# Spatiotemporal Dynamics of Total Suspended Solids in the Yellow River Estuary Under New Water-Sediment Regulation: Insights from Sentinel-3 OLCI

Yafei Luo, Zhengyu Hou, Yanxia Liu, David Doxaran, Dongyang Fu, Liwen

Yan, Haijun Huang

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Yafei Luo, Zhengyu Hou, Yanxia Liu, David Doxaran, Dongyang Fu, et al.. Spatiotemporal Dynamics of Total Suspended Solids in the Yellow River Estuary Under New Water-Sediment Regulation: Insights from Sentinel-3 OLCI. Remote Sensing, 2025, 17, pp.3083. 10.3390/rs17173083. hal-05374279

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Article

# Spatiotemporal Dynamics of Total Suspended Solids in the Yellow River Estuary Under New Water-Sediment Regulation: Insights from Sentinel-3 OLCI

Yafei Luo<fnref n="1" /> ID, Zhengyu Hou<fnref n="2" />*, Yanxia Liu<fnref n="3" />, David Doxaran<fnref n="4" />, Dongyang Fu<fnref n="1" /> ID, Liwen Yan<fnref n="3" /> and Haijun Huang<fnref n="3" />

## Abstract

The Water and Sediment Regulation Scheme (WSRS), implemented since 2002, has been essential for controlling water flow and mitigating sediment siltation in the lower Yellow River. However, WSRS was suspended for the first time in 2016 and 2017 due to extremely low water flow. The rapid floodwater discharge over roughly 20 days conducted by WSRS strongly impacts total suspended solids (TSS) distribution in the Yellow River Estuary (YRE). This study employs high-frequency Sentinel-3 OLCI satellite imagery to investigate intraday TSS variations in the YRE under new water-sediment regulation conditions from 2016 to 2023. TSS concentrations were generally low during the 2016 and 2017 flood seasons, but increased markedly after WSRS resumed in 2018. Peak TSS values occurred in July or August, sometimes extending into September and October during autumn floods. A moderately strong positive correlation was observed between TSS concentrations at the river mouth and sediment load at Lijin Station during the flood seasons. The 2018 WSRS event generated an extensive river plume, with average TSS concentrations at the river mouth exceeding 400 g·m⁻<fnref n="3" />. From 2018 to 2023, TSS concentrations exhibited a declining trend during flood seasons, attributed to reduced sediment discharge and ongoing sediment accretion in the Yellow River Delta. Our findings highlight Sentinel-3 OLCI as a powerful tool to resolve WSRS-driven sediment dynamics, offering critical guidance for estuarine management.

**Keywords:** water and sediment regulation scheme; total suspended solids; Sentinel-3 OLCI imagery; Yellow River estuary

## 1. Introduction

With an annual sediment load of approximately ~1.08 × 10⁹ tons transported into the Bohai Sea, the Yellow River exhibits one of the highest total suspended solids (TSS) concentrations globally [1]. Lower reaches of the river are particularly prone to sediment deposition due to an imbalance between water flow and sediment transport, which leads to riverbed elevation and increases the risk of flooding. To address this water-sediment imbalance, the Yellow River Water Conservancy Commission of the Ministry of Water

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updates

Academic Editors: Daosheng Wang, Jungang Yang and Haidong Pan

Received: 3 July 2025

Revised: 29 August 2025

Accepted: 29 August 2025

Published: 5 September 2025

**Citation:** Luo, Y.; Hou, Z.; Liu, Y.; Doxaran, D.; Fu, D.; Yan, L.; Huang, H. Spatiotemporal Dynamics of Total Suspended Solids in the Yellow River Estuary Under New Water-Sediment Regulation: Insights from Sentinel-3 OLCI. *Remote Sens.* **2025**, *17*, 3083. https://doi.org/10.3390/rs17173083

**Copyright:** © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

<footnote n="1">¹ College of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China; luoyafei@gdou.edu.cn (Y.L.)</footnote>

<footnote n="2">² School of Ocean Engineering and Technology, Sun Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China</footnote>

<footnote n="3">³ Key Laboratory of Marine Geology and Environment, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China</footnote>

<footnote n="4">⁴ Laboratoire d'Océanographie de Villefranche, UMR 7093-CNRS/SU, 06230 Villefranche-sur-Mer, France</footnote>

<footnote>* Correspondence: houzhy7@mail.sysu.edu.cn</footnote>
Resources has implemented the Water-Sediment Regulation Scheme (WSRS) for 13 consecutive years since 2002, then interrupted it in 2016 and 2017. The implementation of WSRS advanced high monthly water discharge and sediment load to as early as June, occurring at least two months prior to the typical high-flow season from August to October [2,3]. During the WSRS, both runoff and sediment loads are increased, facilitating the transport of substantial volumes of water and sediment to the sea. The WSRS is conducted in two phases: the first phase involves the release of water from the Xiaolangdi Reservoir, while the second phase is focused on flushing sediment from the reservoir [4]. The duration of WSRS operations averaged approximately 20 days per year, contributing to 27.6% and 48.9% of the annual water and sediment delivery to the sea, respectively [5]. Between 2002 and 2015, approximately 1.98 × 10⁹ tons of sediments entered the Bohai Sea, with 1.31 × 10⁹ tons being deposited along the coast of the Yellow River Estuary (YRE) [6]. By influencing suspended sediment transport, the WSRS directly affects both the ecological environment and the geomorphological evolution of the YRE [7]. In recent years, however, due to the combined effects of climate change and human activities, such as WSRS interventions, the hydrological conditions of the Yellow River have shifted, transitioning from a state of “less water and more sediment” to “less water and less sediment.” Especially, in 2016 and 2017, the sediment load of the Yellow River experienced a significant decline, prompting the interruption of the WSRS for two years before its resumption in 2018. This disruption may have had new and profound effects on the ecological environment of both the delta and adjacent coastal areas.

The investigation of the spatiotemporal distribution of TSS in the YRE under the evolving conditions of water-sediment regulation is of considerable scientific significance. Such studies provide crucial theoretical support for understanding changes in marine sedimentation processes and the physical environment of the YRE. Traditionally, in-situ measurements have been the primary method for quantifying TSS. However, remote sensing techniques now provide an indispensable way to monitor large-scale TSS variations in estuarine and coastal waters.

Previous studies have frequently utilized Landsat imagery to investigate monthly and annual TSS variations in the YRE and to assess key influencing factors, including wind, currents, runoff, and sediment load [8–13]. Other higher resolution sensors, such as Sentinel 2-MSI [10] and Gaofen (GF) satellite images [14], were also used to map the spatiotemporal variations of the total suspended particulate matter in the Yellow River estuary. While these studies have provided robust insights into long-term TSS dynamics, Landsat's 16-day revisit cycle prevents monitoring interannual and daily variations. This constraint precludes comprehensive observation of shorter-term TSS fluctuations, such as those occurring during WSRS periods.

Alternatively, studies have shown that the ocean-color sensor, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Land Colour Instrument (OLCI), with a spatial resolution of 250 m/500 m/1000 m and 300 m, can be effectively applied for monitoring estuarine turbidity and TSS, and can also provide specific-term time series monitoring records [15]. For instance, Zhang et al. employed MODIS data with the red band to study river plume induced by heavy rainfall events in Lake Taihu [16]. Luo et al. utilized MODIS data to analyze the spatial distribution characteristics and influencing factors of different quantile trends of TSS in the Pearl River Estuary [17]. MODIS is prone to saturation in the highly turbid waters of YRE, probably due to high sensitivity or signal-to-noise ratio [8], posing persistent challenges for accurate TSS retrieval in such environments.

The daily revisit capability of the Sentinel-3 Ocean and Land Colour Instrument (OLCI) satellite sensors, launched on 16 February 2016 (Sentinel-3A) and 25 April 2018 (Sentinel-3B), coupled with its spatial resolution of 300 m, offers a promising solution to
overcome these limitations. By providing near-daily observations of the YRE, Sentinel-3 OLCI (S3-OLCI) facilitates detailed analysis of TSS dynamics during WSRS events. This capability enables a more nuanced understanding of how TSS within the estuary responds to WSRS operations, addressing critical gaps in current research and advancing knowledge in this domain.

