Satellite views of the Bohai Sea, Yellow Sea, and East China Sea

Progress in Oceanography 104 (2012) 30–45

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Satellite views of the Bohai Sea, Yellow Sea, and East China Sea Wei Shi a,b,⇑, Menghua Wang a a

NOAA National Environmental Satellite, Data, and Information Service, Center for Satellite Applications and Research, E/RA3, Room 102, 5200 Auth Road, Camp Springs, MD 20746, USA b CIRA at Colorado State University, Fort Collins, CO, USA

a r t i c l e

i n f o

Article history: Received 19 October 2011 Received in revised form 16 May 2012 Accepted 17 May 2012 Available online 1 June 2012

a b s t r a c t A comprehensive study of water properties for the Bohai Sea (BS), Yellow Sea (YS), and East China Sea (ECS) has been carried out with 8-year observations between 2002 and 2009 from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua platform. Normalized water-leaving radiance spectra (nLw(k)), chlorophyll-a concentration (Chl-a), diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), total suspended matter (TSM), and sea surface temperature (SST) are used to quantify and characterize the physical, optical, biological, and biogeochemical properties and their seasonal and interannual variability in the BS, YS, and ECS regions. The BS, YS, and ECS feature highly turbid waters in the coastal regions and river estuaries with high Kd(490) over 3 m1 and TSM concentrations reach over 50 g m3. The optical, biological, and biogeochemical property features in these three seas show considerable seasonal variability. The dominant empirical orthogonal function (EOF) mode for Kd(490) and TSM variability in the BS, YS, and ECS regions is the seasonal mode, which accounts for about two-thirds of the total variance. Phytoplankton dynamics in open oceans of the BS, YS, and ECS is also found to play an important role in the Kd(490) variation, while its impact on the ocean turbidity (Kd(490)) is much less than that of seasonal winds and sea surface thermodynamics in coastal regions. The first EOF mode in SST for the regions is seasonal and accounts for nearly 90% of the total SST variance. The major mechanisms that drive ocean color property variations in the BS, YS, and ECS are the seasonal winds, ocean stratification, and sea surface thermodynamics due to the seasonal climate change, as well as coastal bathymetry, seasonal phytoplankton blooms, and river discharges. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction The Bohai Sea (BS), Yellow Sea (YS), and East China Sea (ECS) are the three major marginal seas in the western Pacific Ocean bounded by China, Korea, and Japan (Fig. 1). The Bohai Sea, Yellow Sea, and East China Sea cover 7.8  104, 3.8  105, and 1.25  106 km2, respectively, for a total coverage of 1.71  106 km2. The mean depths of the BS, YS, and ECS are about 20, 44, and 188 m, respectively. All three of these oceans are characterized with high loadings of sediment concentrations (Guo and Yanagi, 1998; Shi and Wang, 2010b; Wang et al., 2007) in the continental shelf regions. The Yellow River, which is China’s second largest river, transports 1.64  109 tons of sediment into the Bohai Sea yearly (Milliman and Meade, 1983). The sediment discharge into the ECS from the Yangtze River is approximately 5.0  108 tons annually (Milliman and Meade, 1983). Other major rivers, such as the Liaohe,

⇑ Corresponding author at: NOAA National Environmental Satellite, Data, and Information Service, Center for Satellite Applications and Research, E/RA3, Room 102, 5200 Auth Road Camp Springs, MD 20746, USA. E-mail address: [email protected] (W. Shi). 0079-6611/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.pocean.2012.05.001

Yalu, and Haihe, also transport significant amounts of sediment into these three seas. Seasonal monsoons, the Kuroshio Current, and semidiurnal and diurnal tides (Fang, 1994) are primary processes that dominate the ocean hydrography, ocean circulations, sea surface temperature (SST), ocean stratification, and ocean fronts in these three oceans. Due to the sediment deposited in these three oceans via rivers, the BS, YS, and ECS are among the most turbid ocean regions in the world (Shi and Wang, 2010a,b). In the BS, huge sediment transportations and depositions from the Liaohe River and the Yellow River form two major delta wetlands: the Liaohe River estuary and the Yellow River estuary. In the YS, sediment depositions from the ancient Yellow River help form the Subei Shoal off China’s Jiangsu province. The outer shelf mud in the south of Cheju Island has been found to contain calcites in its clay fraction derived from the erosion of the old Yellow River submarine delta (Milliman et al., 1985). Satellite remote sensing has long been used to study physical, optical, biological, and biogeochemical processes in these ocean regions. SST images from the Advanced Very High Resolution Radiometer (AVHRR) were used to characterize sea surface features in the ECS (Tseng et al., 2000). Major ocean fronts in the three oceans

W. Shi, M. Wang / Progress in Oceanography 104 (2012) 30–45

Fig. 1. Map of Bohai Sea (BS), Yellow Sea (YS), and East China Sea (ECS). The study area is outlined in the box and locations of the eight pseudo-stations for detailed quantifications are also marked.

have also been distinguished with satellite SST observations (Hickox et al., 2000). With satellite remote sensing, clear ocean-to-atmospheric feedback in the Yellow and East China Seas, triggered by the submerged ocean bottom topography (Xie et al., 2002), is also revealed. In recent years, satellite ocean color remote sensing has been increasingly used to study ocean optical, biological, and biogeochemical processes as well as to monitor the natural hazards in these regions. Monthly variation of pigment concentrations has been studied with satellite Coastal Zone Color Scanner (CZCS) observations (Tang et al., 1998). Images of chlorophyll-a concentration (Chl-a) of the Moderate Resolution Imaging Spectroradiometer (MODIS) show significant cross-shelf penetrating fronts off the southeast coast of China (Yuan et al., 2005). The occurrences of green algae blooms in the YS are also investigated with satellite ocean color observations (Hu et al., 2010; Shi and Wang, 2009b). Most recently, ocean sand ridge signatures in the BS (Shi et al., 2011a), sea ice effects in the BS (Shi and Wang, 2012a,b), ocean optical and biological properties in the Korean dump site of the YS (Son et al., 2011), and the seasonal sediment plume in central ECS (Shi and Wang, 2010b), as well as the spring-neap tidal effects on satellite ocean color remote sensing in the BS, YS, and ECS (Shi et al., 2011b) have been studied using MODIS-Aqua measurements. For the BS, YS, and ECS regions, significant ocean radiance contributions at the near-infrared (NIR) wavelengths can be found along the coast of the YS and ECS (Shi and Wang, 2009a; Wang et al., 2007). For example, the normalized water-leaving radiance (nLw(k)) at the NIR band can reach 2–3 mW cm2 lm1 sr1 in the Hangzhou Bay (Wang et al., 2007). The bio-optical complexities in the BS, YS, and ECS regions suggest that the standard (NIR) atmospheric correction algorithm (Gordon and Wang, 1994; IOCCG, 2010) for MODIS ocean color data processing is unable to produce valid ocean color products in order to quantify ocean optical, biological, and biogeochemical properties in these three seas. Indeed, limited ocean color products from the NASA standard (NIR) data processing, such as nLw(k) spectra (Gordon, 2005; Gordon and Wang, 1994; Morel and Gentili, 1991; Wang, 2006b), chlorophyll-a concentration (O’Reilly et al., 1998), diffuse attention

