Search Results (113)

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

The chlorophyll-a (Chl-a) concentration, a critical parameter for characterizing marine primary productivity and ecological health, plays a vital role in providing ecological environment monitoring and climate change assessment while serving as a core retrieval product in ocean color remote sensing. Currently, more than ten ocean color satellites operate globally, including China’s HY-1C, HY-1D and HY-1E satellites. However, significant spatial data gaps exist in Chl-a concentration retrieval from satellites because of cloud cover, sun-glint, and limitation of sensor swath. This study aimed to systematically enhance the spatiotemporal integrity of ocean monitoring data through multisource data merging and reconstruction techniques. We integrated Chl-a concentration datasets from four major sensor types—Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Ocean and Land Color Instrument (OLCI), and Chinese Ocean Color and Temperature Scanner (COCTS)—and quantitatively evaluated their global coverage performance under different payload combinations. The key findings revealed that single-sensor 4-day continuous observation achieved effective coverage levels ranging from only 10.45–26.1%, while multi-sensor merging substantially increased coverage, namely, homogeneous payload merging provided 25.7% coverage for two MODIS satellites, 41.1% coverage for three VIIRS satellites, 24.8% coverage for two OLCI satellites, and 37.1% coverage for three COCTS satellites, with 10-payload merging increasing the coverage rate to 55.4%. Employing the Data Interpolating Empirical Orthogonal Functions (DINEOFS) algorithm, we successfully reconstructed data for China’s ocean color satellites. Validation against VIIRS reconstructions indicated high consistency (a mean relative error of 26% and a linear correlation coefficient of 0.93), whereas self-verification yielded a mean relative error of 27% and a linear correlation coefficient of 0.90. Case studies in Chinese offshore and adjacent waters, waters east of Mindanao Island and north of New Guinea, demonstrated the successful reconstruction of spatiotemporal Chl-a dynamics. The results demonstrated that China’s HY-1C, HY-1D, and HY-1E satellites enable daily global-scale Chl-a reconstruction. Full article

The Leizhou Peninsula, located in the northern South China Sea, features coastal waters with dual functions as both marine ranch demonstration zones and ecological protection areas. Remote sensing monitoring of Chlorophyll-a (Chl-a) concentration in this region holds strategic significance for assessing primary productivity, red tide risk, and the sustainability of the blue food economy. This study integrates in situ survey data from four cruises conducted between 2020 and 2024 with Sentinel-3 OLCI remote sensing imagery, constructing and comparing the performance of six machine learning inversion models. The results show that for the inversion scenarios of the Leizhou Peninsula waters, the GBDT model performs best among the evaluated models (R² = 0.79, RMSE = 0.36 mg/m³, MAE = 0.30 mg/m³). Based on the GBDT model, pixel-by-pixel inversion maps with 300 m spatial resolution were generated for four seasons in 2024, revealing a spatial gradient of “high nearshore–low offshore, high in the east–low in the west” and a seasonal pattern of “low in spring–rise in summer–stable in autumn–high in winter.” In addition, the study verified the operational potential of machine learning in complex type-II waters, analyzed the distribution characteristics and influencing factors of Chl-a concentration in this region, and provided scientific and technical support for marine ranch carrying capacity assessment, eutrophication early warning, and carbon sink accounting in the Leizhou Peninsula. Full article

