Satellites Reveal Frontal Controls on Phytoplankton Dynamics off the Jiangsu Coast, China

Abstract

The Jiangsu Coastal Thermal Front (JCF), a persistent feature in Chinese marginal seas, plays a significant role in modulating phytoplankton dynamics and carbon cycling. However, the multi-scale spatiotemporal variability of the persistent JCF and the underlying mechanisms driving its ecological effects remain limited. Using satellite observations and reanalysis data, this study systematically investigates the JCF’s distribution and its regulatory impact on phytoplankton chlorophyll-a (Chla) and particulate organic carbon (POC). Results show the persistent JCF is most active in summer and winter, primarily in Haizhou Bay and the Jiangsu Shoal. In summer, stratification-induced nutrient limitation within the Haizhou Bay thermal front decreases Chla and POC (by ~−20% and ~−40%, respectively), whereas nutrient-replete non-frontal waters support higher biomass. In the Jiangsu Shoal, the thermal front blocks the southward transport of POC, helping to maintain stable POC levels in the nearshore non-frontal region; meanwhile, the shift from southward to northward transport leaves the offshore non-frontal area without sufficient replenishment, resulting in a ~35% decrease in POC. In winter, the Haizhou Bay thermal frontal barrier effect restricts suspended particulate matter, alleviating light limitation inside the front and enhancing Chla (up to 15%) while reducing POC due to diminished resuspension. We elucidate that the JCF shapes ecological patterns through two primary pathways: by directly acting as a barrier to material transport and by interacting with ancillary processes like upwelling. These findings advance the mechanistic understanding of frontal impacts on coastal ecosystems and provide a mechanistic basis for understanding synergistic coastal carbon sinks.

1. Introduction

Marine phytoplankton, key drivers of oceanic carbon sequestration through the conversion of atmospheric CO₂ into particulate organic carbon (POC) [1,2,3], exhibit spatiotemporal dynamics closely linked to the characteristics of their surrounding water masses [4,5]. Oceanic fronts—transition zones between distinct water masses characterized by sharp gradients in temperature, salinity, and nutrients [6]—often support elevated biological productivity and act as ecological hotspots [7,8,9]. The movement and aggregation of marine organisms largely depend on changes in water movement, light intensity, and nutrient concentrations. In the frontal regions, strong vertical circulation and processes such as convergence provide critical mechanical energy and a favorable environment for growth [9,10,11,12,13]. Notably, under certain circumstances, frontal systems may also create unfavorable habitat conditions that inhibit the growth of marine phytoplankton [14,15,16]. Moreover, under global warming, frontal activity, and chlorophyll concentrations (Chla) have declined in equatorial and subtropical gyres but increased at high latitudes [17]. These trends are expected to intensify with further warming, potentially reshaping marine biomass and species distributions [17]. Importantly, shifts in fronts and phytoplankton overlap with key global fishing grounds, highlighting the central role of frontal processes in regulating phytoplankton dynamics and ecosystem responses in a changing climate. However, due to regional differences in environmental and hydrodynamic conditions, the physical–biological coupling mechanisms within frontal zones are poorly understood.

Satellite observations provide a powerful tool for studying thermal frontal ecological impacts [18,19]. Surveys based on sea surface temperature (SST) data have identified numerous persistent thermal fronts in the China Sea, including 14 typical fronts such as the tidal front, shelf-break front, plume front, upwelling front, and western boundary current front [12,18]. These thermal fronts exhibit seasonal variation, primarily occurring in winter and summer [20,21]. Moreover, the high-resolution satellites such as the Geostationary Ocean Color Imager (GOCI) has made it possible to observe ecologically significant phenomena associated with frontal structures [22]. GOCI-II provides high-resolution (250 m) hourly data, significantly enhancing the detection of oceanic fronts and sub-mesoscale structures such as filaments, spirals, and jets. Compared to traditional polar-orbiting satellites, it offers improved insights into biological fronts and captures their diurnal variations [23]. This enhanced observational capability allows for clearer identification of the physical processes driving Chla variability, thereby advancing our understanding of marine biophysical interactions.