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coefficient at the wavelength of 490 nm (Kd(490)) (Lee et al., 2005; Morel et al., 2007; Mueller, 2000; Wang et al., 2009a), and total suspended matter (TSM) concentration (Miller and McKee, 2004; Tassan, 1993; Zhang et al., 2010), can be used to quantitatively evaluate and characterize the short-term and long-term variability in these three seas, and consequently study the ocean and atmospheric processes contributing to these changes. Recently, a shortwave infrared (SWIR) atmospheric correction algorithm (Wang, 2007; Wang and Shi, 2005) for satellite ocean color data processing has been proposed and demonstrated to significantly improve the ocean color products in the coastal and inland turbid waters (Wang et al., 2007, 2009b, 2011; Zhang et al., 2010). In addition, a tuned (NIR-corrected) atmospheric correction algorithm using NIR bands for MODIS-Aqua ocean color data processing is also proposed for these regions (Wang et al., 2012). Indeed, Shi and Wang (2009a) showed that the black pixel assumption, on which the atmospheric correction for satellite ocean color data processing is based, is generally valid for the SWIR bands for the turbid coastal regions such as in the BS, YS, and ECS regions. In this study, the combined NIR-SWIR atmospheric correction algorithm (Wang, 2007; Wang and Shi, 2005, 2007; Wang et al., 2009b) is used to derive ocean optical property data for both the open ocean and coastal region waters. Seasonal and interannual changes of physical, optical, biological, and biogeochemical properties in these three seas are characterized and quantified with SST (Minnett et al., 2004), nLw(k) (Gordon and Wang, 1994; IOCCG, 2010), Chl-a (O’Reilly et al., 1998), Kd(490) (Wang et al., 2009a), and TSM concentration (Zhang et al., 2010) from the measurements of MODIS-Aqua. Annual and seasonal climatologies of nLw(k), Chl-a, Kd(490), TSM, and SST in the BS, YS, and ECS regions are derived and quantified. The evaluations of seasonal and interannual variations of all these physical, optical, biological, and biogeochemical parameters are also provided and correlated to the ocean and atmospheric processes. 2. MODIS ocean color and SST data processing in the BS, YS, and ECS Regions MODIS is a key instrument aboard the NASA Terra (1999 to present) and Aqua (2002 to present) satellites. MODIS provides radiometric measurements in 36 spectral bands ranging in wavelengths from 412 nm to 14.2 lm. It can provide a wide range of land, atmosphere, and ocean products (Salomonson et al., 1989). Specifically for the ocean, it can provides SST retrievals from the measurements of the thermal infrared bands at 11–12 lm wavelengths and ocean color products in the visible and NIR bands between the deep blue at 412 nm and the NIR wavelengths at 859 and 869 nm (Esaias et al., 1998; McClain et al., 2006; Wang, 2007). 2.1. MODIS ocean color data processing It is well known that the atmospheric and ocean surface contributions can account for up to and even over 90% of radiometric signal measured by a satellite sensor in the visible area over the ocean. This makes atmospheric correction crucial for deriving accurate normalized water-leaving radiance spectra and, consequently, retrieval of the ocean’s biological and biogeochemical parameters, such as Chl-a and Kd(490). The current NASA standard atmospheric correction algorithm for producing the global ocean color product from MODIS uses the Gordon and Wang (1994) algorithm. Specifically, the algorithm uses two NIR bands centered at MODIS 748 and 869 nm to determine aerosol type and estimate the atmospheric effects in the visible by extrapolating the aerosol effect from the NIR into visible bands using aerosol models (Gordon and Wang,

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1994). An important assumption for the Gordon and Wang (1994) algorithm is that the NIR ocean contributions are negligible for open oceans. For productive ocean waters, some modifications for estimating the NIR ocean contributions have been employed (Bailey et al., 2010). Even though high-quality ocean color products have been produced in the global open oceans with the NIR-based atmospheric correction algorithm (Bailey and Werdell, 2006; McClain, 2009; McClain et al., 2006; Stumpf et al., 2003; Wang et al., 2005), there are still significant issues with products from coastal regions, e.g., the satellite-derived nLw(k) spectra at the blue are often biased low and sometimes even go negative in coastal regions such as the BS, YS, and ESC. Thus, the ocean color products, such as nLw(k) spectra, Chl-a, and Kd(490), may have significant errors in coastal turbid waters. This problem often results from NIR ocean contributions in turbid waters (Lavender et al., 2005; Ruddick et al., 2000; Siegel et al., 2000; Wang and Shi, 2005). Because the water is strongly absorbing in the SWIR wavelengths (Hale and Querry, 1973) and the ocean is generally still black at the SWIR wavelengths even for very turbid waters (Shi and Wang, 2009a), the SWIR atmospheric correction algorithm for turbid waters has been proposed (Wang, 2007; Wang and Shi, 2005). Two MODIS-Aqua SWIR bands at 1240 and 2130 nm are used for atmospheric correction in deriving the ocean radiance contributions (Wang, 2007), and it has been demonstrated that the SWIR-based algorithm can derive the improved ocean color products in the coastal turbid and inland waters (Wang and Shi, 2007; Wang et al., 2007, 2009b, 2011). Although the SWIR method shows improved ocean color products in the coastal regions, its performance in the non-turbid ocean waters is usually worse than the standard (NIR) method with significant noise in the derived products (Wang et al., 2009b; Werdell et al., 2010). This is due mainly to the fact that the MODIS SWIR bands are designed for the land and atmosphere applications with substantially lower sensor signal–noise ratio (SNR) values (Wang, 2007; Wang and Shi, 2012). In this study, the NIR-SWIR combined atmospheric correction algorithm (Wang and Shi, 2007; Wang et al., 2009b) (with MODIS-Aqua bands of 748 and 869 nm for the NIR algorithm and 1240 and 2130 nm for the SWIR algorithm) is used for generating ocean nLw(k) spectra data, which are then used to compute the ocean biological and biogeochemical parameters in these regions, i.e., Chl-a (O’Reilly et al., 1998), Kd(490) product (Wang et al., 2009a), and TSM data (Zhang et al., 2010). Chl-a algorithm is derived from Case 1 waters, thus it is only valid for the open oceans of the BS, YS, and ECS. For the complex turbid waters in the coastal regions, the Chl-a retrievals can be significantly biased. In this study, it is used as an index to represent the ratio of the normalized water-leaving radiance between blue and green bands for the turbid regions. It is also noted that the TSM algorithm is a regional model, which was developed based on the in situ data made from the 2003 spring and autumn cruises over the YS and ECS (Zhang et al., 2010). In this algorithm, nLw(k) at wavelengths of 488, 555, and 645 nm are used to derive TSM concentrations in the BS, YS, and ECS. Specifically, TSM is derived as

Log10 TSM ¼ c0 þ c1 ½qwN ð555Þ þ qwN ð645Þ þ c2 ½ðqwN ð488Þ=ðqwN ð555Þ;

ð1Þ

where coefficients c0 = 0.6311, c1 = 7.0715, and c2 = 0.5239 were derived based on the least square fit of the in situ data (Zhang et al., 2010), and the normalized water-leaving reflectance qwN(k) is defined as qwN(k) = p nLw(k)/F0(k), where F0(k) is the extra-terrestrial solar irradiance (Gordon and Wang, 1994; IOCCG, 2010). Results show that this regional TSM algorithm can derive improved estimations of TSM data in the YS and ECS regions (Zhang et al., 2010), compared to results from the other models such as Miller and McKee (2004) and Tassan (1993).

