# The Ocean Surface Current in the East China Sea Computed by the Geostationary Ocean Color Imager Satellite

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## Abstract

**:**

_{2}tide is 0.0335 m/s, the mean deviation of the short semiaxis is 0.0276 m/s, and the mean deviation of the tilt angle is 6.89°. Moreover, the spatially averaged flow velocity corresponds with the observed pattern of tidal elevation changes, thus showcasing the field’s significant reliability. Afterward, we calculated the sea surface current fields in the East China Sea for the years 2013 to 2019 and created distribution maps for both climatology and seasonality. The resulting current charts provide an intuitive display of the spatial structure and seasonal variations in the East China Sea circulation. Lastly, we performed a diagnostic analysis on the surface TSS variation mechanism in the frontal zone along the Zhejiang coast, utilizing inverted flow data collected on 3 August 2013, which had a high spatial coverage and complete time series. Our analysis revealed that the intraday variation in TSS in the local surface layer was primarily influenced by tide-induced vertical mixing. The research findings of this article not only provide valuable data support for the study of local ocean dynamics but also verify the reliability of short-period surface flow inversion of high-turbidity waters near the coast using geostationary satellites.

## 1. Introduction

_{2}tidal currents in the East China Sea (ECS). The derived results demonstrate good consistency with observations from a comprehensive set of twenty-eight surface drifters and four mooring instruments, as well as with the high-resolution regional tidal model data from Oregon State University. Based on the hourly SST data obtained from the Himawari-8 satellite, Taniguchi [10] employed the MCC algorithm to analyze the short-term surface flow variations in the area south of the Lombok Strait during the northern summer. The findings indicate a southward flow from the Lombok Strait to the Indian Ocean, hindering the eastward movement of the South Java Current along the northern summer and the Java Current along the southern coast of Nusa Tenggara. Zhu [11] utilized the 10 min data from the Himawari-8 satellite to retrieve the coastal currents in Hangzhou Bay. The comparative analysis with the numerical model data from the Taiwan Strait current forecasting system demonstrates that the Himawari-8 satellite data can be efficaciously employed for the precise estimation of oceanic currents.

## 2. Data and Methods

#### 2.1. Data

#### 2.1.1. GOCI TSS Data

#### 2.1.2. Tide Data

_{2}tidal constituent exhibits consistency with the co-tidal chart obtained from 10 years of satellite measurements and coastal island station observations. The numerical simulation results impeccably replicate the tidal flow field in the coastal regions of Zhejiang.

_{f}and M

_{m}), three shallow-water tidal components (M

_{4}, MS

_{4}, and MN

_{4}), as well as 2N

_{2}, S

_{1}, and other additional tidal components. The data can be downloaded from the website (https://www.tpxo.net/global/tpxo8-atlas, accessed on 15 December 2022).

#### 2.2. Method

#### 2.2.1. Maximum Cross-Correlation (MCC) Algorithm

**,**${\mathit{Y}}_{\mathbf{0}}$) and radius

**r**is selected in the image ${\mathit{T}}_{\mathbf{0}}$. Next, a square search window with a center (${\mathit{X}}_{\mathbf{0}}$

**,**${\mathit{Y}}_{\mathbf{0}}$) and a larger side length

**R**(

**R > r**) is chosen in the image ${\mathit{T}}_{\mathbf{1}}$. Within the search window, similarly sized target windows are sequentially selected, and the cross-correlation coefficient ($CC$) between the source window and each target window is calculated. The formula for computing $CC$ is as follows [4]:

**,**${\mathit{Y}}_{\mathbf{1}}$). The horizontal displacement of the source window $\Delta L$ within the time interval $\Delta t$ is calculated as $\Delta \mathrm{L}=\sqrt{{\left({X}_{1}-{X}_{0}\right)}^{2}+{\left({Y}_{1}-{Y}_{0}\right)}^{2}}$. The horizontal velocity of the water mass, which is equivalent to the flow speed

**V**and direction $\theta $ of the ocean current at that location, can be calculated as:

#### 2.2.2. Selection of the Radius r for the Source Window

**r**) on the inversion of the flow field to determine the optimal value of r. The size of the search window only needs to be large enough to include the expected maximum flow velocity in the study area. Based on historical data, the search window radius (