Studies have examined the long-term impacts of the WSRS on aspects such as the dispersion of freshwater and sediment in the YRE [5,18,19] and its effects on estuarine morphology, hydrodynamics, and biogeochemical processes [20–23]. Data on the daily variations in TSS distribution within the estuary remain limited [24,25]; even less is understood about the effects of WSRS under varying hydrological conditions on these daily TSS dynamics.

To enhance our understanding of the impacts of regulated water flow on ocean color in the YRE, particularly since the WSRS interruption in 2016, this study leverages eight years of S3-OLCI remote sensing satellite observations from 2016 to 2023. This research advances the understanding of WSRS impacts on TSS dynamics in the YRE, providing a framework for monitoring the current state and evolving trends of TSS not only during WSRS events but also over the post-event periods.

The objectives of the present study are to (i) propose a new algorithm to quantify TSS evolutions and distributions in the YRE using S3-OLCI images and validate the Dark Spectrum Fitting (DSF) atmospheric correction applied to S3-OLCI satellite data recorded over the YRE turbid estuarine waters; (ii) monitor TSS dynamics in the YRE before, during, and after the WSRS implementation, using S3-OLCI satellite data; (iii) provide a detailed understanding of WSRS impacts on TSS dynamics in the YRE by comparing TSS in the YRE with the river freshwater discharge and sediment load from the hydrologic station (Lijin Station on the Yellow River).

## 2. Materials and Methods

### 2.1. Study Area

The study area is the Yellow River Estuary (YRE), containing the Yellow River Mouth (YRM) and Laizhou Bay (LZB). It is located at the confluence of the Bohai Bay and Laizhou Bay (Figure 1). The isobaths in the near-shore area of the YRE are tortuous and irregularly distributed. The sediment transported from the Yellow River into the sea and accumulated in the northwest and northeast direction of the mouth of the estuary, and extends towards the offshore in the shape of a tongue [26].

The typical duration of WSRS operations prior to flooding usually spans approximately 20 days in June and July, though such events occasionally occur during flood periods in July and August. The start and end dates of the WSRS from 2016 to 2023 are detailed in Table 1. The data is recorded from the Yellow River Sediment Bulletin.

### 2.2. Data

#### 2.2.1. In-Situ Data

The in-situ dataset was collected during a dedicated field campaign in the YRE from 21 to 22 and 25 to 27 June 2004 during the WSRS period. Water reflectance was measured, and surface water samples were collected to determine the concentration of suspended solids. Water sampling adhered strictly to national technical standards, specifically those outlined in GB/T13909-92 for marine geological and geophysical surveys. The TSS concentration was determined as the dried sediment mass divided by the known volume of the filtered water sample, resulting in values ranging from 1.7 to 1896.5 $g·m^{-3}$. The remote sensing reflectance signal (Rrs, in $sr^{-1}$) was measured using an ASD FieldSpec Dual VNIR radiometer, covering the 350–1050 nm spectral range. In total, 44 pairs of high-quality co-located TSS and Rrs
values were obtained. This high-quality dataset was used to establish the TSS algorithm for retrieving TSS using S3-OLCI satellite data during the flood seasons. In addition, twelve in-situ Rrs measurements collected on 7–8 November 2024 and 17, 19 and 20 January 2025 were used to validate the Rrs values retrieved from S3-OLCI. Seven in-situ TSS measurements collected on 19–21 July 2019 and 9 July 2023 were used to validate the TSS model by comparing the S3-OLCI-retrieved TSS with the quasi-synchronous (±3 h) in-situ TSS. The geographical locations of the observation stations are shown in Figure 1, and related information is listed in Table 2.

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Geographic location and hydrometric stations of the study area (a). Sampling stations in the Yellow River Estuary (b). The study area (YRE), as shown with the S3-OLCI image (30 August 2023), is divided into the Yellow River Mouth (YRM) area and the Laizhou Bay (LZB) area, respectively in red polygon and in yellow polygon (c). The Yellow River Conservancy Commission has implemented the WSRS in the Yellow River since 2002 (d).</figcaption></figure>

<table><caption>**Table 1.** The start and end times of the WSRS from 2016 to 2023.</caption><thead><tr><th rowspan="2">Year</th><th colspan="2">WSRS Before Flood</th><th colspan="2">WSRS During Flood</th><th rowspan="2">Max. Q (m<sup>3</sup>·s<sup>-1</sup>) <sup>1</sup>/Day</th><th rowspan="2">Max. SC (10<sup>3</sup> g·m<sup>-3</sup>) <sup>2</sup>/Day</th><th colspan="2">Water Discharge To The Sea</th><th colspan="2">Sediment Load To The Sea</th></tr><tr><th>Starting</th><th>Ending</th><th>Starting</th><th>Ending</th><th>Q<sub>w</sub>/(km<sup>3</sup>)</th><th>(%) <sup>3</sup></th><th>Q<sub>s</sub>/(Mt)</th><th>(%) <sup>4</sup></th></tr></thead><tbody><tr><td>2016</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1480/July 28</td><td>4.4/July 26</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>2017</td><td>-</td><td>-</td><td>-</td><td>-</td><td>813/December 8</td><td>3.14/April 28</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>2018</td><td>July 3</td><td>July 27</td><td>August 7</td><td>August 28</td><td>3670/July 11</td><td>48.1/July 20</td><td>8.9</td><td>26.7</td><td>164.7</td><td>55.5</td></tr><tr><td>2019</td><td>June 21</td><td>July 5</td><td>July 6</td><td>August 16</td><td>4060/August 12</td><td>26.3/July 21</td><td>11.3</td><td>36.2</td><td>163.3</td><td>60.3</td></tr><tr><td>2020</td><td>June 24</td><td>July 10</td><td>July 4</td><td>August 18</td><td>4580/July 3</td><td>24.1/July 30</td><td>12.1</td><td>33.6</td><td>168.1</td><td>53.5</td></tr><tr><td>2021</td><td>June 19</td><td>July 13</td><td>-</td><td>-</td><td>5240/October 8</td><td>37.9/July 10</td><td>6</td><td>13.6</td><td>48.9</td><td>20.1</td></tr><tr><td>2022</td><td>June 19</td><td>July 13</td><td>July 17</td><td>July 28</td><td>4030/July 2</td><td>39.2/July 24</td><td>7.3</td><td>28</td><td>71.4</td><td>57.1</td></tr><tr><td>2023</td><td>June 21</td><td>July 16</td><td>-</td><td>-</td><td>3780/July 3</td><td>37.9/July 12</td><td>5.87</td><td>25.9</td><td>53.5</td><td>55.2</td></tr><tr><td>Average</td><td></td><td></td><td></td><td></td><td></td><td></td><td>8.58</td><td>27.3</td><td>111.65</td><td>50.3</td></tr></tbody></table>

<footnote n="1">¹ Maximum water flow at Lijin station; ² Maximum suspended sediment concentration at Lijin station; ³ Water discharge during the WSRS in percentage of annual water discharge to the sea; ⁴ Sediment load during the WSRS in percentage of annual sediment load to the sea; —No WSRS.</footnote>
<table><caption>**Table 2.** Information on the in-situ measurements.</caption><thead><tr><td>Date</td><td>Number of Stations</td><td>Measurements</td></tr></thead><tbody><tr><td>21–22 and 25–27 June 2004</td><td>44</td><td>Rrs, TSS</td></tr><tr><td>19–21 July 2019</td><td>5</td><td>TSS</td></tr><tr><td>9 July 2023</td><td>2</td><td>TSS</td></tr><tr><td>7–8 November 2024</td><td>4</td><td>Rrs</td></tr><tr><td>17 and 19–20 January 2025</td><td>8</td><td>Rrs</td></tr></tbody></table>

### 2.2.2. Remote Sensing Dataset

The Sentinel-3A&B OLCI satellite data were used to derive TSS during the flood season from June to October since 2016. Both Sentinel-3A OLCI and Sentinel-3B OLCI images were obtained from the Copernicus Data Space Ecosystem server. OLCI revisit time with the two-satellite configuration is ~1 day at the latitude of 37°~38° for YRE. With 21 bands, a resolution of 300 m over all surfaces and a design optimized to minimize sun-glint effects, OLCI marks a new generation of measurements over the ocean and land. Cloud-free S3-OLCI images were considered during flood seasons (June to October) from 2016 to 2023.