2.2. MODIS SST data processing In addition to the MODIS-Aqua ocean color data products, nighttime MODIS-Aqua SST data from 2002 to 2009 are used in this study. The thermal infrared ‘‘split window’’ algorithm with MODIS measurements at bands 31 and 32 (wavelengths of 11 and 12 lm) is used to derive the nighttime SST data (Minnett et al., 2004). The algorithm differentiates atmospheric water vapor loading using the difference between the brightness temperatures for the 11 and 12 lm bands (MODIS bands 31 and 32). It is noted that the nighttime SST data are used to reduce the diurnal effect on SST values in these regions. In summary, for this study all MODIS-Aqua-measured (2002– 2009) nLw(k) spectra, Chl-a, Kd(490), TSM, and SST data are used for deriving the physical, optical, biological, and biogeochemical properties and for characterizing and quantifying the seasonal and interannual variability in the BS, YS, and ECS regions.

3. Results 3.1. Annual climatology Fig. 2 shows MODIS-Aqua-derived climatology maps from measurements of 2002–2009 for nLw(k) spectra at wavelengths of 443 nm (nLw(443)) (Fig. 2a), 555 nm (nLw(555)) (Fig. 2b), 645 nm (nLw(645)) (Fig. 2c), and 859 nm (nLw(859)) (Fig. 2d), as well as for Chl-a (Fig. 2e), Kd(490) (Fig. 2f), TSM (Fig. 2g), and SST (Fig. 2h) in the BS, YS, and ECS regions. Fig. 2a-d show the annual mean fields of nLw(443), nLw(555), nLw(645), and nLw(859) in the region, respectively. The enhancements of nLw(k) at blue (443 nm), green (555 nm), and red (645 nm) bands are consistent with the elevated Kd(490) and TSM patterns in the coastal regions. It is interesting to note that for the turbid regions in the three seas as reflected in the Kd(490) map (Fig. 2f) and TSM map (Fig. 2g), nLw(k) at the blue band nLw(443) (Fig. 2a) is also enhanced. This draws an important distinction between the turbid waters and the productive waters, which are normally featured with reduced nLw(443) values. It further suggests that Chl-a retrievals (Fig. 2e) in the turbid region might be biased. On the other hand, modestly high nLw(443) in the southeastern ECS matches well with the high SST for the western Pacific open ocean waters, which are separated from the ECS by the Kuroshio Current (Fig. 2h). This suggests that ocean is clearer for the western Pacific open ocean waters than waters in the central YS and ECS even though Kd(490) values are also below 0.1 m1 for most of the YS and ECS regions and no visual difference can be identified in the Kd(490) map. In most of the ECS and YS regions, Chl-a is less than 1 mg m3, while coastal regions of the YS, ECS, and entire BS are dominated with high Chl-a waters. Significantly enhanced nLw(555) and nLw(645) are located at the Yellow River estuary in the BS, the Subei Shoal in the YS, and Yangtze River estuary and the Hangzhou Bay in the ECS. The values of nLw(555) and nLw(645) are over 5 mW cm2 lm1 sr1 for these highly turbid regions. For the modestly turbid regions, such as the central ECS plume (Shi and Wang, 2010b), nLw(k) is more enhanced in the green band than in the red band. At the NIR wavelength, nLw(859) is still significantly high in the coastal turbid regions (Fig. 2d). Particularly, in the Yellow River estuary, the Subei Shoal, and the Hangzhou Bay, nLw(859) can be over 2 mW cm2 lm1 sr1, demonstrating that the SWIR-based atmospheric correction algorithm is necessary for the entire BS, YS, and ECS regions. The high ocean turbidity (with high Kd(490)) can be identified in the three gulfs of the BS (Fig. 2f). In the YS and ECS regions, the west coastal region in the southern YS, Yangtze River estuary,

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Fig. 2. Climatology images derived from MODIS-Aqua measurements from 2002 to 2009 in the BS, YS, and ECS regions for parameters of (a) nLw(443), (b) nLw(555), (c) nLw(645), (d) nLw(859), (e) Chl-a, (f) Kd(490), (g) TSM, and (h) SST. It is noted that Chl-a estimation is only valid for the open oceans. In the coastal turbid regions, there might be some large errors. Thus, its value reflects the ratio of normalized water-leaving radiance in the green and blue wavelengths for figure (e).

and the Hangzhou Bay of the ECS are all shown with higher Kd(490) over 2 m1. In the central ECS, modestly high Kd(490) values due to sediment plume (Shi and Wang, 2010b) can also be identified with an annual mean value of 1 m1. Climatology of TSM concentration distribution is provided in Fig. 2g. In general, the spatial patterns of the TSM are similar to those of Kd(490) due to the intrinsic correlation between the sediment loadings and the light attenuation in the water column. In the open ocean of the ECS, TSM is normally in the range of 0.1– 0.2 g m3, while TSM values in the central YS are 0.5–1.0 g m3. Enhanced TSM can be found in the Subei Shoal off Jiangsu province in the western Yellow Sea and the estuary of Yellow River in the BS, with TSM reaching over 50 g m3. In addition, the features of the central ECS plume are also more pronounced in the TSM map (Fig. 2g), compared with plumes in the central ECS indicated from the Kd(490) map (Fig. 2f). The TSM concentration in the central ECS plume is 10 g m3, consistent with features of modestly turbid waters from Kd(490) climatology. However, the annual SST climatology from MODIS-Aqua 2002– 2009 observations is considerably different (Fig. 2h), compared with Chl-a, Kd(490), and TSM maps (Fig. 2e–g). This reflects the fact that the ocean and atmospheric processes contributing to the changes of SST are different from those of the biological and biogeochemical variability in these regions. In the BS, values of SST are 13–14 °C, with the annual mean SST increasing gradually from the BS in the north to the ECS in the south. The warm SST caused by the Kuroshio Current can be clearly seen flowing northeastward off the southern Taiwan. The SST front in the southern ECS due to the Kuroshio Current stretches over 900 km with SST difference across the front reaching over 3–4 °C. However, the other SST fronts in the region (Hickox et al., 2000) cannot

be discriminated easily from the MODIS-Aqua climatology SST image. 3.2. Seasonal climatology 3.2.1. Spatial distributions in various seasons 3.2.1.1. Spring (April) season. Among all four seasons (represented by the months of April, July, October, and January), spring (April) is the season during which nLw(k) (Fig. 3a–d), Chl-a (Fig. 3e), Kd(490) (Fig. 3f), and TSM (Fig. 3g) most resemble the annual climatology of these parameters in terms of spatial patterns and their magnitudes. The spatial pattern of SST is also similar to the multiyear mean SST patterns, except the values of SST are a couple degrees lower than the SST climatology in a broad coverage of the BS, YS, and ECS regions. In the turbid regions along the west coast of the BS, YS, and ECS, Kd(490) and TSM are shown to be slightly higher than the annual climatology in Fig. 2 with enlarged coverage. The slightly elevated sediment loadings in the water column are also shown in the nLw(555) (Fig. 3b) and nLw(645) (Fig. 3c) maps. Chl-a also shows a remarkable increase in broad areas of the YS and ECS except for coastal turbid regions, although it is still around or below 1 mg m3 and cannot be differentiated in Fig. 3e due to the color scale limitation. In corresponding to the pronounced Chl-a due to the spring phytoplankton bloom in this region (Tang et al., 1998), nLw(k) at the blue band of 443 nm in open oceans of the YS and ECS had noticeable decrease due to pronounced phytoplankton absorption (Roesler and Perry, 1995) (Fig. 3a). It is noted that during the spring, the modestly high nLw(443) in the southeastern ECS matches well with the high SST of the western Pacific open ocean

