Hourly turbidity monitoring using Geostationary Ocean Color Imager fluorescence bands

Amin, Ruhul; Shulman, Igor

doi:10.1117/1.jrs.9.096024

Cited by 5 publications

(5 citation statements)

References 34 publications

(58 reference statements)

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“…GOCIs produce eight images at a 500 m spatial resolution and hourly interval from 8:15 a.m. to 3:45 p.m. local time every day, and the images cover eight spectral bands with blue to near-infrared wavelengths [22]. Due to their wide coverage and high spatiotemporal resolution, the GOCI data have been used to study the optical characteristics of water [21,23,24], ocean surface currents [25][26][27][28], biological features of waters [29][30][31], water quality [32][33][34], harmful algal bloom [35,36], sea surface salinity [37], and sea fog [38]. However, these high-quality data have not yet been applied to the characteristics of Bohai Sea ice, where fast-changing first-year ice poses challenges for traditional satellites.…”

Section: Bandmentioning

confidence: 99%

Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data

Huang

Shao

et al. 2019

Sustainability

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Satellite remote sensing data, such as moderate resolution imaging spectroradiometers (MODIS) and advanced very high-resolution radiometers (AVHRR), are being widely used to monitor sea ice conditions and their variability in the Bohai Sea, the southernmost frozen sea in the Northern Hemisphere. Monitoring the characteristics of the Bohai Sea ice can provide crucial information for ice disaster prevention for marine transportation, oil field operation, and regional climate change studies. Although these satellite data cover the study area with fairly high spatial resolution, their typically limited cloudless images pose serious restrictions for continuous observation of short-term dynamics, such as sub-seasonal changes. In this study, high spatiotemporal resolution (500 m and eight images per day) geostationary ocean color imager (GOCI) data with a high proportion of cloud-free images were used to monitor the characteristics of the Bohai Sea ice, including area and thickness. An object-based feature extraction method and an albedo-based thickness inversion model were used for estimating sea ice area and thickness, respectively. To demonstrate the efficacy of the new dataset, a total of 68 GOCI images were selected to analyze the evolution of sea ice area and thickness during the winter of 2012–2013 with severe sea ice conditions. The extracted sea ice area was validated using Landsat Thematic Mapper (TM) data with higher spatial resolution, and the estimated sea ice thickness was found to be consistent with in situ observation results. The entire sea ice freezing–melting processes, including the key events such as the day with the maximum ice area and the first and last days of the frozen season, were better resolved by the high temporal-resolution GOCI data compared with MODIS or AVHRR data. Both characteristics were found to be closely correlated with cumulative freezing/melting degree days. Our study demonstrates the applicability of the GOCI data as an improved dataset for studying the Bohai Sea ice, particularly for purposes that require high temporal resolution data, such as sea ice disaster monitoring.

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Section: Bandmentioning

confidence: 99%

Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data

Huang

Shao

et al. 2019

Sustainability

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“…The nflh algorithm, for example, uses 748 nm for Band 3, which is over 60 nm away from the fluorescence peak. Such a distant anchor point works reasonably well for average Chlorophyll waters (Figure 3A), but produces an order of magnitude higher nflh under high suspended sediment load with no fluorescence peak (Figure 3B) (e.g., Zhao and Ghedira, 2014;Amin and Shulman, 2015).…”

Section: Band Mathmentioning

confidence: 74%

Living up to the Hype of Hyperspectral Aquatic Remote Sensing: Science, Resources and Outlook

Dierssen

Ackleson

Joyce

et al. 2021

Front. Environ. Sci.

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Intensifying pressure on global aquatic resources and services due to population growth and climate change is inspiring new surveying technologies to provide science-based information in support of management and policy strategies. One area of rapid development is hyperspectral remote sensing: imaging across the full spectrum of visible and infrared light. Hyperspectral imagery contains more environmentally meaningful information than panchromatic or multispectral imagery and is poised to provide new applications relevant to society, including assessments of aquatic biodiversity, habitats, water quality, and natural and anthropogenic hazards. To aid in these advances, we provide resources relevant to hyperspectral remote sensing in terms of providing the latest reviews, databases, and software available for practitioners in the field. We highlight recent advances in sensor design, modes of deployment, and image analysis techniques that are becoming more widely available to environmental researchers and resource managers alike. Systems recently deployed on space- and airborne platforms are presented, as well as future missions and advances in unoccupied aerial systems (UAS) and autonomous in-water survey methods. These systems will greatly enhance the ability to collect interdisciplinary observations on-demand and in previously inaccessible environments. Looking forward, advances in sensor miniaturization are discussed alongside the incorporation of citizen science, moving toward open and FAIR (findable, accessible, interoperable, and reusable) data. Advances in machine learning and cloud computing allow for exploitation of the full electromagnetic spectrum, and better bridging across the larger scientific community that also includes biogeochemical modelers and climate scientists. These advances will place sophisticated remote sensing capabilities into the hands of individual users and provide on-demand imagery tailored to research and management requirements, as well as provide critical input to marine and climate forecasting systems. The next decade of hyperspectral aquatic remote sensing is on the cusp of revolutionizing the way we assess and monitor aquatic environments and detect changes relevant to global communities.

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“…Water clarity is a critical parameter for the optical properties of ICWs [146,[164][165][166][167], vital for characterizing the turbidity and absorption/scattering of light by water bodies. It directly relates to the inherent optical properties of waterbodies, such as Chla, SPM, and phytoplankton biomass [168][169][170][171][172][173].…”

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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Hourly turbidity monitoring using Geostationary Ocean Color Imager fluorescence bands

Cited by 5 publications

References 34 publications

Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data

Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data

Living up to the Hype of Hyperspectral Aquatic Remote Sensing: Science, Resources and Outlook

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

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