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

Choi, Jong-Kuk; Min, Jee-Eun; Noh, Jae Hoon; Han, Taek Hee; Yoon, Seong‐Min; Park, Young Je; Moon, Jai-Woon; Ahn, Jae-Seong; Ahn, Sung Min; Park, Jaehun

doi:10.1016/j.hal.2014.08.010

Cited by 64 publications

(37 citation statements)

References 35 publications

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“…Unlike polar-orbiting ocean color satellites (e.g., SeaWiFs and MODIS), GOCI has higher temporal resolution which can capture diurnal changes. Plenty of researches showed applications in suspended particulate materials (Ge et al, 2015;He et al, 2013;Pan et al, 2018) and phytoplankton (Choi et al, 2014;Lee et al, 2012;Lou & Hu, 2014). The atmospheric correction utilized in this study (i.e., KOSC standard in GDPS system) is the same as that in the researches of diurnal variation of turbidity fronts (Hu et al, 2016a), tidal currents (Hu et al, 2016b), and salinity (Liu et al, 2017).…”

Section: Diurnal Variation Of Pscsmentioning

confidence: 99%

In Situ and Satellite Observations of Phytoplankton Size Classes in the Entire Continental Shelf Sea, China

et al. 2018

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Phytoplankton size classes (PSCs) is of great significance for exploring marine ecological and biogeochemical processes. Remote sensing of PSCs has been successfully applied to open oceans; however, it is still quite limited for optically complex coastal oceans. In this study, the entire continental shelf sea of China including Bohai Sea (BS), Yellow Sea (YS), and East China Sea (ECS) characterized by distinctive turbid waters and impacted by plumes of large world‐class river (the Changjiang River) was taken as an example of turbid coastal ocean for remotely sensed spatial‐temporal distributions of PSCs. In situ data were collected from cruises during April to June in 2014 and an improved algorithm for PSCs retrieval was proposed. PSCs derived from GOCI (Geostationary Ocean Color Imager) images revealed that microplankton was dominant in the BS, the YS, and the nearshore ECS and nanoplankton distributed widely in the entire study area, while picoplankton mainly distributed in the offshore ECS in April, which was consistent with in situ investigation and related to environmental factors. Validation indicated that the improved algorithm provided a more accurate estimation of PSCs, with the root mean square error (RMSE) between estimated and measured size‐fractionated concentrations been 0.774, 0.257, and 0.142 mg m−3 for micro, nano, and picoplankton, respectively. Diurnal variations of PSCs were mainly affected by tidal currents and light intensity depending on different water types. These illustrated that remote sensed spatial distributions as well as diurnal variations of PSCs are effective in turbid continental shelf seas of China.

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Section: Diurnal Variation Of Pscsmentioning

confidence: 99%

In Situ and Satellite Observations of Phytoplankton Size Classes in the Entire Continental Shelf Sea, China

et al. 2018

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“…Because of the elevated NIR reflectance used to generate FCC images, these patches were detected as red features in the FCC images. Though the MODIS‐Aqua chlorophyll patterns (>2 mg m −3 ) are closely consistent with features in the FCC and SVD images, the chlorophyll product alone is not sufficient to distinguish between C. polykrikoides and N. scintillans blooms because of its positive statistically significant relationship with bloom density of these species [ Ahn and Shanmugam , ; Gomes et al ., ; Choi et al ., ]. In contrast, SVD demonstrated the potential to accurately map C. polykrikoides bloom from the N. scintillans blooms in coastal waters of Oman and Gulf waters.…”

Section: Resultsmentioning

confidence: 99%

“…Recent studies indicated that nutrient sources including upwelling and dust deposition cause regular outbreaks of high‐density C. polykrikoides blooms in coastal regions of the western Arabian Sea and Gulf of Oman [ Richlen et al ., ; Zhao and Ghedira , ]. Earlier studies reported that blooms of C. polykrikoides have increased in geographic extent, frequency, and duration in the world's ocean [ Shanmugam et al ., ; Kudela and Gobler , ; Rountos et al ., ] leading to mass mortalities of wild and formed fish in specific regional waters [ Ahn and Shanmugam , ; Shanmugam et al ., ; Mulholland et al ., ; Choi et al ., ; Wang and Wu , ]. Current approaches for detection of the C. polykrikoides blooms are largely based on the visual interpretation of the features in true color images, chlorophyll‐ a and FLH (fluorescence line height) products, red tide index, sea surface temperature (SST), or combination of these products [ Ahn and Shanmugam , ; Richlen et al ., ; Zhao and Ghedira , ].…”

