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Knowledge of the chlorophyll-a dynamics and their long-term changes is important for assessing marine ecosystems, especially for coastal waters. In this study, the spatial and temporal variability of sea surface chlorophyll-a concentration (Chl-a) in the Bohai Sea were investigated using 13-year (2000-2012) satellite-derived products from MODIS and SeaWiFS observations. Based on linear regression analysis, the results showed that the entire Bohai Sea experienced an increase in Chl-a on a long-term scale, with the largest increase in the central Bohai Sea and the smallest increase in the Bohai strait. Distinct seasonal patterns of Chl-a existed in different sub-regions of the Bohai Sea. A long-lasting Chl-a peak was observed from May to September in coastal waters (Liaodong bay, Qinhuangdao coast, and Bohai bay) and the central Bohai Sea, whereas Laizhou bay had relatively low Chl-a in early summer. In the Bohai strait, two pronounced Chl-a peaks occurred in March and September, but the lowest Chl-a was in summer. This pattern was quite different from those in other regions of the Bohai Sea. The water column condition (stratified or mixed) was likely an important physical factor that affects the seasonal pattern of Chl-a in the Bohai Sea. Meanwhile, increased human activity (e.g., river discharge) played a significant role in changing the Chl-a distribution in both coastal waters and the central Bohai Sea, especially in summer. The increasing trend of Chl-a in the Bohai Sea might be attributed to the increase in nutrient contents from riverine inputs. The Chl-a dynamics documented in this study provide basic knowledge for the future exploration of marine biogeochemical processes and ecosystem evolution in the Bohai Sea. [ABSTRACT FROM AUTHOR]

Particle size distribution (PSD) is an important parameter that is relevant to many aspects of marine ecosystems, such as phytoplankton functional types, optical absorption and scattering from particulates, sediment fluxes, and carbon export. However, only a handful of studies have documented the PSD variability in different regions. Here, we investigate the PSD properties and variability in two shallow and semi-enclosed seas (the Bohai Sea (BS) and Yellow Sea (YS)), using in situ laser diffraction measurements (LISST-100X Type C) and other measurements at 79 stations in November 2013. The results show large variability in particle concentrations (in both volume and number concentrations), with volume concentrations varying by 57-fold. The median particle diameter (Dv50) from each of the water samples also covers a large range (22.4-307.0 μm) and has an irregular statistical distribution, indicating complexity in the PSD. The PSD slopes (2.7-4.5), estimated from a power-law model, cover nearly the entire range reported previously for natural waters. Small mineral particles (with large PSD slopes) are characteristic of near-shore waters prone to sediment resuspension by winds and tides, while large biological particles (with small PSD slopes) dominate the total suspended particulates for waters away from the coast. For the BS and YS, this study provides the first report on the properties and spatial variability of the PSD, which may influence the optical properties of the ocean surface and remote sensing algorithms that are based on estimations of particle concentrations and sizes. [ABSTRACT FROM AUTHOR]

Suspended particles in waters play an important role in determination of optical properties and ocean color remote sensing. To link suspended particles to their optical properties and thereby remote sensing reflectance (Rrs(λ)), cross-sectional area is a key factor. Till now, there is still a lack of methodologies for derivation of the particle cross-sectional area concentration (AC) from satellite measurements, which consequently limits potential applications of AC. In this study, we investigated the relationship between AC and Rrs(λ) based on field measurements in the Bohai Sea (BS) and Yellow Sea (YS). Our analysis confirmed the strong dependence of Rrs(λ) on AC and that such dependence is stronger than on mass concentration. Subsequently, a remote sensing algorithm that uses the slope of Rrs(λ) between 490 and 555 nm was developed for retrieval of AC from satellite measurements of the Geostationary Ocean Color Imager (GOCI). In situ evaluations show that the algorithm displays good performance for deriving AC and is robust to uncertainties in Rrs(λ). When the algorithm was applied to satellite data, it performed well, with a coefficient of determination of 0.700, a root mean squared error of 2.126 m−1 and a mean absolute percentage error of 40.7%, and it yielded generally reasonable spatial and temporal distributions of AC in the BS and YS. The satellite-derived AC using our algorithm may offer useful information for modeling the inherent optical properties of suspended particles, deriving the water transparency, estimating the particle composition and possibly improving particle mass concentration estimations in future. [ABSTRACT FROM AUTHOR]

The emergence of hyperspectral optical satellite sensors for ocean observation provides potential for more detailed information from aquatic ecosystems. The German hyperspectral satellite mission EnMAP (enmap.org) currently in the production phase is supported by a project to explore the capability of using EnMAP data and other future hyperspectral data from space. One task is to identify phytoplankton taxonomic groups. To fulfill this objective, on the basis of laboratory-measured absorption coefficients of phytoplankton cultures (aph(λ)) and corresponding simulated remote sensing reflectance spectra (Rrs(λ)), we examined the performance of spectral fourth-derivative analysis and clustering techniques to differentiate six taxonomic groups. We compared different sources of input data, namely aph(λ), Rrs(λ), and the absorption of water compounds obtained from inversion of the Rrs(λ)) spectra using a quasi-analytical algorithm (QAA). Rrs(λ) was tested as it can be directly obtained from hyperspectral sensors. The last one was tested as expected influences of the spectral features of pure water absorption on Rrs(λ) could be avoided after subtracting it from the inverted total absorption. Results showed that derivative analysis of measured aph(λ) spectra performed best with only a few misclassified cultures. Based on Rrs(λ) spectra, the accuracy of this differentiation decreased but the performance was partly restored if wavelengths of strong water absorption were excluded and chlorophyll concentrations were higher than 1 mg·m-3. When based on QAA-inverted absorption spectra, the differentiation was less precise due to loss of information at longer wavelengths. This analysis showed that, compared to inverted absorption spectra from restricted inversion models, hyperspectral Rrs(λ) is potentially suitable input data for the differentiation of phytoplankton taxonomic groups in prospective EnMAP applications, though still a challenge at low algal concentrations. [ABSTRACT FROM AUTHOR]