Variability of the Primary Productivity in the Yellow and Bohai Seas from 2003 to 2020 Based on the Estimate of Satellite Remote Sensing

Abstract

Monitoring marine primary productivity (PP) is crucial for understanding changes in the marine ecosystem. Based on satellite data and the vertically generalized production model (VGPM), this study investigated the spatiotemporal distribution and long-term trend of PP in the Yellow and Bohai Seas (YBSs) from 2003 to 2020. By using the calibrated satellite data and optimized parameterization scheme, the accuracy of the PP results in the YBSs was significantly improved compared to online PP products. The annual mean PP in the YBSs from 2003 to 2020 was 523.8 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$, with significant seasonal and interannual differences. Seasonally, PP in the Yellow Sea and the Bohai Sea exhibited bimodal (two peaks in May and October) and unimodal (one peak in June) variation, respectively. The magnitude of mean PP in the YBSs was ranked as spring > summer > autumn > winter, with spring PP (~1000 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$) contributing more than 40% of the annual PP. The annual mean PP in the YBSs showed an overall decrease from 2003 to 2020, with a decrease rate of 5–6 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})/\mathrm{y}$. The interannual variation of the PP was mainly related to the variability of the chlorophyll-a concentration and was essentially inverse to the phases of the Pacific Decadal Oscillation and the El Niño-Southern Oscillation.

1. Introduction

Marine primary productivity (PP) refers to the rate at which marine phytoplankton produce organic matter through photosynthesis and is crucial for the marine ecosystem and environment [1,2,3,4,5,6,7,8,9]. Although shelf seas only account for 10% of the world’s oceans, they provide over one third of the PP. Due to their high PP, shelf seas play an important role in the global carbon cycle, especially in the mid-low latitudes, where they have become the most important carbon sink region worldwide [10,11]. Therefore, it is essential to quantify the temporal and spatial variation characteristics and long-term trends of PP in shelf seas to comprehend and predict the patterns and potential trends of marine carbon sinks in the context of climate change, and to formulate relevant management strategies.

Marine PP can be measured using field observations [12,13,14], which is important for accurately understanding the level of PP in oceans. However, field measurements are inefficient and costly, and cannot meet the needs of large spatiotemporal data research due to the limited duration and number of in situ observation stations. Satellite remote sensing has the advantage of monitoring the ocean for a long time and on a large scale, and is increasingly becoming an important method to estimate marine PP, providing a possibility for a clearer understanding of the continuous spatial and temporal variability of marine PP.

Satellite remote sensing estimates marine PP by using inversion models, which are mainly of two types. One is the empirical statistical model, which fits the linear equations by measuring data in the study area to estimate the PP values [15,16,17,18,19]. The other is the ecological process model, in which some environmental and ecological parameters in the ecological mathematical model are obtained by remote sensing and then calculated to estimate PP [20,21,22]. Among them, the vertically generalized production model (VGPM), which is based on satellite remote sensing parameters to estimate the vertical integrated net PP of the marine euphotic layer, is one of the most widely used methods for estimating marine PP due to its high estimation accuracy and its strong applicability. Thus, the VGMP model has been widely used in various marine areas [23,24,25].

The Yellow and Bohai Seas are important continental shelf seas in the northwestern Pacific, covering an area of approximately 450,000 km², with an average water depth of around 40 m (Figure 1). These seas exhibit high levels of primary productivity and deposition rates, which play a crucial role in the carbon cycling of China’s shelf seas [26]. Previous studies have utilized remote sensing data and the VGPM model to investigate the fundamental characteristics and seasonal variations of PP in the Yellow and Bohai Seas, but the results have shown significant differences. Tan and Shi [25] estimated the PP of the eastern shelf seas of China using the VGPM model and concluded that the annual average PP was about 564.39 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$ in the Bohai Sea, 363.08 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$ in the North Yellow Sea, and 536.47 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$ in the South Yellow Sea. However, Yang [27] estimated the annual mean PP in the Yellow Sea using the VGPM model to be > 1000 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$. Cong [28] estimated PP using the data from the SeaWiFS and MODIS sensors, and the results showed that the PP reached 500–700 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$ in most regions of the Yellow Sea during spring, and that it was highest during summer, with values of 800–1000 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$. However, the summer PP of the Yellow Sea estimated by Jia et al. [29] was only 530 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$. The VGPM results of Ding [30] showed that the maximum PP in the Yellow and Bohai Seas was > 2000 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$ in summer, and the PP in the Yellow Sea showed a bimodal distribution with two peaks in June and September. The VGPM results of Li et al. [31] showed that the PP of the Bohai Sea was low between January and April, with a monthly average of only 677 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$ in January, and it was 5265 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$ in August and 825 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$ in December. Previous studies have mainly focused on the seasonal variation of PP in the Yellow and Bohai Seas, while the interannual variability of PP and its long-term trend have not been well understood.