Section: Introductionmentioning

confidence: 99%

An optical system for detecting and describing major algal blooms in coastal and oceanic waters around India

Gokul

Shanmugam

2016

JGR Oceans

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An optical system is developed with the aim to detect and monitor three major algal blooms (including harmful algal blooms ''HABs'') over ecologically relevant scales around India and to strengthen algal forecasting system. This system is designed to be capable of utilizing remote sensing, in situ, and radiative transfer techniques to provide species-specific data necessary for increasing capabilities of an algal forecasting system. With the ability to measure in-water optical properties by means of remote sensing and in situ observations, the optical system developed infers the desired phytoplankton signal from spectral distributions and utilize these data in a numerical classification technique to generate species-specific maps at given spatial and temporal scales. A simple radiative transfer model is adopted for this system to provide a means to optimally interpolate to regions with sparse in situ observation data and to provide a central component to generate in-water optical properties from remotely sensed data. For a given set of inherent optical properties along with surface and bottom boundary conditions, the optical system potentially provides researchers and managers coverage at different locations and depths for tracking algal blooms in the water column. Three major algal blooms focused here include Noctiluca scintillans/miliaris, Trichodesmium erythraeum, and Cochlodinium polykrikoides, which are recurring events in coastal and oceanic waters around India. Because satellite sensors provide a synoptic view of the ocean, both spatially and temporally, our initial efforts tested this optical system using several MODIS-Aqua images and ancillary data. Validation of the results with coincident in situ data obtained from either surface samples or depth samples demonstrated the robustness and potential utility of this approach, with an accuracy of 80-90% for delineating the presence of all three blooms in a heterogeneous phytoplankton community. Despite its limitation in detecting specific species during their prebloom phases and indicating whether a particular bloom is toxic or harmful, the proposed optical system will provide managers with the specific phytoplankton bloom maps to structure monitoring efforts and a powerful tool for studying the dynamics of algal blooms at various temporal and spatial scales.

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“…Such studies utilized polar-orbiting satellite imageries from the Advanced Very High Resolution Radiometer (e.g., Emery et al, 1986;Kelly & Strub, 1992;Vigan et al, 2000), Coastal Zone Color Scanner (e.g., Garcia & Robinson, 1989;Tokmakian et al, 1990), and Moderate Resolution Imaging Spectroradiometer (e.g., Crocker et al, 2007;Marcello et al, 2008). Since the world's first geostationary orbital ocean color sensor GOCI (Geostationary Ocean Color Imager) launched by Korea on 27 June 2010, research has been fruitful in the past few years due to its hourly observations (e.g., Choi et al, 2014;Huang et al, 2015;Ryu et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Improving Surface Current Estimation From Geostationary Ocean Color Imager Using Tidal Ellipse and Angular Limitation

Chen

Cao

et al. 2019

JGR Oceans

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Sea surface currents (SSCs) in coastal regions can be mapped from successive optical images observed by satellite. We implemented the maximum cross‐correlation (MCC) algorithm to Geostationary Ocean Color Imager‐derived total suspended matter in the East China Sea in this study. We developed a new data filtering method to improve the accuracy of SSC vector sequence using estimated tidal ellipse. The proposed method eliminated spurious vectors by considering direction order and suitable angular limitation. The obtained average velocity of SSC reached 0.72 m/s, which was close to the buoy‐measured average speed of 0.64 m/s. Compared with existing spurious vector eliminating method, the average angular error was improved significantly by 28–38% and the average relative magnitude error was improved by 3% in the case study.

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

Cited by 64 publications

References 35 publications

In Situ and Satellite Observations of Phytoplankton Size Classes in the Entire Continental Shelf Sea, China

In Situ and Satellite Observations of Phytoplankton Size Classes in the Entire Continental Shelf Sea, China

An optical system for detecting and describing major algal blooms in coastal and oceanic waters around India

Improving Surface Current Estimation From Geostationary Ocean Color Imager Using Tidal Ellipse and Angular Limitation

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