The Jiangsu coastal area, located in the southwestern Yellow Sea, is a dynamic region influenced by river discharges (e.g., from the Yangtze River), the Yellow Sea Warm Current (YSWC), the Yellow Sea Cold Water Mass, and intense tidal mixing [24,25] (Figure 1). This area hosts several persistent thermal fronts, collectively known as the Jiangsu Coastal Fronts (JCFs)—including the Haizhou Bay Coastal Front (HBCF), Jiangsu Shoal Front (JSF), and South Jiangsu Coastal Front (SJCF)—which are identified based on the climatological distribution of SST gradients (Figure 1b). The JCF plays a vital role in sustaining high phytoplankton productivity and supporting abundant fishery resources [12]. During summer, intensified solar heating enhances stratification in offshore waters [26,27], leading to a shallower the mixed layer depth (~15 m) in the southwestern Yellow Sea [27]. Therefore, the JCF (including the HBCF and SJCF) is primarily influenced by strong tidal energy dissipation and turbulent mixing, forming tidal fronts between well-mixed nearshore waters and stratified offshore waters [12]. Previous studies indicate that thermal fronts in the southwestern Yellow Sea are associated with upwelling, which supplies nutrients to surface waters and alleviates limitations in phosphorus and silicon, thereby enhancing phytoplankton growth [28,29,30]. Furthermore, the El Niño–Southern Oscillation (ENSO) may affect phytoplankton dynamics by modulating summer wind forcing, which in turn influences the intensity of thermal frontal upwelling during the summer months [28,29,30]. During winter, enhanced hydrodynamic forcing destroys stratification, leading to a vertically homogeneous water column and allowing the mixed layer to deepen to near the seabed [26,27]. Thus, the JCF (including the HBCF and JSF) is mainly formed by the convergence of the warm YSWC and the colder nearshore waters (Figure 1b). The barrier effects of the fronts restrict cross-shore transport of suspended sediments, thereby reducing turbidity, alleviating light limitation within the frontal zone, and promoting phytoplankton biomass [29,31]. While these studies highlight the ecological importance of the JCF, the precise mechanisms governing phytoplankton and POC dynamics across different seasons and individual fronts remain unclear.

This study aims to investigate the seasonal variability of persistent oceanic thermal fronts in the Jiangsu coastal waters and their regulatory effects on phytoplankton biomass and carbon cycling using multi-sensor satellite and reanalysis data. Moreover, Chla has been established as a reliable proxy for primary productivity (PP), given its strong positive correlation with and dominant influence on the interannual variability of PP [32,33,34,35]. Therefore, we will analyze the spatiotemporal characteristics of Chla and POC in relation to frontal activity in summer and winter and explore the underlying physical–ecological coupling mechanisms.

2. Materials and Methods

2.1. Data

2.1.1. Satellite-Derived Data

The ocean color data utilized in this study, comprising Chla, POC, diffuse attenuation coefficient (K_d), photosynthetically available radiation (PAR), and suspended particulate matter (SPM), were acquired from the GlobColour database (https://hermes.acri.fr/, accessed on 30 April 2025). The dataset spans the period from 2012 to 2022, with a spatial resolution of 4 km and daily temporal resolution. These products are derived from the merging of multiple satellite sensors, including SeaWiFS, MERIS, MODIS, VIIRS, VIIRS-JPSS, OLCI, and OLCI-B [36].

This study also evaluated the potential of GOCI for identifying and monitoring front-induced phytoplankton variability in the Jiangsu coastal area. The GOCI-II-derived Chla and total suspended material (TSM) products at 250 m spatial resolution from 1 May 2023, 10 May 2024, and 28 December 2024, were downloaded from the National Ocean Satellite Center (NOSC) of South Korea (https://nosc.go.kr/opendap/, accessed on 6 November 2025).

2.1.2. Reanalysis Data

SST data were obtained from two Level-4 (L4) gap-free daily products distributed by the Copernicus Marine Environment Monitoring Service (CMEMS): the Operational Sea Surface Temperature and Ice Analysis (OSTIA) product (doi:10.48670/moi-00168), covering 2012–2022, and the European Space Agency SST Climate Change Initiative & Copernicus Climate Change Service (ESA SST CCI & C3S) product (doi:10.48670/moi-00169), used for selected dates in 2023–2024. Both datasets merge satellite and in situ observations to provide globally consistent SST on a 0.05° grid. OSTIA, produced by the UK Met Office, is an operational analysis system optimised for near-real-time applications, while the ESA SST CCI & C3S product represents a climate-quality reprocessed dataset developed for long-term climate studies, integrating multi-sensor satellite records under frameworks led by the European Space Agency and the Copernicus Climate Change Service [37,38].

