River estuaries are connectors of terrestrial and marine ecosystems. Riverine particulate organic carbon (POC) is discharged into oceans after a series of biogeochemical reactions in estuaries. Satellite monitoring of POC will improve our understanding of the carbon dynamics of these water bodies. Based on in situ data from four seasonal survey cruises, we developed an algorithm for estimating POC concentrations in the Pearl River Estuary (PRE). Reflectance ratios, Rrs(678)/Rrs(488) and Rrs(748)/Rrs(412), were set as inputs to calculate POC concentration in the PRE. The algorithm was then applied to MODIS/AQUA data to inverse POC concentrations in the PRE from 2002 to 2014. Additionally, sources, impact factors, and seasonal distributions of POC were also investigated. Phytoplankton contributed more to POC in off-shore waters than that in in-shore waters in autumn and spring, but showed the opposite pattern in winter. Under the influence of freshwater from the Pearl River, underwater topography, tides, winds, etc., the seasonal POC concentrations along a specific section, vertical to water depth gradient, decreased in different seasons. These decreases could be described by exponential functions (y = ae^bx, b < 0). The distribution of POC concentrations in the PRE resulted from complex physical and biogeochemical processes, which can change spatially and seasonally.

Due to the tremendous flux of terrestrial nutrients from the Changjiang River, the waters in the coastal regions of the East China Sea (ECS) are exposed to heavy eutrophication. Satellite remote sensing was proven to be an ideal way of monitoring the spatiotemporal variability of these nutrients. In this study, satellite retrieval models for nitrate and phosphate concentrations in the coastal regions of the ECS are proposed using the back-propagation neural network (BP-NN). Both the satellite-retrieved sea surface salinity (SSS) and remote-sensing reflectance (R_rs) were used as inputs in our model. Compared with models that only use R_rs or SSS, the newly proposed model performs much better in the study area, with determination coefficients (R²) of 0.98 and 0.83, and mean relative error (MRE) values of 18.2% and 17.2% for nitrate and phosphate concentrations, respectively. Based on the proposed model and satellite-retrieved R_rs and SSS datasets, monthly time-series maps of nitrate and phosphate concentrations in the coastal regions of the ECS for 2015–2017 were retrieved for the first time. The results show that the distribution of nutrients had a significant seasonal variation. Phosphate concentrations in the ECS were lower in spring and summer than those in autumn and winter, which was mainly due to phytoplankton uptake and utilization. However, nitrate still spread far out into the ocean in summer because the diluted Changjiang River water remained rich in nitrogen.

Shenzhen Bay (SZB), situated between Shenzhen and Hong Kong, is a typical bay system. The water quality of the bay is notably affected by domestic and industrial discharge. Rivers and various types of drainage outlets carry terrestrial pollutants into SZB, resulting in elevated concentrations of nitrogen and phosphorous as well as relatively poor water quality. For over 200 years, Hong Kong has practiced oyster farming within brackish estuarine waters. Oyster farming is a type of mariculture which includes oyster breeding in oyster rafts. Remote sensing is a monitoring technique characterized by large spatial coverage, high traceability, and low cost, making it advantageous over conventional point-based and ship-borne monitoring methods. In this study, remote-sensing models were established using machine-learning algorithms to retrieve key water-quality factors (dissolved inorganic nitrogen (DIN) and orthophosphate-phosphorous (PO₄_P) concentrations, C_DIN and C_{PO4_P}, respectively) from long-term time-series data acquired by the Landsat satellites. (1) Spatially, the water quality in Inner SZB was worse than that in Outer SZB. (2) The water quality temporarily deteriorated between the end of the 20th century and the beginning of the 21st century; then it gradually improved in the late 2000s. (3) Monitoring the water quality in an oyster-farming area revealed that oyster farming did not adversely affect the water quality. (4) The result of monitoring the water quality in river estuaries in SZB shows that water quality was mainly affected by river input.

Little is known about the comprehensive factors influencing temporal changes in microplastic abundance in marine ecosystems. We investigated seasonal variations in the microplastic distribution in marine sediments in multiple-used zones of Eastern Guangdong in the South China Sea. The top 10 cm sediments from 26 sites were collected by grab sampling in the spring, summer, and winter of 2021. Marine sediments had the lowest microplastic abundance in summer, significantly lower than in other seasons. The size of microplastics varied from 22.5 to 4363.3 µm but the 50–200 µm range was the most abundant. Transparent and fragmented microplastics were the most frequently observed composition. Microplastic abundance negatively correlated to distance to the coast. However, seaweed ecosystems impacted microplastic abundance by changing the microenvironment and/or direct contact and entrapment. Microplastic abundance is closely related to coastal mariculture and local residential and industrial activities. Polypropylene, polypropylene-polyethylene copolymer, polyethylene terephthalate, and polyethylene were the most frequently detected compositions, probably from packaging materials, textiles, and electronic/electrical/building industries. This work helps to understand the role of multiple-used zones and their influence on microplastic distributions in marine ecosystems. Appropriate management of the use and disposal of plastic waste on land was recommended to alleviate microplastic pollution in the marine environment.