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Fig. 3. Climatology images for the month of April (spring) derived from MODIS-Aqua measurements from 2002 to 2009 in the BS, YS, and ECS regions for parameters of (a) nLw(443), (b) nLw(555), (c) nLw(645), (d) nLw(859), (e) Chl-a, (f) Kd(490), (g) TSM, and (h) SST.

waters, which are separated from the ECS by the Kuroshio Current. This indicates that ocean waters in the east of the Kuroshio Current are oligotrophic with warm SST. 3.2.1.2. Summer (July) season. Significant physical, optical, biological, and biogeochemical changes are observed during the summer (July). In comparison to the annual climatology and spring (April) climatology results, the summer season is the least turbid for the BS, YS, and ECS regions. The coverage of the highly turbid waters shrinks significantly in the regions of the YS estuary, Subei Shoal, Yangtze River estuary, and Hangzhou Bay (Fig. 4f). The magnitude of Kd(490) (Fig. 4f) and TSM (Fig. 4g) are also at their annual low in the above highly turbid regions. In the central ECS, the sediment plume cannot be identified in the maps of nLw(k) (Fig. 4a-d), nor the Chl-a (Fig. 4e), Kd(490) (Fig. 4f), or TSM (Fig. 4g) maps. The summer SST map (Fig. 4h) shows a broad SST elevation in the BS, YS, and ECS regions as compared to SST in the spring, due to seasonal warming. In fact, SST becomes quite uniform in the ECS and the open ocean waters west of the Kuroshio Current. The SST front as shown in both the spring SST climatology (Fig. 3h) and annual SST climatology (Fig. 2h) cannot be identified anymore. In contrast, the difference of nLw(k) at the blue band between the ECS and Kuroshio Current oligotrophic waters is still similar to the spring season and discernible in Fig. 4a. 3.2.1.3. Autumn (October) season. In general, the spatial patterns and coverage areas of nLw(k) spectra (Fig. 5a–d), Kd(490) (Fig. 5f), and TSM (Fig. 5g) are similar to those in the spring season, although the turbid water coverage in these three seas is slightly less than those in the spring season. The ocean turbidity in terms of Kd(490) (Fig. 5f) and TSM level (Fig. 5g) in the autumn in the regions show considerable increase from the summer season. Larger areal coverage of turbid waters and enhanced Kd(490) and TSM

features are present along the coastal regions. In autumn, the modestly turbid waters, due to the central ECS plume, can be observed with enhanced Kd(490), and TSM, as well as overall increased nLw(k) spectra (Fig. 5a–c). However, unlike coastal turbid regions, nLw(k) at the blue band actually shows decrease in the central YS and the clear water region in the ECS, suggesting that biological and biogeochemical processes in clear oceans and turbid waters in the YS and ECS regions are different. On the other hand, SST (Fig. 5h) during the month of October shows the seasonal cooling in the BS and YS regions with a 3– 5 °C drop from the summer season. In the ECS region, the seasonal drop of SST is not as significant as in the BS and YS regions.

3.2.1.4. Winter (January) season. Winter is the most turbid season in the BS, YS, and ECS regions in terms of the turbid water coverage, the magnitude of light attenuation represented by Kd(490) (Fig. 6f), and the TSM concentration (Fig. 6g). Similar to the spatial distributions of Kd(490) and TSM, nLw(443) (Fig. 6a), nLw(555) (Fig. 6b), and nLw(645) (Fig. 6c) all show notable increases in the coastal region of these three seas as well as the central ECS region. The NIR nLw(859) (Fig. 6d) reaches over 2 mW cm2 lm1 sr1 in part of the above highly turbid region. The turbid water coverage expands significantly in the coastal regions of the BS, YS, and ECS, as well as the central ECS region. In the Yellow River estuary, Subei Shoal, and Hangzhou Bay, Kd(490) and TSM reach the highest values in a year with Kd(490) over 3–4 m1 and TSM 100 g m3. In the central ECS region, the sediment plume, as represented by the elevated Kd(490) and TSM concentration, covers significantly larger area than the annual and other seasonal climatology results. On the other hand, the Chl-a map (Fig. 6e) shows a similar pattern of the enhancement and enlargement as for the other

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Fig. 4. Climatology images for the month of July (summer) derived from MODIS-Aqua measurements from 2002 to 2009 in the BS, YS, and ECS regions for parameters of (a) nLw(443), (b) nLw(555), (c) nLw(645), (d) nLw(859), (e) Chl-a, (f) Kd(490), (g) TSM, and (h) SST.

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Fig. 5. Climatology images for the month of October (autumn) derived from MODIS-Aqua measurements from 2002 to 2009 in the BS, YS, and ECS regions for (a) nLw(443), (b) nLw(555), (c) nLw(645), (d) nLw(859), (e) Chl-a, (f) Kd(490), (g) TSM, and (h) SST.

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Fig. 6. Climatology images for the month of January (winter) derived from MODIS-Aqua measurements from 2002 to 2009 in the BS, YS, and ECS regions for parameters of (a) nLw(443), (b) nLw(555), (c) nLw(645), (d) nLw(859), (e) Chl-a, (f) Kd(490), (g) TSM, and (h) SST.

parameters, such as nLw(k), Kd(490), and TSM. This indicates that nLw(k) enhancement in the green band is even more significant than that in the blue wavelength. It is also noted that nLw(k) at the blue band (nLw(443)) also shows increase from the month of October in the central YS and the clear waters of the ECS, suggesting that the central YS and large expanse of the ECS open waters are less productive (low Chl-a) during the winter season. For the SST distribution (Fig. 6h), significant SST gradient can be found from the north in the BS and the south in the ECS. In the BS region, SST is 2–4 °C. It gradually increases to the south, and reaches over 25 °C in the southern ECS. The SST front between the ECS and the Kuroshio Current waters can be clearly identified in this season. 3.2.2. Seasonal optical property variation To understand the optical property variation in the BS, YS, and ECS regions, eight stations as marked in Fig. 1 are selected for further quantitative evaluations. Climatology values of normalized water-leaving reflectance qwN(k) in the winter (January), spring (April), summer (July), and autumn (October) from MODIS-Aqua observations from 2002 to 2009 are used to characterize and quantify the optical property in these three seas. Of the eight stations as shown in Fig. 1, stations 1, 3, 5, and 7 are in the Yellow River estuary, the Subei Shoal of the YS, the Hangzhou Bay of the ECS, and the southern coastal region of the ECS, respectively. Stations 2, 4, 6, and 8 correspond to the central BS, central YS, central ECS, and open oceans of the southern ECS, respectively. Fig. 7 reveals the seasonal changes of qwN(k) spectra at various stations as well as inter-station differences in qwN(k) spectra. In the Yellow River estuary, the reflectance spectra show significant seasonal variations (Fig. 7a). As an example, at this station values of qwN(k) at 555 nm (qwN(555)) are 5% and over 12% in the summer and winter (Fig. 7a), respectively, while qwN(412) shows little

