Search Results (102)

In this work, pCh-MWCNTs@Ag-TiO₂/S and pCh-MWCNTs@Ag-TiO₂ nanocomposites were synthesized through a combined phosphorylation and cross-linked polymerization method. The materials were thoroughly characterized using several analytical techniques, including SEM/EDS, FTIR, TGA, and BET analysis. SEM images revealed that the pCh-MWCNTs@Ag-TiO₂/S nanocomposite displayed a smooth, flake-like morphology with spherical, dark greenish particles. EDS analysis confirmed the presence of Si, S, P, and Ag as prominent elements, with Ti, C, and O showing the most intense peaks. The TGA curves indicated significant weight loss between 250–610 °C for pCh-MWCNTs@Ag-TiO₂ and 210–630 °C for pCh-MWCNTs@Ag-TiO₂/S, corresponding to the decomposition of organic components. FTIR spectra validated the existence of functional groups such as hydroxyl (-OH), carboxyl (-COOH), and carbonyl (-C=O) on the surface of the nanocomposites. Following characterization, the materials were evaluated for their capacity to adsorb Hg²⁺ at parts-per-billion (ppb) concentrations in contaminated water. Batch adsorption experiments identified optimal conditions for mercury removal. For pCh-MWCNTs@Ag-TiO₂, the best performance was observed at pH 4, with an adsorbent dose of 4.0 mg, initial mercury concentration of 16 ppb, and a contact time of 90 min. For pCh-MWCNTs@Ag-TiO₂/S, optimal conditions were at pH 6, a dosage of 3.5 mg, the same initial concentration, and a contact time of 100 min. Each parameter was optimized to determine the most effective conditions for Hg²⁺ removal. The nanocomposites showed high efficiency, achieving more than 95% mercury removal under these conditions. Kinetic studies indicated that the adsorption process followed a pseudo-second-order model, while the equilibrium data aligned best with the Langmuir isotherm, suggesting monolayer adsorption behavior. Overall, this research highlights the effectiveness of sulfur-modified chitosan-based nanocomposites as eco-friendly and efficient adsorbents for the removal of mercury from aqueous systems, offering a promising solution for water purification and environmental protection. Full article

Water color remote sensing is vital for the monitoring and quantification of marine suspended sediment dynamics and their distributions. Yet validations of these observables in coastal regions and deltaic estuaries, including the Hangzhou Bay in the East China Sea, remain challenging, primarily due to the pronounced complex oceanic dynamics that exhibit high spatiotemporal variability in the signals of the suspended sediment concentration (SSC) in the ocean. Here, we integrate satellite images from the sun-synchronous satellites, China’s Huanjing (Chinese for environmental, HJ)-1A/B (charged couple device) CCD (30 m), and from Korea’s Geostationary Ocean Color Imager GOCI (500 m) to the spatiotemporal scale effects to validate SSC remote sensing-retrieved data products. A multi-scale validation framework based on coefficient of variation (CV)-based zoning was developed, where high-resolution HJ CCD SSC data were resampled to the GOCI scale (500 m), and spatial variability was quantified using CV values within corresponding HJ CCD windows. Traditional validation, comparing in situ point measurements directly with GOCI pixel-averaged data, introduces significant uncertainties due to pixel heterogeneity. The results indicate that in regions with high spatial heterogeneity (CV > 0.10), using central pixel values significantly weakens correlations and increases errors, with performance declining further in highly heterogeneous areas (CV > 0.15), underscoring the critical role of spatial averaging in mitigating scale-related biases. This study enhances the quantitative assessment of uncertainties in validating medium-to-low-resolution water color products, providing a robust approach for high-dynamic oceanic environment estuaries and bays. Full article

