Remote-Sensing Monitoring of Green Tide and Its Drifting Trajectories in Yellow Sea Based on Observation Data of Geostationary Ocean Color Imager

2020 ◽  
Vol 40 (3) ◽  
pp. 0301001
Author(s):  
陈莹 Chen Ying ◽  
孙德勇 Sun Deyong ◽  
张海龙 Zhang Hailong ◽  
王胜强 Wang Shengqiang ◽  
丘仲锋 Qiu Zhongfeng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Yellow Sea ◽  
Ocean Color ◽  
Green Tide ◽  
Observation Data ◽  
Remote Sensing Monitoring

scholarly journals A Lagrangian-based Floating Macroalgal Growth and Drift Model (FMGDM v1.0): application in the green tides of the Yellow Sea

2021 ◽  
Author(s):  
Fucang Zhou ◽  
Jianzhong Ge ◽  
Dongyan Liu ◽  
Pingxing Ding ◽  
Changsheng Chen
Keyword(s):  
Remote Sensing ◽  
Yellow Sea ◽  
Surface Wind ◽  
Ocean Model ◽  
Biological Data ◽  
The Yellow Sea ◽  
Green Tide ◽  
Observation Data ◽  
Green Tides ◽  
Drift Model

Abstract. Massive floating macroalgal blooms in the ocean have had an array of ecological consequences; thus, tracking their drifting pattern and predicting their biomass are important for their effective management. However, a high-resolution ecological dynamics model is lacking. In this study, a physical–ecological model, Floating Macroalgal Growth and Drift Model (FMGDM v1.0), was developed to determine the dynamic growth and drift pattern of floating macroalgal, based on the tracking, replication and extinction of Lagrangian particles. The position, velocity, quantity and represented biomass of particles are updated synchronously between the tracking module and the ecological module. The former is driven by ocean flows and sea surface wind, while the latter is controlled by the temperature, salinity, and irradiation. Based on the hydrodynamic models of the Finite-Volume Community Ocean Model and parameterized using a culture experiment of Ulva prolifera, which caused the largest bloom worldwide of the green tide in the Yellow Sea, China, this model was applied to simulate the green tides around the Yellow Sea in 2014 and 2015. The simulation result, distribution and biomass of green tides, was validated using remote sensing observation data and reasonably modeled the entire process of green tide bloom and its extinction from early spring to late summer. Given the prescribed spatial initialization from remote sensing observation, the model could provide accurate short-term (7–8 d) predictions of the spatial and temporal developments of the green tide. With the support of the hydrodynamic model and biological data of macroalgae, this model can forecast floating macroalgae blooms in other regions.

scholarly journals The Lagrangian-based Floating Macroalgal Growth and Drift Model (FMGDM v1.0): application to the Yellow Sea green tide

2021 ◽  
Vol 14 (10) ◽  
pp. 6049-6070
Author(s):  
Fucang Zhou ◽  
Jianzhong Ge ◽  
Dongyan Liu ◽  
Pingxing Ding ◽  
Changsheng Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Yellow Sea ◽  
Surface Wind ◽  
Late Summer ◽  
Ocean Model ◽  
The Yellow Sea ◽  
Green Tide ◽  
Observation Data ◽  
Macroalgal Blooms ◽  
Drift Model

Abstract. Massive floating macroalgal blooms in the ocean result in many ecological consequences. Tracking their drifting pattern and predicting their biomass are essential for effective marine management. In this study, a physical–ecological model, the Floating Macroalgal Growth and Drift Model (FMGDM), was developed. Based on the tracking, replication, and extinction of Lagrangian particles, FMGDM is capable of determining the dynamic growth and drift pattern of floating macroalgae, with the position, velocity, quantity, and represented biomass of particles being updated synchronously between the tracking and the ecological modules. The particle tracking is driven by ocean flows and sea surface wind, and the ecological process is controlled by the temperature, irradiation, and nutrients. The flow and turbulence fields were provided by the unstructured grid Finite-Volume Community Ocean Model (FVCOM), and biological parameters were specified based on a culture experiment of Ulva prolifera, a phytoplankton species causing the largest worldwide bloom of green tide in the Yellow Sea, China. The FMGDM was applied to simulate the green tide around the Yellow Sea in 2014 and 2015. The model results, e.g., the distribution, and biomass of the green tide, were validated using the remote-sensing observation data. Given the prescribed spatial initialization from remote-sensing observations, the model was robust enough to reproduce the spatial and temporal developments of the green tide bloom and its extinction from early spring to late summer, with an accurate prediction for 7–8 d. With the support of the hydrodynamic model and biological macroalgae data, FMGDM can serve as a model tool to forecast floating macroalgal blooms in other regions.

