Search Results (334)

Accurate prediction of key ocean elements (e.g., chlorophyll-a concentration, sea surface temperature, etc.) is imperative for maintaining marine ecological balance, responding to marine disaster pollution, and promoting the sustainable use of marine resources. Existing spatio-temporal prediction models primarily rely on either physical or data-driven approaches. Physical models are constrained by modeling complexity and parameterization errors, while data-driven models lack interpretability and depend on high-quality data. To address these challenges, this study proposes OKG-ConvGRU, a domain knowledge-guided remote sensing prediction framework for ocean elements. This framework integrates knowledge graphs with the ConvGRU network, leveraging prior knowledge from marine science to enhance the prediction performance of ocean elements in remotely sensed images. Firstly, we construct a spatio-temporal knowledge graph for ocean elements (OKG), followed by semantic embedding representation for its spatial and temporal dimensions. Subsequently, a cross-attention-based feature fusion module (CAFM) is designed to efficiently integrate spatio-temporal multimodal features. Finally, these fused features are incorporated into an enhanced ConvGRU network. For multi-step prediction, we adopt a Seq2Seq architecture combined with a multi-step rolling strategy. Prediction experiments for chlorophyll-a concentration in the eastern seas of China validate the effectiveness of the proposed framework. The results show that, compared to baseline models, OKG-ConvGRU exhibits significant advantages in prediction accuracy, long-term stability, data utilization efficiency, and robustness. This study provides a scientific foundation and technical support for the precise monitoring and sustainable development of marine ecological environments. Full article

As economically important sympatric species in the Northwest Pacific, the Japanese sardine (Sardinops melanostictus) and Chub mackerel (Scomber japonicus) exhibit significant biological interactions. Understanding the impact of interspecies competition on their habitat dynamics can provide crucial insights for the sustainable development and management of these interconnected species resources. This study utilizes fisheries data of S. melanostictus and S. japonicus from the Northwest Pacific, collected from June to November between 2017 and 2020. We integrated various environmental parameters, including temperature at different depths (0, 50, 100, 150, and 200 m), eddy kinetic energy (EKE), sea surface height (SSH), chlorophyll-a concentration (Chl-a), and the oceanic Niño index (ONI), to construct interspecific competition species distribution model (icSDM) for both species. We validated these models by overlaying the predicted habitats with fisheries data from 2021 and performing cross-validation to assess the models’ reliability. Furthermore, we conducted correlation analyses of the habitats of these two species to evaluate the impact of interspecies relationships on their habitat dynamics. The results indicate that, compared to single-species habitat models, the interspecific competition species distribution model (icSDM) for these two species exhibit a significantly higher explanatory power, with R² values increasing by up to 0.29; interspecific competition significantly influences the habitat dynamics of S. melanostictus and S. japonicus, strengthening the correlation between their habitat changes. This relationship exhibits a positive correlation at specific stages, with the highest correlations observed in June, July, and October, at 0.81, 0.80, and 0.88, respectively; interspecific competition also demonstrates stage-specific differences in its impact on the habitat dynamics of S. melanostictus and S. japonicus, with the most pronounced differences occurring in August and November. Compared to S. melanostictus, interspecific competition is more beneficial for the expansion of the optimal habitat (HIS ≥ 0.6) for S. japonicus and, to some extent, inhibits the habitat expansion of S. melanostictus. The variation in migratory routes and predatory interactions (with larger individuals of S. japonicus preying on smaller individuals of S. melanostictus) likely constitutes the primary factors contributing to these observed differences. Full article