Temperature, salinity, ocean currents, and mixed layer depth data were obtained from the CMEMS Mercator Global Ocean Analysis and Forecasting System (product ID: GLOBAL_ANALYSISFORECAST_PHY_001_024). This dataset provides daily fields at a horizontal resolution of 1/12° with 50 vertical levels. It is based on the Nucleus for European Modelling of the Ocean (NEMO) model—a widely used ocean circulation model developed by a European consortium—and is forced by ERA-Interim/ERA5 atmospheric reanalysis from the European Centre for Medium-Range Weather Forecasts. The system assimilates satellite SST, altimetry, sea ice concentration, and in situ profiles through a 3D-Var and Kalman filter scheme [39,40,41].

2.2. Methods

2.2.1. Front Detection

The magnitude of the temperature gradient can be used to determine the location of oceanic fronts. This study adopts the gradient magnitude (GM) method proposed by Belkin [42] for front detection. The formula is as follows:

$${\displaystyle \frac{\partial T}{\partial x}}=\left[\begin{array}{ccc}-1& 0& +1\\ -2& 0& +2\\ -1& 0& +1\end{array}\right]\times {\displaystyle \frac{1}{4}}\cdot T, {\displaystyle \frac{\partial T}{\partial y}}=\left[\begin{array}{ccc}+1& +2& +1\\ 0& 0& 0\\ -1& -2& -1\end{array}\right]\times {\displaystyle \frac{1}{4}}\cdot T$$

(1)

$$GM=\sqrt{{\left({\displaystyle \frac{\partial T}{\partial x}}\right)}^2+{\left({\displaystyle \frac{\partial T}{\partial y}}\right)}^2}$$

(2)

where T represents the 3 × 3 SST matrix centered on the target grid point. ${\displaystyle \frac{\partial T}{\partial x}}$ denotes the temperature gradient in the zonal (longitudinal) direction, while ${\displaystyle \frac{\partial T}{\partial y}}$ denotes the gradient in the meridional (latitudinal) direction. GM represents the horizontal gradient magnitude of temperature.

Although gradient-based methods enable efficient oceanic front detection, they are often susceptible to substantial noise interference and may fail to render fronts distinctly discernible in the temperature field [43]. To improve localization accuracy, the present study complements the gradient method with an analysis of front frequency, defined as the proportion of time a front is detected at a given location over the study period, for comparison and validation. For this purpose, fronts were identified from satellite-derived SST data using the CCAIM algorithm [18]. And the study area was subsequently divided into frontal and non-frontal regions according to the magnitude of front frequency.

2.2.2. Calculation of Chla_(ratio), POC_(ratio) and Chla/POC

To reflect the variability of Chla and POC in frontal zones relative to their climatological states (2012–2022), this study defines two indices: ${ Chla}_{\left(ratio\right)}$ and ${POC}_{\left(ratio\right)}$. The calculation formulas are as follow:

$$Chl{a}_{\left(\mathrm{ratio}\right)}={\displaystyle \frac{Chla-\overline{Chla}}{\overline{Chla}}} PO{C}_{\left(\mathrm{ratio}\right)}={\displaystyle \frac{POC-\overline{POC}}{\overline{POC}}}$$

(3)

where $\overline{Chla}$ and $\overline{POC}$ represent the annual climatological values of Chla and POC (2012–2022), respectively. Meanwhile, the extent of cellular pigmentation was estimated by the ratio between Chla and POC (Chla/POC). An increase in Chla/POC indicates an increase in intracellular Chla (cells being more pigmented).

