽  
Vol 8 ◽  
Author(s):  
Junfang Lin ◽  
Peter I. Miller ◽  
Bror F. Jönsson ◽  
Michael Bedington
Keyword(s):  
Chlorophyll A ◽  
Early Warning ◽  
Algal Bloom ◽  
Harmful Algal Bloom ◽  
Marine Ecosystem ◽  
Ocean Color ◽  
Algal Species ◽  
Satellite Observations ◽  
Prediction Scheme ◽  
Satellite Ocean Color

Combining Lagrangian trajectories and satellite observations provides a novel basis for monitoring changes in water properties with high temporal and spatial resolution. In this study, a prediction scheme was developed for synthesizing satellite observations and Lagrangian model data for better interpretation of harmful algal bloom (HAB) risk. The algorithm can not only predict variations in chlorophyll-a concentration but also changes in spectral properties of the water, which are important for discrimination of different algal species from satellite ocean color. The prediction scheme was applied to regions along the coast of England to verify its applicability. It was shown that the Lagrangian methodology can significantly improve the coverage of satellite products, and the unique animations are effective for interpretation of the development of HABs. A comparison between chlorophyll-a predictions and satellite observations further demonstrated the effectiveness of this approach: r2 = 0.81 and a low mean absolute percentage error of 36.9%. Although uncertainties from modeling and the methodology affect the accuracy of predictions, this approach offers a powerful tool for monitoring the marine ecosystem and for supporting the aquaculture industry with improved early warning of potential HABs.

scholarly journals A Deep Learning Model Using Satellite Ocean Color and Hydrodynamic Model to Estimate Chlorophyll-a Concentration

2021 ◽  
Vol 13 (10) ◽  
pp. 2003
Author(s):  
Daeyong Jin ◽  
Eojin Lee ◽  
Kyonghwan Kwon ◽  
Taeyun Kim
Keyword(s):  
Deep Learning ◽  
Chlorophyll A ◽  
Hydrodynamic Model ◽  
Ocean Color ◽  
Learning Model ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Temporal And Spatial Distribution ◽  
Deep Learning Model ◽  
Satellite Ocean Color

In this study, we used convolutional neural networks (CNNs)—which are well-known deep learning models suitable for image data processing—to estimate the temporal and spatial distribution of chlorophyll-a in a bay. The training data required the construction of a deep learning model acquired from the satellite ocean color and hydrodynamic model. Chlorophyll-a, total suspended sediment (TSS), visibility, and colored dissolved organic matter (CDOM) were extracted from the satellite ocean color data, and water level, currents, temperature, and salinity were generated from the hydrodynamic model. We developed CNN Model I—which estimates the concentration of chlorophyll-a using a 48 × 27 sized overall image—and CNN Model II—which uses a 7 × 7 segmented image. Because the CNN Model II conducts estimation using only data around the points of interest, the quantity of training data is more than 300 times larger than that of CNN Model I. Consequently, it was possible to extract and analyze the inherent patterns in the training data, improving the predictive ability of the deep learning model. The average root mean square error (RMSE), calculated by applying CNN Model II, was 0.191, and when the prediction was good, the coefficient of determination (R2) exceeded 0.91. Finally, we performed a sensitivity analysis, which revealed that CDOM is the most influential variable in estimating the spatiotemporal distribution of chlorophyll-a.

scholarly journals Detection and Analysis of the Causes of Intensive Harmful Algal Bloom in Kamchatka Based on Satellite Data

2021 ◽  
Vol 9 (10) ◽  
pp. 1092
Author(s):  
Valery Bondur ◽  
Viktor Zamshin ◽  
Olga Chvertkova ◽  
Ekaterina Matrosova ◽  
Vasilisa Khodaeva
Keyword(s):  
Chlorophyll A ◽  
Suspended Matter ◽  
Algal Bloom ◽  
Harmful Algal Bloom ◽  
Simulated Data ◽  
Region Of Interest ◽  
Chlorophyll A Concentration ◽  
Ocean Level ◽  
Radar Satellite ◽  
Sentinel 2A