The results of previous studies suggest a large difference in the estimates of PP in the Yellow and Bohai Seas. This difference may be attributed to the use of different satellite data and parameterization schemes in the VGPM model, as well as the lack of validation of in situ measurements. This difference also implies a potential bias in the previous PP estimates, which could result from the bias of the satellite product and the parameterization schemes. For instance, global sea surface chlorophyll products, such as MODIS or SeaWiFS chlorophyll-a concentration data, were commonly used in previous studies, but they were mainly produced for Case-I marine waters and may not be accurate for shelf seas like the Yellow and Bohai Seas. The accuracy of these products can be significantly influenced by suspended sediments and colored matters, leading to large errors in chlorophyll-a concentration exceeding 100% [32]. Additionally, errors in the traditional euphotic depth for the satellite data [33] can also contribute to inaccuracies in the VGPM model results. These errors can accumulate and seriously affect the accuracy of PP estimation results. Furthermore, the parameterization schemes of the maximum photosynthetic rate in the VGPM model can vary in different sea areas, which can also affect the accuracy of the PP estimates [34,35].

In this study, we improved the accuracy of the satellite-estimated PP in the Yellow and Bohai Seas by adopting an optimized parameterization scheme for the maximum photosynthetic rate and using calibrated satellite-derived chlorophyll-a concentrations and euphotic depth data in the VGPM model. We further analyzed the temporal and spatial variability of PP, and focused on its interannual variability in particular.

2. Materials and Methods

2.1. VGPM Model

The vertical integral PP in the euphotic zone can be calculated using the VGPM equation, which is given by [22]:

$$PP=0.66125\times P_{opt}^B\times [\frac{E_0}{E_0+4.1}]\times C_{opt}\times Z_{eu}\times D_{irr},$$

(1)

where $PP$ is the integrated net PP in the euphotic zone ($\mathrm{mg}/({\mathrm{m}}^2·\mathrm{d})$), $P_{opt}^B$ is the maximum photosynthetic rate within a water column ($\mathrm{mg}/(\mathrm{mg}·\mathrm{d})$), $E_0$ is the photosynthetically active radiation (PAR) at the sea surface ($\mathrm{mol}/({\mathrm{m}}^2·\mathrm{d})$), $Z_{eu}$ is the depth of the euphotic zone ($\mathrm{m}$), $C_{opt}$ is the corresponding chlorophyll-a concentration at $P_{opt}^B$ ($\mathrm{mg}/{\mathrm{m}}^3$), which can be conveniently replaced by the sea surface chlorophyll concentration ($C_{sat}$) multiplied by 0.9899 [22]. $D_{irr}$ is the photoperiod ($h$), which can be calculated from the latitude of the study area and solar declination.

The parameterization scheme of $P_{opt}^B$ given by Behrenfeld and Falkowski [22] is based on the empirical relationship between $P_{opt}^B$ and the monthly mean sea surface temperature (SST) for the global ocean as follows (hereafter referred to as $P_{opt}^B\_B$):

$$P_{opt}^B\_B=\{\begin{array}{c}1.13, T\le -1.0;\\ 4.00, T\ge 28.5;\\ P_{opt}^{B^{\prime }}, -1.0<T<28.5\end{array},$$

(2)

where $T$ is the sea surface temperature ($°C$), and the expression of $P_{opt}^{B^{\prime }}$ is expressed as follows:

$$\begin{array}{ll}{P}_{opt}^{B^{\prime }}=1.2956+& 2.749\times {10}^{-1}T+6.17\times {10}^{-2}{T}^2-2.05\times {10}^{-2}{T}^3\\ & +2.462\times {10}^{-3}{T}^4-1.348\times {10}^{-4}{T}^5+3.4132\times {10}^{-6}{T}^6\\ & -3.27\times {10}^{-8}{T}^7\end{array}$$

(3)

However, $P_{opt}^B\_B$ may not be appropriate for shelf seas. Yoon and Yoo et al. [35,36] found that the parameterization scheme of $P_{opt}^B\_B$ overestimated the maximum photosynthetic rate in the Yellow Sea and modified it by a quadratic function of temperature, i.e., Expression (4), based on in situ observation data.

$$P_{opt}^B\_Y=-0.013\times T^2+0.27\times T+3(T<27.2°\mathrm{C}).$$

(4)

Thus, the optimized parameterization scheme $P_{opt}^B\_Y$ was used in the VGPM model of this study. $P_{opt}^B\_Y$ was calculated by using the satellite-derived SST data introduced below.