2.2.3. Estimation of Mixed-Layer Averaged Irradiance

To quantify the light availability for phytoplankton within the mixed layer, the mixed-layer mean irradiance ($I_m$) was calculated as follows [13]:

$$I_m={\displaystyle \frac{1}{k_d{z}_{ml}}}{I}_0{e}^{-k_d{z}_{ml}}\left(1-e^{-k_d{z}_{ml}}\right)$$

(4)

where $k_{d }$ denotes the diffuse attenuation coefficient, $I_{0 }$ represents the photosynthetically available radiation at the sea surface, and $Z_{ml }$ indicates the mixed layer depth.

3. Results

3.1. Climatological Patterns of JCF, Front Frequency and SST

Figure 2 shows the spatial and temporal distributions of SST, SST gradient, and front frequency during summer (May–September) and winter (November–March) in the coastal waters off Jiangsu. Two persistent thermal frontal systems are identified: the HBCF and the SJCF. To examine their environmental influence, two transects were selected across areas of strong frontal variability: transect A across the HBCF and transect B across the SJCF (Figure 2(a1)). Along transect A (HBCF), the nearshore non-frontal region shows an SST gradient of ~0.07 °C km⁻¹, front frequency of ~5%, and SST ranging between 15–30 °C (Figure 2a–e). Within the frontal zone, the gradient strengthens to ~0.1 °C km⁻¹, with a front frequency of ~10% and an average SST near 17 °C (Figure 2a–e). The offshore non-frontal region exhibits a gradient of ~0.05 °C km⁻¹, front frequency of ~3%, and SST varying from 15–27 °C (Figure 2a–e).

Along transect B (SJCF, Figure 2a–e), the nearshore non-frontal region features an SST gradient of ~0.07 °C km⁻¹, front frequency of ~4%, and SST between 15–30 °C (Figure 2a–e). The frontal zone shows a gradient of ~0.1 °C km⁻¹, front frequency of ~8%, and SST ranging from 14–28 °C (Figure 2a–e). Offshore non-frontal waters exhibit a gradient of ~0.03 °C km⁻¹, front frequency of ~3%, and SST of 12–25 °C (Figure 2a–e). In both transects, the frontal zones are clearly distinguished by elevated temperature gradients and frontal frequencies relative to adjacent non-frontal areas (Figure 2a). Additionally, offshore non-frontal waters are consistently cooler than their nearshore counterparts (Figure 2(a1)).

During winter (November–March), the two persistent thermal frontal systems in the Jiangsu coastal waters—the HBCF and the JSF—coalesce into a single, continuous front (Figure 2(g1)). To analyze its environmental influence, transect C (HBCF) was established across an area of pronounced frontal variability within the Haizhou Bay region (Figure 2f–j). Along transect C (purple line in Figure 2f–j), the nearshore non-frontal region exhibits an SST gradient of ~0.05 °C km⁻¹, a front frequency of ~3%, and SST values between 5–15 °C (Figure 2f–j). Within the frontal zone, the gradient increases to ~0.15 °C km⁻¹, accompanied by a front frequency of ~12% and SST ranging from 7–17 °C (Figure 2f–j). In the offshore non-frontal region, the SST gradient weakens to ~0.03 °C km⁻¹, with a front frequency of ~3% and SST varying from 10–20 °C (Figure 2f–j). Overall, the frontal zone is clearly distinguished by stronger temperature gradients and higher frontal frequencies compared to both nearshore and offshore non-frontal areas (Figure 2(g2)). Additionally, offshore non-frontal waters are noticeably warmer than those in the nearshore region (Figure 2(g1)).

3.2. Effects of Front on Chla and POC

The spatiotemporal distribution characteristics of Chla and POC in thermal frontal zones are crucial for a further understanding of the regulatory mechanisms of frontal activity. In early summer (May–June), both Chla_(ratio), POC_(ratio) and Chla/POC exhibit distinct spatial patterns (Figure 3a–e). Chla_(ratio) is significantly lower in frontal zones (areas of higher frontal frequency) than in adjacent non-frontal regions, whereas POC_(ratio) is markedly higher in offshore non-frontal waters compared to nearshore areas. In contrast, Chla/POC is clearly higher in nearshore non-frontal waters than in offshore regions (Figure 3a,b).