Besides, Landsat-8 Operational Land Imager (OLI) satellite data with a spatial resolution of 30 m and a revisit time of 16 days were used. Landsat-8 OLI (L8-OLI) image recorded on 30 August 2023 over YRE at 10:53 UTC, 17 min before the OLCI image was collected.

### 2.2.3. Atmospheric Correction for OLCI

To retrieve the water-leaving reflectance, defined as $ρ = Rrs × π$ (dimensionless), OLCI data were processed using the ACOLITE (v20221114.0) software, applying the Dark Spectrum Fitting (DSF) atmospheric correction method [27].

This DSF approach assumes negligible surface reflectance in at least one of the sensor bands, selected from the darkest pixels in the image. The DSF atmospheric correction algorithm was originally developed for the processing of metre-scale sensors for aquatic applications in inland and coastal waters [28,29], but it also performed well for decametre-scale sensors [30,31]. A new OLCI version of DSF was implemented in ACOLITE, and imagery was processed to water reflectances using a fixed aerosol optical thickness $τ_a$ Vanhellemont and Ruddick (2021) [27].

The comparison between Rrs measured in situ and derived from S3-OLCI satellite data (3 × 3 pixels) corrected using the DSF method in the 560, 665 and 865 nm spectral bands and for the 665/560 nm band ratio was made. The in-situ Rrs were measured in November 2024 and January 2025 in the YRE. The station information can be found in Figure 1. From Figure 2, we can see that the comparison is overall good in red and green bands, even better when considering the band ratio between 665 and 560 nm, with a MAPE of 12% and close to a 1:1 line. The S3-OLCI-derived Rrs values in the NIR band are slightly lower than in situ measured Rrs.

ACOLITE has been shown to perform well in the highly turbid waters of the YRE, a finding previously validated through step-by-step comparisons with field measurements for Landsat data [8,10,32]. Cloud-free L8-OLI and S3-OLCI satellite images recorded on 30 August 2023, with a time difference of 17 min, were considered for the sensor-to-sensor Rrs validation. The two images were reprojected onto a common grid by resampling OLI data to a spatial resolution of 300 m, using mean averaging. The OLCI-derived water reflectance values at Green, Red, and NIR wavebands were then compared to the corresponding OLI water reflectance values.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Scatterplots of S3-OLCI Rrs versus in situ Rrs measured in November 2024 and January 2025 at (a) 560 nm band, (b) 665 nm band, (c) 865 nm band, and (d) 665/560 band ratio.</figcaption></figure>

### 2.2.4. Hydrologic Data

The Lijin Hydrologic Station (Figure 1a), located approximately 80 km upstream of the river mouth, is the last station along the Yellow River. Daily water discharge and Sediment Concentration (SC) data measured at 8:00 a.m. (local time) each day were sourced from the daily water condition reports published by the Yellow River Conservancy Commission of the Ministry of Water Resources. The SC data generally represent vertically-averaged or cross-sectionally averaged measurements. Monthly river discharge and sediment load data at Stations Lijin, Xiaolangdi and Huayuankou were retrieved from the Yellow River Sediment Bulletin (http://www.yrcc.gov.cn/ (accessed on 1 June 2024)).

## 2.3. Methods

### 2.3.1. TSS Inversion Algorithm

The TSS inversion algorithm relies on the range of the TSS observed and the form of the model equation [33]. TSS in the YRE can exceed 1000 g·m⁻³, as evidenced by in-situ measurements and previous studies, particularly during the WSRS. For such highly turbid waters, Rrs in visible bands, notably in blue and green bands, tends to saturate while TSS increases. Rrs in near-infrared (NIR) bands have basically low values when TSS is lower than 100 g·m⁻³, then increase proportionally to TSS up to concentrations of about 1500 g·m⁻³ [34,35].

To avoid the saturation of the water reflectance signal, a NIR spectral band is therefore required to estimate TSS concentrations over the wide range encountered in the YRE (0 to >1000 g·m⁻³) [35,36]. Also, according to the correlation analysis between TSS and Rrs, the correlation coefficients for the green, red and NIR bands are all higher than 0.5, especially for the NIR band, with a r of 0.94. Therefore, Rrs(865) is considered in our TSS modelling. Single-band algorithms are highly sensitive to particle type (size and composition) and
environmental changes such as illumination conditions and glint effects, making them inadequate for comprehensively characterizing the spectral signatures of waters with varying suspended sediment concentrations. In contrast, band-ratio approaches can partially eliminate or mitigate the influence of particle size and illumination on the retrieval of suspended sediment concentrations [37]. Previous studies have shown that TSS retrieved using the Landsat images using the NIR band, combined with the ratio of red to green bands and best-fitted exponential relationships, exhibited the strongest agreement with in-situ TSS measurements in the YRE [10,36]. Consequently, we developed an empirical method based on the Rrs(665)/Rrs(560) ratio and the Rrs(865) band with a total of 44 coincident TSS and Rrs measurements, which were converted to OLCI equivalent Rrs values in each band by considering the corresponding spectral response functions (SRF).

$$ \log_{10}(TSS) = 1.9 \times X_1 + 9.96 \times X_2 - 0.006 \quad (1) $$

$$ \text{With } X_1 = \text{Rrs}(665)/\text{Rrs}(560) \text{ and } X_2 = \text{Rrs}(865) $$

where, Rrs(560), Rrs(665) and Rrs(865) are the S3-OLCI equivalent Rrs values. Equation (1) yields a determination coefficient R² of 0.94, RMSE of 97 g·m⁻³, and MAPE of 19% (Figure 3a). For low TSS concentrations (<27 g·m⁻³), the relationship is also well-defined with a R² of 0.89, RMSE of 1.4 g·m⁻³, and MAPE of 16.6% (Figure 3b). Figure 4 compares S3-OLCI estimated and in situ measured TSS concentrations drawn from seven observation stations over a 3 h time window between satellite overpasses and field measurements, during the WSRS periods of 19–21 July 2019 and 9 July 2023. The in-situ measured TSS concentrations range from 13 to 2019 g·m⁻³. The results showed a good agreement with a highly significant linear relationship (R² of 0.96), with a MAPE of 29.6% and RMSE of 222 g·m⁻³ (Figure 4).

### 2.3.2. Accuracy Assessment

The Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and BIAS (Bias) were used to quantitatively compare the TSS or Rrs values:

$$ \text{RMSE} = \sqrt{\frac{1}{N} \sum_{i=1}^{N} (x_i - y_i)^2} \quad (2) $$

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 3.** Comparison between estimated and in situ measurements of TSS values for the dataset generated during the WSRS period of June 2004 (a), with a zoom on TSS concentrations below 27 g·m⁻³ (blue dashed frame in Figure 3a) (b). The black dashed line indicates the 1:1 relationship.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Figure 4. Comparison between S3-OLCI estimated TSS and in-situ measurements for the dataset generated during the 19–21 July 2019 and 9 July 2023 WSRS periods (a), with a zoom on TSS concentrations below 50 g·m⁻³ (blue dashed frame in Figure 4a) (b). The black dashed line indicates the 1:1 relationship. Error bars correspond to the associated standard deviations of 3 × 3 extracted satellite pixels.</figcaption></figure>

$$ \text{MAPE} = \sum_{i=1}^{N} \frac{|y_i - x_i|}{N \cdot x_i} \times 100\%, \qquad (3) $$

$$ \text{BIAS} = \frac{\sum_{i=1}^{N} (y_i - x_i)}{\sum_{i=1}^{N} x_i} \times 100\%, \qquad (4) $$

where $x_i$ and $y_i$ are respectively the in-situ measured TSS or Rrs (including already-validated OLI products) and modelled TSS or Rrs retrieved from OLCI, N is the total number of samples.

