With the planning and construction of marine ranching in China, water quality has become one of the critical limiting factors for the development of marine ranching. Due to geographical differences, marine ranches exhibit varying water quality conditions under the influence of the continental shelf. To the best of our knowledge, there is limited research on satellite-based water quality monitoring for marine ranching and the spatiotemporal variations in marine ranches in different geographical locations. Chlorophyll-a (Chl-a) is a key indicator of the ecological health and disaster prevention capacity of marine ranching, as it reflects the conditions of eutrophication and is crucial for the high-quality, sustainable operation of marine ranching. Using a physically based model, this study focuses on the retrieval of Chl-a concentration in Daya Bay. The coefficient of determination (R²) between the model retrieval values and the in situ Chl-a data is 0.69, with a root mean square error (RMSE) of 1.52 μg/L and a mean absolute percentage error (MAPE) of 44.25%. Seasonal variations in Chl-a concentration are observed in Daya Bay and are higher in spring–summer and lower in autumn–winter. In the YangMeikeng waters, Chl-a concentration shows a declining trend with the development of marine ranching. A comparison between the YangMeikeng (nearshore) and XiaoXingshan (offshore) marine ranches suggests that offshore ranching may be less impacted by terrestrial pollutants. The primary sources of Chl-a input in Daya Bay are the Dan’ao River and the aquaculture areas in the northeastern part of the bay. This study can provide valuable information for the protection and management of marine ranching.

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of the most urbanized and industrialized coastal regions in China, where intense human activities contribute to substantial terrestrial sediment discharge into the adjacent marine environment. However, complex hydrodynamic conditions and high spatiotemporal variability pose challenges for accurate suspended sediment concentration (SSC) retrieval. Developing water quality retrieval models based on different classifications of water bodies could enhance the accuracy of SSC inversion in coastal waters. Therefore, this study classified the coastal waters of the GBA into clear and turbid zones based on Hue angle α, and established retrieval models for SSC using a single-scattering approximation model for clear zones and a secondary-scattering approximation model for turbid zones based on radiative transfer processes. Model validation with in-situ data shows a coefficient of determination (R²) of 0.73, a root mean square error (RMSE) of 8.30, and a mean absolute percentage error (MAPE) of 42.00%. Spatial analysis further reveals higher SSC in the waters around Qi’ao Island in the Pearl River Estuary (PRE) and along the coastline of Guanghai Bay, identifying these two areas as priorities for attention. This study aims to offer valuable insights for SSC management in the coastal waters of the GBA.

Organic pollution poses a significant threat to water security, making the monitoring of organic pollutants in water environments essential for the protection of water resources. Remote sensing technology, with its wide coverage, continuous monitoring capability, and cost-efficiency, overcomes the limitations of traditional methods, which are often time-consuming, labor-intensive, and spatially restricted. As a result, it has become an effective tool for monitoring organic pollution in water environments. In this study, we propose a physically constrained remote sensing algorithm for the quantitative estimation of organic pollution in inland waters based on radiative transfer theory. The algorithm was applied to the Feilaixia Basin using Sentinel-2 data. Accuracy assessment results demonstrate good performance in the quantitative assessment of organic pollution, with a coefficient of determination (R²) of 0.79, a mean absolute percentage error (MAPE) of 13.03%, and a root mean square error (RMSE) of 0.39 mg/L. Additionally, a seasonal variation map of organic pollutant concentrations in the Feilaixia Basin was generated, providing valuable scientific support for regional water quality monitoring and management.