In China’s coastal regions, severe seawater eutrophication has led to frequent occurrences of red tides, causing significant damage to marine fisheries and aquatic resources. Therefore, red tide detection and prediction are of great research importance. Although current deep learning-based red tide detection methods perform well in detecting single-day red tides, they struggle with continuous multi-day detection due to insufficient mining of temporal features and difficulties in accurately capturing dynamic variations, limiting further improvements in detection accuracy. To address these issues, this study proposes a time-series fusion network model (CSF-RTDNet) for red tide detection using time-continuous GOCI data from the East China Sea. By integrating multi-temporal GOCI data, the model comprehensively captures spatiotemporal characteristics of red tides, enhancing dynamic process modeling. The CSF-RTDNet method improves feature discrimination by introducing NDVI to enhance red tide characteristics and increase separability between red tides and seawater. Additionally, an ECA channel attention mechanism is employed to fully exploit spectral features across different bands for deeper feature extraction. A novel feature extraction module, ASPC-DSC, combines atrous spatial pyramid convolution with depthwise separable convolution to effectively fuse multi-scale contextual features while improving computational efficiency. Furthermore, ConvLSTM is introduced to integrate temporal and spatial features, effectively addressing the insufficient mining of sequential characteristics in multi-day red tide detection. Experimental results demonstrate that CSF-RTDNet achieves robust detection of red tides with complex boundaries and continuous temporal patterns, attaining an accuracy of 95.89%, precision of 93.03%, recall of 96.34%, and a Kappa coefficient of 0.95. This method significantly enhances red tide detection accuracy and provides valuable technical support for marine environmental monitoring. Full article

Accurately predicting the drift trajectory of green tides is crucial for assessing potential risks and implementing effective countermeasures. This paper proposes a short-term green-tide drift prediction method that combines green-tide patch characteristics, 1 h interval drift distances from GOCI-II images, and driving-factor data using the XGBoost machine learning model to enhance prediction accuracy. The results demonstrate that the proposed method outperforms the traditional OpenDrift model in short-term predictions. Specifically, at time intervals of 3, 5, and 7 h, the root mean square errors (RMSEs) of the OpenDrift model in the zonal direction are 1.81 km, 2.89 km, and 3.55 km, respectively, whereas the RMSEs of the proposed method are 0.80 km, 0.98 km, and 1.20 km, respectively; in the meridional direction, the RMSEs of the OpenDrift model are 1.77 km, 2.67 km, and 3.10 km, while the RMSEs for the proposed method are 0.82 km, 1.10 km, and 1.25 km, respectively. Furthermore, the proposed XGBoost method more-accurately tracks the actual positions of green-tide patches compared to the OpenDrift model. Specifically, at the 25 h interval, the proposed method continues to accurately predict patch positions, while the OpenDrift model exhibits significant deviations. This study demonstrates that the proposed method, by learning drift patterns from historical data, effectively predicts the short-term drift process of green tides. It provides valuable support for early warning systems, thereby helping to mitigate the ecological and economic impacts of green-tide disasters. Full article

The offshore transport of coastal water masses in the East China Sea is vital for maintaining ecological stability. Understanding its spatial-temporal pathways helps clarify material transport and ecological responses. This study used total suspended sediment (TSS) data from the Korean Geostationary Ocean Color Imager to analyze TSS distribution and anomalies, combined with satellite-derived surface residual currents. Results show significant seasonal variations: coastal water masses expand to the 50 m isobath in winter and contract to the 20 m isobath in summer. Offshore transport pathways vary spatially, extending to the shelf edge north of 28° N but restricted by the Taiwan Warm Current south of 28° N. A persistent transport pathway near 28° N shifts from northeastward to eastward. Other pathways include one south of Hangzhou Bay (spring and autumn) linked to tidal mixing and another north of the Yangtze River estuary (summer) following the Yangtze River Diluted Water. These findings provide crucial observational insights for modeling material cycling in the East China Sea shelf. Full article

The delimitation of an ecotone is an important reference for ecosystem conservation; however, the assessment of a marine ecotone from an ecological point of view represents a knowledge gap. The Yellow River Estuary (YRE) serves as both spawning and feeding grounds for numerous economically important organisms. Delineating the boundary of YRE and assessing the boundary change have great importance in maintaining its ecosystem health. This study attempts to apply a Moving Split Window (MSW) to determine marine boundary in YRE. Level 2 remote sensing satellite data spanning from 2012 to 2020 sourced from the Geostationary Ocean Color Imager (GOCI) were utilized. Chlorophyll-a, Chromophoric Dissolved Organic Matter (CDOM), and Total Suspended Solids (TSS) were employed as variables, with Squared Euclidean Distance (SED) serving as the determinant for identifying the marine ecological ecotone within the Yellow Estuary and its adjacent waters. Results indicate the following: (1) SED values exhibit distinct peaks and valleys, facilitating the accurate identification of marine ecotones via MSW. (2) Evident ecotones are observable in both the gate and coastal regions. (3) The influence range of TSS on the gate spans between 10 km and 14 km. In synthesis, the ensuing conclusions are drawn: MSW proves to be a reliable method for quantitatively determining ecotones in marine environments. Furthermore, MSW introduces a novel approach to the delineation of marine ecotones. Full article

