Search Results (58)

The Changjiang estuary–Hangzhou Bay region is a critical zone of land–sea interaction, where Total Suspended Matter (TSM) dynamics significantly influence coastal ecology and engineering. While previous studies have examined individual factors affecting TSM variability, the synergistic effects of “tide–monsoon–current” interactions and the actual pathways of turbid plume transport remain poorly understood. Using GOCI satellite data, in situ buoy measurements, and voyage data from 2020, this study applied Data Interpolating Empirical Orthogonal Functions (DINEOFs) and comprehensive spatio-temporal analysis to reconstruct continuous high-resolution TSM fields and elucidate multi-factor controls on TSM dynamics. Based on this high-resolution dataset of TSM, we found that, during the dry season, elevated TSM concentrations are primarily driven by wind–tide resuspension and transport under the comprehensive forcing of the Jiangsu Alongshore Current (JAC), the Yellow Sea Warm Current (YSWC), and wind–tide-induced flows. Contrary to the conventional understanding, the Jiangsu-origin surface TSM can transport to the outer sea without supplementing the TSM in the Turbidity Maximum Zone (TMZ). The YSWC in autumn can cause either low C_TSM gradients or high gradients nearshore depending on whether it is carrying Korean coastal turbid water or not. During the wet season, stratification induced by the Changjiang freshwater discharge suppresses wind–tide resuspension, reducing TSM concentrations in the TMZ and the Qidong water. However, the Changjiang freshwater combined with the Taiwan Warm Current (TWC) dilutes surface TSM in Hangzhou Bay, where the two water masses meet on the 10 m isobath. These insights into factor interactions and TSM plume pathways provide a scientific basis for improved environmental monitoring and coastal management. Full article

Traditionally, sea fog detection technologies have relied primarily on in situ observations. However, point-based observations suffer from limitations in extensive monitoring in marine environments due to the scarcity of observation stations and the limited nature of measurement data. Satellites effectively address these issues by covering vast areas and operating across multiple spectral channels, enabling precise detection and monitoring of sea fog. Despite the increasing adoption of deep learning in this field, achieving further improvements in accuracy and reliability necessitates the simultaneous use of multiple satellite datasets rather than relying on a single source. Therefore, this study aims to achieve higher accuracy and reliability in sea fog detection by employing a deep learning-based advanced co-registration technique for multi-satellite image fusion and autotuning-based optimization of State-of-the-Art (SOTA) semantic segmentation models. We utilized data from the Advanced Meteorological Imager (AMI) sensor on the Geostationary Korea Multi-Purpose Satellite 2A (GK2A) and the GOCI-II sensor on the Geostationary Korea Multi-Purpose Satellite 2B (GK2B). Swin Transformer, Mask2Former, and SegNeXt all demonstrated balanced and excellent performance across overall metrics such as IoU and F1-score. Specifically, Swin Transformer achieved an IoU of 77.24 and an F1-score of 87.16. Notably, multi-satellite fusion significantly improved the Recall score compared to the single AMI product, increasing from 88.78 to 92.01, thereby effectively mitigating the omission of disaster information. Ultimately, comparisons with the officially operational GK2A AMI Fog and GK2B GOCI-II Marine Fog (MF) products revealed that our deep learning approach was superior to both existing operational products. Full article

Water pollution is a growing concern for aquatic ecosystems worldwide, with threats like plastic waste, nutrient pollution, and oil spills harming biodiversity and impacting human health, fisheries, and local economies. Traditional methods of monitoring water quality, such as ground sampling, are often limited in how frequently and widely they can collect data. Satellite imagery is a potent tool in offering broader and more consistent coverage. This review explores how Multispectral Imagery (MSI) and Synthetic Aperture Radar (SAR), including polarimetric SAR (PolSAR), are utilised to monitor harmful algal blooms (HABs) and other types of aquatic pollution. It looks at recent advancements in satellite sensor technologies, highlights the value of combining different data sources (like MSI and SAR), and discusses the growing use of artificial intelligence for analysing satellite data. Real-world examples from places like Lake Erie, Vembanad Lake in India, and Korea’s coastal waters show how satellite tools such as the Geostationary Ocean Colour Imager (GOCI) and Environmental Sample Processor (ESP) are being used to track seasonal changes in water quality and support early warning systems. While satellite monitoring still faces challenges like interference from clouds or water turbidity, continued progress in sensor design, data fusion, and policy support is helping make remote sensing a key part of managing water health. Full article