seasonal variation. In the NIR band, qwN(859) reaches over 4%, which is much higher than 1–2% in the other seasons. It is interesting to note that the maximum qwN(k) at this station shifts from the green band at 555 nm for the spring, summer, and autumn seasons to the red band at 645 nm in the winter. This is attributed to the enhanced particle backscattering from increased suspended sediment in the estuary, similar to the case in the Amazon River estuary (Shi and Wang, 2010a). In contrast to station 1 in the Yellow River estuary, the spectral shape of qwN(k) in the central BS (station 2) is flat (Fig. 7b). Even though qwN(555) and qwN(645) are significantly reduced due to the lower sediment loading level in the central BS, the reflectance spectral features, e.g., low qwN(412), maxima qwN(555) and qwN(645), and high NIR reflectance qwN(859) at this station, still reflect the fact that waters in the central BS are not clear for all seasons. Similar to the results of station 1, enhanced reflectance spectrum appears in the winter, while the reduced sediment concentrations from winter to summer at this station lead to much weaker qwN(555) and qwN(645). Indeed, the value of qwN(645) is 1–2% in the summer, only one-third the value in the winter at this station. qwN(k) at station 3 in the Subei Shoal is the highest among all eight stations; in particular, qwN(645) reaches 15% in the winter (Fig. 7c). In addition to enhanced qwN(645), the NIR qwN(859) reaches 9%, twice its value at station 1 in the winter. Similar to the station 1 observations in the Yellow River estuary, peak reflectance shifts from green to red band due to the seasonal increase of the suspended sediment loading. At this station, qwN(412) also shows little change in all four seasons. In comparison to station 3, the optical features at station 4 in the central YS (Fig. 7d) are entirely different. In addition to significantly reduced qwN(k) values, especially values in the red and NIR wavelengths, this station also features significant seasonal variations of qwN(k) spectra. In the

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summer, waters in the central YS are mostly clear, represented with high qwN(k) in the blue (443 nm) and deep blue (412 nm) wavelengths. For the other three seasons (spring, autumn, and winter), however, qwN(k) peaks at 488 nm. This shows that, in the central YS, the water property is impacted by the enhanced

algal concentration and/or advection of turbid waters off the coastal regions. The reflectance spectra at station 5 (Fig. 7e) in the Hangzhou Bay are similar to that at station 1 in the Yellow River estuary except with less seasonal variability. qwN(k) peaks at 645 nm for all

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four seasons. In the summer, qwN(645) reaches its minimum in a year, while qwN(k) shows little difference in the red (645 nm) and NIR (859 nm) for the other seasons. In the central ECS plume region at station 6 (Fig. 7f), the optical property shows significant seasonal variability. In the summer, qwN(k) values are small in all wavelengths from 412 to 859 nm. In contrast, qwN(k) reaches its maximum in all wavelengths during the winter season, while qwN(k) values in the spring and autumn seasons range among the middle values between the summer and winter. The seasonal variations of qwN(k) are significant. For example, qwN(k) at this station is below 1.5% in the green and red bands during the summer, while it jumps to 8% in the winter. Unlike the coastal waters of the BS, YS, and ECS regions as represented by stations 1, 3, and 5, the optical property at station 7 shows different features (Fig. 7g). This is demonstrated with the modest enhancement of qwN(k) in the green band, and negligible qwN(k) in the NIR. During the spring, summer, and autumn seasons, qwN(k) spectra are similar at this location. In the winter season, however, significantly enhanced qwN(k) can be found in the wavelengths ranging between 488 and 555 nm. At station 8 in the open ocean of the ECS (Fig. 7h), ocean optical properties are typical open ocean waters with the highest qwN(k) at 412 nm. It is also noted that qwN(k) at wavelengths of 412 and 443 nm are lower in the winter than in the other seasons, consistent with nLw(k) maps shown in Figs. 3–6. The seasonal climatology maps shown in Figs. 3–6 and seasonal optical properties in Fig. 7 are calculated with all the available MODIS-Aqua observations between 2002 and 2009 for the corresponding period. Large number of MODIS-Aqua observations makes day-to-day, intra-seasonal and inter-annual variability becoming trivial when generating Figs. 3–6 and 7. As an example, the nLw(443) and nLw(645) values at station 2 as shown in Fig. 6 and qwN(443) and qwN(645) in Fig. 7b are computed from 89 valid satellite retrievals for the January (winter) between 2002 and 2009. The standard deviations of qwN(443) and qwN(645) are 0.89% and 1.3%, respectively, while mean values of qwN(443) and qwN(645) are 2.72% and 4.66%, respectively. The standard deviation values represent about 25–35% of the mean values. This range is also true for the other bands. Similar statistics can also be found at the other stations in different seasons. The assessment of the ocean optical property range provides further confidence that the seasonal climatology as shown in Figs. 3–6 and climatology optical properties in Fig. 7 are statistically significant for quantifying the seasonal changes of the ocean environments in the BS, YS, and ECS. 3.3. Ocean property variability 3.3.1. Variability of Chl-a, Kd(490), TSM, and SST Data Fig. 8 shows temporal variations of Chl-a, Kd(490), TSM, and SST at the eight stations between 2002 and 2009 and provides insight into the variability of the ocean environments in the BS, YS, and ECS. Fig. 8a–d show that the variations of Chl-a, Kd(490), TSM, and SST are predominantly seasonal at stations 1, 3, 5, and 7. Since all four of these stations are located in the coastal regions, the differences in physical, optical, biological, and biogeochemical properties in the coastal regions of these three oceans can be compared. In Fig. 8a, Chl-a at station 7 shows that interannual variability is small in comparison to the seasonal Chl-a variability ranging between 0.3 and 2.5 mg m3, even though Chl-a in highly turbid regions may not reflect the actual values due to algorithm uncertainty. For Kd(490) (Fig. 8c), the interannual variability is small in comparison to the seasonal one at these four stations. High Kd(490) normally occurs in the winter season and low values in the summer season. Even though the magnitude of the interannual changes of Kd(490) is small, the phases of Kd(490) indeed show interannual variation at these four stations. For station 1, the minimum

Kd(490) occurred in late summer during 2003 and 2007, but for some other years, such as 2003 and 2008, Kd(490) was at a minimum during late spring and early summer. At station 7, peak Kd(490) also shows interannual variation. Normally, it occurs in January as shown in Fig. 8c. However, during 2005 and 2008, the maximum Kd(490) occurred in late winter and early spring. TSM concentrations at stations 1, 3, 5, and 7 show similar temporal variations as for Kd(490) (Fig. 8e) with significant seasonal variability. The interannual variations at stations 1 and 7 are small compared to their seasonal ones. At station 3 in the YS, the interannual variability can be as significant as the seasonal one. Normally, TSM is in the range of 10–20 g m3 in the summer. However, in the summer of 2005, TSM actually reached 50 g m3. It is also worth noting that at station 5 in the Hangzhou Bay, TSM has been kept high for all 8 years. Both the seasonal and interannual variability in TSM and Kd(490) are smaller, compared to those at stations 1 and 3. At stations 2, 4, 6, and 8, Chl-a (Fig. 8b), Kd(490) (Fig. 8d), TSM (Fig. 8f) also show that the interannual variability is less significant than the seasonal one. Particularly, the interannual variability of each sea is not in sync with that of the others. In January of 2007, Kd(490) reached maxima at stations 2 and 4 in the BS and YS regions. On the other hand, Kd(490) values at stations 6 and 8 do not show significant difference from the other years. During the summer of 2005, station 8 had anomalous Kd(490) and TSM values, while these values were similar to those of normal years at the other stations. This suggests that the physical, biological, and biogeochemical processes that contributed to the interannual variability of these three seas may not be exactly the same, leading to the asynchronous interannual variations in the regions. In comparison to Chl-a, Kd(490), and TSM data, the interannual variability of SST is even smaller for all eight stations in these three seas compared to their seasonal cycles (Fig. 8g and h). The SSTs at stations in the north (stations 1, 2, 3, and 4) show much enhanced seasonal variations than those at the stations in the south (stations 7, 8). In particular, unlike Chl-a, Kd(490), and TSM, SST at the eight stations shows synchronous changes. Compared to other years, the SST minimum during early 2007 was 1–2 °C higher at stations 1 and 2 in the BS. Similar anomalously high SST values are also observed at the other stations in the YS and ECS regions, showing a broad SST increase in the entire three seas for that year. 3.3.2. Empirical orthogonal function (EOF) analysis Since climatology data only provide the mean situations of the BS, YS, and ECS for a year (Fig. 2) or a season (Figs. 3–6), to further analyze and characterize the ocean seasonal and interannual variability, empirical orthogonal function (EOF) analyses have been conducted to identify different processes that drive the Kd(490), TSM, and SST changes in the regions. It is noted that the EOF analyses have been carried out for Kd(490), TSM, and SST parameters for their variances, i.e., the EOF analyses have been conducted for these parameters with their corresponding mean values removed. Since Chl-a algorithm may not be valid for all BS, YS, and ECS regions, i.e., significant uncertainty may occur in turbid regions, which accounts for about 1/3 of the total areal coverage, EOF analysis of Chl-a has not been conducted in this study. 3.3.2.1. EOF analysis in Kd(490). Fig. 9a-c show the spatial patterns of the first, second, and third EOF modes of Kd(490) in the BS, YS, and ECS regions. The first EOF mode is dominant, contributing 62.32% of the total Kd(490) variance (Fig. 9a), while the second (Fig. 9b) and third (Fig. 9c) EOF modes account for 4.85% and 3.32% of the total Kd(490) variance, respectively. Correspondingly, Fig. 10a shows the temporal variations for the three EOF modes of Kd(490). For the first mode, the spatial pattern in Fig. 9a shows that the magnitude of the first mode is significantly higher in the