Taihu Lake is a large lake with high levels of eutrophication. Cyanobacterial outbreaks significantly affect the ecological environment and socioeconomic development. The chlorophyll-a (Chl-a) concentration, which is crucial for monitoring eutrophication, can be obtained through remote sensing inversion, and the random, sudden, and complex changes impose stringent requirements on the monitoring scale. However, single remote sensing images often fail to meet both the high temporal and spatial resolution requirements for Chl-a monitoring. This study took Taihu Lake as the research object, combined COMS-1 GOCI (1 h/500 m resolution) and Sentinel-2 MSI (5 d/10 m resolution) inverted Chl-a data, and developed a precorrection-based spatiotemporal downscaling method (PC-STDM). After eliminating systematic bias, the model used temporal weighting downscaling (TWD) and regression trend assessment downscaling (TRAD) methods to downscale the inverted Chl-a data, improving the temporal resolution of the Sentinel-2 MSI Chl-a inversion data from 5 d to 1 h. The verification resulted in an average R² of 0.87 between the COMS-1 GOCI and Sentinel-2 MSI Chl-a data after adaptive correction. A comparison with the measured Chl-a data yielded a maximum fitting coefficient of 0.98, verifying the credibility of the model. The downscaled Chl-a concentration data detailed hourly changes and development trends, providing support for water quality monitoring in the Taihu Lake area. Full article

Algal bloom is a major ecological and environmental problem caused by abnormal algal reproduction in water, and it poses a serious threat to the aquatic ecosystem, drinking water safety, and public health. Because of the high dynamic and spatiotemporal heterogeneity of bloom outbreaks, the process often presents significant changes in a short time. Therefore, it has important scientific research value and practical application significance to construct an accurate and effective bloom warning model. This study constructs an integrated model combining sequence features, attention mechanisms, and random forest using machine learning algorithms for bloom prediction, based on watercolor geostationary satellite observations and meteorological data from GOCI in South Korea. In the process, high spatial resolution Sentinel-2 satellite data is also utilized for sample extraction. With a 10-m resolution, Sentinel-2 provides more precise spatial information compared to the 500-m resolution of GOCI, which significantly enhances the accuracy of the model, especially in monitoring local water body changes. The experimental results demonstrate that the model exhibits excellent accuracy and stability in the spatiotemporal prediction of water blooms. The average AUC value is 0.88, the F1 score is 0.72, and the accuracy is 0.79 when identifying the dynamic change of water bloom on the hourly scale. At the same time, this study summarized four typical diurnal change modes of effluent bloom, including dispersal mode, persistent outbreak mode, dispersal-regression mode, and subsidence mode, revealing the main characteristics of diurnal dynamic change of bloom. The research results provided strong technical support for water environment monitoring and water quality safety management and showed a good application prospect. Full article