In this paper, the causes of the anomalous harmful algal bloom which occurred in the fall of 2020 in Kamchatka have been detected and analyzed using a long-term time series of heterogeneous satellite and simulated data with respect to the sea surface height (HYCOM) and temperature (NOAA OISST), chlorophyll-a concentration (MODIS Ocean Color SMI), slick parameters (SENTINEL-1A/B), and suspended matter characteristics (SENTINEL-2A/B, C2RCC algorithm). It has been found that the harmful algal bloom was preceded by temperature anomalies (reaching 6 °C, exceeding the climatic norm by more than three standard deviation intervals) and intensive ocean level variability followed by the generation of vortices, mixing water masses and providing nutrients to the upper photic layer. The harmful algal bloom itself was manifested in an increase in the concentration of chlorophyll-a, its average monthly value for October 2020 (bloom peak) approached 15 mg/m3, exceeding the climatic norm almost four-fold for the region of interest (Avacha Gulf). The zones of accumulation of a large amount of biogenic surfactant films registered in radar satellite imagery correlate well with the local regions of the highest chlorophyll-a concentration. The harmful bloom was influenced by river runoff, which intensively brought mineral and biogenic suspensions into the marine environment (the concentration of total suspended matter within the plume of the Nalycheva River reached 10 mg/m3 and more in 2020), expanding food resources for microalgae.

Exploration of the antioxidant system and photosynthetic system of a marine algicidal Bacillus and its effect on four harmful algal bloom species

2016 ◽  
Vol 62 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Shaoling Hou ◽  
Wanjiao Shu ◽  
Shuo Tan ◽  
Ling Zhao ◽  
Pinghe Yin
Keyword(s):  
Antioxidant System ◽  
Algal Bloom ◽  
Harmful Algal Bloom ◽  
Skeletonema Costatum ◽  
Algal Species ◽  
Photosynthetic System ◽  
Genes Expression ◽  
Total Antioxidant ◽  
Bacterium Strain ◽  
Harmful Algal Bloom Species

A novel marine bacterium, strain B1, initially showed 96.4% algicidal activity against Phaeocystis globosa. Under this situation, 3 other harmful algal species (Skeletonema costatum, Heterosigma akashiwo, and Prorocentrum donghaiense) were chosen to study the algicidal effects of strain B1, and the algicidal activities were 91.4%, 90.7%, and 90.6%, respectively. To explore the algicidal mechanism of strain B1 on these 4 harmful algal species, the characteristics of the antioxidant system and photosynthetic system were studied. Sensitivity to strain B1 supernatant, enzyme activity, and gene expression varied with algal species, while the algicidal patterns were similar. Strain B1 supernatant increased malondialdehyde contents; decreased chlorophyll a contents; changed total antioxidant and superoxide dismutase activity; and restrained psbA, psbD, and rbcL genes expression, which eventually resulted in the algal cells death. The algicidal procedure was observed using field emission scanning electron microscopy, which indicated that algal cells were lysed and cellular substances were released. These findings suggested that the antioxidant and photosynthetic system of these 4 algal species was destroyed under strain B1 supernatant stress. This is the first report to explore and compare the mechanism of a marine Bacillus against harmful algal bloom species of covered 4 phyla.

scholarly journals Questiomycins, Algicidal Compounds Produced by the Marine Bacterium Alteromonas sp. D and Their Production Cue

Molecules ◽  
2019 ◽  
Vol 24 (24) ◽  
pp. 4522 ◽  
Author(s):  
Umetsu ◽  
Kanda ◽  
Imai ◽  
Sakai ◽  
Fujita
Keyword(s):  
Marine Bacterium ◽  
Algal Bloom ◽  
Close Contact ◽  
Harmful Algal Bloom ◽  
Marine Ecosystem ◽  
Algicidal Bacteria ◽  
Chattonella Antiqua ◽  
Algicidal Compounds ◽  
New Compounds ◽  
Cultivation Experiment