**R**) is set to

**r+8**in this study.

**r**, a series of experiments were conducted in this study. The values of

**r**were tested incrementally from 5 to 20, and the resulting surface flow fields were computed and subjected to harmonic analysis to derive the M

_{2}tidal ellipse. Subsequently, the root-mean-square error of the long and short axes and inclination angle of the M

_{2}tidal ellipse of the inverted flow field were calculated using the simulated M

_{2}tidal ellipse of the flow field as a reference. The experimental results (see Figure 3) showed a decrease in the root-mean-square errors of the long and short axes and inclination angle with an increase in

**r**. Notably, the inclination angle error reached a significant minimum at

**r**= 17. Although the inversion error continued to decrease for

**r**values greater than 20, the rate of decrease had slowed down. Hence, considering the efficiency and accuracy of the inversion algorithm, we concluded that the optimal value of

**r**for the source window radius is 17.

## 3. Results and Discussion

#### 3.1. Verification of Inversion Results Based on Numerical Modeling and In Situ Measurements

_{2}constituent. The dependability of the MCC inverted flow field was evaluated by comparing the tidal ellipses of the inverted flow field and the model flow field and calculating the root-mean-square error of the long and short axes, and the inclination angle between the two. The comparative results reveal that the M

_{2}tidal ellipses of the inverted flow field and the model flow field exhibit substantial consistency in the sea area south of the Yangtze River Estuary (Figure 4a). However, the consistency is comparatively poor in the Subei Shoal area. The calculated errors reveal that the average error of the long axis is 0.0335 m/s, the average error of the short axis is 0.0276 m/s, and the average error of the inclination angle is 6.89°. Moreover, among the data points with good consistency, the accuracy in the shelf area is superior to that in the nearshore area, which might be attributable to the fact that the vertical sediment settling motion of nearshore water is more vigorous, and the variation in TSS is not dominated by the horizontal flow. We performed a spectrum analysis for one month’s worth of tidal hourly elevation data, and the results (Figure 4b) showed two distinct peaks with frequencies of 2.2401 × 10

^{−5}Hz and 2.3148 × 10

^{−5}Hz, with a magnitude of 366.8782 and 102.1478, corresponding to periods of 12.4 h and 12.0 h. We consider that these two tidal components are M

_{2}and S

_{2}, which are the main components of tidal currents in the East China Sea and have a significant impact on the distribution of surface flow fields.

#### 3.2. Pattern and Seasonal Variations of the Surface Residual Currents in the East China Sea

#### 3.3. Diurnal Variability and Mechanisms of TSS in the Zhejiang Coastal Front

^{−5}, while the magnitude for the advection term was 10

^{−6}, the magnitude for the horizontal diffusion term was 10

^{−7}, and the magnitude for the vertical processes was 10

^{−5}. Thus, it can be inferred that the vertical processes, which involve vertical convective and diffusive transport of water masses, sediment settling, and resuspension, play a crucial role throughout the entire process of TSS changes. This is also evident from the degree of conformity between the two lines as they vary with time (The blue line and the purple line in Figure 9). In comparison to the contribution of vertical processes, the contribution of the diffusion term is almost negligible, while the contribution of the advection term is of the same order as that of the vertical processes only after 12:00. Figure 10 displays the spatial distribution of the absolute values of hourly variations in TSS, horizontal advection term, horizontal diffusion term, and vertical term from 10:00–11:00 and 11:00–12:00. It can be observed that the hourly variations in TSS exhibit a stable band-shaped distribution parallel to the coastline, with decreasing intensity offshore, indicating that water depth influences TSS changes. The distribution of horizontal advection and vertical terms is similar to that of hourly variations in TSS, and the intensity of the vertical term is greater than that of the horizontal advection term. The horizontal diffusion term is of the smallest magnitude and almost negligible. Thus, it is evident that vertical dynamic processes dominate the variations in surface TSS in the local area, with some contribution from horizontal advection, while the contribution from horizontal diffusion can be ignored.

