The semi-enclosed bays impacted by heavy anthropogenic activities have weak water exchange and purification capacities. Most of the sea bays have suffered severe eutrophication, water quality deterioration, ecosystem degradation and other problems. Although many countries and local governments have carried out corresponding environmental protection actions, the evaluation of their effectiveness still requires monitoring technology and data support for long-term water environment change. In this study, we take Yueqing Bay, the fourth largest bay in China, as a case to study the satellite-based water quality monitoring and variation analysis. We established a nutrient retrieval model for Yueqing Bay to produce a long-term series of nutrient concentration products in Yueqing Bay from 2013 to 2020, based on Landsat remote sensing images and long-term observation data, combined with support vector machine learning and water temperature and satellite spectra as input parameters, and then we analyzed its spatiotemporal variations and driving factors. In general, nutrient concentrations in the western part of the bay were higher than those in the eastern part. Levels of dissolved inorganic nitrogen (DIN) were lower in summer than in spring and winter, and reactive phosphate (PO₄-P) levels were lower in summer and higher in autumn. In terms of natural factors, physical effects (e.g., seasonal variations in flow field) and biological effects (e.g., seasonal differences in the intensity of plankton photosynthesis) were the main causes of seasonal differences in nutrient concentration in Yueqing Bay. Nutrient concentration generally increased from 2013 to 2015 but decreased slightly after 2015. Over the past decade, the economy and industry of Yueqing Bay basin have developed rapidly. Wastewater resulting from anthropogenic production and consumption was transported via streams into Yueqing Bay, leading to the continuous increase in nutrient concentrations (the variation rates: $a_{DIN}>0, a_{P{O}_4-P}>0$), which directly or indirectly caused high nutrient concentrations in some areas of the bay (e.g., Southwest Shoal at the mouth of Yueqing Bay). After 2015, the various ecological remediation policies adopted by cities around Yueqing Bay have mitigated, to some extent, the increasing nutrient concentration trends (the variation rates: $a_{DIN}<0, a_{P{O}_4-P}<0$), but not significantly (P > 0.1). The environmental restoration of Yueqing Bay also requires continuous and long-term ecological protection and restoration work to be effective. This research can provide a reference for ecological environment monitoring and remote sensing data application for similar semi-enclosed bays, and support the sustainable development of the bay.

Real-time monitoring of riverine-dissolved organic carbon (DOC) and its controlling factors is critical for formulating strategies regarding the river basin and marginal seas pollution prevention and control. In this study, we established a linear regression formulation that relates the permanganate index (COD_Mn) to the DOC concentration based on in situ measurements collected on five field surveys in 2023–2024. This regression formulation was used on a large number of data collected from automatic monitoring stations in the Qiantang River area to construct a daily quasi-in situ database of DOC concentration. By combining the quasi-in situ DOC data and Sentinel-2 measurements, an enhanced algorithm for empirical DOC estimation was developed (R² = 0.66) using the extreme gradient boosting (XGBoost) method and its spatial and temporal variations in the Qiantang River were analyzed from 2016 to 2023. Spatially, the main stream of the Qiantang River exhibited an overall decreasing and increasing trend influenced by population density, economic development, and pollutant discharge in the basin area, and the temporal distribution of DOC was controlled by meteorological conditions. The DOC contents had the highest in summer, primarily due to high rainfall and leaching. The inter-annual variation in DOC concentration was influenced by the total annual runoff volumes, with a minimum level of 2.24 mg L⁻¹ in 2023 and a maximum level of 2.45 mg L⁻¹ in 2019. The monthly DOC fluxes ranged from 6.3 to 13.8 × 10⁴ t, with the highest values coinciding with the maximum river discharge volumes in June and July. The DOC levels in the Qiantang River remained relatively high in recent years (2016–2023). This study enables the concerned stakeholders and researchers to better understand carbon transportation and its dynamics in the Qiantang River and its coastal areas.