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coastal region than that in the open ocean. This is especially true for the Yellow River estuary, as well as for the Subei Shoal region. Particularly, the spatial magnitude of the first EOF mode in Kd(490) at the estuary is two orders higher than that in the open ocean for the YS and ECS regions.

Time series of the first EOF mode in Fig. 10a shows a typical seasonal signal. Peaks of the first mode in time series occur in the winter, while troughs are in the summer. It is worth noting that the spatial pattern of the first mode (Fig. 9a) resembles the annual mean fields of Kd(490) (Fig. 2b). Since the first EOF mode contrib-

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utes 62.32% of the entire Kd(490) variance, the dominance of this mode suggests that the highest variability of Kd(490) occurs in the coastal region as specified in Fig. 9a. On the other hand, the seasonality of the first mode in time series further shows that seasonal ocean and atmospheric processes (such as wind speeds and SST) responsible for this mode are the main reasons that lead to the entire Kd(490) changes in this region. It is also noted that in the Hangzhou Bay, the spatial magnitude of the first mode is not as high as that in the Yellow River estuary and the Subei Shoal region. This reflects lack of Kd(490) seasonal variability in the Hangzhou Bay and is consistent with the Kd(490) (Fig. 8c) time series at station 5. The second and third EOF modes of Kd(490) play minor roles in the Kd(490) variation because they only contribute 4.85% and

3.32% of the total Kd(490) variance budget. The temporal variation of the second EOF mode in Kd(490) (Fig. 10a) shows seasonal cycles of this mode. Unlike the first mode, the peak of the second and third mode temporal variations occurs in the spring instead of the winter (first mode). This suggests that these two modes might be attributed to the seasonal spring bloom in the region. The phytoplankton bloom not only leads to higher Chl-a, but also results in enhanced Kd(490). It is also intriguing that the temporal variation of the second mode shows an overall upward trend from 2002 to 2009, indicating increased bloom events in the period. 3.3.2.2. EOF analysis in TSM. Similar to Kd(490) EOF analysis, Fig. 9d–f are the spatial patterns of the first, second, and third modes in TSM. In all the TSM modes, the first mode is again

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dominant. Contributions of the first, second, and third modes to the total TSM variance are about 66.51%, 3.86%, and 3.05%, respectively. The spatial pattern of the first EOF mode in TSM is similar to that in Kd(490). Significantly enhanced values can be found in the Yellow River estuary and the Subei Shoal regions. Modest enhancement can also be found in the Yangtze River estuary, the Hangzhou Bay, and the central ECS plume regions. Temporal variation (Fig. 10b) also shows periodic seasonal changes for the first mode. Seasonal peak occurs during the winter, while the trough occurs in the summer. This mode is attributed to the seasonal change of vertical mixing and sediment resuspension. In fact, the mechanism is the same as that of the first mode in Kd(490). In comparison to the second mode of Kd(490), the temporal variation of the second mode in TSM does not have any seasonal cycles. This indicates that the seasonal phytoplankton bloom responsible for the second and third EOF modes in Kd(490) does not play any role in the TSM variation in this region. In addition, the temporal variation of the third EOF mode in TSM (Fig. 10b), which only contributes 3.05% to the total TSM variance budget, shows no apparent connection to any known physical, biological, or geochemical processes.

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3.3.2.3. EOF analysis in SST. For the SST variation, the first EOF mode accounts for 89.03% of the total SST variance, while the second and third EOF modes are only responsible for 1.93% and 1.00%, respectively. Figs. 9g and 10c show the spatial patterns and temporal variation corresponding to the first EOF mode in SST. The spatial pattern for the first EOF mode in SST (Fig. 9g) is obviously different from those of Kd(490) (Fig. 9a) and TSM (Fig. 9d). Significant enhancements of Kd(490) and TSM in the coastal region, such as the Yellow River estuary and the Subei Shoal, cannot be identified anymore for SST. Instead, it shows generally decreasing amplitudes from the BS in the north to the ECS in the south. In addition, values of the spatial patterns in the first SST EOF mode in Fig. 9g are all positive. This demonstrates that SST changes caused in this mode are synchronous for the BS, YS, and ECS regions. Periodical seasonal cycle of temporal variation in the first mode for SST is obvious (Fig. 10c). Peak and trough values are found in the summer and winter, respectively. Results of combining the spatial pattern (Fig. 9g) and temporal variation (Fig. 10c) for the first SST EOF mode show higher SST variation in the BS in the north and lower in the south. This reflects the SST variation for the three different seas as shown in Fig. 8g and h, and can be attributed to the seasonal climate changes in the regions. In the time series of the first SST EOF mode, early 2007 marked the highest point during the winter in the 8 years. During the winter, the high extreme and the broad positive spatial patterns of the first EOF mode in SST produce higher-thannormal SST values since the first mode accounts for nearly 90% of the SST variance. Indeed, SST reached the multi-year low during the early 2007 for all eight stations as shown in Fig. 8g and h. Temporal variation of the second EOF mode in SST does not have any periodic changes (Fig. 10c). The spatial pattern of the second EOF mode in SST (Fig. 9h) shows uniform (flat) distributions for the BS, YS, and ECS regions. Abnormally high amplitudes are identified in the pixels close to land. This indicates that this mode actually represents the contribution of the SST noise caused by the land contamination. Even though the third EOF mode in SST only contributes 1% of the total SST variance in the region, temporal variation of the third SST mode actually shows a seasonal variation, e.g., highly negative during each winter season. The spatial pattern of the third SST mode (Fig. 9i) also shows enhanced positive pattern in the Taiwan Strait. This can be attributed to seasonal transport change of northward Taiwan Strait Current (Wang et al., 2003). A tongue-shaped negative pattern extending northwestward along the YS trough (not identifiable due to the color scale limitation in Fig. 9i) also exists near Cheju Island. The spatial pattern and the temporal variations of the third SST mode, thus, can lead to positive contribution during the winter over the southeast YS. This is consistent with the pronounced SST patterns during the winter in the southeast YS as shown in Moon et al. (2009). This consistency evidently demonstrates that the third EOF mode in SST variation is driven by the ocean circulations that are featured with the seasonal northward intrusion of the Yellow Sea Warm Current (YSWC) in the winter (Pang et al., 1992; Riedlinger and Jacobs, 2000; Teague and Jacobs, 2000).

