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An attention U-Net was proposed to reconstruct the missing chlorophyll-a concentration (Cchla) data. The U-Net is a lightweight full convolution neural network architecture consisting of an enccoder-decoder (i.e., down-sampling and up-sampling). The attention gates (AGs) were integrated into the U-Net. Training the U-Net with AGs could implicitly teach it to suppress irrelevant areas and highlight the salient features in the missing data areas, which would increase the network sensitivity and reconstruction accuracy. The neural network uses the satellite-derived Cchla anomalies and its variance as the input, and the reconstructed fields along with their variances as outputs. The trained network was applied to long-term daily MODIS/Aqua Cchla products in the Pearl River estuary (PRE) and adjacent continental shelf area. The model performance was evaluated by using an independent test dataset from both satellite-derived and in-situ measurements. The results showed that the proposed neural network not only had good performance in the reconstruction of valid pixels, but also provided a more reasonable reconstruction compared to the standard U-Net without AGs. This study provided a feasible method for the reconstruction task in the field of ocean color, which should be helpful in producing a creditable dataset to study the ecological effects of extreme weather conditions such as typhoons on the upper ocean in the PRE waters. Based on the reconstructed Cchla products, the footprints of the typhoons were studied. An increase in surface Cchla near the typhoons’ track and a decrease in estuary were found. The composite results illustrated that the Cchla increases occurred for almost the entire area within a radius of 100 km. The time series analysis showed that the Cchla peak appeared on the fifth day after the typhoon’s passage.

Phytoplankton dynamics, which are highly sensitive to the ecosystem condition and change, are different in coastal waters and open ocean. Previous researches mainly focused on the open ocean dynamic in the South China Sea (SCS), but few research on the Eastern Guangdong (EGD), especially during a period of downwelling-favorable winds. In fact, the influence of topographic irregularities, downwelling, continental outflows, and cold, nutrient-rich currents from Fujian-Zhejiang coastal waters (ZFC) of the East China Sea (ECS) on the spatial distribution of Chlorophyll a (Chl a) in EGD coastal waters could be significant. This study utilized ocean color time series and meteorological and hydrographic data jointly from July 2002 to June 2020 to investigate the heterogeneous regional specific distribution pattern of Chl a in EGD and the environmental determinants in different regions subdivided by water depth. Then the temporal and spatial distribution pattern of Chl a in EGD in winter were discussed in detail by applying an Empirical Orthogonal Function (EOF) analysis, GaoFeng-1 (GF-1) satellite data and in situ measured dataset. The sea surface temperature (SST) in February was negatively related with Chl a in shallow coastal waters less than 60 m deep due to the nutrient-rich, cold waters from ZFC. The monthly mean photosynthetically active radiation (PAR) and precipitation showed significant effects on the phytoplankton growth over regions with a depth less than 10 m. An area with higher Chl a concentration in the downwelling zone were detected in winter. By an examining the temporal variability in meridional distribution of the mean Chl a at 22.41°N and 22.21°N, a symmetrical peak was observed. The coastal fronts extended southwestward from the southeastern coast of Guangdong Province to Dangan Island in the SCS. In addition, a cross-shelf filament was detected near the coast of Shanwei, Guangdong, China on 8 January 2020.

The abundance of phytoplankton is generally estimated by measuring the chlorophyll-a concentration (Cchla), which is an important factor in photosynthesis and can be used to analyze the density and biomass of phytoplankton in the ecosystem. The band-ratio-based empirical or semi-analytical algorithms are operationally applied to retrieve Cchla in global oceans, which generally experience difficulties from the diversity of optical properties and the complexity of the radiative transfer equations in analytical analyses, respectively. With an attempt to develop an accurate Cchla retrieval model for the optically complex coastal and estuarine waters, this study aimed to explore the deep learning (DL) methods in satellite retrieval of Cchla. A two-stage convolutional neural network (CNN), named Cchla-Net, was proposed, which utilized the spectral information of remote sensing reflectances at MODIS/Aqua’s visible bands. In the first-stage phase, the Cchla-Net was pretrained by a set of remote sensing patches, in which the Cchla was generated from an existing model (OC3M). The pretrained results were than used as the initial values to refine the network with the synthetic oversampled in-situ dataset in the second-stage training phase. Using in-situ samples for training with the new initial values has a higher probability to reach the global optimum. The quantitative analyses showed that the two-stage training was more likely to achieve a global optimum in the optimization than the one-stage training. Matchups of the in-situ Cchla measurements were used to evaluate the retrieval models. Results showed that the proposed Cchla-Net produced obvious better performance than the empirical and semi-analytical algorithms, implying the DL method was more effective for optically complex waters with extremely high Cchla. This study provided an applicable method for remote sensing retrieval of Cchla, which should be helpful for studying the spatial distribution and temporal variability in the productive Pearl River estuary (PRE) waters.

We evaluated six empirical and semianalytical models of the diffuse attenuation coefficient at 490 nm (Kd(490)) using an in situ dataset collected in the Pearl River estuary (PRE). A combined model with the most accurate performance (correlation coefficient, R2 = 0.92) was selected and applied for long-term estimation from 2003 to 2017. Physical and biological processes in the PRE over the 14-year period were investigated by applying satellite observations (MODIS/Aqua data) and season-reliant empirical orthogonal function analysis (S-EOF). In winter, the average Kd(490) was significantly higher than in the other three seasons. A slight increasing trend was observed in spring and summer, whereas a decreasing trend was observed in winter. In summer, a tongue with a relatively high Kd(490) was found in southeastern Lingdingyang Bay. In Eastern Guangdong province (GDP), the relatively higher Kd(490) value was found in autumn and winter. Based on the second mode of S-EOF, we found that the higher values in the eastern GDP extended westward and formed a distinguishable tongue in winter. The grey relational analysis revealed that chlorophyll-a concentration (Cchla) and total suspended sediment concentration (Ctsm) were two dominant contributors determining the magnitude of Kd(490) values. The Ctsm-dominated waters were generally located in coastal and estuarine turbid waters; the Cchla-dominated waters were observed in open clear ocean. The distribution of constituents-dominated area was different in the four seasons, which was affected by physical forces, including wind field, river runoff, and sea surface temperature.