Spatiotemporal changes and influencing factors of water clarity in the Yellow Sea over the past 20 years

Yu, Dingfeng; Yang, Lei; Li, Yunzhou; Xiang, Jie; Zhao, Chongbin

doi:10.1016/j.marpolbul.2023.114904

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…Keivan Kabiri [17] proposed a method for the estimation of Zsd based on MODIS Kd(490) data and apply it in the northern Persian Gulf, the Strait of Hormuz, and the Gulf of Oman. Many other researchers have studied the spatial and temporal distributions of transparency in global oceans, and inland waters [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28]. However, few scholars discuss the influence of different atmospheric correction (AC) algorithms on the accuracy of retrieved transparency.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Transparency Retrieved From GOCI Under Four Different Atmospheric Correction Algorithms in Jiaozhou Bay and Qingdao Coastal Area

Liu,

Hu,

Tian

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Water transparency is one of the important parameters to describe the optical properties of seawater. It is a visual reflection of the degree of turbidity of seawater and the degree of absorption and scattering of light by seawater. First, this article verified and analyzed the suitability of the water transparency retrieval method proposed by Lee in 2015 (denoted as Zsd_lee_2015) for Jiaozhou Bay and Qingdao offshore waters by using the in-situ data. Second, the water transparency estimation results retrieved from Geostationary Ocean Color Imager (GOCI) using four atmospheric correction (AC) algorithms were compared and analyzed with in-situ data in the study area. The four AC algorithms are the standard AC algorithms of Korea Ocean Satellite Center (KOSC) GOCI GDPS1.3 and GDPS2.0, the standard near-infrared AC algorithm of NASA, and management unit of the North Sea mathematical models. At last, GOCI data on Mar 09, 2018, were used to analyze the differences in hourly changes of transparency caused by different AC algorithms in detail. The results show that the Zsd_lee_2015 model is suitable for the study area (R 2 >0.8, MAPE = 16.4%). The difference in transparency value caused by the AC algorithm cannot be ignored completely. Moreover, there are great differences in the moment when the maximum value of transparency is presented based on different AC algorithms, presented is basically the same, mostly occurring at 00:16. The results cast doubt on the previous conclusions about the hourly variations of biochemical parameters using GOCI because the hourly variations obtained under different AC algorithms are not completely consistent. Index Terms-Atmospheric correction (AC), geostationary ocean color imager (GOCI), hourly change, Jiaozhou bay and Qingdao coastal area, water transparency.

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Section: Introductionmentioning

confidence: 99%

Comparative Study on Transparency Retrieved From GOCI Under Four Different Atmospheric Correction Algorithms in Jiaozhou Bay and Qingdao Coastal Area

Liu,

Hu,

Tian

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…It covers an area of approximately 78,000 square kilometers and is known for its economic significance, hosting major ports and industrial centers [22]. The Yellow Sea is renowned for its rich fishing grounds, serves as a crucial habitat for marine species, and spans around 380,000 square kilometers [23,24]. Both seas suffer environmental challenges such as pollution, eutrophication, and overfishing, highlighting the need for sustainable management and conservation efforts [25].…”

Section: Introductionmentioning

confidence: 99%

Variations of Remote-Sensed Forel-Ule Index in the Bohai and Yellow Seas during 1997–2019

et al. 2023

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Water color, often quantified using the Forel-Ule Index (FUI), is a crucial parameter for assessing the water quality and ecological health of coastal waters. However, there is limited research on the spatiotemporal variations of FUI and the associated influencing factors in the Bohai and Yellow Seas. In this study, we utilized multi-sensor satellite datasets to retrieve monthly FUI products for the Bohai and Yellow Seas spanning the period from September 1997 to December 2019. Subsequently, we examined significant spatial disparities and variations across multiple timescales in the remotely sensed FUI time series. The climatological annual mean FUI map reveals a decreasing trend from nearshore to offshore regions, with similar spatial patterns observed in terms of overall and interannual FUI variability. The annual variations in wind field, sea surface temperature (SST), and ocean stratification play a key role in the seasonal dynamics of FUI by modulating the sediment resuspension process, resulting in low FUI values in summer and high FUI values in winter. Linear regression analysis of FUI anomaly indicates a long-term decreasing trend in FUI for the three bays of the Bohai Sea, while upward trends in FUI predominantly prevail in the central Yellow Sea. Factors related to interannual FUI variations, such as surface winds, SST, river outflow, rainfall, and anthropogenic activities, are qualitatively discussed. The findings of this study provide the first comprehensive evaluation of water color variations and their underlying mechanisms in the Bohai and Yellow Seas.

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The development research on marine ecological environment online monitoring equipment system in China

Yan,

Yunzhou,

Yumei

et al. 2024

Marine Pollution Bulletin

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Spatiotemporal changes and influencing factors of water clarity in the Yellow Sea over the past 20 years

Cited by 4 publications

References 34 publications

Comparative Study on Transparency Retrieved From GOCI Under Four Different Atmospheric Correction Algorithms in Jiaozhou Bay and Qingdao Coastal Area

Comparative Study on Transparency Retrieved From GOCI Under Four Different Atmospheric Correction Algorithms in Jiaozhou Bay and Qingdao Coastal Area

Variations of Remote-Sensed Forel-Ule Index in the Bohai and Yellow Seas during 1997–2019

The development research on marine ecological environment online monitoring equipment system in China

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