Along transect A (HBCF) in May, the nearshore non-frontal zone (within 30 km) shows a Chla_(ratio) between −20% and −15%, POC_(ratio) near 0% and Chla/POC between 0.007 g:g and 0.010 g:g (Figure 4a–c). Within the frontal zone (30–60 km), Chla_(ratio) ranges from −15% to 5%, POC_(ratio) from 0% to 10% and Chla/POC from 0.006 g:g to 0.007 g:g. The offshore non-frontal zone (beyond 60 km) exhibits higher values, with Chla_(ratio) at 5–18%, POC_(ratio) at 10–40% and Chla/POC at 0.004–0.006 g:g (Figure 4a,b). By June, nearshore non-frontal zone and frontal zone show increased values: nearshore Chla_(ratio) rises to 25–40%, POC_(ratio) to 10–20% and Chla/POC to 0.008–0.011 g:g (Figure 4a–c); in the frontal zone, Chla_(ratio) reaches 20–40% and POC_(ratio) 20–60% and Chla/POC to 0.004–0.011 g:g; offshore, Chla_(ratio) varies from −15% to 28%, POC_(ratio) from 40% to 70% and Chla/POC to 0.004–0.005 g:g (Figure 4a–c). Overall, both ratios increase from May to June, with the most pronounced Chla_(ratio) rise occurring nearshore (Figure 4a). A notable concave depression in Chla_(ratio) is observed within the June frontal transect, where values are lower than in adjacent non-frontal regions (Figure 4a). Meanwhile, Chla/POC increases rapidly in the nearshore region and then gradually decreases from the frontal zone toward offshore non-frontal waters with increasing distance (Figure 4a–c).

In transect B (SJCF) during May, Chla_(ratio) ranges from −15% to 0%, POC_(ratio) from 5% to 10% across all zones and Chla/POC is near 0.0075 g:g in the nearshore and frontal regions, but rapidly decreases to 0.005 in the offshore region (Figure 4e–g). By June, values remain relatively stable: nearshore Chla_(ratio) is 5–15%, POC_(ratio) 5–12% and Chla/POC is 0.008–0.010 g:g (Figure 4e–g); in the frontal zone (15–80 km), Chla_(ratio) is −5% to 15%, POC_(ratio) −5% to 5% and Chla/POC is 0.0075–0.008 g:g; offshore, Chla_(ratio) falls between −30% and 5%, POC_(ratio) ranging from −35% to 5% and Chla/POC is 0.005–0.0065 g:g (Figure 4e–g). The POC_(ratio) in the offshore non-frontal region during May is significantly elevated relative to other zones and periods (Figure 4f). Meanwhile, Chla/POC in the offshore non-frontal region decreases rapidly during May and June (Figure 4g).

In winter (November–March), both Chla_(ratio), POC_(ratio) and Chla/POC are elevated within the frontal zone relative to adjacent non-frontal areas (Figure 4i–k). Along transect C (HBCF), the nearshore non-frontal region (within 20 km) exhibits a Chla_(ratio) range of −60% to 8%, POC_(ratio) range of −10% to 5% and Chla/POC range of 0.006 to 0.010 g:g (Figure 4i–k). Within the frontal zone (20–60 km), Chla_(ratio) increases to −60% to 15%, POC_(ratio) ranges from −10% to 4% and Chla/POC range of 0.006 to 0.009 g:g (Figure 4i–k). In the offshore non-frontal region (beyond 60 km), Chla_(ratio) varies widely from −75% to 30%, POC_(ratio) ranges from −5% to 25% and Chla/POC range of 0.003 to 0.007 g:g (Figure 4i–k). Overall, the frontal zone is characterized by a sharp increase in Chla_(ratio)and Chla/POC, accompanied a concurrent decrease in POC_(ratio) relative to non-frontal waters (Figure 4i–k).

Given the complexity of the spatial patterns observed across transects A–C, we synthesize the most prominent features and contrasts among the nearshore, frontal, and offshore zones in

Table 1. Key biogeochemical findings for frontal and non-frontal regions across seasons and transects.

Season	Transect	Key Findings
Summer	A	1. Chla in the frontal region are approximately 10–20% lower than those in adjacent non-frontal regions (June). 2. Chla/POC is higher in the nearshore region than in the offshore region.
Summer	B	1. POC is markedly higher in offshore non-frontal regions in May than in June. 2.while Chla/POC is higher in the nearshore region than in the offshore region.
Winter	C	1. Chla is enhanced within the frontal zone. 2. Chla/POC is enhanced within the frontal zone. 3. POC is reduced within the frontal zone.