2.2. Data Sources

This study focused on the variability of the PP in the Yellow and Bohai Seas from 2003 to 2020. The monthly mean satellite data, including sea surface PAR, SST, chlorophyll-a concentration (Chl-a), and euphotic depth (Zeu) data from 2003 to 2020 were used in the VGPM model. The spatial resolution of these data was about 4 km. PAR and SST data were obtained from the products of the MODIS satellite of NASA (National Aeronautics and Space Administration) (https://oceandata.sci.gsfc.nasa.gov/, accessed on 1 July 2021). The sea surface Chl-a data for the Yellow and Bohai Seas used in this study were from Wang et al. [32], which were calibrated based on the generalized additive model (GAM) and field observation Chl-a data. The GAM Chl-a data have been used in studies of the Yellow and Bohai Seas (e.g., [37,38]), and their high accuracy has been demonstrated even in highly turbid water during winter [37].

The $Z_{eu}$ data were obtained from the VIIRS and MODIS satellite inversion (https://oceancolor.gsfc.nasa.gov/, accessed on 1 July 2021), which employed the inherent optical properties approach (IOP approach) developed by Lee et al. [39]. Shang et al. [33] evaluated the accuracy of the IOP approach $Z_{eu}$ in the China seas, including the Yellow and Bohai Seas, and found that the overall average difference (ε) between the satellite inversion $Z_{eu}$ and the in situ measurement was 21.8% with a root mean square error in log scale (RMSE) of 0.118 by the IOP approach. This value was much smaller than that obtained by the traditional approach (ε = 49.9% and RMSE = 0.205), indicating the reliability of the $Z_{eu}$ data for this study.

The online global PP products from the standard VGPM estimates using the MODIS reprocessed results were used for comparing with the PP results of this study. Monthly PP data products from 2003 to 2020 were obtained from the Oregon State University website (http://orca.science.oregonstate.edu/1080.by.2160.monthly.hdf.vgpm.m.chl.m.sst.php, accessed on 1 July 2021). The correlation between the PP in the Yellow and Bohai Seas and the climate variability of the Pacific, including the Pacific Decadal Oscillation (PDO) and the El Niño-Southern Oscillation (ENSO), was analyzed. The PDO and Niño 3.4 indices downloaded from the World Meteorological Organization (http://climexp.knmi.nl/selectindex.cgi?id=someone@somewh, accessed on 1 July 2021) were used to represent the variability of the PDO and ENSO, respectively.

2.3. Data Analysis

The four seasons were defined as spring (April, May, and June), summer (July, August, and September), autumn (October, November, and December), and winter (January, February, and March) for the analysis of the seasonal variability. The Yellow Sea was divided into three subregions based on the water depth (h), including the nearshore Yellow Sea (h < 20 m), the continental slope region (20 m < h < 50 m), and the central Yellow Sea (h > 50 m) for regional comparison. To analyze the long-term trend of the PP, the linear regression analysis was performed on the annual average PP in the study area from 2003 to 2020, using the least square method to linearly fit the data. The slope of the linear regression analysis was used to identify the trend of the PP from 2003 to 2020, where a positive (negative) slope indicated an increasing (decreasing) trend.

