Research on the dissipation of green tide and its influencing factors in the Yellow Sea based on Google Earth Engine

Li, Dongxue; Gao, Zhiqiang; Xu, Fuxiang

doi:10.1016/j.marpolbul.2021.112801

Cited by 27 publications

(8 citation statements)

References 49 publications

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“…Green tides MODIS (0.5-1 km) No [41] - [74,195,200] Macroalgae (1) Green - (1) This parameter was mapped and monitored together with other parameters; (2) hyperspectral data. (1) Coastal aquaculture ponds Sentinel-2 (10-20-60 m) No [212] [77-79] -Aquaculture (1) Coastline change Landsat (15-30 m) No - [213] [117,214] Aquaculture (1) Habitat Sentinel-1 (~10 m) No [165,215] -- Aquaculture (1) Habitat Sentinel-2 (10-20-60 m) No [215] --Aquaculture (1) Ecosystem services value Landsat (15-30 m) No --[216] Aquaculture (1) LU/LC change Landsat (15-30 m) --[217] Aquaculture (1) LU/LC change Aerial photos No [218] -Aquaculture (1) LU/LC change GaoFen5-HIS (30 m) (2) No -[219] -Aquaculture (1) LU/LC change Landsat (15-30 m) No - [218,220] [217] Aquaculture (1) LU Marine aquaculture ZY1-02D-HIS (30 m) (2) No -[222] -Aquaculture (1) Seaweed aquaculture HY-1C (50 m) No -[80] -Aquaculture (1) Seaweed aquaculture…”

Section: Macroalgaementioning

confidence: 99%

“…] -SST (1) Effects of extreme events -Yes [59,153] --SST (1) Fishing zones MODIS (0.5-1 km) Yes [303,382] --SST (1) Giant kelp NOAA Yes --[228] SST (1) Green tide AVHRR (5 km) Yes - [199] -SST (1) Green tide Landsat (15-30 m) No -- [200] SST (1) Green tide MODIS (0.5-1 km) No --[200] SST (1) Harmful algal bloom MODIS (0.5-1 km) Yes [304] [305]…”

Section: Parametermentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Macroalgaementioning

confidence: 99%

Section: Parametermentioning

confidence: 99%

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

Cavalli

2024

Remote Sensing

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“…Previous studies, including Li et al (2021b), have proposed various methods to integrate green tide datasets derived from optical and SAR images, aiming to enhance compatibility and extend the temporal sequences of daily green tide observations. Figure 6 illustrates simultaneously observed optical and SAR images alongside their corresponding green tide coverages (Fig.…”

Section: Integrating Daily Optical and Sar Productsmentioning

confidence: 99%

“…However, due to the lack of a comprehensive and scientific understanding of the green tide bloom mechanism, the root cause of the "jumping" changes in green algae coverage area remains elusive. Particularly when various factors are interdependent and mutually influencing each other (Jin et al, 2018;Li et al, 2021a and2021b), the absence of collaborative joint analysis impedes accurate elucidation of the critical response mechanism and interactions between environmental factors and green tide outbreaks. Hence, it is imperative to investigate the interplay between green tide occurrences and the environment by examining the correlation between environmental factors and outbreaks of green algae.…”

mentioning

confidence: 99%

Weekly Green Tide Mapping in the Yellow Sea with Deep Learning: Integrating Optical and SAR Ocean Imagery

Gao,

Guo,

2024

Preprint

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Abstract. Since 2008, the Yellow Sea has experienced a world's largest-scale marine disasters, known as the green tide, marked by the rapid proliferation and accumulation of large floating algae. Leveraging advanced AI models, namely AlgaeNet and GANet, this study comprehensively extracted and analyzed green tide occurrences using optical Moderate Resolution Imaging Spectroradiometer (MODIS) images and microwave Sentinel-1 Synthetic Aperture Radar (SAR) images. Most importantly, this study presents a continuous and seamless weekly average green tide coverage dataset with the resolution of 500 m, by integrating high precise daily optical and SAR data during each week during the green tide breakout. The uncertainty assessment of this weekly product shows it is completely consistent with the overall direct average of the daily product (R2=1 and RMSE=0). Additionally, the individual case verification in 2019 also shows that the weekly product conforms to the life pattern of green tide outbreaks and exhibits parabolic curve-like characteristics, with an low uncertainty (R2=0.89 and RMSE=275 km2).This weekly dataset offers reliable long-term data spanning 15 years, facilitating research in forecasting, climate change analysis, numerical simulation and disaster prevention planning in the Yellow Sea. The dataset is accessible through the Oceanographic Data Center, Chinese Academy of Sciences (CASODC), along with comprehensive reuse instructions provided at http://dx.doi.org/10.12157/IOCAS.20240410.002 (Gao et al., 2024).

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“…The study revealed the combined promoting effects of SST, nutrient pollution, and "nori" cultivation on green tide outbreaks, as well as the impacts of environmental factors like SST, sea surface salinity (SSS), and radiation [20]. SST and precipitation influence the dissipation time of green tides; higher temperatures speed up the dissipation process, while precipitation slows it down [21].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Monitoring of Green Tide Disaster Using MODIS and GF-1 Data: A Case Study in the Yellow Sea

Men,

Liu,

et al. 2023

JMSE

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Satellites with low-to-medium spatial resolution face challenges in monitoring the early and receding stages of green tides, while those with high spatial resolution tend to reduce the monitoring frequency of such phenomena. This study aimed to observe the emergence, evolution, and migratory patterns of green tides. We integrated GF-1 and MODIS imagery to collaboratively monitor the green tide disaster in the Yellow Sea during 2021. Initially, a linear regression model was employed to adjust the green tide coverage area as captured using MODIS imagery. We jointly observed the distribution range, drift path, and coverage area of the green tide and analyzed the drift path in coordination with offshore wind field and flow field data. Furthermore, we investigated the influence of SST, SSS, and rainfall on the 2021 green tide outbreak. The correlations calculated between SST, SSS, and precipitation with the changes in the area of the green tide were 0.43, 0.76, and 0.48, respectively. Our findings indicate that the large-scale green tide outbreak in 2021 may be associated with several factors. An increase in SST and SSS during the initial phase of the green tide established the essential conditions, while substantial rainfall during its developmental stage provided favorable conditions. Notably, the SSS exhibited a close association with the outbreak of the green tide.

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Research on the dissipation of green tide and its influencing factors in the Yellow Sea based on Google Earth Engine

Cited by 27 publications

References 49 publications

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

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

Weekly Green Tide Mapping in the Yellow Sea with Deep Learning: Integrating Optical and SAR Ocean Imagery

Remote Sensing Monitoring of Green Tide Disaster Using MODIS and GF-1 Data: A Case Study in the Yellow Sea

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