Arctic shelf waters exhibit high optical variability due to terrestrial inputs and elevated colored dissolved organic matter (CDOM) concentrations, posing significant challenges for the accurate retrieval of chlorophyll-a (Chl-a) and downwelling diffuse attenuation coefficients (${{\rm K}}_{\mathrm{d}}\left(\lambda \right)$). These retrieval biases contribute to substantial uncertainties in estimates of primary productivity and upper-ocean heat flux in the Arctic Ocean. However, the performance and constraints of existing ocean color algorithms in Arctic shelf environments remain insufficiently characterized, particularly under seasonally variable and optically complex conditions. In this study, we present a systematic multi-year evaluation of commonly used empirical and semi-analytical ocean color algorithms across the western Arctic shelf, based on seven expeditions and 240 in situ observation stations. Building on these evaluations, regionally optimized retrieval schemes were developed to enhance algorithm performance under Arctic-specific bio-optical conditions. The proposed OCx-AS series for Chl-a and ${{\rm K}}_{\mathrm{d}}$-DAS models for ${{\rm K}}_{\mathrm{d}}\left(\lambda \right)$ significantly reduce retrieval errors, achieving RMSE improvements of over 50% relative to global standard algorithms. Additionally, we introduce QAA-LS, a modified semi-analytical model specifically adapted for the Laptev Sea, which addresses the strong absorption effects of CDOM and corrects the significant overestimation observed in previous QAA versions. Full article

Chlorophyll-a (Chla) and suspended particulate matter (SPM) are key indicators of water quality, playing critical roles in understanding marine biogeochemical processes and ecosystem health. Although satellite data from the Chinese Ocean Color and Temperature Scanner (COCTS) onboard the Haiyang-1C/D satellites is freely available, there has been limited validation of its standard Chla and SPM products. This study is a first step to address this gap by evaluating COCTS-derived Chla and SPM products against in situ measurements in French coastal waters. The matchup analysis showed robust performance for the Chla product, with a median symmetric accuracy (MSA) of 50.46% over a dynamic range of 0.13–4.31 mg·m⁻³ (n = 24, Bias = 41.11%, Slope = 0.93). In contrast, the SPM product showed significant limitations, particularly in turbid waters, despite a reasonable performance in the matchup exercise, with an MSA of 45.86% within a range of 0.18–10.52 g·m⁻³ (n = 23, Bias = −14.59%, Slope = 2.29). A comparison with another SPM model and Moderate Resolution Imaging Spectroradiometer (MODIS) products showed that the COCTS standard algorithm tends to overestimate SPM and suggests that the issue does not originate from the input radiometric data. This study provides the first regional assessment of COCTS Chla and SPM products in European coastal waters. The findings highlight the need for algorithm refinement to improve the reliability of COCTS SPM products, while the Chla product demonstrates suitability for water quality monitoring in low to moderate Chla concentrations. Future studies should focus on the validation of COCTS ocean color products in more diverse waters. Full article

Investigating biodiversity in remote and harsh environments, particularly in the Southern Ocean, remains costly and challenging through traditional sampling methods such as trawling. Environmental DNA (eDNA) sampling, which refers to sampling genetic material shed by organisms from environmental samples (e.g., water), provides a more cost-effective and sustainable alternative to traditional sampling approaches. To study the biogeographic patterns of two typical mesopelagic fishes, Antarctic lanternfish (Electrona antarctica) and Antarctic deep-sea smelt (Bathylagus antarcticus), in the Cosmonaut Sea in the Indian Ocean sector of the Southern Ocean, we conducted both eDNA and trawling sampling at a total of 86 stations in the Cosmonaut Sea during two cruises in 2021–2022. Two sets of species-specific primers and probes were developed for a quantitative eDNA analysis of two fish species. Both the eDNA and trawl results indicated that the two fish species are widely distributed in the Cosmonaut Sea, with no significant difference in eDNA concentration, biomass, or abundance between stations. Spatially, E. antarctica tended to be distributed in shallow waters, while B. antarcticus tended to be distributed in deep waters. Vertically, E. antarctica was more abundant above 500 m, while B. antarcticus had a wider range of habitat depths. The distribution patterns of both species were affected by nutrients, with E. antarctica additionally affected by chlorophyll, indicating that their distribution is primarily influenced by food resources. Our study provides broader insight into the biogeographic patterns of the two mesopelagic fishes in the remote Cosmonaut Sea, demonstrates the potential of combining eDNA with traditional methods to study biodiversity and ecosystem dynamics in the Southern Ocean and even at high latitudes, and contributes to future ecosystem research and biodiversity conservation in the region. 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