.

Moreover, this study analyzed high-resolution GOCI-II data to examine the effects of frontal processes on Chla and particulate matter distribution (Figure 5). GOCI-II observations of Chla and TSM for two thermal frontal systems: the HBCF (Figure 5a–d) in summer (10 May 2024) and winter (28 December 2024), and the SJCF (Figure 5e–h) in summer (1 May 2023).

For the HBCF in summer, three distinct zones were delineated between 35.0° N and 35.6° N (Figure 5a–d). The frontal zone (II) exhibited significantly lower concentrations of both Chla and TSM compared to the nearshore (I) and offshore (III) zones, which corresponded with intermediate SST and a moderate frontal gradient (Figure 5a–d).

For the SJCF in summer, elevated levels of Chla and TSM were observed along the Jiangsu coast and to the south of the Shandong Peninsula (Figure 5e–h). The frontal zone displayed the most pronounced SST gradient among the three zones. A clear cross-shelf transport of materials from the Shandong Peninsula toward the Jiangsu Shoal was evident (Figure 5e–h).

For the HBCF in winter, high Chla and TSM were predominantly concentrated in coastal areas (Figure 5i–l). The frontal zone exhibited elevated levels of both Chla and TSM, as indicated by black arrows (Figure 5k,l), forming a distinct band that contrasted with the adjacent waters. These high-resolution results both confirmed the broader patterns from the GlobColour database and refined them with finer-scale spatial details (Table 1, Figure 5).

3.3. Impacts of Fronts on the Hydrographic Environment

Phytoplankton growth and productivity in the frontal zone are strongly mediated by local environmental conditions, such as temperature-salinity structure, current transport, and light availability. Examining the spatial patterns and temporal variations of these factors helps elucidate the mechanisms through which frontal dynamics regulate ecosystem-level biological processes.

Figure 6 presents temperature-salinity profiles along transect A (summer), B (summer), and C (winter), illustrating seasonal hydrographic structures near the fronts. In summer (Transects A and B), the nearshore non-frontal zones (within 30 km for A, 15 km for B) exhibited vertically uniform temperature and salinity, indicating strong mixing. Within the frontal zones (30–60 km for A, 15–80 km for B), isotherms and isohalines were horizontally compressed, forming sharp gradients consistent with the high SST gradients in Figure 2. Offshore of the fronts (beyond 60 km for A, 80 km for B), isopleths sloped upward, corresponding to the cooler surface layer shown in Figure 2. In winter (Transect C), nearly vertical isopleths reflected strong vertical mixing throughout. The nearshore zone (within 20 km) was characterized by cold, low-salinity water, the frontal zone (20–60 km) showed tightly spaced contours indicating steep gradients, and the offshore area (beyond 60 km) exhibited warmer, saltier properties.

Figure 7 illustrates the evolution of summer ocean currents and their corresponding influence on the transport and spatial distribution of Chla and POC. In May, a distinct southward current, with speeds reaching approximately 0.2 m/s, flowed from the southern Shandong Peninsula toward the offshore region associated with the South Jiangsu Coastal Front (SJCF, Transect B in Figure 7). This southward transport was closely linked to a marked accumulation of POC in the same area, where the POC_(ratio) peaked at nearly 40% (Figure 7a,c). By June, the current pattern had reversed, shifting to a northward flow that directed water masses toward the Jiangsu Shoal before recirculating back to the southern Shandong Peninsula. Concurrent with this circulation shift, the previously elevated POC_(ratio) dissipated significantly, indicating a clear temporal and spatial coupling between current dynamics and POC distribution (Figure 7b,d).

Figure 8 illustrates winter light conditions and their role in shaping the spatial distributions of Chla and POC, mediated by current-driven particulate transport. A clear cross-frontal gradient is evident: in the nearshore non-frontal zone (within 20 km), high concentrations of suspended particulate matter (~70 g m⁻³) correspond to low light levels (I_m ≈ 4 μmol m⁻² s⁻¹). Across the frontal zone (20–60 km), suspended matter decreases sharply from 70 to 10 g m⁻³, while I_m increases from 4 to 8 μmol m⁻² s⁻¹. Further offshore (beyond 60 km), particulate concentrations stabilize around 10 g m⁻³, and I_m, declines rapidly back to approximately 4 μmol m⁻² s⁻¹ (Figure 8).

