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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A support vector machine (SVM) regression algorithm was developed for estimating the chlorophyll-a concentration (C chla) in the turbid estuarine waters of the Pearl River estuary (PRE) based on visible infrared imaging radiometer suite (VIIRS) ocean color data. The proposed SVM algorithm outperformed the OC3 algorithm in the turbid waters. The temporal variation and spatial distribution of SVM-based monthly C chla products were determined during 2012 and 2018. By analyzing the monthly average C chla products and ancillary data, the underlying mechanisms of the C chla coastal summer plume jet and winter bloom were revealed. The C chla coastal plume jet was often observed in June, when the zonal wind direction changed from westward to eastward and the river runoff reached its maximum value of the year. The winter bloom in the ocean area was attributed to strong mixing in the upper layer, which resulted from cooling-induced buoyancy changes. • A SVM model was developed for the estimation of chlorophyll-a concentration. • Spatial distribution and temporal variation of chlorophyll-a concentration was reveal. • The underlying mechanisms of the summer plume jet and winter bloom were discussed. [ABSTRACT FROM AUTHOR]

Abstract Spatio-temporal variability of the suspended sediment concentration (SSC) in the Pearl River Estuary (PRE) is analyzed over 13 years (2003–2015) using MODIS-Aqua Level-1B data. A unique seasonal SSC variability was found. During the wet season, the SSC in the northwestern estuary (NWE) is high, which could reach up to 100 mg/L, while during the dry season it suffers from a markedly decrease. Over the southeastern estuary (SEE), however, the pattern is reversed. This seasonality is mainly affected by freshwater discharge, winds and water column stratification. During the wet season, freshwater discharge dominates the SSC in the NWE while stratification and water discharge control the SSC variation in the SEE. During the dry season, freshwater discharge also plays a more important role in changing the SSC in the NWE, while the combined effect of wind mixing and tidal mixing dominates the SSC in the SEE. The interannual variability of the SSC is revealed by an EOF analysis. The first mode is highly related to water discharge of the Pearl River, indicating that the interannual variability is mainly subject to the rainfall over the Pearl River basin. The long-term SSC trend is characterized by a general decrease across the entire estuary and proved to be closely related to human activity. The reduction rate of the SSC varies spatially and seasonally. Spatially, the northern West Shoal and upper eastern part of the PRE suffer from a much greater SSC decrease, while the rest of the estuary, especially the southern West Shoal, is relatively less influenced. Seasonally, the decreasing rate in the dry season (49.3%) is higher than that in the wet season (30.1%). Highlights • Seasonal variability of the SSC in the NWE and SEE are controlled by different factors. • Interannual variability of the SSC is subject to the freshwater discharge. • A significant decreasing trend of the SSC is detected, presumably due to dam constructions. • The reduction rate of the SSC in the dry season is higher than that in the wet season. [ABSTRACT FROM AUTHOR]