Estimation of Hourly Sea Surface Salinity in the East China Sea Using Geostationary Ocean Color Imager Measurements

Kim, Dae‐Won; Park, Young-Je; Jeong, Jin-Yong; Jo, Young‐Heon

doi:10.3390/rs12050755

Cited by 22 publications

(28 citation statements)

References 43 publications

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“…For the MJR estuary, direct observation suggested an excellent linear relationship between SSS and CDOM absorption measured in m −1 at 280 nm wavelength (Yang et al., 2019). Compared with other complex deep learning approaches (Kim et al., 2020), this linear regression method is easier to implement and more robust to extrapolate. For the wavelength of 400 nm, we also measured and regressed the linear relation (SSS = 32.5–28.7*CDOM 400nm , R 2 = 0.77).…”

Section: Methodsmentioning

confidence: 99%

“…GOCI is the first geostationary ocean color satellite launched on 26 June 2010. It has become increasingly popular due to its high temporal resolution, providing various studies of monitoring coastal environments (Kim & Hong, 2019), including the indirect estimation of SSS in the estuarine region (Kim et al., 2020). It monitors the Northeast Asian region every hour from 00:15 to 07:45 GMT (8:15 to 15:15 local time) at 500‐m spatial resolution (Ryu et al., 2012).…”

Section: Methodsmentioning

confidence: 99%

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Data‐Driven Method With Numerical Model: A Combining Framework for Predicting Subtropical River Plumes

Wang

Jiang

et al. 2022

JGR Oceans

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Numerical models are of fundamental usage for estuarine and coastal sciences. Although numerical simulations are widely applied, analyzing and improving them are often challenging tasks given their large volume and huge parameter space. In this study, a novel data‐driven framework is introduced to study the Minjiang River Plume (MJRP). The framework combines Self‐Organizing Map (SOM) clustering with a Hidden Markov Model (HMM). A three‐dimensional Regional Ocean Model System for MJRP is first configurated with realistic atmospheric, oceanic, and riverine forcings. By applying SOM clustering to the modeled sea surface salinity (SSS) with ∼2,000 2‐day averaged records from 2010 to 2020, we identify six major patterns of MJRP. Each pattern exhibits distinct circulation and plume structures. These MJRP patterns contain not only seasonal signals, but also rich short‐term variabilities driven by the riverine inputs and oceanic dynamics. Then, the SOM‐HMM method was applied to predict the future of the hidden state (i.e., patterns of MJRP) from the observable states (wind and river runoff). With a hypothetic SSS product from a geostationary satellite as the ground truth, we show that the SOM‐HMM method can predict MJRP patterns considerably high prediction accuracy and computational efficiency. Further, these patterns were translated back to SSS with high forecast skills. Combining a conventional numerical model with a data‐driven method, this approach can be promisingly applied in the short‐term marine forecast to support the utilization and management of other estuaries.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Data‐Driven Method With Numerical Model: A Combining Framework for Predicting Subtropical River Plumes

Wang

Jiang

et al. 2022

JGR Oceans

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“…같은 대륙붕 해역에서 수행된 관측선 및 계류 관측의 경우 에는 정확도와 관측 자료의 처리 절차에서 다소 문제가 있 으며 (Oh et al, 2006;Kim et al 2020;Park et al 2020 (Ha et al, 2019;Kim et al 2019;Byun et al, 2021). 수온과 염분을 관측하기 위해 이어도 해양과학 기지는 표층(3m), 중층(20.5m), 저층(38m), 그리고 가거초와 소청초 해양과학기지는 표층(3m)에 Aanderaa사의 CT3919 전도도-수온(Conductivity-Temperature, CT) 센서를 설치하 여 운영하고 있다.…”

Section: 이재익⋅이수찬⋅정종민⋅민용침⋅정진용⋅김용선unclassified

Salinity drift prevention experiments in the Korea ocean research stations and suggestions for high quality salinity observation