## 4. Conclusions

_{2}and S

_{2}semidiurnal tides are the main tidal components that dominate the dynamic processes in the East China Sea. Among them, the magnitude of M

_{2}is much larger than that of S

_{2}. The inversion results were compared with the modeled tidal current data and the measured tidal elevation data for verification. The results of the verification demonstrated that the mean deviation of the long semiaxis of the tidal ellipse of the inverted M

_{2}tide was 0.0335 m/s, the mean deviation of the short semiaxis was 0.0276 m/s, and the mean deviation of the tilt angle was 6.89°. Moreover, the spatially averaged flow velocity corresponded to the observed pattern of tidal elevation changes, thus showcasing the field’s significant reliability.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**(

**a**) Spatial distribution of GOCI climatological TSS in the East China Sea; (

**b**) the schematic diagram of ocean circulation (the background color in (

**b**) is the water depth).

**KC**: Kuroshio Current;

**TWC**: Taiwan Warm Current;

**CDW**: Changjiang Diluted Water;

**ZJCC**: Zhejiang Coastal Current.

**Figure 2.**Two consecutive remote-sensing image images ${\mathit{T}}_{\mathbf{0}}$ (

**a**) and ${\mathit{T}}_{\mathbf{1}}$ (

**b**), used for calculating the cross-correlation coefficient of the source window (red square) and the target window in the search window (blue square) to search for the position of the highest cross-correlation value and to obtain the velocity vector.

**Figure 3.**The RMSE of semimajor (solid blue line), semiminor (blue dotted line) axis, and inclination (solid red line) under different source window radius $r$.

**Figure 4.**(

**a**) M

_{2}tidal ellipse of MCC−derived current (blue) and the model flow (red), with a red star indicating the location of the local tide gauge station; (

**b**) spectrum analysis of one month’s worth of tidal hourly elevation data; and (

**c**) tidal elevation variation and spatially averaged flow velocity on 14 February 2017.

**Figure 5.**(

**a**) GOCI−derived climatic current field; (

**b**) spatial distribution of the number of velocity vectors in the flow field.

**KC**: Kuroshio Current;

**TWC**: Taiwan Warm Current;

**CDW**: Changjiang Diluted Water. The red arrows in 5(

**a**) show the trajectory of the currents.

**Figure 6.**Seasonal surface mean flow in the East China Sea (red arrows indicate the main circulation). (

**a**) Spring. (

**b**) Summer. (

**c**) Autumn. (

**d**) Winter.

**KC**: Kuroshio Current;

**TWC**: Taiwan Warm Current;

**CDW**: Changjiang Diluted Water;

**ZJCC**: Zhejiang Coastal Current.

**Figure 7.**Seasonal distribution of the number of derived current vectors in the East China Sea. (

**a**) Spring. (

**b**) Summer. (

**c**) Autumn. (

**d**) Winter.

**Figure 8.**(

**a**–

**g**) Surface current field (red line represents the 70 m isobath); (

**h**) tidal elevation observed from local tide gauge station (blue line) and spatially averaged TSS variation within 70 m isobath (red line); and (

**i**) regional mean current velocity within 70 m isobath and tidal elevation from 8:00 to 14:00 on 3 August 2013.

**Figure 9.**The hourly variation in TSS, advection term, horizontal diffusion term, and vertical term from 08:00 to 13:00 on 3 August 2013.

**Figure 10.**Spatial distribution of hourly variation in TSS, advection term, horizontal diffusion term, and vertical term at 10:00–11:00 (

**a**–

**d**) and 11:00–12:00 (

**e**–

**h**) on 3 August 2013.

**Figure 11.**The evolution of Simpson−Hunter index k versus hourly variations in TSS from 8:00 to 14:00.

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## Share and Cite

**MDPI and ACS Style**

Ma, Y.; Yin, W.; Guo, Z.; Xuan, J.
The Ocean Surface Current in the East China Sea Computed by the Geostationary Ocean Color Imager Satellite. *Remote Sens.* **2023**, *15*, 2210.
https://doi.org/10.3390/rs15082210

**AMA Style**

Ma Y, Yin W, Guo Z, Xuan J.
The Ocean Surface Current in the East China Sea Computed by the Geostationary Ocean Color Imager Satellite. *Remote Sensing*. 2023; 15(8):2210.
https://doi.org/10.3390/rs15082210

**Chicago/Turabian Style**

Ma, Youzhi, Wenbin Yin, Zheng Guo, and Jiliang Xuan.
2023. "The Ocean Surface Current in the East China Sea Computed by the Geostationary Ocean Color Imager Satellite" *Remote Sensing* 15, no. 8: 2210.
https://doi.org/10.3390/rs15082210