The rapid expansion of seaweed aquaculture has profound impacts on coastal ecosystems, yet the lack of long-term, high-precision spatiotemporal monitoring methods has constrained systematic understanding of aquaculture dynamics and their environmental effects. This study integrated Landsat (1984–2025) and Sentinel-2 (2015–2025) imagery with an attention-enhanced U-Net deep learning model to achieve 41 years of continuous monitoring of seaweed aquaculture in the Dongtou Archipelago, Zhejiang Province, China. The model achieved high extraction accuracy for both Landsat and Sentinel-2 aquaculture areas (F1 scores of 0.972 and 0.979, respectively). On this basis, the cultivation zones were further classified into Porphyra sp. and Sargassum fusiforme cultivation areas by incorporating local aquaculture planning and field survey data. Results showed that the aquaculture area underwent three developmental stages: slow initiation (1984–2000, <3 km²), rapid expansion (2001–2015, 3–8 km²), and high-level fluctuation (post-2015, typically 8–20 km²), reaching a peak of ~30 km² during 2018–2019. Long-term retrieval of water quality parameters revealed that the decline in total suspended matter (from ~80 to 60 mg/L) and chlorophyll (from ~3 to 2 μg/L) within aquaculture zones was significantly greater than that in non-aquaculture areas, providing direct observational evidence for local water quality improvement by appropriately scaled aquaculture. Meanwhile, sea surface temperature showed a sustained increasing trend, with extremely high-temperature days (≥25 °C) exhibiting strong interannual variability, posing potential thermal stress risks to cold-preferring seaweed species. The NDVI (Normalized Difference Vegetation Index) and FAI (Floating Algae Index) indices effectively captured aquaculture phenology (seeding, growth, maturation, harvest), with their interannual peaks exhibiting an inverted U-shaped correlation with corresponding yields (R = 0.82 and 0.79, respectively, based on quadratic regression fitting), preliminarily demonstrating the potential of remote sensing in indicating density-dependent effects. This study systematically demonstrates the comprehensive capability of multi-source satellite remote sensing in long-term dynamic monitoring, environmental effect assessment, and yield relationship analysis of seaweed aquaculture, providing key technical support and scientific basis for aquaculture carrying capacity management and ecological risk prevention in island waters.

Sea surface temperature (SST) anomalies represent a critical threat to the operational safety and productivity of marine ranching systems. Taking a typical marine ranching area in the Northern Yellow Sea as the study area, this study developed a lightweight two-stage field-to-site forecasting framework. In the first stage, a Convolutional Long Short-Term Memory network (ConvLSTM) was employed to generate 1–5 days regional SST forecasts. Through experiments involving 24 input configurations, the combination of Optimum Interpolation Sea Surface Temperature (OISST), seasonal and trend components, and ERA5 meteorological variables was identified as an optimal configuration, providing spatial-evolution constraints for the target location. In the second stage, the 5-day target-site forecasts were concatenated with OISST to construct a 60-day sequence, which was then used to drive a lightweight Gated Recurrent Unit (GRU) for extended forecasts from days 6 to 15. This strategy reformulates the forecasting task into a spatially constrained 10-day extension forecast, thereby suppressing long-lead error accumulation. The results showed that, in the regional forecasting stage, an RMSE of 0.83 °C and MAE of 0.63 °C were achieved on Day 5, while in the extended-forecasting stage, an RMSE of 0.89 °C and MAE of 0.69 °C were achieved on Day 15. Compared with single-stage ConvLSTM and GRU models performing direct 15-day forecasting, the field-to-site strategy reduced RMSE by approximately 25.21% and 9.18%, respectively, and reduced MAE by approximately 24.18% and 8.00%, respectively. Independent validation against in situ buoy observations further showed that the proposed framework introduced only limited additional errors comparable to the inherent discrepancy between OISST and buoy observations. Additional tests under representative marine heatwave (MHW) conditions showed that the framework retained useful forecasting skill under anomalous warming conditions. Furthermore, its extended application experiments at five additional marine ranching sites in the Northern Yellow Sea produced consistent forecasting performance, with mean RMSE and MAE ranging from 0.71 °C to 0.75 °C and from 0.53 °C to 0.56 °C, respectively. The proposed method can therefore provide a risk-warning window of at least two weeks for marine ranching management and support timely operational decisions for temperature-related risk mitigation and refined aquaculture management.