### 2.3.3. Time-Series Analysis of TSS During the WSRS

To investigate the dynamic characteristics of TSS under new sediment transport conditions from 2016 to 2023, this study conducted a detailed analysis of TSS distribution during the Yellow River flood season (WSRS period) from June to October.

Time series of TSS data were obtained from regional mean values derived from the 300 m spatial resolution and daily revisit S3-OLCI imagery, considering separately the Yellow River Mouth (YRM), Laizhou Bay (LZB), and the Yellow River Estuary (YRE) as regions of interest (ROIs). In Figure 1, these ROIs are delineated by the red (YRM), yellow (LZB), and the combined red and yellow polygons (YRE), respectively. In total, 224 cloud-free images were considered in the daily TSS time series analysis. Visual inspection was conducted to ensure that the study area was not covered by clouds, and abnormal pixels were masked in the daily TSS time series analysis.

A systematic assessment of TSS was performed at daily and monthly time-scales for the YRM, LZB and YRE during the flood seasons. Mean TSS values during the annual floods were calculated using the images collected during the flood seasons (June to October) from 2016 to 2023. The variation in TSS during the flood season is primarily governed by river discharge and sediment load. To further explore the impact of WSRS on TSS dynamics, this study calculated Pearson correlation coefficients (r) and examined the relationships between daily and monthly sediment load, as well as the monthly discharge at the Lijin Station, with daily and monthly TSS in the YRM, Laizhou Bay, and over the entire study area.
# 3. Results

## 3.1. *Trend of Daily TSS in the YRE During the Flood Seasons*

From 2016 to 2017, the TSS at the Lijin Station consistently remained lower than 100 $g·m^{-3}$. Since the resumption of the WSRS in 2018, daily TSS has exhibited a pronounced variability within the flood season. WSRS events significantly increased TSS values. For instance, during the WSRS period from June to July 2018, the TSS in the YRM rose sharply, peaking at 446 $g·m^{-3}$. From July to October, the TSS displayed a fluctuating declining trend (Figure 5). Overall, TSS concentrations in the YRM were higher than those observed in the LZB and over the entire study region (YRE) (Figure 5).

<figure><img src="image_8.png" /><figcaption>**Figure 5.** Time series of daily TSS in the river mouth area (YRM), the entire YRE and the Laizhou Bay area (LZB) during the flood seasons of 2016–2023. The black trend line indicates the linear fitting of TSS vs. Date.</figcaption></figure>

## 3.2. *Trends of Daily River Discharge and SC at the LIJIN STATION*

Based on the daily records at station Lijin (2016–2023), 10 events of WSRS were identified (see also Table 1). These events were characterized by episodic increases in water discharge and SC, except in 2016 and 2017. The daily maximum river flow and maximum SC always occurred during the flood seasons, especially during the WSRS period and were observed to vary slightly from 2018 to 2023, with daily maximum river flow ranging from 3670 to 4580 $m^3·s^{-1}$ and daily maximum SC ranging from $24.1 × 10^3$ to 48.1 $× 10^3 g·m^{-3}$ (Table 1, Figure 6). The WSRS contributed nearly 27.3% and 50.3% of the total annual water discharge and sediment load to the sea, respectively, within periods of 14 to 45 days (Table 1), which greatly modified the natural hydrological cycle.

<figure><img src="image_9.png" /><figcaption>**Figure 6.** Trend of daily Sediment Concentration (SC) and river discharge at the Lijin Station from 2016 to 2023.</figcaption></figure>

## 3.3. *Spatiotemporal Variations of TSS Before, During, and After the Occurrence of a Typical WSRS Event in 2018*

In 2018, the impact of a strong WSRS event on the TSS concentration in the coastal region of the Yellow River was significant. During the period from June 1 to July 2, prior to the strong WSRS event, OLCI imagery analysis revealed an average TSS concentration of
less than 100 g·m⁻³ (Figure 7). However, during the event, particularly on July 16 and 20, the imagery showed a marked increase in TSS concentration in the river plume, reaching approximately 400 g·m⁻³, which indicates a substantial influx of TSS from the Yellow River basin. By August 1, five days after the initial WSRS event, the average TSS concentration in the plume had decreased to approximately 30 g·m⁻³. During the second WSRS event, on August 12 and 25, TSS concentrations in the plume increased to around 100 g·m⁻³, significantly higher than in the surrounding coastal waters. Although the intensity and spatial extent of the river plume were considerably reduced in the OLCI images from September to October, compared to the observations on July 16 and 20, the plume was still observable (Figure 7).

<figure><img src="image_10.png" /><figcaption>**Figure 7.** TSS concentrations derived from OLCI images before (a–d), during (e–i) and after (j–r) the typical WSRS event of 2018.</figcaption></figure>

### 3.4. Interannual Variations of TSS During the Flood Seasons

Figure 8 illustrates the interannual variability of TSS from 2016 to 2023 during the flood season (June to October). Among the years considered, the peak average TSS concentration in the YRM was observed in 2018, reaching 117 g·m⁻³. The analysis reveals that the average
TSS concentration in the YRM region remained relatively elevated, whereas that in Laizhou Bay was notably lower.

<figure><img src="image_11.png" /><figcaption>**Figure 8.** Average TSS maps in the YRE during the flood seasons from 2016 to 2023.</figcaption></figure>

# 4. Discussion

## 4.1. Performance of Daily-Revisit OLCI Data in Monitoring TSS

The ACOLITE DSF atmospherically-corrected water reflectance values derived from S3-OLCI data exhibited a good consistency with the reference (OLI-derived) products (Figure 9a-c). Linear regressions with slopes close to 1 were obtained between OLCI and OLI water reflectance values for the green, red and NIR wavebands. The corresponding R² coefficients are 0.94, 0.97, and 0.90, with RMSE of 0.006, 0.013 and 0.006 and MAPE of 5.8%, 19.6% and 41.4%, respectively. OLCI-derived water reflectance values were, however, observed to be slightly lower than OLI-derived ones, with BIAS of -3.2%, -16% and -29%, respectively, in the green, red and NIR bands. The results showed that ACOLITE DSF atmospheric correction is hence appropriate for the retrieval of TSS in the highly turbid waters of the YRE. The disparity between the TSS derived from S3-OLCI (300 m) and that derived from Landsat-OLI (30 m) was not conspicuous, with MAPE of 11.47% (Figure 9d). The BIAS between the 30 and 300 m products is -1.8% (Figure 9d), indicating that 300 m imagery would only slightly underestimate TSS values in YRE. Therefore, the spatial resolution of OLCI may not be a disadvantage for monitoring TSS in YRE, as a limited difference between 300 m and 30 m resolution images was found. Compared to the 16-day revisit time of OLI, the daily revisit associated with OLCI data is a major advantage in
order to observe and monitor the variation of TSS over short-time-scale events such as the
WSRS in the YRE.

<figure><img src="image_12.png" /><figcaption>**Figure 9.** Comparisons between OLCI and OLI water reflectance values in the YRE on 30 August 2023 in the green (a), red (b) and NIR (c) wavebands. Comparison between the resulting TSS values derived from OLCI (300 m) and OLI (30 m) satellite data (d).</figcaption></figure>

4.2. Daily TSS Fluctuation in the YRM and Daily SC at the Lijin Station

The variations of the diurnal TSS in the YRM and of the diurnal SC at the Lijin station from 2016 to 2023 are presented in Figure 10. Overall, the TSS concentration in the YRM during the flood seasons of 2016 and 2017 remained relatively stable, with average values mostly below 100 g·m⁻³ and no anomalously high peaks detected. This stability is primarily attributed to the cessation of the WSRS in 2016 and 2017, which substantially reduced the sediment load entering the marine environment.

