Using geostationary satellite ocean color data and superpixel to map the diurnal dynamics of water transparency in the eastern China seas

Ding, Xiaosong; Gong, Fang; Zhu, Qiankun; Li, Jiajia; Wang, Xiao; Bai, Ruofeng; Xu, Yuzhuang

doi:10.1016/j.ecolind.2022.109219

Cited by 8 publications

(4 citation statements)

References 41 publications

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“…The high-frequency observation characteristics of GOCI's 8 daily images provide us with convenient conditions to study the differences of retrieved transparency obtained based on various AC algorithms within a short time frame. Some researchers use GOCI data to analyze the temporal and spatial variation of transparency and its influencing factors in specific sea areas [50], [62]. For example, Zhou et al used GOCI data to analyze the hourly variation of transparency in Jiaozhou Bay, and analyzed the relationship between transparency with tide and solar zenial angle based on Zsd_lee_2015 [62].…”

Section: A Uncertainties Of Zsd Retrievalmentioning

confidence: 99%

“…The high-frequency observations of GOCI make it possible to analyze the hourly changes of marine geochemical parameters compared with the traditional polar orbit ocean color satellite. It is helpful to monitor the hourly changes in water quality, red tide, and green tide in offshore waters [50], [51], [52]. Jiaozhou Bay is a semi-closed shallow water bay located in Shandong Peninsula with an area of nearly 500 square kilometers.…”

Section: Introductionmentioning

confidence: 99%

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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: A Uncertainties Of Zsd Retrievalmentioning

confidence: 99%

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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“…They replaced the original 𝐾 𝑑 (490) estimation method using a weighted 𝐾 𝑑 (490) proposed by [177] combined by two 𝐾 𝑑 (490) estimation methods (one for clear and one for turbid water) and a weighted coefficient. [178] used the GOCI-derived Chla and SPM products to calculate 𝑎(𝜆) and 𝑏 𝑏 (𝜆) and then input them into the semi-analytical model proposed by [179] and [180] (denoted as 𝑆𝐷𝐷 𝐻𝑒 ) to retrieve SDD in the eastern China seas with Equation ( 23):…”

Section: Water Claritymentioning

confidence: 99%

A Systematic Review of the Application of GOCI to Water Quality Monitoring of Inland and Coastal Waters

Shao,

Wang,

Liu

et al. 2024

Preprint

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In recent decades, as eutrophication in inland and coastal waters (ICWs) has increased due to anthropogenic activities and global warming, so has the need for timely monitoring. Compared with traditional sampling and laboratory analysis methods, satellite remote sensing technology can provide macro-scale, low-cost, and near real-time water quality monitoring services. The Geostationary Ocean Color Imager (GOCI), integrated onboard the Communication Ocean and Meteorological Satellite (COMS) from the Republic of Korea, was the first geostationary ocean color observation satellite and was operational from April 1, 2011 to March 31, 2021. Over ten years, GOCI has observed oceans, coastal waters, and inland waters within its 2,500 km×2,500 km target area centered on the Korean Peninsula. The most attractive feature of GOCI, compared with other commonly used watercolor sensors, was its high temporal resolution (1h, eight times daily from 0 UTC to 7 UTC), providing the opportunity to monitor the quality of ICWs, where optical properties can change rapidly throughout the day. This systematic review aims to comprehensively review GOCI features and applications in ICWs, analyzing progress in atmospheric correction algorithms and water quality monitoring. Analyzing 123 articles from the Web of Science and China National Knowledge Infrastructure (CNKI) through a bibliometric quantitative approach, we examined GOCI’s strength and performance with different processing methods. These articles reveal that GOCI played an essential role in monitoring the ecological health of ICWs in its observation area in East Asia. GOCI has led the way to a new era of geostationary ocean satellites, providing new technical means for monitoring water quality in oceans, coastal zones, and inland lakes. We also discuss the challenges encountered by geostationary satellites in monitoring water quality and provide suggestions for improvements.

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“…They replaced the original K d (490) estimation method using a weighted K d (490) proposed by Mao [176] combined with two K d (490) estimation methods (one for clear and one for turbid water) and a weighted coefficient. Ding [177] used the GOCI-derived Chla and SPM products to calculate a(λ) and b b (λ) and then input them into the semi-analytical model proposed by He [178] and He [179] (denoted as SDD He ) to retrieve SDD in the eastern Chinese seas with Equation (23).…”

Section: Water Claritymentioning

confidence: 99%

A Systematic Review of the Application of the Geostationary Ocean Color Imager to the Water Quality Monitoring of Inland and Coastal Waters

Shao,

Wang,

Liu

et al. 2024

Remote Sensing

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In recent decades, eutrophication in inland and coastal waters (ICWs) has increased due to anthropogenic activities and global warming, thus requiring timely monitoring. Compared with traditional sampling and laboratory analysis methods, satellite remote sensing technology can provide macro-scale, low-cost, and near real-time water quality monitoring services. The Geostationary Ocean Color Imager (GOCI), aboard the Communication Ocean and Meteorological Satellite (COMS) from the Republic of Korea, marked a significant milestone as the world’s inaugural geostationary ocean color observation satellite. Its operational tenure spanned from 1 April 2011 to 31 March 2021. Over ten years, the GOCI has observed oceans, coastal waters, and inland waters within its 2500 km × 2500 km target area centered on the Korean Peninsula. The most attractive feature of the GOCI, compared with other commonly used water color sensors, was its high temporal resolution (1 h, eight times daily from 0 UTC to 7 UTC), providing an opportunity to monitor ICWs, where their water quality can undergo significant changes within a day. This study aims to comprehensively review GOCI features and applications in ICWs, analyzing progress in atmospheric correction algorithms and water quality monitoring. Analyzing 123 articles from the Web of Science and China National Knowledge Infrastructure (CNKI) through a bibliometric quantitative approach, we examined the GOCI’s strength and performance with different processing methods. These articles reveal that the GOCI played an essential role in monitoring the ecological health of ICWs in its observation coverage (2500 km × 2500 km) in East Asia. The GOCI has led the way to a new era of geostationary ocean satellites, providing new technical means for monitoring water quality in oceans, coastal zones, and inland lakes. We also discuss the challenges encountered by Geostationary Ocean Color Sensors in monitoring water quality and provide suggestions for future Geostationary Ocean Color Sensors to better monitor the ICWs.

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Using geostationary satellite ocean color data and superpixel to map the diurnal dynamics of water transparency in the eastern China seas

Cited by 8 publications

References 41 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

A Systematic Review of the Application of GOCI to Water Quality Monitoring of Inland and Coastal Waters

A Systematic Review of the Application of the Geostationary Ocean Color Imager to the Water Quality Monitoring of Inland and Coastal Waters

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