Sea surface currents (SSCs) play a pivotal role in material transport, energy exchange, and ecosystem dynamics in coastal marine environments. While traditional methods to obtain wide-range SSCs, such as satellite altimetry, often struggle with limited performance in coastal regions due to waveform contamination, deriving SSCs from sequential ocean color data using maximum cross-correlation (MCC) has emerged as a promising approach. In this study, we proposed a novel SSC estimation method, called tide-restricted maximum cross-correlation (TRMCC), and implemented it on hourly ocean color data obtained from the Geostationary Ocean Color Imager II (GOCI-II) and the global tide model FES2014 to derive SSCs in coastal seas and turbid estuaries. Cross-comparison over three years with buoy data, high-frequency radar, and numerical model products shows that TRMCC is capable of obtaining high-resolution SSCs with good accuracy in coastal and estuarine areas. Both large-scale ocean circulation patterns in seas and fine-scale surface current structures in estuaries can be effectively captured. The deriving accuracy, especially in coastal and estuarine areas, can be significantly improved by integrating tidal current data into the MCC workflow, and the influence of invalid data can be minimized by using a flexible reference window size and normalized cross-correlation in the Fourier domain technique. Seasonal SSC structure in the Bohai Sea and diurnal SSC variation in the Yangtze River Estuary were depicted via the satellite method, for the first time. Our study highlights the vast potential of TRMCC to improve the understanding of current dynamics in complex coastal regions. Full article

Rapid and accurate dynamic monitoring and quantitative analysis of red tide disasters are of significant practical importance to national economic development. Remote sensing technology is an effective means for monitoring red tides. This paper utilizes GOCI satellite data and employs a quasi-analytical algorithm (QAA) to retrieve the spectral curves of phytoplankton absorption coefficients. On the basis of a detailed analysis of the differences in the spectral curves of the phytoplankton absorption coefficients between red tide and non-red tide waters, we establish a red tide identification algorithm for the East China Sea on the basis of phytoplankton absorption coefficients. The algorithm is applied to multiple red tide events in the East China Sea. The results indicate that this algorithm can effectively determine the occurrence locations of red tides and extract relevant information about them. Full article

The hourly L2-level chlorophyll-a (CHL-a) concentration spatial energy spectra of GOCI-II from 2021 to 2023 are employed to investigate the characteristics of the CHL-a spatial energy spectrum slopes in three regions of the East China Sea, namely nearshore, offshore, and open ocean. The seasonal trends of the spatial energy spectrum slopes are also examined for the nearshore and offshore regions. It is observed that the slopes of the CHL-a spatial energy spectrum are −2 at scales larger than 5 km, whereas at smaller scales, they are −5/3, −1, and −0.3 from the nearshore region to the open sea, respectively. On the larger scales, the spatial energy spectrum slopes are consistent with surface quasi-geostrophic (sQG) theory, but this is not the case on smaller scales. An insufficient regional CHL-a concentration leads to a flattening of the slope at the smaller scales. On the submesoscale, the slope of the nearshore CHL-a concentration spatial energy spectrum is steeper in summer and flatter in winter, a pattern that contrasts with changes observed offshore. This seasonal variation is attributed to the southward flow of ZheMin Coastal Current (ZMCC) during winter, which carries freshwater and enhances the horizontal buoyancy gradient in the nearshore region. Full article

The recurring occurrence of green tides as an ecological disaster has been reported annually in the Yellow Sea. While remote sensing technology effectively tracks the scale, extent, and duration of green tide outbreaks, there is limited research on the underlying driving mechanisms of green tide drift transport and the determination of the leeway coefficient. This study investigates the green tide transport mechanism and evaluates the feasibility of estimating the leeway coefficient by analyzing green tide drift velocities obtained from Geostationary Ocean Color Imager-II (GOCI-II) images using the maximum cross-correlation (MCC) technique and leeway method across various time intervals alongside ocean current and wind speed data. The results reveal the following: (1) Significant spatial variations in green tide movement, with a distinct boundary at 34°40′N. (2) Short-term green tide transport is primarily influenced by tidal forces, while wind and ocean currents, especially the combined Ekman and geostrophic current component, predominantly govern net transport. (3) Compared to 1, 3, and 7 h intervals, estimating the leeway coefficient with a 25 h interval is feasible for moderate-resolution geostationary images, yielding values consistent with previous studies. This study offers new insights into exploring the transport mechanisms of green tides through remote sensing-driven velocity. Full article