From 2018 to 2023, significant changes in the daily SC at the Lijin station were observed from June to October. Prior to the implementation of the WSRS, the variations were not substantial, with daily SC values consistently below 10 × 10³ g·m⁻³ and average TSS values in the YRM remaining below 100 g·m⁻³. In contrast, during the WSRS period, daily SC exhibited a fluctuating increasing trend, with values reaching up to 20 × 10³ g·m⁻³. Concurrently, the average TSS concentration in the YRM showed an increasing trend, peaking in 2018 with an average value exceeding 400 g·m⁻³. Following the implementation of the WSRS, daily SC at Lijin station decreased, and the average TSS concentration in the YRM gradually declined. The TSS values in 2019, 2021, and 2022 indicate occasional rebounds in TSS concentrations in September and October, surpassing levels observed during the WSRS period.
<figure><img src="image_13.png" /><figcaption>**Figure 10.** Daily TSS variations in the Yellow River Mouth (YRM) and daily SC variations at the Lijin Station during the flood seasons from 2016 to 2023.</figcaption></figure>

As shown in Table 3, during the years 2016 and 2017, when the WSRS was not implemented, the correlation coefficient ($r$) between the daily SC at the Lijin station and TSS in the YRE was relatively low. However, following the reactivation of the WSRS from 2018 to 2023, a moderate to strong positive correlation emerged between the average TSS concentration in the YRM region and the daily SC at the Lijin station, with $r$ ranging from
0.25 to 0.81. In contrast, the correlation between the average TSS concentration across the entire YRE and LZB region and the daily SC at the Lijin station was systematically lower.

<table><caption>**Table 3.** Correlation coefficient (r) between the diurnal variations of TSS concentration in the YRE and SC at the Lijin Station.</caption><thead><tr><th rowspan="2">Year</th><th colspan="2">In the YRM</th><th colspan="2">In the YRE</th><th colspan="2">In the LZB</th></tr><tr><th>r</th><th>p-Value</th><th>r</th><th>p-Value</th><th>r</th><th>p-Value</th></tr></thead><tbody><tr><td>2016</td><td>0.06</td><td>&gt;0.05</td><td>0.20</td><td>&gt;0.05</td><td>0.24</td><td>&gt;0.05</td></tr><tr><td>2017</td><td>0.27</td><td>&gt;0.05</td><td>0.25</td><td>&gt;0.05</td><td>0.24</td><td>&gt;0.05</td></tr><tr><td>2018</td><td>0.64</td><td>&lt;0.05</td><td>0.60</td><td>&lt;0.05</td><td>0.42</td><td>&gt;0.05</td></tr><tr><td>2019</td><td>0.25</td><td>&gt;0.05</td><td>0.14</td><td>&gt;0.05</td><td>0.08</td><td>&gt;0.05</td></tr><tr><td>2020</td><td>0.54</td><td>&lt;0.05</td><td>0.35</td><td>&gt;0.05</td><td>0.04</td><td>&gt;0.05</td></tr><tr><td>2021</td><td>0.44</td><td>&lt;0.05</td><td>0.22</td><td>&gt;0.05</td><td>0.16</td><td>&gt;0.05</td></tr><tr><td>2022</td><td>0.81</td><td>&lt;0.05</td><td>0.48</td><td>&lt;0.05</td><td>0.21</td><td>&gt;0.05</td></tr><tr><td>2023</td><td>0.31</td><td>&lt;0.05</td><td>0.22</td><td>&gt;0.05</td><td>0.14</td><td>&gt;0.05</td></tr></tbody></table>

### 4.3. Correlation Between River Discharge, Sediment Load and TSS Concentration

During the 2016–2017 period, the suspension of the WSRS operations resulted in a lower monthly sediment load (consistently below $5.46 \times 10^6$ tons) and in a monthly runoff lower than $2.1 \times 10^9$ m$^3$. Correspondingly, the monthly average TSS in YRM remained generally below $70 \text{ g} \cdot \text{m}^{-3}$, with relatively stable variations during the flood seasons. In comparison to the river flow and sediment transport volumes of 2016 and 2017, the corresponding values from 2018 to 2023 showed a significant increase. During the 2018 WSRS period, the maximum monthly sediment load reached $1.4 \times 10^8$ tons. During the WSRS from 2018 to 2023, the highest river discharge and sediment transport rates typically occurred in July and August. Compared to the pre-WSRS period, where monthly sediment load was less than $32 \times 10^6$ tons, the monthly sediment load during the WSRS period significantly increased, surpassing $44.45 \times 10^6$ tons. Notably, between 2018 and 2020, the sediment load sometimes exceeded $100 \times 10^6$ tons.

Additionally, the concentration of TSS exhibited a marked upward trend during the WSRS. Specifically, as shown in Figure 11, the sediment load peaked in July 2018 during the WSRS, while the average TSS concentration in the YRM region in July reached $268 \text{ g} \cdot \text{m}^{-3}$.

<figure><img src="image_14.png" /><figcaption>**Figure 11.** Monthly variations of river discharge and sediment load at the Lijin Station, together with monthly variations of TSS concentration in the YRM from June to October, from 2016 to 2023. Error bars correspond to the associated standard deviations. Trend lines 1 and 2 represent the linear fitting TSS variations vs. Date in the YRM from 2016–2023 and 2018–2023, respectively.</figcaption></figure>
A similar pattern was observed in 2020, where, as depicted in Figures 8 and 12, the TSS concentration in the estuarine region peaked from late July to early August. During this period, both the daily SC and the monthly average sediment load at the Lijin station also reached their highest levels.

<figure><img src="image_15.png" /><figcaption>**Figure 12.** River discharge and sediment load at the Lijin Station and average TSS concentration in the YRM during flood seasons. Error bars correspond to the associated standard deviations.</figcaption></figure>

In 2023, TSS concentrations remained consistently high during the WSRS period compared to before and after the event. In summary, the trends in daily SC and monthly average sediment load at the Lijin station in 2018, 2020 and 2023 were closely correlated with changes in TSS concentration, all showing an increase in TSS during the WSRS, followed by a decline post-scheme.

From June to October in the YRM region, a significant correlation was observed between the river discharge (and sediment load) and TSS ($r = 0.52$ and $0.73$) (Table 4). During the WSRS period from June to August, TSS exhibited a more pronounced correlation with the sediment load ($r = 0.79$) and river discharge ($r = 0.60$). During June–October, river discharge and sediment load exhibited significant yet weak correlations with TSS in the YRE ($r = 0.33$ and $0.39$, respectively). Conversely, in the LZB, sediment load showed no correlation with TSS ($r = 0.06$).

<table><caption>**Table 4.** Correlation coefficient ($r$) between the monthly-averaged TSS, river discharge and sediment load from 2016 to 2023.</caption><thead><tr><th rowspan="2">Station</th><th rowspan="2">Period</th><th colspan="2">TSS vs. Sediment Load</th><th colspan="2">TSS vs. River Discharge</th></tr><tr><th>YRM</th><th>YRE</th><th>LZB</th><th>YRM</th><th>YRE</th><th>LZB</th></tr></thead><tbody><tr><td rowspan="2">Lijin</td><td>June–October</td><td>0.73<br/>p &lt; 0.05</td><td>0.39<br/>p &lt; 0.05</td><td>0.06<br/>p &gt; 0.05</td><td>0.52<br/>p &lt; 0.05</td><td>0.33<br/>p &lt; 0.05</td><td>0.11<br/>p &gt; 0.05</td></tr><tr><td>June–August</td><td>0.79<br/>p &lt; 0.05</td><td>0.45<br/>p &lt; 0.05</td><td>-0.009<br/>p &gt; 0.05</td><td>0.60<br/>p &lt; 0.05</td><td>0.27<br/>p &gt; 0.05</td><td>-0.12<br/>p &gt; 0.05</td></tr><tr><td rowspan="2">Xiaolangdi</td><td>June–October</td><td>0.55<br/>p &lt; 0.05</td><td>0.23<br/>p &gt; 0.05</td><td>-0.03<br/>p &gt; 0.05</td><td>0.42<br/>p &lt; 0.05</td><td>0.20<br/>p &gt; 0.05</td><td>0.014<br/>p &gt; 0.05</td></tr><tr><td>June–August</td><td>0.63<br/>p &lt; 0.05</td><td>0.36<br/>p &gt; 0.05</td><td>0.009<br/>p &gt; 0.05</td><td>0.37<br/>p &lt; 0.05</td><td>0.09<br/>p &gt; 0.05</td><td>-0.18<br/>p &gt; 0.05</td></tr><tr><td rowspan="2">Huayuankou</td><td>June–October</td><td>0.72<br/>p &lt; 0.05</td><td>0.33<br/>p &lt; 0.05</td><td>-0.007<br/>p &gt; 0.05</td><td>0.45<br/>p &lt; 0.05</td><td>0.24<br/>p &gt; 0.05</td><td>0.04<br/>p &gt; 0.05</td></tr><tr><td>June–August</td><td>0.80<br/>p &lt; 0.05</td><td>0.45<br/>p &lt; 0.05</td><td>0.004<br/>p &gt; 0.05</td><td>0.45<br/>p &lt; 0.05</td><td>0.12<br/>p &gt; 0.05</td><td>-0.20<br/>p &gt; 0.05</td></tr></tbody></table>

This suggests that the high TSS values at the river mouth are directly related to increased river discharge and sediment load due to WSRS events and autumn floods.
Less sediment has thus been supplied to the LZB located far away from the river mouth (Figure 7) [11].

