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.

Based on characteristics analysis about remote sensing reflectance, the Secchi Disk Depth (SDD) in the Qiandao Lake was predicted from the Landsat8/OLI data, and its changing rates on a pixel-by-pixel scale were obtained from satellite remote sensing for the first time. Using 114 matchups data pairs during 2013–2019, the SDD satellite algorithms suitable for the Qiandao Lake were obtained through both the linear regression and machine learning (Support Vector Machine) methods, with remote sensing reflectance (Rrs) at different OLI bands and the ratio of Rrs (Band3) to Rrs (Band2) as model input parameters. Compared with field observations, the mean absolute relative difference and root mean squared error of satellite-derived SDD were within 20% and 1.3 m, respectively. Satellite-derived results revealed that SDD in the Qiandao Lake was high in boreal spring and winter, and reached the lowest in boreal summer, with the annual mean value of about 5 m. Spatially, high SDD was mainly concentrated in the southeast lake area (up to 13 m) close to the dam. The edge and runoff area of the lake were less transparent, with an SDD of less than 4 m. In the past decade (2013–2020), 5.32% of Qiandao Lake witnessed significant (p < 0.05) transparency change: 4.42% raised with a rate of about 0.11 m/year and 0.9% varied with a rate of about −0.09 m/year. Besides, the findings presented here suggested that heavy rainfall would have a continuous impact on the Qiandao Lake SDD. Our research could promote the applications of land observation satellites (such as the Landsat series) in water environment monitoring in inland reservoirs.

Small island countries in the South Pacific are ecologically fragile areas, vulnerable to climate change, and the long-term ecological changes in the sea and land have an important impact on their sustainable development. This study takes Fiji, a typical small island country in the South Pacific, as an example, to analyze the change and connection of marine and terrestrial ecosystem environments based on 30 years of multi-source, satellite, remote-sensing data. From 1991 to 2019, according to the change in forest area in Fiji, three stages were delineated: first was a period of stability, then a decrease, and then a recovery in recent years. From 1991 to 2002, Fiji’s vegetation accounted for 73% of the total area; sea environment surrounding the islands, such as sea level height and sea surface temperature, were relatively low, with high water transparency. From 2002 to 2014, with the development of forestry and tourism, vegetation decreased by 6.89% and bare land increased, which changes the runoff erosion in the drainage basin; correspondingly, the chlorophyll a concentration in three major estuaries was found to be slightly increased with low water transparency. Meanwhile, coupled with the rising sea temperature, the area of Fiji’s coral reefs shrank significantly, with 51.13% of the total loss of coral reefs occurring in the Vanua Levu, where bare land and runoff were more distributed in its drainage basin. From 2014 to 2019, Fiji’s vegetation and coral reef areas recovered from the former stage; affected by short-term climate oscillations such as El Niño-Southern Oscillation (ENSO), the sea surface temperature showed a significant abnormal drop and the water transparency decreased. In the past 30 years (1993–2018), the sea level rise rate around Fiji reached 4 mm/year, and the temperature increased by 0.3 °C, which threatens the coastal ecosystem environment, including coral reefs and mangrove; inappropriate land-use change would worsen the situation in these ecologically fragile areas.

In naturally accretional salt marshes, pioneer species typically expand seaward and colonize tidal flats. However, this process can be influenced by disturbances such as human activities and species invasions. Understanding the spatiotemporal patterns and driving mechanisms of vegetation succession in salt marshes is critical for wetland conservation, restoration, and management. Using southern Hangzhou Bay as a case study, we developed a remote sensing algorithm to distinguish the dominant species Scirpus mariqueter (S. mariqueter) and Spartina alterniflora (S. alterniflora). Based on long-term time-series remote sensing data (1990–2023) and twelve parameters representing environmental variables, human activity, and interspecific competition, we analyzed the seaward expansion of the dominant salt marsh species and quantified the effects of various drivers on vegetation. The results showed that as a pioneer species, S. mariqueter expanded at a rate of 0.26 km² yr⁻¹ and was gradually replaced by S. alterniflora, which expanded at a rate of 0.52 km² yr⁻¹. Over the 34-year period, both species exhibited phased expansion–decline–recovery dynamics. During the relatively stable periods (1990–2003 and 2015–2023), temperature, sea level anomaly, and sea surface salinity were the key drivers of vegetation succession. During the disturbance period (2004–2014), S. mariqueter remained primarily influenced by environmental factors, whereas S. alterniflora was primarily influenced by human activities. This study provides the first satellite-based analysis of salt marsh species dynamics in southern Hangzhou Bay over a 34-year period, revealing nonlinear, staged, and species-specific succession patterns and providing new perspectives for invasive species management and the conservation of dynamic coastal wetlands.

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.