Monitoring chlorophyll-a concentration (Chl-a) is essential for assessing aquatic ecosystem health, yet its retrieval using remote sensing remains challenging in turbid coastal waters because of the intricate optical characteristics of these environments. Elevated levels of colored (chromophoric) dissolved organic matter (CDOM) and suspended sediments (aka total suspended solids, TSS) interfere with satellite-based Chl-a estimates, necessitating alternative approaches. One potential solution is machine learning, indirectly including non-Chl-a signals into the models. In this research, we develop machine learning models to predict Chl-a concentrations in the Chesapeake Bay, one of the largest estuaries on North America’s East Coast. Our approach leverages the Extra-Trees (ET) algorithm, a tree-based ensemble method that offers predictive accuracy comparable to that of other ensemble models, while significantly improving computational efficiency. Using the entire ocean color datasets acquired by the satellite sensors MODIS-Aqua (>20 years) and VIIRS-SNPP (>10 years), we generated long-term Chl-a estimates covering the entire Chesapeake Bay area. The models achieve a multiplicative absolute error of approximately 1.40, demonstrating reliable performance. The predicted spatiotemporal Chl-a patterns align with known ecological processes in the Chesapeake Bay, particularly those influenced by riverine inputs and seasonal variability. This research emphasizes the potential of machine learning to enhance satellite-based water quality monitoring in optically complex coastal waters, providing valuable insights for ecosystem management and conservation. Full article

The Arabian Sea has undergone significant warming since the mid-20th century, highlighting the importance of assessing how decadal climate patterns influence chlorophyll-a (Chl-a) and broader marine ecosystem dynamics. This study investigates the variability of Chl-a, sea surface temperature (SST), and sea level anomaly (SLA) over the past three decades, and their relationships with the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO). The mean Chl-a concentration was 1.10 mg/m³, with peak levels exceeding 2 mg/m³ between 2009 and 2013, and the lowest value (0.6 mg/m³) was recorded in 2014. Elevated Chl-a levels were consistently observed in February and March across both coastal and offshore regions. Empirical orthogonal function (EOF) analysis revealed distinct spatial patterns in Chl-a and SST, indicating dynamic regional variability. The SST increased by 0.709 °C over the past four decades, accompanied by a steady rise in the SLA of approximately 1 cm. The monthly mean Chl-a exhibited a strong inverse relationship with both the SST and SLA and a positive correlation with SST gradients (R² > 0.5). A positive correlation (R² > 0.5) was found between the PDO and Chl-a, whereas the PDO was negatively correlated with the SST and SLA. In contrast, the AMO was negatively correlated with Chl-a but positively associated with warming and SLA rise. These findings underline the contrasting roles of the PDO and AMO in modulating productivity and ocean dynamics in the Arabian Sea. This study emphasizes the need for continued monitoring to improve predictions of ecosystem responses under future climate change scenarios. Full article

This work introduces the use of Bayesian Neural Networks (BNNs) for inferring chlorophyll-a concentration ([CHL-a]) from remotely sensed data. BNNs are probabilistic models that associate a probability distribution to the neural network parameters and rely on Bayes’ rule for training. The performance of the proposed probabilistic model is compared to that of standard ocean color algorithms, namely ocean color 4 (OC4) and ocean color index (OCI). An extensive in situ bio-optical dataset was used to train and validate the ocean color models. In contrast to established methods, the BNN allows for enhanced modeling flexibility, where different variables that affect phytoplankton phenology or describe the state of the ocean can be used as additional input for enhanced performance. Our results suggest that BNNs perform at least as well as established methods, and they could achieve 20–40% lower mean squared errors when additional input variables are included, such as the sea surface temperature and its climatological mean alongside the coordinates of the prediction. The BNNs offer means for uncertainty quantification by estimating the probability distribution of [CHL-a], building confidence in the [CHL-a] predictions through the variance of the predictions. Furthermore, the output probability distribution can be used for risk assessment and decision making through analyzing the quantiles and shape of the predicted distribution. Full article