Lee¹,

Lee²,

Jeong³

et al. 2021

J Coast Disaster Prev

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The importance of salinity has been highlighted to cope with climate changes and disaster prevention. The salinity of accuracy up to 0.005 is normally required in an open ocean to understand various oceanic and climatic phenomena; however, the reliability of salinity measured on the coast and open seas around Korea was low due to the lack of a standardized observation system and post-processing of quality verification. Korea Ocean Research Stations (KORS) has been producing salinity time series since 2003 through the Aanderaa conductivity-temperature (CT) 3919 inductive sensors, which have an advantage of on-site maintenance but tend to drift toward a lower conductivity because of biological attachments to the sensor. This study applied copper taping and UV light exposure techniques to the sensors and then compared its salinity measurements with RBR CTD mooring observations and SeaBird19 CTD profiles to assess a biofouling effect on salinity observations. This experiment shows that the salinity from the CT sensor without biofouling prevention starts to drift in a week, particularly for a surface sensor. This biofouling induced the decrease of salinity up to 10 in a month. The copper taping methodology efficiently suppressed the biological attachment but disturbed an electromagnetic field around the sensor, thus resulting in unrealistic salinity values. When UV light was periodically exposed at a distance of about 5 cm away from the CT sensor, relatively stable salinity could be observed without significant drift at least in two months. Besides, the SBE37 CTD, an electrode-type sensor, seems to be relatively free from biofouling but has difficulties in sensor maintenance and a sensor calibration process. Our results underline a double installation of salinity observation equipment with UV light exposure. In addition, the pre-calibration of a CT(D) sensor and post-verification should be included in a standard procedure for high-quality salinity measurement.

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“…Low-salinity water (LSW) plumes in the East China Sea in the summer are derived from the Changjiang Diluted Water, which extends to the south of the Korean Peninsula by wind forcing and influences the marine environment in the coastal region, leading to extensive damage to aquaculture. It also affects the optical characteristics of the surface water between the Changjiang River mouth and Jeju Island [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

The Applicability of the Geostationary Ocean Color Imager to the Mapping of Sea Surface Salinity in the East China Sea

et al. 2021

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During the summer season, low-salinity water (LSW) inputs from the Changjiang River are observed as filamentous or lens-like features in the East China Sea. Sea surface salinity (SSS) is an important factor in ocean science, and is used to estimate oceanic carbon fluxes, trace red tides, and calculate other physical processes at the surface. In this study, a proxy was developed using remote sensing reflectance (Rrs) from the Geostationary Ocean Color Imager (GOCI) centered at 490 nm (band 3), 555 nm (band 4), 660 nm (band 5), and 680 nm (band 6), and salinity (data from summer cruises during the period of 2011–2016). It was then validated to map LSW plumes in the East China Sea. The GOCI-derived surface salinity was determined by the empirical relationships between Rrs at the four bands and in situ wave glider SSS data (August 2016), and was validated with synchronous in situ hydrographic SSS data (August 2011, 2012, 2013, and 2016). The GOCI-derived SSS was considered reliable in terms of the validation with the in situ measurement with a high coefficient of determination along with a low RMSE (R2 = 0.803, RMSE = 0.914, N = 21), and in comparisons with two previous models that were used to derive SSS in the East China Sea. The GOCI-derived SSS was successfully used to examine time-series variations on diurnal and daily scales, and the effects of a typhoon in terms of marine physical and biological properties in combination with the chlorophyll-a concentration and sea surface temperature.

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Estimation of Hourly Sea Surface Salinity in the East China Sea Using Geostationary Ocean Color Imager Measurements

Cited by 22 publications

References 43 publications

Data‐Driven Method With Numerical Model: A Combining Framework for Predicting Subtropical River Plumes

Data‐Driven Method With Numerical Model: A Combining Framework for Predicting Subtropical River Plumes

Salinity drift prevention experiments in the Korea ocean research stations and suggestions for high quality salinity observation

The Applicability of the Geostationary Ocean Color Imager to the Mapping of Sea Surface Salinity in the East China Sea

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