The estimation of accurate and precise Satellite-Derived Bathymetries (SDBs) is important in marine and coastal applications for a better understanding of the ecosystems and science-based decision-making. Despite the advancements in related Machine Learning (ML) studies, quantifying the anticipated bias per pixel in the SDBs remains a significant challenge. This study aims to address this knowledge gap by developing a spatially explicit uncertainty index of a ML-derived SDB, capable of providing a quantifiable anticipation for biases of 0.5, 1, and 2 m. In addition, we explore the usage of this index for model optimization via the exclusion of training points of high or moderate uncertainty via a six-fold iteration loop. The developed methodology is applied across the national coastal extent of Belize in Central America (~7017 km²) and utilizes remote sensing data from the European Space Agency’s twin satellite system Sentinel-2 and Planet’s NICFI PlanetScope. In total, 876 Sentinel-2 images, nine NICFI six-month basemaps and 28 monthly PlanetScope mosaics are processed in this study. The training dataset is based on NASA’s system Ice, Cloud and Elevation Satellite (ICESat-2), while the validation data are in situ measurements collected with scientific equipment (e.g., multibeam sonar) and were provided by the National Oceanography Centre, UK. According to our results, the presented approach is able to provide a pixel-based (i.e., spatially explicit) uncertainty index for a specific prediction bias and integrate it to refine the SDB. It should be noted that the efficiency of the optimization of the SDBs as well as the correlations of the proposed uncertainty index with the absolute prediction error and the true depth are low. Nevertheless, spatially explicit uncertainty information produced by a ML-related SDB provides substantial insight to advance coastal ecosystem monitoring thanks to its capability to showcase the difficulty of the model to provide a prediction. Such spatially explicit uncertainty products can also aid the communication of coastal aquatic products with decision makers and provide potential improvements in SDB modeling. Full article

The Chinese sturgeon (Acipenser sinensis), a critically endangered anadromous fish species, spends over 90% of its life cycle in marine habitats, yet research on its marine ecology and habitat requirements is limited due to sparse data. To address this, we integrated satellite remote sensing with ecological modeling to assess spatiotemporal dynamics in marine habitat suitability across China’s continental shelf (2003–2020). Nine key habitat factors were derived from multi-source remote sensing data and inverted transparency algorithms. Species occurrence data were coupled with the Maximum Entropy (MaxEnt) model to evaluate habitat preferences and seasonal shifts. Results revealed distinct environmental preferences: shallow depths (≤20 m), sea surface and bottom temperature (10–30 °C and 10–25 °C), salinity (10–35‰), transparency (0.40–3.00 m), eastward and northward seawater velocity (−0.20–0.15 m/s and −0.20–0.20 m/s), moderate productivity (1000–3000 mg/m²), and zooplankton carbon (0.20–6.00 g/m²). Habitat factor importance varied seasonally—salinity, depth, and net primary productivity dominated in spring; bottom temperature and productivity in summer/autumn; salinity and transparency in winter. Spatially, high-suitability areas peaked in autumn (70% total suitable habitat), concentrating near the Yangtze Estuary, northern Jiangsu coast, and Zhoushan Archipelago. This study emphasizes the need to prioritize these areas for protection and inform proliferation and release schemes for Chinese sturgeon. It also demonstrates the efficacy of remote sensing for mapping essential habitats of migratory megafauna in complex coastal ecosystems and provides actionable insights for targeted conservation strategies. Full article

The Chacopampean Plain is a major physiographic unit in Argentina, bounded by the Colorado River to the south, the Sierras Pampeanas and Subandinas to the west, and the Paraná River, Río de la Plata Estuary, and the Argentine Sea to the east. Its subsurface preserves sediments from the Miocene marine transgression, while the surface hosts some of the country’s most productive soils. Two main geomorphological domains are recognized: fluvial systems dominated by alluvial megafans in the north, and aeolian systems characterized by loess accumulation and wind erosion in the south. The southern sector exhibits diverse landforms such as deflation basins, ridges, dune corridors, lunettes, and mantiform loess deposits. Despite their regional extent, the origin and chronology of many aeolian features remain poorly constrained, as previous studies have primarily focused on depositional units rather than wind-sculpted erosional features. This study integrates remote sensing data, field observations, and a synthesis of published chronometric and sedimentological information to characterize these aeolian landforms and elucidate their genesis. Our findings confirm wind as the dominant morphogenetic agent during Late Quaternary glacial stadials. These aeolian morphologies significantly influence the region’s hydrology, as many permanent and ephemeral water bodies occupy deflation basins or intermediate low-lying sectors prone to flooding under modern climatic conditions, which are considerably wetter than during their original formation. Full article