The Zhejiang coastal waters (ZCW), which exhibit various turbidity levels, including low, medium, and high turbidity levels, are vital for regional ecological balance and sustainable marine resource utilization. Dissolved oxygen (DO) significantly affects marine organism survival and ecosystem health, yet there is limited research on remote sensing monitoring of DO in the ZCW, and the underlying mechanisms are unclear. This study addresses this gap by utilizing high-resolution Landsat 8/9 imagery and sea surface temperature (SST) data to develop a multiple linear regression (MLR) model for DO estimation. Compared to previous studies that utilize remote sensing band reflectance data as inputs, the results show that the red and blue bands are more suitable for establishing DO inversion models for such water bodies. The model was applied to analyze variations in the DO concentrations in the ZCW from 2013 to 2023, with a focus on Hangzhou Bay (HZB), Xiangshan Bay (XSB), Sanmen Bay (SMB), and Yueqing Bay (YQB). The temporal and spatial distributions of DO concentrations and their relationships with environmental factors, such as chlorophyll-a (Chl-a) concentrations, total suspended matter (TSM) concentrations, and thermal effluents, are analyzed. The results reveal significant seasonal fluctuations in DO concentrations, which peak in winter (e.g., 9.02 mg/L in HZB) and decrease in summer (e.g., 6.83 mg/L in HZB). Changes in the aquatic environment, particularly in the thermal effluents from the Sanmen Nuclear Power Plant (SNPP), significantly decrease coastal dissolved oxygen (DO) concentrations near drainage outlets. Chl-a and TSM directly or indirectly affect DO concentrations, with notable correlations observed in XSB. This study offers a novel approach for monitoring and managing water quality in the ZCW, facilitating the early detection of potential hypoxia issues in critical zones, such as nuclear power plant heat discharge outlets.

New generation ocean color imagers on geostationary orbits are designed to provide a much higher temporal resolution along with enhanced spatial and spectral resolutions that will open up obvious opportunities for improving the sampling frequency and resolving diurnal variability of phytoplankton and other biogeochemical properties in dynamic coastal waters. Despite the capabilities of such new generation sensors to detect the diurnal cycles of various ocean phenomena, there is a lack of knowledge on their radiometric sensitivity and signal detectability for observing the ocean color at morning or evening hours. This paper aims to explore the capability of geostationary satellite ocean color sensor for detecting ocean biogeochemical properties [chlorophyll (CHL); total suspended matter (TSM); colored dissolved organic matter (CDOM)] under high solar zenith angles (SZAs). The analysis is based upon simulations from the vector radiative transfer model for the coupled ocean-atmosphere system (PCOART-SA), which considers the earth curvature effects. The unitless differential signal-to-noise ratio (ASNR) is used as a discriminant parameter to indicate the radiometric sensitivity to variation of different biogeochemical properties. The results showed that the SZAs have a significant impact on the signal detectability for CHL variation. For typical shelf water (CHL = 1 μg/L, TSM = 1 mg/L, CDOM = 0.15 m ^-1 ), with the typical observation zenith angle (OZA) = 30°, changes on theorder of ΔCHL = 0.024 μg/L (2.4% to background CHL) were detectable when SZA = 30°; when SZA > 75°, the detectable minimal ΔCHL increased to 0.77 μg/L (77%), indicating the difficulty of detecting CHL under high SZA. For CDOM, the detectability of changes (ΔCDOM) was also found to be closely related to the SZAs, i.e., changes on the order of ten times depending on the SZA conditions. However, even under extremely high SZA conditions (SZA = 80°, OZA = 30°), ACDOM = 0.007 m ^-1 which is about 4.7% of the background CDOM was still detectable at 412 nm. On the other hand, under high SZA conditions (SZA = 80°, OZA = 30°), ΔTSM = 0.211 mg/L (2.1% to the background TSM) was also detectable. Overall, our results indicate that under high SZAs conditions, the geostationary satellite ocean color sensor may experience difficulty in detecting a slight change in CHL variation in productive waters, but it still can detect small changes in TSM and CDOM contents despite a reduced sensitivity at the steeper SZAs.