4. Discussion

Frontal structures directly influence phytoplankton primary productivity and biomass by modulating key physical processes—including horizontal transport, vertical advection, and shear mixing—which in turn reshape nutrient distributions, material fluxes, and light conditions [9,10,11,13,20]. This study demonstrates that the spatial distribution of surface phytoplankton biomass is regulated by water mass boundaries defined by frontal structures and their associated dynamics. Through the combined effects of frontal convergence and vertical mixing, nutrients and light are redistributed, thereby modulating phytoplankton variability spatiotemporally (Figure 6, Figure 7 and Figure 8). In the following section, we systematically analyze how frontal structures, via these physical processes, regulate nutrient supply, material transport, and light availability to shape phytoplankton spatial patterns.

4.1. The Regulatory Role of Oceanic Fronts in Nutrient Dynamics

In the HBCF (transect A, summer), In the offshore region, the temperature-salinity profiles show a pronounced upward tilting of isotherms and isohalines on the offshore side, accompanied by low-temperature and high-salinity surface water, which indicates the presence of upwelling in this area (Figure 6a,b,f,g), consistent with previous studies [12,30,44]. In summer, a strong offshore decline in surface nutrients drives nutrient-limited phytoplankton growth (Chla) in the southwestern Yellow Sea, as observed in field studies [45,46,47,48]. The nearshore region itself has a higher concentration of nutrients, which can promote phytoplankton growth (Figure 9) [12,44]. Conversely, fronts with strong temperature-salinity gradients suppress the upwelling of nutrient-rich bottom waters and lack the high-concentration nutrients found in the nearshore regions (Figure 9) [49]. Phytoplankton primary production is the dominant source of summer POC [34,50,51]. As a result, the nutrient concentration in these areas is lower than in the adjacent regions, leading to reduced Chla and POC compared to the surrounding areas (Figure 9).

Moreover, GOCI-II observation show that both Chla and TSM are lower within the frontal zone than in adjacent waters (Figure 5c,d), a spatial pattern consistent with the measured Chla/POC ratio. Summer POC is primarily derived from phytoplankton primary production, as indicated by Chla [50], this ratio was significantly reduced in frontal and offshore areas compared to nearshore regions (Figure 4c), indicating a physiological adjustment in phytoplankton under summer nutrient limitation [30,52]. Upwelling near the Jiangsu coastal front enriches surface layers with nutrients [12,52]. In response to variations in frontal mixing intensity [30,44], the phytoplankton community undergoes succession—a shift evidenced by the significant correlation between pelagophyte distribution and upwelling strength [44,52]. Thus, the observed decline in Chla/POC ratios in upwelling regions can be attributed, at least in part, to these structural changes, as different algal groups employ distinct pigment–carbon allocation strategies [53]. This, in turn, suggests that the frontal environment influences carbon export: by modulating phytoplankton physiology and community structure, it may enhance the efficiency of POC export through biological pump-related pathways.

4.2. Current-Driven Transport and Frontal Barrier Effect

In the SJCF (transect B, summer), upwelling near the front supports comparable Chla_(ratio) levels in frontal and offshore non-frontal zones (Figure 4e and Figure 6c). However, POC_(ratio) shows a pronounced seasonal difference: values in May are significantly elevated (~8%) compared to June (~−30%; Figure 4f). Under relatively stable Chla_(ratio) conditions, the marked fluctuations in POC_(ratio) suggest potential contributions from allochthonous POC sources or lateral transport processes, in addition to local phytoplankton production (Table 1).

In summer, phytoplankton growth and primary productivity are most active. The southern waters of the Shandong Peninsula exhibit high levels of Chla and POC content (Figure 7a,d). In May, the southward currents transport high concentrations of POC to the offshore region of the SJCF (Figure 10). However, the strong stratification at the front traps these POC-rich waters outside the front [12]. By June, the southward current shifts to a northward flow, preventing the transport of high-concentration POC from the southern part of the Shandong Peninsula to the offshore area of the Jiangsu Shoal Front. As a result, POC levels in the offshore region in June are significantly lower than in May. In contrast, the nearshore region remains unaffected by the current shift due to the front’s blocking effect, and thus, POC levels remain stable.