Typhoons can significantly alter ocean hydrodynamic processes through their powerful external forces, greatly affecting marine biogeochemistry and ocean productivity. However, the specific impacts of typhoons with different tracks on coastal dynamics, including frontal activities and phytoplankton lateral transport, are not well understood. This study captured two distinct types of typhoons, namely Merbok (2017) and Nuri (2020), which landed from the right and left sides of the Pearl River Estuary (PRE), respectively, utilizing satellite remote sensing data to study their impacts on frontal dynamics and marine productivity. We found that after both typhoons, the southwest monsoon amplified geostrophic currents significantly (increased ~14% after Nuri (2020) and 48% after Merbok (2020)). These stronger currents transported warmer offshore seawater from the South China Sea to the PRE and intensified the frontal activities in nearshore PRE (increased ~47% after Nuri (2020) and ~2.5 times after Merbok (2020)). The ocean fronts limited the transport of high-chlorophyll and eutrophic water from the PRE to the offshore waters due to the barrier effect of the front. This resulted in a sharp drop in chlorophyll concentrations in the offshore-adjacent waters of PER after Typhoon Nuri (2020) (~37%). By contrast, despite the intensified geostrophic current induced by the summer monsoon following Typhoon Merbok (2020), its stronger offshore force, driven by the intense offshore wind stress (characteristic of the left-side typhoon), caused the nearshore front to move offshore. The displacement of fronts lifted the restriction of the front barrier and led more high-chlorophyll (increased ~4 times) and eutrophic water to be transported offshore, thereby stimulating offshore algal blooms. Our findings elucidate the mechanisms by which different track typhoons influence chlorophyll distribution through changes in frontal dynamics, offering new perspectives on the coastal ecological impacts of typhoons and further studies for typhoon impact modeling or longshore management. Full article

Marine atmospheric boundary-layer height (MABLH) is crucial for ocean heat, momentum, and substance transfer, affecting ocean circulation, climate, and ecosystems. Due to the unique geographical location of the South China Sea (SCS), coupled with its complex atmospheric environment and sparse ground-based observation stations, accurately determining the MABLH remains challenging. Coherent Doppler wind lidar (CDWL), as a laser-based active remote sensing technology, provides high-resolution wind profiling by transmitting pulsed laser beams and analyzing backscattered signals from atmospheric aerosols. In this study, we developed a stacking optimal ensemble model (SOEM) to estimate MABLH in the vicinity of the site by integrating CDWL measurements from a representative SCS site with ERA5 (fifth-generation reanalysis dataset from the European Centre for Medium-Range Weather Forecasts) data from December 2019 to May 2021. Based on the categorization of the total cloud cover data into weather conditions such as clear/slightly cloudy, cloudy/transitional, and overcast/rainy, the SOEM demonstrates enhanced performance with an average mean absolute percentage error of 3.7%, significantly lower than the planetary boundary-layer-height products of ERA5. The SOEM outperformed random forest, extreme gradient boosting, and histogram-based gradient boosting models, achieving a robustness coefficient ($R^2$) of 0.95 and the lowest mean absolute error of 32 m under the clear/slightly cloudy condition. The validation conducted in the coastal city of Qingdao further confirmed the superiority of the SOEM in resolving meteorological heterogeneity. The predictions of the SOEM aligned well with CDWL observations during Typhoon Sinlaku (2020), capturing dynamic disturbances in MABLH. Overall, the SOEM provides a precise approach for estimating convective boundary-layer height, supporting marine meteorology, onshore wind power, and coastal protection applications. 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