Particulate organic carbon (POC) plays a crucial role in oceanic climate change. However, existing research is limited by several factors, including the scarcity of long-term data, extensive datasets, and a comprehensive understanding of POC dynamics. This study utilizes monthly average POC remote sensing data from the MODIS/AQUA satellite to analyze the spatiotemporal variations of POC in the East China Sea from 2003 to 2022. Employing correlation analysis, spatial autocorrelation models, and the Geodetector model, we explore responses to key influencing factors such as climatic elements. The results indicate that POC concentrations are higher in the western nearshore areas and lower in the eastern offshore regions of the East China Sea (ECS). Additionally, concentrations are observed to be lower in southern regions compared to northern ones. From 2003 to 2022, POC concentrations exhibited a fluctuating downward trend with an average annual concentration of 121.05 ± 4.57 mg/m³. Seasonally, monthly average POC concentrations ranged from 105.48 mg/m³ to 158.36 mg/m³; notably higher concentrations were recorded during spring while summer showed comparatively lower levels. Specifically, POC concentrations peaked in April before rapidly declining from May to June—reaching a minimum—and then gradually increasing again from June through December. Correlation analysis revealed significant influences on POC levels by particulate inorganic carbon (PIC), sea surface temperature (SST), chlorophyll (Chl), and photosynthetically active radiation (PAR). The Geodetector model further elucidated that these factors vary in their impact: Chl was identified as having the strongest influence (q = 0.84), followed by PIC (q = 0.75) and SST (q = 0.64) as primary influencing factors; PAR was recognized as a secondary factor with q = 0.30. This study provides new insights into marine carbon cycling dynamics within the context of climate change. Full article

Sthenoteuthis oualaniensis is one of the most commercially important marine cephalopod species distributed throughout tropical and subtropical waters of the Indo-Pacific Seas. The Indian Ocean is a main fishing ground for S. oualaniensis with a high population density. To explore the distribution of S. oualaniensis in the east equatorial Indian Ocean, four surveys were carried out using light-lift-net fishing vessels. Meanwhile, marine environmental data were also collected, including the sea surface temperature, sea temperature at 100 m depth, mixed layer depth, sea surface chlorophyll-a concentration, sea surface height, and eddy kinetic energy. Generalized Additive Models were used to analyze the relationship between the catch per unit effort (CPUE) for S. oualaniensis and environmental factors. The results showed that the average CPUE of S. oualaniensis was 14.55 kg/h in the four surveys, which was considerably lower than in the South China Sea and Northwest Indian Ocean. In terms of seasonal distribution, the high-CPUE stations were closer to the continental shelf in spring, while they shifted towards the deeper and offshore water in autumn, demonstrating a seasonal migration trend. Pearson correlation analysis showed that CPUE reflected a significant negative correlation with both sea temperature at 100 m depth and eddy kinetic energy (p < 0.001). The Generalized Additive Models revealed that sea surface height was the most significant factor affecting CPUE with a variance explanation of 30.1%. Furthermore, the optimal CPUE prediction model was established by stepwise regression, which contains two factors, sea surface height and eddy kinetic energy, with a variance explanation of 34.9%. This study provides insights into the environmental factors influencing the distribution of S. oualaniensis, which is essential for the sustainable utilization and management of this species. Full article