3. Results

3.1. Regional and Seasonal Variations

As estimated by the VGPM model, the PP of the Yellow and Bohai Seas showed a significant seasonal variability, with an annual mean of 523.8 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$. Figure 2 illustrates the distributions of the climatological mean PP for the four seasons from 2003 to 2020. During winter, the PP was the lowest, ranging from 200 to 300 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$. The Bohai Sea had significantly lower PP during winter compared to the Yellow Sea, while the PP in the offshore water of the southeast of the Shandong Peninsula had relatively higher PP, reaching 600–800 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$. In spring, the PP increased significantly, with an average of approximately 1000 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$ throughout the sea area. The spring PP contributed 40.74% of the annual PP, with the highest PP occurring in the North Yellow Sea and the nearshore water along the west coast of Korea, exceeding 1200 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$. In most areas, the PP in summer was lower than that in spring, especially in the central Yellow Sea. However, the PP of the Bohai Sea in summer was comparable to that in spring. Generally, the summer PP decreased from shallow to deep areas, with the highest value appearing in the coastal water along the Liaodong Peninsula to the Korean Peninsula, exceeding 1000 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$. In contrast, the PP in the deep waters (h > 40 m) was relatively small, less than 500 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$. Compared to summer, the PP in autumn decreased significantly in the Bohai Sea and the coastal water of the Yellow Sea, but slightly increased in the deep water of the Yellow Sea. The pattern of the PP in autumn was similar to that in winter. Additionally, regions of high PP roughly corresponded to oceanic fronts, particularly in spring and autumn.

The monthly variability of spatially averaged PP in different regions is shown in Figure 3. For the entire study area (Figure 3a), the PP exhibited a bimodal distribution, with two peaks occurring in May and October with values of 935.4 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$ and 512.8 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$, respectively. However, the peak in October was weak and significantly lower than the peak in May. The minimum PP occurred in January and December, with a value of approximately 200 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$. The ranking of PP magnitude of the four seasons was spring (818.4 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$) > summer (541.4 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$) > autumn (357.1 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$) > winter (305.9 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$). The standard deviations from May to August exceeded 100 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$, indicating significant interannual differences in the PP from 2003 to 2020. The seasonal variation of the mean PP in the Yellow Sea (Figure 3b) was similar to that of the entire area, with slightly larger monthly mean values and standard deviations. In the Bohai Sea (Figure 3c), a single peak occurred in June (957.2 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$), and the standard deviation was over 250 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$ from June to August. The annual mean PP in the Bohai Sea was approximately 432.7 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$, which was lower than that in the Yellow Sea (564.1 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$).

As shown in Figure 2, PP in the Yellow Sea exhibited strong spatial inhomogeneity related to the water depth. The monthly variability of the PPs in the three subregions was significantly different (Figure 3d). The annual mean PP was the highest in the continental slope region (682.9 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$), where shelf fronts occurred frequently [37,40], while it was the lowest in the central Yellow Sea (537.9 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$). The monthly variation in the PP of the central Yellow Sea was bimodal, while that of the nearshore Yellow Sea was unimodal. The peak timings of the PP in different subregions of the Yellow Sea were also different.

3.2. Interannual Variations

The PP in the Yellow and Bohai Seas showed significant interannual differences (Figure 4). The average PP in the entire sea area reached its highest level (~700 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$) in 2009 and its lowest level (~380 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$) in 2018. The average PPs in the Yellow Sea and the Bohai Sea followed a similar trend (Figure 4a–c), with a rapid increase from 2003 to 2009 and a subsequent rapid decrease until 2018. After 2018, there was a slight increase. The maximum PP in 2009 were 756.3 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$ and 624.7 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$, and the minimum PP in 2018 which were 416.8 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$ and 320.5 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$ for the Yellow Sea and the Bohai Sea, respectively. The PPs showed an overall decrease from 2003 to 2020, with a decrease rate of about 5–6 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})/\mathrm{year}$. The interannual variability of PPs in different subregions of the Yellow Seas are similar (Figure 4d). The magnitude of the interannual variability of the PP of the central Yellow Sea was smaller than that of the other two subregions with relatively shallow water depth.

The linear regression analysis on the PP of the Yellow and Bohai Seas from 2003 to 2020 was conducted to further investigate regional differences in the interannual trend of PP. The slope of the linear regression was used to quantify the rate of the interannual change. As shown in Figure 5, there is a significant difference in the slope of the linear regression between the nearshore and offshore areas. In most areas, the slope value was negative, indicating a decreasing trend in PP. In the Yellow Sea, the decreasing rate in the central region was less than 8 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})/\mathrm{year}$, which was only about a half of that in the shallow water region (h < 50 m), indicating that the latter had a faster decrease in PP in the past 20 years. In contrast, in the Bohai Sea, the decline rate in the central region was larger than that in the coastal waters, indicating that the central Bohai Sea had a faster decrease in PP.

