The Ganges–Brahmaputra estuary, located in the northern Bay of Bengal, is situated within the largest delta in the world. This river basin features a complex river system, a dense population, and significant variation in watershed vegetation cover. Human activities have significantly impacted the concentration of total suspended matter (TSM) in the estuary and the ecological environment of the adjacent bay. In this study, we utilised the Landsat series of satellite remote sensing data from 1990 to 2020 for TSM retrieval. We applied an atmospheric correction algorithm based on the general purpose exact Rayleigh scattering look-up-table (LUT) and the shortwave-infrared (SWIR) bands extrapolation to Landsat L1 products to obtain high-precision remote sensing reflectance. In conjunction with the normalised difference vegetation index (NDVI), precipitation, and discharge data, we analysed the variation and influencing mechanisms of TSM in the Ganges–Brahmaputra estuary and its surrounding areas. We revealed notable seasonal variation in TSM in the estuary, with higher concentrations during the wet season (May–October) compared to the dry season (the rest of the year). Over the period from 1990 to 2020, the NDVI in the watershed exhibited a significant upward trend. The outer estuarine regions of the Hooghly River and Meghna River displayed significant decreases in TSM, whereas the Baleswar River, which flows through mangrove areas, showed no significant trend in TSM. The declining trend in TSM was mainly attributed to land-use changes and anthropogenic activities, including the construction of embankments, dams, and mangrove conservation efforts, rather than to runoff and precipitation. Surface sediment concentration and chlorophyll in the northern Bay of Bengal exhibited slight increases, which means the limited influence of terrestrial inputs on long-term change in surface sediment concentration and chlorophyll in the northern Bay of Bengal. This study emphasises the impact of human activities on the river–estuary–coast continuum and sheds light on future sustainable management.

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.