The accurate mapping of Spartina alterniflora ( S. alterniflora ) invasion is crucial for controlling its spread and reducing severe ecological problems. Satellite images have been extensively employed for S. alterniflora invasion monitoring; however, there are still several issues that need to be addressed. The spectral similarities between S. alterniflora and surrounding ground objects make it challenging for traditional classifiers to achieve satisfactory extraction accuracy. Since the phenological information and red-edge spectral differences have been considered as informative features for identifying S. alterniflora , current studies mainly used them separately as classification features and seldom considered the differences of red-edge information at different phenological periods. Therefore, we proposed a pixel-based phenological and red-edge feature composite method (PpRef-CM) for S. alterniflora extraction considering both phenological information and red-edge bands derived from Sentinel-2 time series based on the existing pixel-based phenological feature composite method (Ppf-CM). The proposed PpRef-CM and machine-learning algorithms were employed for S. alterniflora extraction in two typical mangrove forests along coastal China. Results indicated that red-edge information at different phenological periods is essential for detecting S. alterniflora . S. alterniflora extraction achieved the highest accuracy of 96.57% by using the eXtreme gradient boost algorithm when compared with other machine-learning algorithms. The PpRef-CM gave 2.72% and 2.61% more extraction accuracies of S. alterniflora than the Ppf-CM in two study sites, separately. These findings provide insights for selecting suitable classification features for S. alterniflora extraction studies and serve as an effective control and management of S. alterniflora .

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.

The Dongjiang River Basin (DRB) is a crucial water source for the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), and its water quality is vital for ensuring regional ecological security and sustainable development. The Potassium Permanganate Index (COD_Mn) is one of the key indicators for assessing water quality in the DRB. However, traditional COD_Mn monitoring methods have limitations in inland waters, making it difficult to comprehensively reveal the spatiotemporal dynamics of water quality in the basin. To address this issue effectively, this study utilized multi-band data from Sentinel-2 imagery and water body type information to construct machine learningbased remote sensing retrieval models for CODMn in the DRB. Through comparative analysis, the optimal retrieval model was identified, demonstrating significant accuracy (R² = 0.71, RMSE = 0.8 mg/L, MAPE = 23.28%). Based on the optimal model, highresolution spatiotemporal distribution products of COD_Mn in the DRB from 2017 to 2024 were generated. The results indicate the COD_Mn in some segments of the midstream and the delta are relatively higher, which is closely related to agricultural activities impacts in the midstream, as well as industrial wastewater discharge pollution in the delta. Temporally, the COD_Mn in most areas of the DRB reach their lowest values in winter, a phenomenon that differs from the water quality characteristics of other basins. This study highlights the significant application value of machine learning methods and water body type attributes in improving COD_Mn remote sensing retrieval, reveals the unique water quality characteristics of the DRB, and emphasizes the importance of region-specific management strategies within the basin. The findings provide a theoretical foundation and data support for the scientific management and protection of the DRB.