3.3. Comparison with Online PP Product and In Situ Measurements

An open online global ocean PP satellite product based on the VGPM model is available on the website of Oregon State University (http://sites.science.oregonstate.edu/ocean.productivity/index.php, accessed on 1 July 2021). This product has been used to investigate the PP variability of the global ocean (e.g., [6]). In this study, the PP estimated here and the online MODIS-based PP product in the Yellow and Bohai Seas are compared with the in situ measurement from the reference to evaluate the accuracy of their PP estimates.

As shown in Figure 6 and Figure 7, the PP obtained from the online PP product from 2003 to 2020 was significantly different from that of this study. The annual mean PP in the Yellow and Bohai Seas was 2228.7 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$, which was ~4 times higher than that of this study (523.8 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$). In the Bohai Sea, the average PP from the online product was relatively high in summer and spring, over 2500 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$, while that in autumn was about 2000 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$ and that in winter was under 1500 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$. In the central Yellow Sea with water depth > 40 m, the mean PP was the lowest in summer and winter, about 1000 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$, and the highest in spring and autumn, more than 1500 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$. In the four seasons, the relatively high PP mainly occurred in the shallow nearshore waters, from the coast of Jiangsu to the estuary of the Yangtze River and the coast of the Korean Peninsula. Additionally, the PP in the Bohai Sea was obviously higher than that in the Yellow Sea, except in winter, which was different from the result of this study. The monthly variability of the spatially averaged PP of the online product showed a similar pattern with that of this study, which was highest in summer and lowest in winter (Figure 7a). However, the timing of the two PP peaks was different from that in this study. The online PP reached the first peak in June and the second peak in September, whereas the PP in this study reached the first peak in May and the second peak in October. The interannual PP from the online product showed a similar variability to that of this study, slowly increasing from 2003 to 2014 and then decreasing after 2014 (Figure 7b).

Based on the beforementioned results, obvious differences were observed between the PPs of the Yellow and Bohai Seas obtained from the online product and those obtained in this study. In order to evaluate the accuracy of their PP estimates, the PP values were compared with in situ measurements. The PP values measured at 37 stations during the Yellow Sea cruise in September by Choi et al. [41] were used for comparison with the results of the satellite estimates. As shown in Figure 8, the PP obtained in this study was found to be much closer to the in situ measurements as compared to that of the online product. The PP from the online product was 3–5 times significantly higher than the measured PP and the PP estimated in this study. The average PP of the measurements was approximately 600 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$, while the average PP estimated by this study for all sites was approximately 500 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$, which is in close agreement with the measurement value. In contrast, the average PP obtained from the online product for the Yellow Sea was approximately 1700 $\mathrm{mgC}/({\mathrm{m}}^2·\mathrm{d})$, which was more than three times the value obtained in this study, suggesting that the online PP product significantly overestimated the PP in the shelf seas. Therefore, it is recommended that, when using the VGPM model to estimate the PP in shelf seas, the input satellite data and the parameterization scheme of $P_{opt}^B$ should be calibrated and improved to reduce the bias.

4. Discussion

4.1. Causes of the Variability of Primary Productivity in the Yellow and Bohai Seas

The results of this study revealed significant seasonal and interannual variability of the PP in the Yellow and the Bohai Seas. The average PP was found to be highest in spring, particularly in the central Yellow Sea, where the spring PP was about twice that of other seasons. This is mainly attributed to the spring phytoplankton bloom in the Yellow Sea [42], which is also a common ecological phenomenon in temperate shelf seas [43]. In summer, strong water stratification resulting from surface warming limits the upward transport of nutrients in the bottom water, thereby reducing the PP, especially in the central Yellow Sea. However, the increased river discharge can bring a lot of terrestrial nutrients to the coastal water and induce a higher PP in the shallow coastal water. In autumn, with the decrease of river discharge, the PP in the coastal water decreases, while the PP in the central Yellow Sea slightly increases due to the increase of vertical mixing and the weakening of stratification. In winter, strong surface cooling and wind cause intense vertical mixing. The weak solar radiation and deep mixing cause a strong light limitation for phytoplankton growth [37]. Therefore, winter is the season with the lowest PP in the entire year, but the accumulation of nutrients in winter lays the foundation for the PP in the following spring.