Consequently, during the flood season, WSRS events involving large river discharge and sediment load play a dominant role in the TSS variations in the YRM. Additionally, rainfall, flooding, and wind also impact the TSS concentration and distribution in the YRE.

## 4.4. Correlation Between Rainfall, Autumn Flood, Wind Speed and TSS

In some instances, in the YRM, the TSS following WSRS events may exceed that during the WSRS period itself. For instance, in 2019, TSS concentrations in July and August were lower than those in September and October (Figure 10). This can be attributed to the sustained rainfall between September 17 and 24, in conjunction with higher wind speeds in September and October (not shown). Wind waves are one of the main factors that control local sediment resuspension in shallow waters [38].

In 2021, TSS concentrations in September and October were similarly elevated due to severe autumn flooding from late August to October. During the autumn period, the main stem of the Yellow River experienced three major flood events. As a result, the sediment load in October 2021 was the highest (Figure 11), with the corresponding TSS concentrations during the autumn flood period even surpassing those observed during the WSRS events (Figures 10 and 11).

The YRE is affected by the East Asian monsoon climate. Previous studies indicated that wave height (WH) and sea surface height (SSH) play a major role in sediment resuspension and dispersion [11,39,40]. Over the study period, the mean WH was about 0.8 m in the cold period, which was nearly twice the value in the warm season. In the summer, the south wind dominates with a moderate speed (3–5 m/s) [10,41]. As a consequence, the wave is gentler and the height of the wave is generally <1 m in the summer. As a result, the waves can cause the limited resuspension of bottom sediment in the warm flood seasons.

The tides in the YRE are irregular semi-diurnal, with an average tidal range of 0.6–0.8 m. Intraday TSS variation is influenced by tidal currents [24]. The shift in the tidal directions results in a shift in TSS plume direction and transportation of suspended sediment near groins [25]. The WSRS-induced increase in freshwater and sediment discharge to the river mouth enhanced the offshore extension of the turbid plume [24].

Current speeds show different seasonal patterns. In the summer, current speeds decrease rapidly to the southern coast of the estuary, suggesting that the sediment carried by the Yellow River is transported primarily to this area. Correspondingly, the contour lines with TSS > 100 g·m⁻³ display a narrow shape along the coast (Figure 7) [26].

## 4.5. Correlation Between WSRS and Sediment Transport

From 2018 to 2023, the monthly average TSS in the YRM manifested a downward trend (Figures 10 and 12). Despite the total sediment load during the flood seasons in 2019 and 2020 being comparable to that in 2018, the TSS concentration in the YRM in 2019–2020 was not as high as it was in 2018. Some of the sediments were retained within the estuary, leading to siltation; a part of the suspended sediments was also transported beyond the estuary, as shown in Figure 7, but this occurred only during the WSRS period. A part of suspended sediments from the Yellow River ended up deposited at the river mouth before entering the ocean (Figure 13).

This sedimentation situation was further exacerbated in 2023. Although the total sediment load during the flood seasons has decreased since 2021, the river mouth has undergone a seaward advancement due to the sediment accumulation during the flood seasons. TSS concentrations in the YRM during the flood seasons of 2023 were systematically lower than those during the flood seasons of 2018 to 2022. As a result, TSS in the
YRE showed a lower correlation with the daily SC at Lijin station. The Water-Sediment Regulation seems to be closely related to shoreline evolution. From 2000 to 2023, the shoreline has continuously advanced, indicating an overall accretion trend [42]. The sediment accumulation around the current river mouth would be a local geomorphic phenomenon (Figure 13). The WSRS of the Yellow River from 2018 to 2023 promoted the spread of TSS into the YRE, so that extremely high TSS concentrations were observed in the YRE, especially in the river mouth area. Due to the elevation of the Yellow River's lower reaches and the reduction in water and sediment supplies, the progradation rate of the active sub-delta at the river mouth will gradually slow down. As a result, the shoreline will tend to stabilize, and the changes in suspended sediments in the estuary will also become more stable.

<figure><img src="image_16.png" /><figcaption>**Figure 13.** The coastlines of the YRM from 2016 to 2023 were obtained with Sentinel2-MSI (Multi-Spectral Instrument) and Landsat multi-spectral images during the flood seasons, obtained using Normalized Difference Water Index (NDWI) combined with visual interpretation.</figcaption></figure>

In the future, climate change and impacts of human activities are expected to result in the elevation buildup of the Yellow River's tail channel and the reduction of incoming water and sediment load, and the progradation rate of the subaqueous delta at the active river mouth will progressively decrease. The shoreline will gradually stabilize, and suspended sediment dynamics in the estuary should also tend to reach equilibrium [42]. The water-sediment regime of the Yellow River has shifted from the former state of "low water and high sediment" to the current condition of "low water and low sediment." It indicates that WSRS will have less effect on the delta evolution with less river sediment supply. Therefore, the coastal zone of the Yellow River Estuary will face further erosion. This process is expected to favor the stabilization of the existing estuarine channel [6].

## 4.6. *Future Improvements of the Present Study*

In the present study, the use of in-situ data collected in 2004 to establish a model to retrieve TSS from satellite data recorded from 2016 to 2023 may raise questions about temporal representativeness. However, the key point is that the field dataset was gener-
ated in 2004 during the WSRS period, so it is expected to be suitable for our study. To verify whether the model is reliable, an independent validation was made using S3-OLCI estimated TSS values and TSS in situ measurements for the dataset measured during the WSRS period on 19–21 July 2019 and 9 July 2023. The results show a good agreement with a highly significant linear relationship (R² of 0.96), with a MAPE of 29.6% and RMSE of 222 g·m⁻³ (Figure 4). Therefore, in this case, the 2004 dataset is a valuable choice for the TSS retrieval. Future efforts should prioritize real-time in situ observations during WSRS periods, enabling more robust TSS validation for a comprehensive monitoring of sediment dynamics and characteristics in the YRE.

OLCI satellite data have a 300-m resolution, which may limit TSS retrievals in channels and small estuaries. However, the MAPE between the 30 and 300 m TSS products in the YRE is 11.5% (Figure 9d). The improvement from 30 m OLI to 300 m OLCI is not that obvious in the highly turbid waters [43]. Discrepancies in spatial scale between the 300 m resolution remote-sensing data and in situ suspended sediment concentration measurements from hydrologic stations may also introduce inconsistencies in the correlation analysis between daily TSS fluctuations in the YRE and daily SC at the Lijin Station.

One limitation in our study is the temporal discontinuity caused by selecting cloud-free images. Although this approach ensures data quality, it may limit the detection of short-duration extreme events and be biased in terms of seasonal coverage. Future work could integrate harmonized datasets (e.g., GOCI) to obtain not only daily but also hourly variations of TSS. Tides and currents also affect the short-term dispersal pattern of freshwater and sediment plume [24,25]. Further developments should include the influence of coastal currents, wave energy, and bathymetry, determined by numerical modeling, to generate a complete assessment of the littoral areas exposed to the Yellow River turbid plume.