Remote sensing monitoring of green tide in the Yellow Sea in 2015 based on GF-1 WFV data

2016 ◽  
Author(s):  
Xiangyu Zheng ◽  
Zhiqiang Gao ◽  
Jicai Ning ◽  
Fuxiang Xu ◽  
Chaoshun Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Yellow Sea ◽  
The Yellow Sea ◽  
Green Tide ◽  
Remote Sensing Monitoring

scholarly journals Remote sensing of early-stage green tide in the Yellow Sea for floating-macroalgae collecting campaign

2018 ◽  
Vol 133 ◽  
pp. 150-156 ◽  
Author(s):  
Qianguo Xing ◽  
Lingling Wu ◽  
Liqiao Tian ◽  
Tingwei Cui ◽  
Lin Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Yellow Sea ◽  
Early Stage ◽  
The Yellow Sea ◽  
Green Tide

scholarly journals Sampling Uncertainties of Long-Term Remote-Sensing Suspended Sediments Monitoring over China’s Seas: Impacts of Cloud Coverage and Sediment Variations

2020 ◽  
Vol 12 (12) ◽  
pp. 1945
Author(s):  
Liqiao Tian ◽  
Xianghan Sun ◽  
Jian Li ◽  
Qianguo Xing ◽  
Qingjun Song ◽  
...  
Keyword(s):  
Remote Sensing ◽  
East China Sea ◽  
Yellow Sea ◽  
Bohai Sea ◽  
Ocean Color ◽  
Suspended Sediments ◽  
The East China Sea ◽  
Cloud Coverage ◽  
Satellite Sensor

Satellite-based ocean color sensors have provided an unprecedentedly large amount of information on ocean, coastal and inland waters at varied spatial and temporal scales. However, observations are often adversely affected by cloud coverage and other poor weather conditions, like sun glint, and this influences the accuracy associated with long-term monitoring of water quality parameters. This study uses long-term (2013–2017) and high-frequency (eight observations per day) datasets from the Geostationary Ocean Color Imager (GOCI), the first geostationary ocean color satellite sensor, to quantify the cloud coverage over China’s seas, the resultant interrupted observations in remote sensing, and their impacts on the retrieval of total suspended sediments (TSS). The monthly mean cloud coverage for the East China Sea (ECS), Bohai Sea (BS) and Yellow Sea (YS) were 62.6%, 67.3% and 69.9%, respectively. Uncertainties regarding the long-term retrieved TSS were affected by a combination of the effects of cloud coverage and TSS variations. The effects of the cloud coverage dominated at the monthly scale, with the mean normalized bias (Pbias) at 14.1% (±2.6%), 7.6% (±2.3%) and 12.2% (±4.3%) for TSS of the ECS, BS and YS, respectively. Cloud coverage-interfering observations with the Terra/Aqua MODIS systems were also estimated, with monthly Pbias ranging from 6.5% (±7.4%) to 20% (±13.1%) for TSS products, and resulted in a smaller data range and lower maximum to minimum ratio compared to the eight GOCI observations. Furthermore, with approximately 16.7% monthly variations being missed during the periods, significant “missing trends” effects were revealed in monthly TSS variations from Terra/Aqua MODIS. For the entire region and the Bohai Sea, the most appropriate timeframe for sampling ranges from 12:30 to 15:30, while this timeframe was narrowed to from 13:30 to 15:30 for observations in the East China Sea and the Yellow Sea. This research project evaluated the effects of cloud coverage and times for sampling on the remote sensing monitoring of ocean color constituents, which would suggest the most appropriate timeframe for ocean color sensor scans, as well as in situ data collection, and can provide design specification guidance for future satellite sensor systems.

scholarly journals A Remote-Sensing Method to Estimate Bulk Refractive Index of Suspended Particles from GOCI Satellite Measurements over Bohai Sea and Yellow Sea

2019 ◽  
Vol 10 (1) ◽  
pp. 23
Author(s):  
Deyong Sun ◽  
Zunbin Ling ◽  
Shengqiang Wang ◽  
Zhongfeng Qiu ◽  
Yu Huan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Refractive Index ◽  
Spatial Distribution ◽  
Yellow Sea ◽  
Bohai Sea ◽  
Suspended Particles ◽  
In Situ Observation ◽  
Observation Data ◽  
Estimation Model

The bulk refractive index (np) of suspended particles, an apparent measure of particulate refraction capability and yet an essential element of particulate compositions and optical properties, is a critical indicator that helps understand many biogeochemical processes and ecosystems in marine waters. Remote estimation of np remains a very challenging task. Here, a multiple-step hybrid model is developed to estimate the np in the Bohai Sea (BS) and Yellow Sea (YS) through obtaining two key intermediate parameters (i.e., particulate backscattering ratio, Bp, and particle size distribution (PSD) slope, j) from remote-sensing reflectance, Rrs(λ). The in situ observed datasets available to us were collected from four cruise surveys during a period from 2014 to 2017 in the BS and YS, covering beam attenuation (cp), scattering (bp), and backscattering (bbp) coefficients, total suspended matter (TSM) concentrations, and Rrs(λ). Based on those in situ observation data, two retrieval algorithms for TSM and bbp were firstly established from Rrs(λ), and then close empirical relationships between cp and bp with TSM could be constructed to determine the Bp and j parameters. The series of steps for the np estimation model proposed in this study can be summarized as follows: Rrs (λ) → TSM and bbp, TSM → bp → cp → j, bbp and bp → Bp, and j and Bp → np. This method shows a high degree of fit (R2 = 0.85) between the measured and modeled np by validation, with low predictive errors (such as a mean relative error, MRE, of 2.55%), while satellite-derived results also reveal good performance (R2 = 0.95, MRE = 2.32%). A spatial distribution pattern of np in January 2017 derived from GOCI (Geostationary Ocean Color Imager) data agrees well with those in situ observations. This also verifies the satisfactory performance of our developed np estimation model. Applying this model to GOCI data for one year (from December 2014 to November 2015), we document the np spatial distribution patterns at different time scales (such as monthly, seasonal, and annual scales) for the first time in the study areas. While the applicability of our developed method to other water areas is unknown, our findings in the current study demonstrate that the method presented here can serve as a proof-of-concept template to remotely estimate np in other coastal optically complex water bodies.