Harmful algal blooms (HABs) pose increasing threats to marine ecosystems and fisheries worldwide, creating an urgent need for efficient wide-area monitoring schemes. Satellite remote sensing offers a promising approach. However, quantitative, real-time HAB monitoring via satellites remains underdeveloped. Here, we evaluated the applicability of the Normalized Red Tide Index (NRTI), originally developed for Korean waters using the Geostationary Ocean Color Imager (GOCI), in detecting and quantifying HAB in the southern California Current. Our integrated monitoring encompassed two distinct regions of the California Current—Monterey Bay (central California) and La Bocana (Baja California)—separated by a 1470-km stretch of coastline and characterized by blooms of multiple HAB species. Our objectives were threefold: (1) to validate the relationship between NRTI and HAB cell densities through field measurements, (2) to evaluate the performance of hyperspectral NRTI derived from in situ reflectance measurements compared to existing multispectral indices including MODIS ocean color products, and (3) to assess the capability of multispectral sensors to represent NRTI by comparing multispectral-derived indices against hyperspectral NRTI measurements. We found species-specific relationships between hyperspectral NRTI and in situ HAB cell densities, with Prorocentrum gracile in Baja California showing a robust logarithmic fit (R² = 0.92) and multi-species assemblage (dominated by Akashiwo sanguinea) in Monterey Bay displaying a weak, positive correlation. MODIS-derived NRTI values were consistently lower than hyperspectral estimates due to reduced spectral resolution, but the two datasets were strongly correlated (R² = 0.97), allowing for reliable tracking of relative bloom intensity. MODIS applications further captured distinct bloom dynamics across regions, with localized nearshore blooms in Baja California and broader offshore expansion in Monterey Bay. These results suggest that the NRTI-based monitoring scheme can effectively quantify HAB intensity across broad geographic scales, but its application requires explicit consideration of regional HAB assemblages. 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

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

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

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

In recent decades, eutrophication in inland and coastal waters (ICWs) has increased due to anthropogenic activities and global warming, thus requiring timely monitoring. Compared with traditional sampling and laboratory analysis methods, satellite remote sensing technology can provide macro-scale, low-cost, and near real-time water quality monitoring services. The Geostationary Ocean Color Imager (GOCI), aboard the Communication Ocean and Meteorological Satellite (COMS) from the Republic of Korea, marked a significant milestone as the world’s inaugural geostationary ocean color observation satellite. Its operational tenure spanned from 1 April 2011 to 31 March 2021. Over ten years, the GOCI has observed oceans, coastal waters, and inland waters within its 2500 km × 2500 km target area centered on the Korean Peninsula. The most attractive feature of the GOCI, compared with other commonly used water color sensors, was its high temporal resolution (1 h, eight times daily from 0 UTC to 7 UTC), providing an opportunity to monitor ICWs, where their water quality can undergo significant changes within a day. This study aims to comprehensively review GOCI features and applications in ICWs, analyzing progress in atmospheric correction algorithms and water quality monitoring. Analyzing 123 articles from the Web of Science and China National Knowledge Infrastructure (CNKI) through a bibliometric quantitative approach, we examined the GOCI’s strength and performance with different processing methods. These articles reveal that the GOCI played an essential role in monitoring the ecological health of ICWs in its observation coverage (2500 km × 2500 km) in East Asia. The GOCI has led the way to a new era of geostationary ocean satellites, providing new technical means for monitoring water quality in oceans, coastal zones, and inland lakes. We also discuss the challenges encountered by Geostationary Ocean Color Sensors in monitoring water quality and provide suggestions for future Geostationary Ocean Color Sensors to better monitor the ICWs. Full article

Annual outbreaks of floating Ulva prolifera blooms in the Yellow Sea have caused serious local environmental and economic problems. Rapid and effective monitoring of Ulva blooms from satellite observations with wide spatial-temporal coverage can greatly enhance disaster response efforts. Various satellite sensors and remote sensing methods have been employed for Ulva detection, yet automatic and rapid Ulva detection remains challenging mainly due to complex observation scenarios present in different satellite images, and even within a single satellite image. Here, a reliable and fully automatic method was proposed for the rapid extraction of Ulva features using the Tasseled-Cap Greenness (TCG) index from satellite top-of-atmosphere reflectance (R_TOA) data. Based on the TCG characteristics of Ulva and Ulva-free targets, a local adaptive threshold (LAT) approach was utilized to automatically select a TCG threshold for moving pixel windows. When tested on HY1C/D-Coastal Zone Imager (CZI) images, the proposed method, termed the TCG-LAT method, achieved over 95% Ulva detection accuracy though cross-comparison with the TCG and VBFAH indexes with a visually determined threshold. It exhibited robust performance even against complex water backgrounds and under non-optimal observing conditions with sun glint and cloud cover. The TCG-LAT method was further applied to multiple HY1C/D-CZI images for automatic Ulva bloom monitoring in the Yellow Sea in 2023. Moreover, promising results were obtained by applying the TCG-LAT method to multiple optical satellite sensors, including GF-Wide Field View Camera (GF-WFV), HJ-Charge Coupled Device (HJ-CCD), Sentinel2B-Multispectral Imager (S2B-MSI), and the Geostationary Ocean Color Imager (GOCI-II). The TCG-LAT method is poised for integration into operational systems for disaster monitoring to enable the rapid monitoring of Ulva blooms in nearshore waters, facilitated by the availability of near-real-time satellite images. Full article