## 5. Conclusions

In order to investigate the variations of total suspended solids in the YRE under the new conditions imposed by the WSRS, S3-OLCI images recorded from 2016 to 2023 during flood periods in the YRE were processed and analyzed. The response of TSS in the YRE to the WSRS was discussed, combining satellite observations and in-situ data from the Lijin Station.

1. Based on the correlation analysis between OLCI-derived Rrs values and in situ measured TSS, an exponential model was established combining the Rrs(665)/Rrs(560) ratio and the Rrs(865) band (R²= 0.94). OLCI images processed using the ACOLITE DSF atmospheric correction showed good agreement with both in situ Rrs and the previously validated OLI images. The results showed that daily OLCI images can be used to monitor the dynamics of terrigenous and anthropogenic TSS in the YRE over short time-scale events such as the WSRS, notably over the recent period of new regulations of the WSRS.

2. Satellite-derived TSS concentrations were generally low with no significant variations as the WSRS was suspended in 2016 and 2017. After the resumption of the WSRS, the daily TSS increased and varied significantly during the flood seasons from 2018 to 2023. The TSS was generally low before the WSRS in June and gradually rose over time due to the large inputs of water and sediment in July and August, then returned to normal values once the WSRS ended. The TSS in the river mouth was observed to be significantly correlated with the sediment load in the river, suggesting that the sediment and river discharges during the WSRS period from June to August were the determining factors controlling the river plume.

3. The flood events, including those caused by the WSRS, gave rise to a significant river plume and outputs of a substantial amount of TSS in adjacent coastal waters. A significant part of the suspended sediments most probably deposited in the Yellow River delta, leading
to continuous shoreline progradation with accretion from 2016 to 2023. The maximum TSS increase was observed during the flood season in 2018, then demonstrated a downward trend from 2018 to 2023.

This study demonstrates the capability of S3-OLCI data to capture short-term WSRS-driven TSS dynamics in the YRE, providing a robust tool for evaluating the scheme's environmental and ecological impacts. Future efforts should prioritize real-time in situ observations during WSRS periods, enabling more robust validation and comprehensive monitoring of sediment characteristics across different water column strata in the YRE.

**Author Contributions:** Conceptualization, Y.L. (Yafei Luo) and Z.H.; methodology, Y.L. (Yafei Luo) and D.D.; validation, Y.L. (Yafei Luo), Y.L. (Yanxia Liu) and L.Y.; formal analysis, Y.L. (Yafei Luo), Y.L. (Yanxia Liu) and L.Y.; investigation, Y.L. (Yanxia Liu) and L.Y.; resources, Y.L. (Yanxia Liu), D.F. and H.H.; data curation, Y.L. (Yanxia Liu) and L.Y.; writing—original draft preparation, Y.L. (Yafei Luo) and D.D.; writing—review and editing, Y.L. (Yafei Luo) and D.D.; supervision, Z.H., D.F. and H.H.; project administration, Y.L. (Yafei Luo) and Z.H.; funding acquisition, Y.L. (Yafei Luo) and Z.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 42206187 and 12374428), the Open Fund of the Key Laboratory of Marine Geology and Environment, Chinese Academy of Sciences (Grant No. MGE2020KG12), the program for scientific research start-up funds of Guangdong Ocean University (Grant No. R20010) and the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (Grant No. 311024018 and SML2024SP006).

**Acknowledgments:** We appreciate Junwu Tang, Wei Zhao, Qingjun Song, and Xiaomei Wang in the National Satellite Ocean Application Service and Song Jin in Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, for collecting in situ data. Acknowledgement for the data support from "National Earth System Science Data Center, National Science and Technology Infrastructure of China (https://www.geodata.cn (accessed on 29 April 2025))".

**Conflicts of Interest:** The authors declare no conflicts of interest.

## References

1. Milliman, J.; Meade, R. World-wide delivery of river sediments to the oceans. *J. Geol.* **1983**, *91*, 1–21. [CrossRef]

2. Liu, S.M.; Li, L.W.; Zhang, G.L.; Liu, Z.; Yu, Z.; Ren, J. Impacts of human activities on nutrient transports in the Huanghe (Yellow River) estuary. *J. Hydrol.* **2012**, *430*, 103–110. [CrossRef]

3. Liu, S.M. Response of nutrient transports to water-sediment regulation events in the Huanghe basin and its impact on the biogeochemistry of the Bohai. *J. Mar. Syst.* **2015**, *141*, 59–70. [CrossRef]

4. Wang, H.; Wu, X.; Bi, N.; Li, S.; Yuan, P.; Wang, A.; Syvitski, J.; Saito, Y.; Yang, Z.; Liu, S.; et al. Impacts of the dam-orientated water-sediment regulation scheme on the lower reaches and delta of the Yellow River (Huanghe): A review. *Glob. Planet Change* **2017**, *157*, 93–113. [CrossRef]

5. Yu, Y.; Shi, X.; Wang, H.; Yue, C.; Chen, S.; Liu, Y.; Hu, L.; Qiao, S. Effects of dams on water and sediment delivery to the sea by the Huanghe (Yellow River): The special role of Water-Sediment Modulation. *Anthropocene* **2013**, *3*, 72–82. [CrossRef]

6. Chen, S.; Gu, S.; Ji, H.; Xu, C. Processes of the Yellow River Mouth on new water and sediment condition. *J. Sediment. Res.* **2019**, *44*, 61–67. (In Chinese)

7. Fan, H.; Huang, H.; Zeng, T.; Wang, K. River mouth bar formation, riverbed aggradation and channel migration in the modern Huanghe (Yellow) River delta, China. *Geomorphology* **2006**, *74*, 124–136. [CrossRef]

8. Qiu, Z.F.; Xiao, C.; Perrie, W.; Sun, D.; Wang, S.; Shen, H.; Yang, D.; He, Y. Using Landsat 8 data to estimate suspended particulate matter in the Yellow River estuary. *J. Geophys. Res. Oceans.* **2017**, *122*, 276–290. [CrossRef]

9. Li, J.; Hao, Y.; Zhang, Z.; Li, Z.; Yu, R.; Sun, Y. Analyzing the distribution and variation of Suspended Particulate Matter (SPM) in the Yellow River Estuary (YRE) using Landsat 8 OLI. *Reg. Stud. Mar. Sci.* **2021**, *48*, 102064. [CrossRef]

10. Li, P.; Ke, Y.; Bai, J.; Zhang, S.; Chen, M.; Zhou, D. Spatiotemporal dynamics of suspended particulate matter in the Yellow River Estuary, China during the past two decades based on time-series Landsat and Sentinel-2 data. *Mar. Pollut. Bull.* **2019**, *149*, 110518. [CrossRef]

11. Jin, S.; Fagherazzi, S.; Fichot, C.G.; Wu, X.; Liu, Y.; Zheng, X.; Zou, T.; Xing, Q. Drivers of suspended sediment dynamics along the shorelines of the Yellow River Delta detected from satellite data. *Earth Surf. Process. Landf.* **2023**, *48*, 3091–3102. [CrossRef]
12. Jin, S.; Zou, T.; Xing, Q.; Zheng, X.; Fagherazzi, S. Particle Size Distribution Slope Changes along the Yellow River Delta Observed from Sentinel 3A/B OLCI Images. *Remote Sens.* **2024**, *16*, 938. [CrossRef]

13. Zhang, M.; Dong, Q.; Cui, T.; Xue, C.; Zhang, S. Suspended sediment monitoring and assessment for Yellow River estuary from Landsat TM and ETM+ imagery. *Remote Sens. Environ.* **2014**, *146*, 136–147. [CrossRef]

14. Yu, Z.; Zhang, J.; Chen, Z.; Hu, Y.; Shum, C.K.; Ma, C.; Song, Q.; Yuan, X.; Wang, B.; Zhou, B. Spatiotemporal evolutions of the suspended particulate matter in the Yellow River Estuary, Bohai Sea and characterized by Gaofen imagery. *Remote Sens.* **2023**, *15*, 4769. [CrossRef]

15. Constantin, S.; Doxaran, D.; Constantinescu, S. Estimation of water turbidity and analysis of its spatio-temporal variability in the Danube River plume (Black Sea) using MODIS satellite data. *Cont. Shelf Res.* **2016**, *112*, 14–30. [CrossRef]