Due to the external environment and the buoyancy of cyanobacteria, the inhomogeneous vertical distribution of phytoplankton in eutrophic lakes affects remote sensing reflectance (R_rs) and the inversion of surface chlorophyll-a concentration (Chla). In this study, vertical profiles of Chla(z) (where z is the water depth) and field R_rs (R_rs_F) were collected and utilized to retrieve the vertical profiles of Chla in Lake Chaohu in China. Chla(z) was categorized into vertically uniform (Type 1: N = 166) and vertically non-uniform (Type 2: N = 58) types. Based on the validation of the atmospheric correction performance of the Geostationary Ocean Color Imager (GOCI), a Chla(z) inversion model was developed for Lake Chaohu from 2011 to 2020 using GOCI R_rs data (R_rs_G). (1) Five functions of non-uniform Chla(z) were compared, and the best result was found for Chla(z) = a × exp(b × z) + c (R² = 0.98, RMSE = 38.15 μg/L). (2) A decision tree of Chla(z) was established with the alternative floating algae index (AFAI_Rrs), the fluorescence line height (FLH), and wind speed (WIN), where the overall accuracy was 89% and the Kappa coefficient was 0.79. The Chla(z) inversion model for Type 1 was established using the empirical relationship between Chla (z = surface) and AFAI_Rrs (R² = 0.58, RMSE = 10.17 μg/L). For Type 2, multivariate regression models were established to estimate the structural parameters of Chla(z) combined with R_rs_G and environmental parameters (R² = 0.75, RMSE = 72.80 μg/L). (3) There are obvious spatial variations in Chla(z), especially from the water surface to a depth of 0.1 m; the largest diurnal variations were observed at 12:16 and 13:16 local time. The Chla(z) inversion method can determine Chla in different layers of each pixel, which is important for the scientific assessment of phytoplankton biomass and lake carbon and can provide vertical information for the short-term prediction of algal blooms (and the generation of corresponding warnings) in lake management. Full article

This study used GOCI-II data to systematically evaluate the feasibility of Rayleigh-corrected reflectance (R_rc) to detect algal blooms in the complex optical environment of the East China Sea (ECS). Based on long-term in situ remote sensing reflectance (R_rs), R_rc spectra demonstrated the similar capability of reflecting the water condition under various atmospheric conditions, and the baseline indices (BLIs) derived from R_rc and R_rs showed good consistency (R² > 0.98). The effectiveness of five R_rc-based BLIs (SS490, CI, DI, FLH, and MCI) for algal bloom detection was assessed, among which SS490 and MCI showed better performances. A synthetic bloom detection algorithm based on the BLIs of R_rc was then developed to avoid the impact of turbid water. The validation of the BLI algorithm was carried out based on the in situ algal abundance data from 2021 to 2023. Specifically, SS490 showed the best bloom detection result (F-measure coefficient, FM = 0.97), followed by MCI (FM = 0.88). Since the 709 nm bands used in MCI were missing in many ocean color satellites, the SS490 algorithm was more useful in application. Compared to R_rs based bloom detection algorithms, synthetical R_rc BLI proposed in this paper provides more effective observation results and even better algal bloom detection performance. In conclusion, the study confirmed the feasibility of utilizing R_rc for algal bloom detection in the coastal areas of the ECS, and recognized the satisfactory performance of synthetical SS490 by comparing with the other BLIs. Full article

During the summer, substantial freshwater discharge from the Changjiang River into the East China Sea (ECS) results in extensive low-salinity water (LSW) plumes that significantly affect regions along the southern Korean Peninsula and near Jeju Island. Previous research developed an empirical equation to estimate sea surface salinity (SSS) in the ECS during the summer season using remote-sensing reflectance (R_rs) data from bands 3–6 (490, 555, 660, and 680 nm) of the Geostationary Ocean Color Imager (GOCI). With the conclusion of the GOCI mission in March 2021, this study aims to ensure the continuity of SSS estimation in the ECS by transitioning to its successor, the GOCI-II. This transition was facilitated through two approaches: applying the existing GOCI-based equation and introducing a new machine learning method using a random forest model. Our analysis demonstrated a high correlation between SSS estimates derived from the GOCI and GOCI-II when applying the equation developed for the GOCI to both satellites, as indicated by a robust R² value of 0.984 and a low RMSD of 0.8465 psu. This study successfully addressed the challenge of maintaining continuous SSS estimation in the ECS post-GOCI mission and evaluated the accuracy and limitations of the GOCI-II-derived SSS, proposing future strategies to enhance its effectiveness. Full article