Since pre-industrial times, anthropogenic methane emissions have increased and are partly responsible for a changing global climate. Natural gas and oil extraction activities are one significant source of anthropogenic methane. While methods have been developed and refined to quantify onshore methane emissions, the ability of methods to directly quantify emissions from offshore production facilities remains largely unknown. Here, we review recent studies that have directly measured emissions from offshore production facilities and critically evaluate the suitability of these measurement strategies for emission quantification in a marine environment. The average methane emissions from production platforms measured using downwind dispersion methods were 32 kg h⁻¹ from 188 platforms; 118 kg h⁻¹ from 104 platforms using mass balance methods; 284 kg h⁻¹ from 151 platforms using aircraft remote sensing; and 19,088 kg h⁻¹ from 10 platforms using satellite remote sensing. Upon review of the methods, we suggest the unusually large emissions, or zero emissions observed could be caused by the effects of a decoupling of the marine boundary layer (MBL). Decoupling can happen when the MBL becomes too deep or when there is cloud cover and results in a stratified MBL with air layers of different depths moving at different speeds. Decoupling could cause: some aircraft remote sensing observations to be biased high (lower wind speed at the height of the plume); the mass balance measurements to be biased high (narrow plume being extrapolated too far vertically) or low (transects miss the plume); and the downwind dispersion measurements much lower than the other methods or zero (plume lofting in a decoupled section of the boundary layer). To date, there has been little research on the marine boundary layer, and guidance on when decoupling happens is not currently available. We suggest an offshore controlled release program could provide a better understanding of these results by explaining how and when stratification happens in the MBL and how this affects quantification methodologies. Full article

River mouths are important indicators and mediators of interactions between rivers and the sea that mark the dispersal point for catchment-based stressors and subsidies. Satellite remote sensing data products and algorithms present many new possibilities for monitoring these dynamic and often inaccessible environments. In this study, we describe a national-scale comparative framework based on proximity to river mouths and show its application to the monitoring of coastal ecosystem health in Aotearoa New Zealand. We present results from light attenuation coefficient (K_d) analyses used to develop the framework considering data products of differing resolution and the effects of coastline geometries which might obscure the influence of catchment-derived stressors. Ten-year (2013–2022) K_d values from the highest-resolution product (500 m) showed significant differences (p < 0.01) in successively larger radii (1–20 km) despite the confounding influence of adjacent river mouths. Smaller radii returned a high variability that dropped markedly > 5 km. Tests of a 10 km radius showed that coastline geometry had a significant influence on K_d (p < 0.001), which is also likely for other water quality indicators. An analytical approach stratified by coastline geometry showed significant effects of stream order on open (p < 0.01) but not enclosed coasts, differences between marine bioregions (p < 0.05), and a degradation trend in the 90th percentile of K_d on enclosed coasts, which is indicative of extreme events associated with catchment erosion or sediment resuspension. We highlight applications of the framework to explore trends across many other meaningful scales (e.g., jurisdictions and ecosystem types) in addition to tracking changes at individual river mouths. Full article

Current marine research that leverages remote sensing data urgently requires gridded data of high spatial and temporal resolution. However, such high-quality data is often lacking due to the inherent physical and technical constraints of sensors. A necessary trade-off therefore exists between spatial, temporal, and spectral resolution in satellite remote sensing technology: increasing spatial resolution often reduces the coverage area, thereby diminishing temporal resolution. This manuscript introduces an innovative remote sensing image fusion algorithm that combines Sentinel-2 (high spatial resolution) and Sentinel-3 (relatively high spectral and temporal resolution) satellite data. The algorithm, based on a cross-attention mechanism and referred to as the Cross-Attention Spatio-Temporal Spectral Fusion (CASTSF) model, accounts for variations in spectral channels, spatial resolution, and temporal phase among different sensor images. The proposed method enables the fusion of atmospherically corrected ocean remote sensing reflectance products (Level 2 OSR), yielding high-resolution spatial data at 10 m resolution with a temporal frequency of 1–2 days. Subsequently, the algorithm generates chlorophyll-a concentration remote sensing products characterized by enhanced spatial and temporal fidelity. A comparative analysis against existing chlorophyll-a concentration products demonstrates the robustness and effectiveness of the proposed approach, highlighting its potential for advancing remote sensing applications. Full article