16. Zhang, Y.; Shi, K.; Zhou, Y.; Liu, X.; Qin, B. Monitoring the river plume induced by heavy rainfall events in large, shallow, Lake Taihu using MODIS 250 m imagery. *Remote Sens. Environ.* **2016**, *173*, 109–121. [CrossRef]

17. Luo, X.; Wang, J.; Chen, G.; Zhu, S.; Huo, Z.; Liu, X.; Deng, W. Research on Distribution of Suspended Sediment Concentration in Long Time Series Based on Mid-Resolution Imaging Spectrometer Data and Quantile Trend Analysis. *Reg. Stud. Mar. Sci.* **2020**, *39*, 101399. [CrossRef]

18. Wang, K. Impact and evaluation of water and sediment regulation in the Yellow River on the estuary and its delta. *J. Sediment. Res.* **2005**, *6*, 31–35. (In Chinese)

19. Wang, Q.; Li, Y. Phosphorus adsorption and desorption behavior on sediments of different origins. *J. Soils Sediments* **2010**, *10*, 1159–1173. [CrossRef]

20. Bi, N.; Yang, Z.; Wang, H.; Xu, C.; Guo, Z. Impact of artificial water and sediment discharge regulation in the Huanghe (Yellow River) on the transport of particulate heavy metals to the sea. *Catena* **2014**, *121*, e232–e240. [CrossRef]

21. Xu, B.; Yang, D.; Burnett, W.C.; Ran, X.; Yu, Z.; Gao, M.; Diao, S.; Jiang, X. Artificial water sediment regulation scheme influences morphology, hydrodynamics and nutrient behavior in the Yellow River estuary. *J. Hydrol.* **2016**, *539*, 102–112. [CrossRef]

22. Wang, Y.; Liu, D.; Lee, K.; Dong, Z.; Di, B.; Wang, Y.; Zhang, J. Impact of Water-Sediment Regulation Scheme on seasonal and spatial variations of biogeochemical factors in the Yellow River estuary. *Estuar. Coast. Shelf Sci.* **2017**, *198*, 92–105. [CrossRef]

23. Wu, X.; Bi, N.; Xu, J.; Nittrouer, J.; Yang, Z.; Saito, Y.; Wang, H. Stepwise morphological evolution of the active Yellow River (Huanghe) delta lobe (1976–2013): Dominant roles of riverine discharge and sediment grain size. *Geomorphology* **2017**, *292*, 115–127. [CrossRef]

24. Guo, K.; Zou, T.; Jiang, D.; Tang, C.; Zhang, H. Variability of Yellow River turbid plume detected with satellite remote sensing during water-sediment regulation. *Cont. Shelf Res.* **2017**, *135*, 74–85. [CrossRef]

25. Li, P.; Ke, Y.; Wang, D.; Ji, H.; Chen, S.; Chen, M.; Lyu, M.; Zhou, D. Human impact on suspended particulate matter in the Yellow River Estuary, China: Evidence from remote sensing data fusion using an improved spatiotemporal fusion method. *Sci. Total Environ.* **2021**, *750*, 141612. [CrossRef]

26. Liu, Y.; Huang, H.; Yang, X. The transportation and deposition of suspended sediment and its dynamic mechanism analysis based on Landsat images in the Laizhou Bay. *Acta Oceanolog. Sin.* **2013**, *35*, 43–53. (In Chinese)

27. Vanhellemont, Q.; Ruddick, K. Atmospheric correction of Sentinel-3/OLCI data for mapping of suspended particulate matter and chlorophyll-a concentration in Belgian turbid coastal waters. *Remote Sens. Environ.* **2021**, *256*, 112284. [CrossRef]

28. Vanhellemont, Q.; Ruddick, K. Atmospheric correction of metre-scale optical satellite data for inland and coastal water applications. *Remote Sens. Environ.* **2018**, *216*, 586–597. [CrossRef]

29. Vanhellemont, Q. Daily metre-scale mapping of water turbidity using CubeSat imagery. *Opt. Express* **2019**, *27*, A1372–A1399. [CrossRef] [PubMed]

30. Vanhellemont, Q. Adaptation of the dark spectrum fitting atmospheric correction for aquatic applications of the Landsat and Sentinel-2 archives. *Remote Sens. Environ.* **2019**, *225*, 175–192. [CrossRef]

31. Vanhellemont, Q. Sensitivity analysis of the dark Spectrum fitting atmospheric correction for metre- and decametre-scale satellite imagery using autonomous hyperspectral radiometry. *Opt. Express* **2020**, *27*, A1372–A1399. [CrossRef] [PubMed]

32. A, R.; Qing, S.; Bao, Y. The inspection and application of atmospheric correction algorithm in Landsat-8 OLI data. *Mar. Sci.* **2018**, *42*, 107–115. (In Chinese)

33. Zhan, C.; Yu, J.; Wang, Q.; Li, Y.; Zhou, D.; Xing, Q.; Chu, X. Remote sensing retrieval of surface suspended sediment concentration in the Yellow River Estuary. *Chin. Geogr. Sci.* **2017**, *27*, 934–947. [CrossRef]

34. Shen, F.; Salama, M.H.D.S.; Zhou, Y.X.; Li, J.F.; Su, Z.; Kuang, D.B. Remote-sensing reflectance characteristics of highly turbid estuarine waters—A comparative experiment of the Yangtze River and the Yellow River. *Int. J. Remote Sens.* **2010**, *31*, 639–2654. [CrossRef]

35. Luo, Y.; Doxaran, D.; Ruddick, K.; Shen, F.; Gentili, B.; Yan, L.; Huang, H. Saturation of water reflectance in extremely turbid media based on field measurements, satellite data and bio-optical modelling. *Opt. Express.* **2018**, *26*, 10435–10451. [CrossRef]

36. Zhou, Y.; Hao, Y.; Liu, D.; Cui, T.; Yu, R.; Zhang, Z. Estimation of suspended particulate matter concentration based on Landsat 8 data in the Yellow River Estuary. *J. Mar. Sci.* **2018**, *36*, 35–45. (In Chinese)
37. Doxaran, D.; Froidefond, J.M.; Lavender, S.; Castaing, P. Spectral signature of highly turbid waters: Application with SPOT data to quantify suspended particulate matter concentrations. *Remote Sens. Environ.* **2002**, *81*, 149-161. [CrossRef]

38. Zhao, G.; Jiang, W.; Wang, T.; Chen, S.; Bian, C. Decadal variation and regulation mechanisms of the suspended sediment concentration in the Bohai Sea, China. *J. Geophys. Res. Ocean.* **2022**, *127*, e2021JC017699. [CrossRef]

39. Wen, M.; Shan, H.; Zhang, S.; Liu, X.; Jia, Y. Contribution of waves and currents to sediment resuspension in the Yellow River Delta. *Mar. Georesour. Geotechnol.* **2018**, *37*, 96-102. [CrossRef]

40. Wang, H.; Yang, Z.; Li, G.; Jiang, W. Wave climate modeling on the abandoned Huanghe (Yellow River) Delta lobe and related deltaic erosion. *J. Coast. Res.* **2006**, *22*, 906-918. [CrossRef]

41. Yang, Z.; Ji, Y.; Bi, N.; Lei, K.; Wang, H. Sediment transport off the Huanghe (Yellow River) delta and in the adjacent Bohai Sea in winter and seasonal comparison. *Estuar. Coast. Shelf Sci.* **2011**, *93*, 173-181. [CrossRef]

42. Zuo, F.; Cao, Y.; Fan, Z.; Yin, P.; Li, R.; Qian, Z.; Wang, H.; Zhan, C.; Wang, Q. Research on the stability of river mouth shoreline in the Yellow River Delta from 1996 to 2022. *Mar. Sci. Bull.* **2024**, *43*, 426-436. (In Chinese)

43. Luo, Y.; Doxaran, D.; Vanhellemont, Q. Retrieval and validation of water turbidity at metre-scale using Pléiades satellite data: A case study in the Gironde estuary. *Remote Sens.* **2020**, *12*, 946. [CrossRef]

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