Fang Gong

Dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) are the two dominant nutrients influencing seawater quality. Due to the non-optically active nature of nutrients and their regional variability, relying solely on optical data is inadequate for achieving high-precision remote sensing retrieval in complex marine environments. We developed a novel remote sensing algorithm for DIN and DIP using MODIS remote sensing reflectance (Rrs) products and the XGBoost machine learning framework. Beyond optical inputs, our model integrates sea surface temperature (SST) and spatiotemporal information, including a shoreline-based pixel location descriptor, which significantly enhances model performance. We generated monthly average distributions of DIN and DIP concentrations across China’s coastal waters from 2012 to 2022. The findings highlight extensive high-nutrient zones in the Bohai Bay and Changjiang River (Yangtze River) Estuary−Hangzhou Bay regions, with a notable declining trend in nutrient concentrations. The Zhujiang River (Pearl River) Estuary also exhibits elevated nutrient levels, albeit with minimal changes. This study pioneers the incorporation of dual-coordinate information in nutrient retrieval for complex marine environments, significantly improving model accuracy and addressing stripping artifacts associated with single-coordinate systems. Moreover, the results provide unprecedented spatiotemporal insights into nutrient distributions in China’s coastal waters, offering valuable support for marine environmental management and policy-making.

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 Changjiang River Estuary and the adjacent East China Sea is one of the world’s largest coastal carbon sinks, with a steadily increasing sink capacity over recent decades. However, the potential changes in its carbon sink and control mechanisms at decadal scales under climate change remain unclear. This study, based on 27 years (1998–2024) of continuous satellite remote sensing data, investigates the spatiotemporal distribution and long-term evolution of this coastal carbon sink. The results reveal a typical carbon sink with a capacity of −5.23 ± 3.73 mmol m⁻² d⁻¹ and significant seasonal variation. High-frequency remote sensing data reduces uncertainty compared to traditional shipborne observations. Over the past 27 years, the air–sea CO₂ flux increased at a rate of 0.24 mmol m⁻² d⁻¹ yr⁻¹, with a five-fold enhancement in carbon sink capacity. However, after atmospheric pCO₂ exceeded 400 μatm in 2014, the rate of increase slowed, indicating stabilization. Control mechanism analysis shows that biogeochemical processes have been persistently active, while over the past decade the influence of Changjiang discharge on seawater pCO₂ increased by 50%, shifting the system from primarily physical dilution to enhanced biogeochemical regulation. The findings provide insights into the evolution and management of coastal carbon cycles under climate change.

Hangzhou Bay (HZB) has become a hot spot in hydro-morphodynamic research due to human impacts and natural influences, as well as the substantial quantities of water discharge and sediment load of the Yangtze River and Qiantang River. Although many previous studies have analyzed the spatial–temporal variations in suspended particulate matter (TSM) from in situ and satellite observations, the long-term changes in suspended sediment dynamics remain unclear. In this study, we quantified the long-term variation in TSM load using MODIS/Aqua data during 2003–2024. The TSM products in the HZB displayed a decreasing trend from 2003 to 2024 (k = −1.90 mg/L/year, p < 0.05), which may be attributed to decreased sediment discharge from the Yangtze River. The spatial variation in TSM provided quantitative results for HZB, with a substantially increasing trend in the southern shallow areas and a decreasing trend in the northern deep troughs and central bay. The interannual variations in TSM in winter displayed a positive correlation with the sediment load from the Yangtze River (R = 0.640 for the data during 2014–2022) and with wind speed (R = 0.676 for the data during 2009–2021). The TSM of HZB was partly affected by the combined impacts of human activities and climate change. A distinct difference in TSM concentrations on both sides of the Hangzhou Bay Bridge was observed, with higher TSM on the western side than on the eastern side for most of the year during 2003–2024. A decline in TSM was observed near Yushan Island from 2003 to 2024, attributed to large-scale land reclamation and associated alterations in tide-dominated areas. This study provides valuable insights into the long-term changes in suspended sediment and water quality in HZB, which is crucial for managing water resources, creating effective water strategies, predicting future needs, and ensuring sustainable water management.