The vertically generalized production model (VGPM) is one of the most important methods for estimating marine net primary productivity (PP) using remote sensing. However, different data sources and parameterization schemes of the input variables for the VGPM can introduce uncertainties to the model results. This study compared the PP results from different data sources and parameterization schemes of three major input variables (i.e., chlorophyll-a concentration ($C_{opt}$), euphotic depth ($Z_{eu}$), and maximum photosynthetic rate ($P_{opt}^B$)) and evaluated the sensitivity of VGPM in the Yellow and Bohai Seas on the inputs. The results showed that the sensitivity in the annual mean PP was approximately 40%. Seasonally, the sensitivity was lowest in the spring (35%), highest in the winter (70%), and approximately 60% in the summer and autumn. Spatially, the sensitivity in nearshore water (water depth < 40 m) was more than 60% and around two times higher than that in deep water areas. Nevertheless, all VGPM results showed a decline trend in the PP from 2003 to 2020 in the Yellow and Bohai Seas. The influence of $P_{opt}^B$ and $C_{opt}$ was important for the magnitude of annual mean PP. The PP seasonal variation pattern was highly related to the parameterization scheme of $P_{opt}^B$, whereas the spatial distribution was mostly sensitive to the data sources of $C_{opt}$. Full article

The vertical distribution of the marine total suspended matter (TSM) concentration significantly influences marine material transport, sedimentation processes, and biogeochemical cycles. Traditional field observations are constrained by limited spatial and temporal coverage, necessitating the use of remote-sensing technology to comprehensively understand TSM variations over extensive areas and periods. This study proposes a remote-sensing approach to estimate the vertical distribution of TSM concentrations using MODIS satellite data, with the Bohai Sea and Yellow Sea (BSYS) as a case study. Extensive field measurements across various hydrological conditions and seasons enabled accurate reconstruction of in situ TSM vertical distributions from bio-optical parameters, including the attenuation coefficient, particle backscattering coefficient, particle size, and number concentration, achieving a determination coefficient of 0.90 and a mean absolute percentage error of 26.5%. In situ measurements revealed two distinct TSM vertical profile types (vertically uniform and increasing) and significant variation in TSM profiles in the BSYS. Using surface TSM concentrations, wind speed, and water depth, we developed and validated a remote-sensing approach to classify TSM vertical profile types, achieving an accuracy of 84.3%. Combining this classification with a layer-to-layer regression model, we successfully estimated TSM vertical profiles from MODIS observation. Long-term MODIS product analysis revealed significant spatiotemporal variations in TSM vertical distributions and column-integrated TSM concentrations, particularly in nearshore regions. These findings provide valuable insights for studying marine sedimentation and biological processes and offer a reference for the remote-sensing estimation of the TSM vertical distribution in other marine regions. Full article

Turbidity serves as a crucial indicator of coastal water health and productivity. Twenty years of remote sensing data (2003–2022) from the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) satellite were used to analyze the spatial and temporal variations in turbidity, as measured by total suspended matter (TSM), in the Berau Coastal Shelf (BCS), East Kalimantan, Indonesia. The BCS encompasses the estuary of the Berau River and is an integral part of the Coral Triangle, renowned for its rich marine and coastal habitats, including coral reefs, mangroves, and seagrasses. The aim of this research is to comprehend the seasonal and interannual patterns of turbidity and their associations with met-ocean parameters, such as wind, rainfall, and climate variations like the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). The research findings indicate that the seasonal spatial pattern of turbidity is strongly influenced by monsoon winds, while its temporal patterns are closely related to river discharge and rainfall. The ENSO and IOD climate cycles exert an influence on the interannual turbidity variations, with turbidity values decreasing during La Niña and negative IOD events and conversely increasing during El Niño and positive IOD events. Furthermore, the elevated turbidity during negative IOD and La Niña coincides with rising temperatures, potentially acting as a compound stressor on marine habitats. These findings significantly enhance our understanding of turbidity dynamics in the BCS, thereby supporting the management of marine and coastal ecosystems in the face of changing climatic and environmental conditions. Full article