Red tide is one of the most devastating phenomena that have impacted coastal environments and fishery on a local scale in the worldwide seas. Satellite imagery can provide a synoptic view of the red tides over the wide region. Previous methods to detect the red tides have not sufficiently performed and revealed limitations in the study region. This study developed a red tide detection scheme based on the deep-learning method by using Landsat-8 OLI data during unprecedented explosive red tide events in the southern coastal region of the Korean Peninsula in 2013. To develop and validate the red tide detection of this study, we conducted cruise campaigns to obtain in-situ water sampling for red tide species and its density, chlorophyll-a concentration, suspended particulate matter (SPM), and spectrum data of red tides as a target object and other reference data using a spectroradiometer in the coastal regions from 2013 to 2015. The seawater shows different spectral shape by its components such as red tide, chlorophyll-a concentration, and SPM. Spectral characteristics of the red tides demonstrated bimodal peaks over visible wavelengths regardless of the species of the red tide. Considering such spectral characteristics, the red tide detection algorithm was constructed by the deep-learning method with Landsat-8 OLI level-2 reflectance values and in-situ red tide observations. The validation results of the algorithm as compared with the map of in-situ red tide measurements showed a high probability of detection values greater than 0.7. These works have made it possible to monitor the red tides with high-spatial resolution satellite data and provide a tool to minimize a lot of socioecological impacts from red tide.

Red tide causes significant damage to marine resources such as aquaculture and fisheries in coastal regions. Such red tide events occur globally, across latitudes and ocean ecoregions. Satellite observations can be an effective tool for tracking and investigating red tides and have great potential for informing strategies to minimize their impacts on coastal fisheries. However, previous satellite-based red tide detection algorithms have been mostly conducted over short time scales and within relatively small areas, and have shown significant differences from actual field data, highlighting a need for new, more accurate algorithms to be developed. In this study, we present the newly developed normalized red tide index (NRTI). The NRTI uses Geostationary Ocean Color Imager (GOCI) data to detect red tides by observing in situ spectral characteristics of red tides and sea water using spectroradiometer in the coastal region of Korean Peninsula during severe red tide events. The bimodality of peaks in spectral reflectance with respect to wavelengths has become the basis for developing NRTI, by multiplying the heights of both spectral peaks. Based on the high correlation between the NRTI and the red tide density, we propose an estimation formulation to calculate the red tide density using GOCI data. The formulation and methodology of NRTI and density estimation in this study is anticipated to be applicable to other ocean color satellite data and other regions around the world, thereby increasing capacity to quantify and track red tides at large spatial scales and in real time.

Reviewing six years of Geostationary Ocean Color Imager (GOCI) suspended particulate matter (SPM) concentration images from 2011 to 2016 revealed unexpected and some enormously high or low values. These speckles are randomly scattered throughout the entire study area or congregated at a certain part, which has strongly restricted the scientific applications of GOCI data thus far. They can be classified into four types: isolated, near-cloud, patch-type, and slot-related speckles, based on spatial distribution and potential causes. These types are investigated. The speckles are induced by a moving-cloud during a complete observation of 8 bands for each slot of GOCI, partly by an incomplete cloud masking procedure near cloud edges, by relatively low reflectance of pixels corresponding to a cloud shadow, by imperfect atmospheric corrections related to water vapor after cloud passage, or by sensor-related troubles related to the effects of stray light. Spatial and temporal variabilities of the identified speckles are investigated and used to develop a methodology for their removal. The SPM concentration values of the error-free pixels, passing through the post-processing of the speckle removal procedure, are compared to those previously with speckles. As a result, the enormously large values of SPM, occupying 6.06% of the pixel numbers, are all eliminated. Typical seasonality of SPM, unrevealed using the speckled images, is clearly presented through the removal procedure. This study addresses the importance of preprocessing for speckle error in SPM concentrations from GOCI data and implies more reliable SPM data without speckles can be used in scientific application research.

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.

Small-scale oil spills can exert substantial ecological impacts on pristine coastal environments, even when the discharged volume is relatively limited. This study investigates a diesel oil spill that occurred on 4 July 2022 along the coast of Jeju Island, Korea, following a fire on moored fishing vessels. Using high-resolution Sentinel-2 optical imagery, we developed a remote sensing-based framework to detect, quantify, and analyze the dispersion of the oil spill. Non-ocean pixels, including land, ships, and clouds, were first removed through spectral thresholding. A spectral unmixing algorithm was then applied to extract endmembers representing oil and seawater. The Spectral Angle Mapper (SAM) method was used to delineate the spatial extent of the spill and a two-beam interference model was employed to estimate oil thickness and total volume. The resulting oil-covered area and volume estimates showed reasonable consistency with reported values. A particularly notable observation was the bifurcation of the oil slick into two distinct branches after exiting the harbor. Hydrodynamic simulations, combined with near-surface wind data, revealed a strong temporal correlation between surface current direction and oil advection, with statistical significance. Additionally, bathymetric analysis derived from Sentinel-2 imagery identified two small seamounts near the harbor outlet, which contributed to the divergence of the oil's trajectory. This study demonstrates the utility of high-resolution optical satellite data for detecting thin oil sheens and highlights the combined influence of wind, surface currents, and local bathymetry on oil dispersion dynamics in shallow, environmentally sensitive coastal waters.

Structured coastal habitats serve as vital fish nurseries globally, yet the role of biotic and abiotic factors in shaping this function remains unclear. Understanding these relationships will allow more informed conservation and restoration decisions and effective communication of their anticipated effects on fish production. We conducted a meta‐analysis of 102 studies (2002 responses) to evaluate the potential drivers of fish recruitment enhancement (RE), defined as the relative increase in young‐of‐year fish abundance in structured vs. unstructured bottom, in North American salt marshes, oyster reefs and submerged aquatic vegetation (SAV). All three habitats significantly enhanced fish recruitment, with SAV and salt marshes exhibiting greater RE than oyster reefs. Restoration increased fish recruitment in all three habitats (130%–180%), but only restored marshes and oyster reefs—not SAV—achieved RE comparable to their natural counterparts. Habitat characteristics were consistently influential predictors of RE across habitat types. In SAV, shoot cover and biomass were positively associated with fish recruitment, with RE being 295% higher in SAV with dense (~80%) compared with sparse (~20%) shoot cover. Fish recruitment increased with oyster cover but declined at greater reef heights, with low (0.5 m). Proximity to SAV and oyster reefs increased RE in salt marshes by 170%; however, this positive effect was not reciprocated in SAV and oyster reefs near marshes. RE remained consistent across temperature and salinity gradients, whereas it was strongly influenced by tidal regime in salt marshes and SAV. Synthesis and applications . Our findings underscore the importance of habitat attributes and physical settings as the key predictors of fish enhancement across threatened coastal nurseries of North America. Restoration and management efforts should aim to optimize these factors to maximize fish recruitment potential amid ecosystem degradation.