Monitoring the Dissipation of the Floating Green Macroalgae Blooms in the Yellow Sea (2007–2020) on the Basis of Satellite Remote Sensing

An, Dong; Yu, Dingfeng; Zheng, Xiaolong; Zhou, Yan; Meng, Lingheng; Xing, Qianguo

doi:10.3390/rs13193811

Cited by 12 publications

(8 citation statements)

References 43 publications

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“…ICWs, due to their high population density, are prone to ABs when certain meteorological conditions arise [86][87][88][89][90][91][92][93]. The impact of ABs, such as the reduction in dissolved oxygen and the release of toxins, presents major challenges to aquatic ecosystem management [9,[94][95][96].…”

Section: Algal Bloomsmentioning

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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Section: Algal Bloomsmentioning

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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“…This situation can lead to biased results derived from images with different resolutions (Kim et al, 2018). Image pairs over the same macroalgae region with the same acquisition dates were selected to investigate the consistency in the retrieved macroalgae coverage area (Xing et al, 2019;An et al, 2021). The areas derived from different image pairs were then compared.…”

Section: Consistency Of Macroalgae Area Detected From Multi-sensor Im...mentioning

confidence: 99%

“…Optical images, such as GOCI (Geostationary Ocean Color I m a g e r ) , M O D I S ( M o d e r a t e R e s o l u t i o n I m a g i n g Spectroradiometer), HJ-1A/1B (HuanJing-1A/1B), and GF-1 (GaoFen-1) are the primarily remote sensing images for monitoring MABs (Hu et al, 2010;Xing et al, 2011;Qiu and Lu, 2015;Xing et al, 2015;Xing and Hu, 2016;Zhang et al, 2016;Xing et al, 2019;An et al, 2021;Cui et al, 2022). However, during the MAB outbreaks, which occur in early June to early August, it is the rainy season in the Yellow Sea because of the influence of the East Asian summer monsoon (Feng et al, 1998).…”

mentioning

confidence: 99%

A simple method for estimating macroalgae area under clouds on MODIS imagery

Xing

et al. 2022

Front. Mar. Sci.

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The presence of clouds interferes with optical remote sensing monitoring of macroalgae blooms. To solve this problem, we propose a simple method for estimating macroalgae area under clouds (Area_cloud_GT) on MODIS imagery using the principle behind the lowpass filter. The method is based on a rectangle with clouds and eight identical adjacent rectangles surrounding it that contain macroalgae. The cloud rectangle is a central ‘pixel’ (Cloud) and the eight adjacent rectangles are ‘pixels’ GT1–GT8. The core operation is to calculate the central ‘pixel’ value, i.e., the macroalgae coverage rate in the Cloud rectangle. The macroalgae area detected by semi-simultaneous fine resolution images in the same region was taken as the ‘real’ value. A comparison of the estimation results and the ‘real’ value shown that the mean relative difference between them (MRD) was 30.09% when the time interval of the images within 10 minutes. When the time interval was over 3 hours, the MRD was more than 60%. The MRD increased significantly with increasing time interval because of the constant movement of the macroalgae and the limitations of the remote sensing image. The results indicate that this simple method is effective to a certain extent. These results can provide a reference for the quantitative analysis of green tide.

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“…Review of near-shore satellite derived bathymetry: Classification and account of five decades of coastal bathymetry research 99 39 46 Bagheri-Gavkosh et al [17] 2021 Land subsidence: A global challenge 91 66 74 Chaturvedi [18] 2021 Disaster management: Tsunami and remote sensing technology 31 0 0 Datta et al [19] 2021…”

Section: Introductionmentioning

confidence: 99%

“…Monitoring the spread of water hyacinth (Pontederia crassipes): Challenges and future developments 74 23 33 Gijsman et al [20] 2021…”

Section: Introductionmentioning

confidence: 99%

Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

Cavalli

2024

Remote Sensing

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Since 1971, remote sensing techniques have been used to map and monitor phenomena and parameters of the coastal zone. However, updated reviews have only considered one phenomenon, parameter, remote data source, platform, or geographic region. No review has offered an updated overview of coastal phenomena and parameters that can be accurately mapped and monitored with remote data. This systematic review was performed to achieve this purpose. A total of 15,141 papers published from January 2021 to June 2023 were identified. The 1475 most cited papers were screened, and 502 eligible papers were included. The Web of Science and Scopus databases were searched using all possible combinations between two groups of keywords: all geographical names in coastal areas and all remote data and platforms. The systematic review demonstrated that, to date, many coastal phenomena (103) and parameters (39) can be mapped and monitored using remote data (e.g., coastline and land use and land cover changes, climate change, and coastal urban sprawl). Moreover, the authors validated 91% of the retrieved parameters, retrieved from remote data 39 parameters that were mapped or monitored 1158 times (88% of the parameters were combined together with other parameters), monitored 75% of the parameters over time, and retrieved 69% of the parameters from several remote data and compared the results with each other and with available products. They obtained 48% of the parameters using different methods, and their results were compared with each other and with available products. They combined 17% of the parameters that were retrieved with GIS and model techniques. In conclusion, the authors addressed the requirements needed to more effectively analyze coastal phenomena and parameters employing integrated approaches: they retrieved the parameters from different remote data, merged different data and parameters, compared different methods, and combined different techniques.

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Monitoring the Dissipation of the Floating Green Macroalgae Blooms in the Yellow Sea (2007–2020) on the Basis of Satellite Remote Sensing

Cited by 12 publications

References 43 publications

A Systematic Review of the Application of the Geostationary Ocean Color Imager to the 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

A simple method for estimating macroalgae area under clouds on MODIS imagery

Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

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