Notably, this study utilizes high temporal and spatial resolution GOCI-II satellite remote sensing data to successfully capture the southward diffusion of Chla and TSM in May, which also explains the source of the anomalously high POC observed in the offshore non-frontal region along transect B (Figure 5g,h). Additionally, the results clearly show the significant blocking effect in the frontal region, indicating that the front plays an important role in regulating material exchange between nearshore and offshore waters from both a dynamical and ecological perspective (Figure 5g,h).

4.3. Modulating Light Availability Through Oceanic Fronts

In winter, the temperature and salinity isopleths are nearly vertical, indicating strong vertical mixing that extends throughout the water column (Figure 6e,j). This enhanced vertical mixing deepens the mixed layer, increases surface nutrient concentrations, and elevates water turbidity, thereby making light availability the primary limiting factor for phytoplankton growth and primary production [34,48,53]. The warm, saline Yellow Sea Warm Current interacts with the cold, less saline coastal freshwater, leading to the formation of a thermal front (Figure 6e,j) [12]. The frontal zone facilitates the settling of suspended particulates, improving light penetration and promoting phytoplankton growth [31]. In the HBCF (transect C, winter), The frontal zone exhibits an increase in Chla_(ratio) (~30%), a decrease in POC_(ratio) (5–10%) and an increase in Chla/POC (~0.003 g:g) (Figure 4i–k). Notably, this study successfully captured and identified this fine-scale mechanism using GOCI-II satellite products (Figure 5k,l). Based on GOCI-II derived Chla observations, the observed high Chla values in the frontal region are consistent with the high Chla distribution pattern identified in this study (Figure 4i).

This study similarly demonstrates that intense vertical mixing and elevated concentrations of SPM (>90 g m⁻³) markedly reduce light availability in nearshore waters, with I_m values declining to approximately 4 μmol m⁻² s⁻¹ (Figure 8a–d). The frontal zone functions as a transport barrier, leading to an abrupt offshore decline in SPM (down to ~10 g m⁻³) (Figure 8b and Figure 11). This sharp reduction facilitates a notable increase in light penetration, raising I_m within the frontal zone to around 8 μmol m⁻² s⁻¹ (Figure 8d and Figure 11). The alleviation of light limitation in this region enhances photosynthetic efficiency, leading to an approximate 30% increase in Chla and enhancing primary productivity (Figure 4i–k and Figure 11) [34,53]. Under improved light conditions, phytoplankton allocate more resources to light-harvesting pigments and photosynthetic apparatus, thereby raising the Chla-to-biomass ratio. Although winter resuspension is the primary control on POC variability, the reduction in suspended particulate matter within the frontal zone leads to a decrease in POC, which in turn partially results in an increase in the Chla/POC ratio [50]. However, accompanied by improved light conditions, a pronounced increase in Chla is also observed in the frontal region [12,34,53,54]. This indicates that the variations in Chla/POC are not solely driven by physical processes, but also reflect complex physiological adjustments of phytoplankton in response to changes in the light environment.

5. Conclusions

This study demonstrates that oceanic fronts modulate phytoplankton biomass and primary productivity in the Jiangsu coastal area through distinct physical–ecological mechanisms. In Haizhou Bay during summer (transect A), tidal-induced frontal upwelling enhances nutrients and productivity offshore; meanwhile, strong stratification within the front limits vertical nutrient supply, suppressing phytoplankton growth. Nearshore productivity is further supported by riverine nutrient inputs. In the Jiangsu Shoal region during summer (transect B), the front acts as a barrier to cross-frontal transport, trapping POC-rich waters derived from the Shandong Peninsula in the offshore non-frontal zone. In the Haizhou Bay during winter (transect C), the frontal barrier reduces suspended particulate matter, alleviating light limitation within the frontal zone and stimulating phytoplankton growth. These findings underscore the role of coastal fronts in structuring marine productivity by regulating nutrient, material, and light conditions.