Introduction: Dinoflagellate and diatom blooms are the most frequent marine ecological disasters in the Yangtze River estuary (YRE) and adjacent areas of the East China Sea (ECS). Distinguishing between these bloom types is essential for accurate monitoring and effective mitigation.Methods: To investigate the physiological differences between bloom types, algal culture experiments were conducted to quantify variations in fluorescence quantum yield (φ) between dinoflagellates and diatoms during the growth phase. Using insights from the experiments, an identification and classification method for harmful algal blooms (HABs) was developed based on Geostationary Ocean Color Imager-II (GOCI-II) data. The method incorporates the difference in remote sensing reflectance (Rrs) between the fluorescence peak and baseline bands and establishes a φ-based classification threshold.Results: Validation of GOCI-II Level-2 products showed that fluorescence-band Rrs products have high accuracy and strong correlation with in situ measurements. A threshold of φ = 0.014 was proposed to distinguish dinoflagellate from diatom blooms. The derived methods were successfully applied to GOCI-II imagery, enabling precise detection and classification of HABs in ECS waters.Discussion: The proposed approach demonstrated strong capability for monitoring the spatial distribution and evolution of HABs. It provides a reliable technique for distinguishing bloom types using geostationary satellite data, offering valuable support for marine environmental management and mitigation efforts.

Accurate prediction of the spatiotemporal distribution of chlorophyll-a (Chl-a) is essential for evaluating marine ecosystem health and predicting ecological disasters. Current methods struggle to capture short-term variability and periodic trends in Chl-a, especially in noise-prone coastal regions. This study aims to enhance the prediction of marine Chl-a concentrations by introducing the chlorophyll-a concentration prediction model (ChlaPM), which was developed on the basis of a convolutional long short-term memory (ConvLSTM) network. The model integrates recent spatiotemporal feature extraction (RSTFE), periodic feature extraction (PFE), and denoising fusion (DNF) modules to effectively capture short-term spatiotemporal changes and periodic variations in Chl-a concentrations. In this study, the performance of ChlaPM in single-step and multistep predictions was evaluated using monthly average Chl-a remote sensing data spanning 1998–2023. The results indicate that compared with the RSTFE model, the ChlaPM model achieves substantial reductions in the root mean square error (RMSE) of 53.84%, 53.58%, and 49.70% for predicting Chl-a concentrations 1 month, 3 months, and 6 months into the future, respectively. These findings highlight the effectiveness of ChlaPM in addressing short-term variability and periodic trends and significantly enhances the accuracy of Chl-a prediction. Future work will focus on integrating additional relevant marine variables into the prediction model to further improve its prediction capabilities.

Acquiring a large number of in situ water spectral measurements is fundamental for constructing water color remote-sensing retrieval models and validating the accuracy of water color remote-sensing products. However, traditional manual site-based water spectral measurements are time-consuming and labor-intensive, resulting in an insufficient number of in situ water spectral samples to date. To resolve this issue, this study develops an unmanned aerial vehicle-based hyperspectral remote-sensing reflectance measurement system (UAV-RRS) capable of continuous on-the-move water spectral measurements. This paper provides a detailed introduction to the system components and conducts precise experiments on the correction and calibration of the spectral sensors. Using this system, an in situ–UAV–satellite multi-source remote-sensing reflectance comparison experiment was conducted in the middle reaches of the Qiantang River, East China, to evaluate the accuracy and reliability of UAV-RRS and extend the analysis to satellite data across different spatial scales. The results demonstrate that, in small-scale water bodies, UAV-RRS achieves higher spatial precision and spectral accuracy, offering a valuable solution for high-precision, low-altitude continuous water body observations.

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 mesoscale eddy structure in the upper ocean is difficult to observe with altimeter satellite data due to their coarse spatiotemporal resolution. However, the world’s first Geostationary Ocean Color Imager (GOCI) makes it possible to refine the eddy structure inversion with observations of high spatiotemporal resolution (500 m and 1 h, respectively). In this study, we proposed a new flow field retrieval algorithm recurrent all-pairs field transforms in mesoscale eddy flow retrieval (RAFT-MesoEddy) based on deep current optical flow architecture in the mesoscale eddy surface flow field for GOCI data. The validation results showed that the flow field matched the particle image velocimetry (PIV) data in both quality and spatial distribution, with mean end-to-end point error (MEPE) and mean average angle error (MAAE) of less than 0.07 pixel and 2.57°, respectively. The GOCI-derived flow field showed good consistency with drifting buoy trajectory data, with MEPE and MAAE values less than 0.34 m/s and 18.78°, respectively. The GOCI-retrieved flow field showed agreement with satellite altimeter geostrophic flow data, with the MEPE and MAAE being 0.28 m/s and 10.12°, respectively. The GOCI-retrieved flow field showed an agreement with Ocean Surface Current Analyses Real-time (OSCAR) surface flow field, with the MEPE and MAAE are 0.28 m/s and 9.62°, respectively. In addition, a specific mesoscale eddy to discuss the hourly dynamics of flow field and eddy kinetic energy (EKE) is taken. The significant diurnal dynamics revealed in the hourly GOCI observations suggest caution in mapping mesoscale eddy flow using conventional altimeter satellite data, as the temporal resolution is insufficient to capture diurnal variations.