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

2021 ◽  
Vol 13 (19) ◽  
pp. 3811
Author(s):  
Deyu An ◽  
Dingfeng Yu ◽  
Xiangyang Zheng ◽  
Yan Zhou ◽  
Ling Meng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Yellow Sea ◽  
Large Scale ◽  
Marine Ecology ◽  
The Yellow Sea ◽  
Green Tide ◽  
Southern Yellow Sea ◽  
Simple Method ◽  
Green Macroalgae ◽  
The Southern Yellow Sea

Large scale green macroalgae blooms (MABs) caused by Ulva prolifera have occurred regularly in the Yellow Sea since 2007. In the MAB dissipation phase, the landing or sinking and decomposition of U. prolifera would alter the physical-chemical environment of seawater and cause ecological, environmental, and economic problems. To understand MAB dissipation features, we used multiple sensors to analyze the spatiotemporal variation of the MAB dissipation phase in the southern Yellow Sea. The results show the variation in the daily dissipation rate (DR) was inconsistent from year to year. Based on the DR variation, a simple method of estimating MAB dissipation days was proposed for the first time. Verification results of the method, from 2018 to 2020, showed the estimated dissipation days were relatively consistent with the results obtained by remote sensing imagery. From 2007 to 2020, the order in which macroalgae landed in the coastal cities of Shandong Peninsula can be roughly divided into two types. In one type, the macroalgae landed first in Rizhao, followed by Qingdao, Rushan, and Haiyang. In the other type, they landed in the reverse order. The MABs annual distribution density showed significant differences in the southern Yellow Sea. These results provided a basis for evaluating the MABs’ impact on marine ecology and formulating the green-tide prevention and control strategies.

scholarly journals Evaluation of Four Atmospheric Correction Algorithms for GOCI Images over the Yellow Sea

2019 ◽  
Vol 11 (14) ◽  
pp. 1631 ◽  
Author(s):  
Xiaocan Huang ◽  
Jianhua Zhu ◽  
Bing Han ◽  
Cédric Jamet ◽  
Zhen Tian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Yellow Sea ◽  
Near Infrared ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
The Yellow Sea ◽  
Optical Parameters ◽  
Band Ratio ◽  
Percentage Difference

Atmospheric correction (AC) for coastal waters is an important issue in ocean color remote sensing. AC performance is fundamental in retrieving reliable water-leaving radiances and then bio-optical parameters. Unlike polar-orbiting satellites, geostationary ocean color sensors allow high-frequency (15–60 min) monitoring of ocean color over the same area. The first geostationary ocean color sensor, i.e., the Geostationary Ocean Color Imager (GOCI), was launched in 2010. Using GOCI data acquired over the Yellow Sea in summer 2017 at three principal overpass times (02:16, 03:16, 04:16 UTC) with ±1 and ±3 h match-up times, this study compared four GOCI AC algorithms: (1) the standard near infrared (NIR) algorithm of NASA (NASA-STD), (2) the Korea Ocean Satellite Center (KOSC) standard algorithm for GOCI (KOSC-STD), (3) the diffuse attenuation coefficient at 490 nm Kd (490)-based NIR correction algorithm (Kd-based), and (4) the Management Unit of the North Sea Mathematical Models (MUMM). The GOCI-estimated remote sensing reflectance (Rrs), aerosol parameters [aerosol optical thickness (AOT), Angström Exponent (AE)], and chlorophyll-a (Chla) were validated using in situ data. For Rrs, AOT, AE, and Chla, GOCI-retrieved results performed well within the ±1 h temporal window, but the number of match-ups was extended within the ±3 h match-up window. For ±3 h GOCI-derived Rrs, all algorithms had an absolute percentage difference (APD) at 490 and 555 nm of <40%, while other bands showed larger differences (APD > 60%). Compared with in situ values, the APD of the Rrs(490)/Rrs(555) band ratio was <20% for all ACs. For AOT and AE, the APD was >40% and >200%, respectively. Of the four algorithms, the KOSC-STD algorithm demonstrated satisfactory performance in deriving Rrs for the region of interest (Rrs APD: 22.23%–73.95%) in the visible bands. The Kd-based algorithm worked well obtaining Ocean Color 3 GOCI Chla because Rrs(443) is more accurate than the KOSC-STD. The poorest Rrs retrievals were achieved using the NASA-STD and the MUMM algorithms. Statistical analysis indicated that all methods had optimal performance at 04:16 UTC.

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