Questiomycin A (1) along with three new compounds, questiomycins C–E (2–4), were isolated from culture of Alteromonas sp. D, an algicidal marine bacterium, guided by algal lethality assay using the raphidophyte, Chattonella antiqua, one of the causative organisms of harmful algal bloom. The structures of 1–4 were assigned on the basis of their spectrometric and spectroscopic data. Compounds 1 to 4 exhibited algicidal activity against C. antiqua with LC50 values ranging from 0.18 to 6.37 M. Co-cultivation experiment revealed that 1 was produced only when the microalgae and the bacterium are in close contact, suggesting that some interactions between them trigger the biosynthesis of questiomycins. These results suggested that the algicidal bacteria such as Alteromonas sp. D can control microalgae chemically in marine ecosystem.

Harmful algal bloom (HAB) in the East Sea identified by the Geostationary Ocean Color Imager (GOCI)

Harmful Algae ◽  
2014 ◽  
Vol 39 ◽  
pp. 295-302 ◽  
Author(s):  
Jong-Kuk Choi ◽  
Jee-Eun Min ◽  
Jae Hoon Noh ◽  
Tai-Hyun Han ◽  
Suk Yoon ◽  
...  
Keyword(s):  
Algal Bloom ◽  
Harmful Algal Bloom ◽  
Ocean Color ◽  
East Sea

scholarly journals Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi-sensor ocean color and sea surface temperature products

2021 ◽  
Author(s):  
Hongyan Xi ◽  
Svetlana N. Losa ◽  
Antoine Mangin ◽  
Philippe Garnesson ◽  
Marine Bretagnon ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Chlorophyll A ◽  
Input Data ◽  
Ocean Color ◽  
Satellite Observations ◽  
Sea Surface ◽  
Chl A ◽  
Functional Types

<p>With the extensive use of ocean color (OC) satellite products, diverse algorithms have been developed in the past decades to observe the phytoplankton community structure in terms of functional types, taxonomic groups and size classes. There is a need to combine satellite observations and biogeochemical modelling to enable comprehensive phytoplankton groups time series data and predictions under the changing climate. A prerequisite for this is continuous long-term satellite observations from past and current OC sensors with quantified uncertainties are essential to ensure their application. Previously we have configured an approach, namely OLCI-PFT (v1), to globally retrieve total chlorophyll a concentration (TChl-a), and chlorophyll a concentration (Chl-a) of multiple phytoplankton functional types (PFTs). This algorithm is developed based on empirical orthogonal functions (EOF) using satellite remote sensing reflectance (Rrs) products from the GlobColour archive (https://www.globcolour.info/). The algorithm can be applied to both, merged OC products and Sentinel 3A OLCI data. Global PFT Chl-a products of OLCI-PFT v1 are available on CMEMS under Ocean Products since July 2020. Lately we have updated the approach and established the OLCI-PFT v2 by including sea surface temperature (SST) as input data. The updated version delivers improved global products for the aforementioned PFT quantities. The per-pixel uncertainty of the retrieved TChl-a and PFT Chl-a products is estimated and validated by taking into account the uncertainties from both input data (satellite Rrs and SST) and model parameters through Monte Carlo simulations and analytical error propagation. The uncertainty of the OLCI-PFT products v2 was assessed on a global scale. For PFT Chl-a products this has been done for the first. The uncertainty of OLCI-PFT v2 TChl-a product is in general much lower than that of the TChl-a product generated in the frame of the ESA Ocean Colour Climate Change Initiative project (OC-CCI). The OLCI-PFT algorithm v1 and v2 have also been further adapted to use a merged MODIS-VIRRS input. Good consistency has been found between the OLCI-PFT products derived from using input data from the different OC sensors. This sets the ground to realize long-term continuous satellite global PFT products from OLCI-PFT. Satellite PFT uncertainty, as provided for our products, is essential to evaluate and improve coupled ecosystem-ocean models which simulate PFTs, and furthermore can be used to improve these models directly via data assimilation.</p>

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