To understand the interannual variability of the PP, the interannual variabilities of three parameters, $C_{opt}$, $Z_{eu}$, and $P_{opt}^B$, were further analyzed (Figure 9). These parameters exhibited high interannual differences. There was a consistent interannual variation between the $C_{opt}$ and PP (Figure 9a and Figure 4a). $C_{opt}$ increased to the highest value of ~1.9 mg/m³ in 2009 and then decreased to approximately half of the highest value in 2018 (~1.0 mg/m³), indicating that the relative change of the interannual $C_{opt}$ could reach ~50%. In contrast, the relative changes of both the interannual $Z_{eu}$ and $P_{opt}^B$ were less than 5% and one order of magnitude smaller than that of $C_{opt}$. Thus, $C_{opt}$, i.e., the chlorophyll-a concentration, is the dominant factor controlling the interannual variability of PP. The chlorophyll-a concentration is an important factor reflecting the marine phytoplankton biomass, and thus, the interannual variability of the phytoplankton biomass primarily controls the interannual variability of PP in the Yellow and the Bohai Seas.

The Pacific climate variability has an important impact on the marine environment in the Yellow and Bohai Seas, and thus, on the phytoplankton biomass. Consequently, it could potentially influence the interannual variability of PP. As shown in Figure 10, the interannual variability of PP in the Yellow and Bohai Seas was basically inversely related to the Pacific climate variability (i.e., PDO and ENSO). From 2003 to 2008, the PP increased with the decrease of PDO and ENSO indexes, while after 2008, the PP declined with the increase of PDO and ENSO indexes. Several studies have suggested that PDO and ENSO could influence the phytoplankton biomass by influencing water temperature, wind, and rainfall in the Yellow and Bohai Seas [44,45], which can explain the climate modulation on PP.

4.2. Implications for the Marine Ecosystem, Fishery, and Climate Change

Marine PP is a crucial indicator for assessing the status of marine ecosystems. The spatial and temporal variability of PP in the Yellow and Bohai Seas provides a valuable research basis for evaluating the ecosystem service value of these regions. This information is vital for formulating effective ecological protection policies and implementing scientific protection measures to promote ecological restoration and sustainable development [46]. Additionally, PP can reflect the production status of each trophic level in the marine food web, making it a useful tool for evaluating marine fisheries resources and for managing marine fisheries. The positive correlation between marine PP and fishery potential highlights the importance of monitoring PP trends in the Yellow and Bohai Seas [47,48]. The results of this study can provide a reference for the development of fisheries in these regions. For example, the observed decline in PP since 2014 may indicate a decline in fishery potential. The PP map and seasonality can also serve as a reference for fishing activities. Moreover, this study suggests a close link between the Pacific Decadal Oscillation (PDO) and the El Niño-Southern Oscillation (ENSO) and PP in the Yellow and Bohai Seas (Figure 10). This finding implies that the PDO and ENSO may modulate the high trophic biomass and fishery potential in these regions by influencing PP. Therefore, the PDO and ENSO may serve as useful indicators for shelf fishery management.

Shelf PP is also an essential factor in global carbon sequestration and plays a vital role in the global carbon cycle and climate change [10]. The estimates of PP in the Yellow and Bohai Seas from 2003 to 2020 showed a high interannual variability, which could significantly impact carbon cycling and potentially influence climate change. Furthermore, this study suggests that climate variability, such as PDO and ENSO, could influence shelf PP (Figure 10) and, therefore, modulate global carbon cycling and climate change.

5. Conclusions

In this study, using the VGPM model, the calibrated high-precision satellite remote sensing chlorophyll-a data, the euphotic depth data, and the optimized parameterization scheme of maximum photosynthesis rate, we estimated the PP of the Yellow and Bohai Seas from 2003 to 2020. Our findings indicate that the PP of the Yellow Sea and Bohai Sea was higher during spring and lower during winter. Furthermore, the PP gradually decreased from coastal waters to offshore waters. Areas with high PP appeared in waters near the Liaodong Peninsula and the Yangtze River estuary. The PP in the Bohai Sea during spring and summer was approximately twice that of autumn and winter. The PP in the Bohai Sea exhibited a unimodal seasonal variation with one peak in June, while the PP in the Yellow Sea displayed a bimodal variation with two peaks in May and October. However, there were regional differences in the PP in the Yellow Sea. The PP in the area with water depth > 20 m showed a bimodal seasonal variation, while the PP in the area with water depth < 20 m displayed a unimodal variation. The annual average PP of different sea areas all exhibited a decline from 2003 to 2020. In addition, the interannual variation of PP in the area with water depth > 50 m was much smaller than that in the area with water depth < 50 m. Our results also indicate that the annual change of PP in the Yellow and Bohai Seas was most affected by the chlorophyll-a concentration and related to the Pacific climate variability.