4. Discussions 4.1. Mechanism driving the satellite observations of the BS, YS, and ECS This study shows that BS, YS, and ECS regions experience significant variability between 2002 and 2009. In the continental shelf of the BS, YS, and ECS, ocean bottom is covered with silty clay south of the Yangtze River due to accumulation of Yangtze River sediments and silty, clay-like sands from the ancient Yellow River to the north (Demaster et al., 1985; Milliman et al., 1985). The spatial patterns of the annual and seasonal climatology (Figs. 2–6) and their

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variability are dominantly driven by sediment re-suspension process, which is highly related to the different physical processes as well as the ocean bathymetry and sediment characteristics and distributions in this region. In addition, biological process also plays a role in determining the ocean’s turbidity (Kd(490)) variability in this region. Seasonal changes of the surface winds, sea surface warming (cooling), and the water stratification are the major forcing that drives the sediment re-suspension process which is responsible for the first EOF mode of the satellite ocean color observations (TSM and Kd(490)) as shown in Figs. 9 and 10. Using satellite ocean color observations, wind speed data from National Centers for Climate Prediction (NCEP), World Ocean Atlas 1998 data, Shi and Wang (2010b) demonstrated that the seasonal variability of the sediment plume in the central ECS is attributed to the strong (weak) vertical mixing and convection driven by the cooling (warming) of the upper layers and the enhanced (weakened) surface winds during the winter (summer) season. In the central ECS region, the mean wind speeds change from 1–2 m/s in the summer to 5–6 m/s in the winter, while the ocean is vertically strongly stratified in the summer and nearly uniform in the winter. The seasonal winds, different ocean stratifications, and ocean surface thermodynamics lead to the seasonal sediment plume in the central ECS. Similar monsoon winds, ocean stratifications, and cooling (warming) process of the ocean upper layer in the continental BS, YS, and ECS imply that the seasonal changes of the winds, ocean stratification, and ocean thermodynamics are also the driving forcing for the seasonal variations of the satellite observations in the BS, YS, and ECS regions. Even though tidal current is another major forcing that leads to the re-suspension of the sediments in the study regions, it is not a factor that drives the satellite observation as shown in this study, thus tidal influence has not been specifically shown in this study. The tide is predominantly semidiurnal in the region (Choi, 1980; Guo and Yanagi, 1998), particularly in the YS and ECS regions. The satellite ocean color observations in the BS, YS, and ECS show notable spring-neap variations due to the significant tidal turbulent energy in a spring-neap cycle (Shi et al., 2011b). The springneap variations of Kd(490) and TSM are in the same order as the seasonal variability revealed in this study (Shi et al., 2011b). Unlike the seasonal change of winds and ocean stratification, tide is not the mechanism that causes the seasonal Kd(490) and TSM variations as observed in this study because the effects of the tidal currents are averaged to become negligible when the annual/seasonal composite (arithmetic mean) is computed using all the observations within a certain period. It is worth noting that the enhanced Kd(490) and TSM as observed by MODIS-Aqua are confined in the continental shelf region. The spatial patterns of high Kd(490) and TSM in Figs. 2–6 are coinciding with 50 m isobath in Fig. 1. This implies that water depth is also critical in defining the spatial distributions of ocean color and SST in addition to seasonal winds, ocean stratification, and tidal currents. In the BS, Shi et al. (2011a) have shown that the bathymetry change due to the existence of the sand ridges can significantly enhance the ocean turbidity measured by the satellite. Annual mean value of Kd(490) decreases from 1.6 m1 over the ridge to 0.7 m1 in the neighboring ocean region. This further suggests that the re-suspension of the sediments is not only determined by the driving forcing such as winds, tidal current, but also impacted by the water depths. Yang et al. (2004) and Uncles et al. (2002) show that in the coastal regions the change of the sediment concentration near the ocean bottom is synchronous with the ocean bottom current, while TSM change in the surface is not directly correlated to the bottom current. Instead it is linked to the accumulation of the bottom current over a certain period. In situ measurements in this region (Lei et al., 2001; Yang et al.,

2007) indeed show that the highest sediment concentration actually occurs in the middle of the water column for locations with bathymetry over 100 m. All these suggest that ocean bathymetry plays a critical role on the spatial patterns of the TSM and Kd(490). For the region with bathymetry larger than 50 m, the sediments can no longer be re-suspended to the sea surface and observed by the satellite. It is also interesting to note that the spatial pattern of the SST variation as represented by the first EOF mode (Fig. 9) more or less matches with the ocean bathymetry. This actually reflects the bathymetric effect on SST in the YS and ECS regions as suggested by Xie et al. (2002). Phytoplankton bloom is the major forcing that drives the ocean turbidity changes in basin scale (Shi and Wang, 2010a). In the BS, YS, and ECS, Chl-a concentrations are highly dynamic in the BS (Wei et al., 2004), YS (Tian et al., 2005), and ECS (Gong et al., 2003). High nutrient input from Yangtze River (Gong et al., 1996; Siswanto et al., 2008) contributes significantly to the eutrophication of the ECS (Wang, 2006a) and results in a trend of increasing phytoplankton standard stock (Zhou et al., 2008). Even though the phytoplankton bloom is responsible for the second mode of the Kd(490) variability and accounts for 4.85% of the total Kd(490) variance, the spatial patterns (Fig. 9b) and the time series (Fig. 10a) still have significant importance and implications to the ecosystem of the BS, YS, and ECS. First, the total effect of the phytoplankton variation on the water turbidity change is one order less than the first EOF mode, which accounts for over 60% of the Kd(490) variance in this region. This does not mean it is insignificant to the ecosystem. Actually, it is highly regional-dependent. Combining of the spatial pattern and the time series for each mode shows that the contribution of the first Kd(490) EOF mode is 1–2 order higher than the contribution of the second Kd(490) EOF mode in the coastal turbid regions and river estuaries. In comparison, the effect of the first Kd(490) EOF mode is in the same order of the contribution in the second Kd(490) EOF mode for open oceans in the YS and ECS. This implies that phytoplankton dynamics still plays a critical role on the marine environments, especially for the open oceans. Second, spatial patterns of the second Kd(490) EOF mode (Fig. 9b) show the enhanced magnitudes in the coastal and estuarine region, suggesting that the seasonal phytoplankton bloom in the continental shelf is more significant in the coastal regions than that in open oceans, even though the contribution of phytoplankton bloom (the second EOF mode) to the Kd(490) variance in the continental shelf is much less than the contribution of the first Kd(490) EOF mode. Third, spatial patterns and temporal variations of the second Kd(490) EOF mode provide us more insight into the phytoplankton dynamics for the entire region. The phase of the phytoplankton bloom represented by the second EOF mode (Fig. 10a) is different from that of the seasonal change driven by the winds and the ocean stratification as shown for the first EOF mode. Negative magnitudes in the BS and positive magnitudes in the YS and ECS in Fig. 9b show that the phytoplankton dynamics in the three seas are not in phase with each other. Indeed, the ECS and YS are featured with spring phytoplankton blooms (Gong et al., 2003; Tian et al., 2005), while the highest ocean primary production occurs in late summer and early autumn in the BS (Wei et al., 2004). As the largest river in this region, the sediment discharge of the Yangtze River is approximately 5  108 tons each year (Milliman and Meade, 1983). Most of the river discharge and the sediment transportations occur in the summer season (Ning et al., 1998). The river discharge of the Yangtze River predominantly flows southward along the coast (Beardsley et al., 1985). The roles of the river discharge, river plume, and ocean circulation are not shown in the top three Kd(490) modes. Some studies (Chen et al., 2008; Lee and Chao, 2003) have shown that the dispersal of the Yangtze River plume is largely confined in the shallow continental shelf and seasonal-dependent. In the summer, the plume disperses

