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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]

Water transparency, measured with Secchi disk depth (SDD), is an important parameter for describing the optical properties of a water body. This study evaluates variations of SDD and related impact factors in the Bohai and Yellow Seas (BYS). Based on a new mechanistic model proposed by Lee et al. (2015) applied to MODIS remote sensing reflectance data, climatological SDD variation from 2003 to 2019 was estimated. The annual mean images showed an increasing trend from the coastal zone to the deep ocean. Lower values were found in the Bohai Sea (BHS), while higher values observed in the center of the southern Yellow Sea (SYS). Additionally, the entire sea has shown a decreasing temporal tend, with the variation rate lowest in the BHS at 0.003 m y−1, and highest in the SYS at 0.015 m y−1. However, the weak increasing trend that appeared since 2017 suggests that water quality seems to have improved. Further, it displayed seasonal patterns of low in winter and spring and high in summer and autumn. The empirical orthogonal function (EOF) analysis of SDD variations over the BYS, shows that the first SDD EOF mode is the highest, strongly correlated with total suspended matter. With the high correlation coefficients of chromophoric dissolved organic matter, it illustrates that the SDD variation is mainly dominated by the optical components in the seawater, although correlation with chlorophyll-a is the weakest. The second and third EOF modes show that photosynthetically available radiation, sea surface temperature, sea surface salinity, and wind speed are the main covariates that cause SDD changes. Water transparency evaluation on a long-term scale is essential for water quality monitoring and marine ecosystem protection. [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]

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]