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to the north and east of the Yangtze River estuary, while it flows to the south in a narrow band along the China’s coastline with the development of the China Coastal Current in fall and winter. Further examining the remaining Kd(490) EOF modes actually shows that the spatial pattern of the fourth mode (not shown here) reflects the above features of the Yangtze River plume in the north and south, thus it is attributed to the Yangtze River plume. This mode only accounts for 2.69% of the total Kd(490) variance. Enhanced contributions of this EOF mode in the continental shelf regions north and south of the Yangtze River estuary suggests that the Yangtze River plume can significantly increase mesoscale water turbidity (e.g., Kd(490)) near the mouth of Yangtze River. This indicates that the Yangtze River discharge can have strong influence on the Chl-a, CDOM, and sediment loadings in the coastal region. On the other hand, the effects of the Yangtze River plume on the open oceans of the ECS and YS are not as pronounced as on the coastal region. 4.2. Optical properties in the BS, YS, and ECS It is noted that, in Shi and Wang (2010a), the relationship between nLw(645) and nLw(859) shows that nLw(645) is flattened when nLw(859) is over 2 mW cm2 lm1 sr1, which equals 0.06 for qwN(859) in the region. These results can be explained from the relationship between water apparent optical properties (AOP) and inherent optical properties (IOP). In the extremely turbid waters, bb(k) is an important term in the function of bb(k)/(a(k) + bb(k)), where bb(k) is backscattering coefficient, and a(k) is total absorption coefficient that is the sum of absorption from water, phytoplankton, detritus and CDOM. When bb(645)  a(645), nLw(645) reaches its maximum value. It suggests that when nLw(859) is over 2 mW cm2 lm1 sr1, nLw(645) is no longer sensitive to the changes of the sediment in the water column. This indicates that the Kd(490) and TSM algorithms, for which both are based on the nLw(645) for the highly turbid waters (Wang et al., 2009a; Zhang et al., 2010), may lead to biased low satellite-derived Kd(490) and TSM values in the BS, YS, and ECS regions. In these three seas, the scenario for nLw(859) over 2 mW cm2 lm1 sr1 normally happens during the winter season and is usually confined to the close-land areas of the Yellow River area and the Subei Shoal region. Actually, the situation with qwN(859) > 0.06 only occurs at one station (station 3) out of all eight stations in the winter season as shown in Fig. 7. From the winter nLw(859) climatology, we estimate these high-nLw(859) (>2 mW cm2 lm1 sr1) regions account for 1.1% and 0.07% of the total area in the BS, YS, and ECS for the winter and summer, respectively. 5. Summary In this study, 8-year MODIS-Aqua observations are used to quantify and characterize the physical, optical, biological, and biogeochemical properties in the BS, YS, and ECS regions. As described in Shi and Wang (2010a), these regions are among the world’s most turbid ocean regions. Even though it has been long known that the BS, YS, and ECS are featured with highly turbid waters in coastal regions and ocean optical, biological, and biogeochemical property features in these three seas show considerable seasonal variability, it only becomes possible to quantify and characterize these water features for the three seas after some major advances in the satellite ocean color data processing and ocean property retrieval algorithms for this region, in particular, the development of the SWIRbased atmospheric correction algorithm (Wang, 2007; Wang and Shi, 2005, 2007) for the highly turbid waters, advanced Kd(490) model for both open oceans and turbid waters (Morel et al., 2007; Mueller, 2000; Wang et al., 2009a), and a regionally-opti-

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mized TSM model (Zhang et al., 2010). In situ validations following the above algorithm developments provide further confidence about the results in this study. The optical, biological, and biogeochemical features in these three seas show significant seasonal variability. These are well reflected in the seasonal maps of Chl-a, Kd(490), TSM, as well as nLw(k) spectra data. In general, the seasonal change weakens from coast to offshore and also from the BS in the north to the ECS in the south. Stronger seasonal variability can be identified in the coast regions, especially in the Yellow River estuary and the Subei Shoal regions with Kd(490) and TSM changing from 4 m1 and over 50 g m3 in the winter to 1 m1 and less than 10 g m3 in the summer. In comparison, the seasonal changes in the open ocean and in the ECS are much smaller. On the other hand, the seasonal patterns of the ocean color retrievals, such as Chl-a, Kd(490), and TSM, are different from the seasonal patterns of SST, showing that the processes contributing to the biological and biogeochemical changes in the regions are different from the ocean and atmospheric processes for the change of SST. EOF analysis shows that the dominant mode for Kd(490) and TSM variation in the BS, YS, and ECS regions is seasonal, which accounts for about two-thirds of the total variance. This mode reflects the impact of the seasonal climate changes on the local ecosystem. No significant EOF mode representing the long-term inter-annual variability is identified for the BS, YS, and ECS. In comparison to the first EOF mode, the second and third modes play minor roles with less than 4% of contributions to the Kd(490) and TSM variance budgets. Seasonal spring phytoplankton bloom and the interannual variability might be responsible for these two modes. Contribution of the first Kd(490) EOF mode can be 1–2 order higher than that of the second Kd(490) EOF mode in the coastal turbid regions and river estuaries. In comparison, the effect of the first Kd(490) EOF mode is in the same order of the contribution of the second Kd(490) EOF mode in the open oceans for the YS and ECS regions. The first EOF mode of SST in the regions is also seasonal and accounts for nearly 90% of the total SST variance. The amplitude for the spatial pattern of the first SST EOF mode in general decreases monotonously from the north to the south, indicating that the BS and YS regions have more seasonality than the ECS in the south. There is no similarity between the spatial pattern of the first EOF mode in SST and those from Kd(490) or TSM. This shows that the driving forcing for the SST variability is different from that of Kd(490) or TSM. The major mechanisms that define the spatial patterns and cause significant variability of water properties in the BS, YS, and ECS regions are the seasonal winds and sea surface thermodynamics driven by seasonal climate change. Coastal bathymetry is also a critical factor in defining the spatial patterns of the ocean color observations. Phytoplankton bloom is identified to plays an important role on the marine environments of the open oceans in the BS, YS, and ECS, while its impact on the ocean turbidity (Kd(490) is much less than that of the seasonal winds and sea surface thermodynamics in the coastal regions. Even though the tidal current is one of important ocean processes that drive both the synopticscale and mesoscale changes of the ocean’s optical, biological, and biogeochemical properties (Shi et al., 2011b), it is not responsible for the seasonal changes of the ocean color observations as revealed in this study. River discharge and river plumes are found to have important mesoscale impacts on the coastal ecosystem and river estuaries. Acknowledgements This research was supported by NASA and NOAA funding and grants. MODIS L1B data and SST data were obtained from the NASA/GSFC MODAPS Services website and ocean color website,

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