Polynyas play a crucial role in polar ecosystems, influencing biodiversity, climate regulation, and oceanic processes. This study employs Synthetic Aperture Radar (SAR) data to determine the optimal sea ice concentration threshold for polynya identification, which is established at 75%. We present a dataset of daily polynya distribution in the Arctic and Antarctic from 2013 to 2022, analyzing their spatial patterns, interannual variability, and seasonal dynamics. Our results indicate that coastal polynyas, primarily located near landmasses, dominate both polar regions. The total polynya area in the Antarctic remained relatively stable, averaging approximately 1.86 × 10⁸ km² per year, with an interannual fluctuation of −3.1 × 10⁵ km² per year. In the Arctic, the average polynya area is around 1.59 × 10⁸ km² per year, with an interannual fluctuation of −7.1 × 10⁵ km² per year. Both regions exhibit distinct seasonal cycles: Arctic polynyas peak in May and reach their minimum in September, whereas Antarctic polynyas expand in November and contract to their smallest extent in February. The polynya formation and development result from a complex interplay of multiple factors, with no single variable fully explaining variations in polynyas’ extent. Additionally, the polynya area in the NOW, and Weddell Sea polynyas, exhibit consistent trends with chlorophyll-a concentration, highlighting their role as critical habitats for primary productivity in polar regions. These findings provide key insights into polynya dynamics and their broader implications for climate and ecological processes in polar regions. Full article

Remote sensing data play a crucial role in capturing and evaluating eutrophication, providing a comprehensive view of spatial and temporal variations in water quality parameters. Chlorophyll-a concentration time series analysis aids in understanding the current trophic state of coastal waters and tracking changes over time, enabling the evaluation of water bodies’ trophic status. This research presents a novel and replicable methodology able to derive accurate phenological patterns using remote sensing data. The methodology proposed uses the two-decade MODIS-Aqua surface reflectance dataset, analyzing data from 30-point stations and calculating chlorophyll-a concentrations from NASA’s Ocean Color algorithm. Then, a correction process is implemented through a robust, simple statistical analysis by applying LOESS smoothing to detect and remove outliers from the extensive dataset. Different scenarios are reviewed and compared with field data to calibrate the proposed methodology accurately. The results demonstrate the methodology’s capacity to produce consistent chlorophyll-a time series and to present phenological patterns that can effectively identify key indicators and trends, resulting in valuable insights into the coastal body’s trophic state. The case study of the Ambracian Gulf is characterized as hypertrophic since algal bloom during August reaches up to 5 mg/m³, while the replicate case study of Aitoliko shows algal bloom reaching up to 2.5 mg/m³. Finally, the proposed methodology successfully identifies the positive chlorophyll-a climate tendencies of the two selected Greek water bodies. This study highlights the value of integrating statistical methods with remote sensing data for accurate, long-term monitoring of water quality in aquatic ecosystems. Full article

Monitoring the oceans and establishing a global ocean observing system is a task of paramount importance for topics as diverse as the study of climate change, the management of marine environments, and the safety of coastal areas and marine traffic. These systems must be based on long-term observations that allow the correct modeling of the behavior of the seas and the proper environmental management of them. Despite the logical present trend toward automation, in situ measurements from oceanographic vessels are still needed at present, especially when dealing with biogeochemical variables or when seeking information from the subsurface or deep layers of the sea. Long-term measurements by oceanographic vessels can be carried out at one single fixed oceanographic station with a high sampling frequency (typically once a month) or across a grid of stations. In the latter case a larger geographical area is usually covered, but the cost is a reduction of sampling frequency. The question that arises is: what objectives can be achieved, and what questions can be answered according to the sampling frequency and the spatial coverage of the monitoring program? In this work, we analyze the influence of the sampling frequency on the capacity of observing programs to capture the temporal variability of ocean variables at different time scales and to estimate average seasonal cycles and long-term trends. This analysis is conducted through the study of sea surface chlorophyll concentrations in the Western Mediterranean. The trade-off between sampling frequency and spatial coverage is addressed. For this purpose, a monitoring program in the Spanish Mediterranean waters is used as a case study. We show that monthly and fortnightly intervals are the best sampling frequencies for describing the temporal variability of ocean variables as well as their average seasonal cycles. Quarterly sampling could also be appropriate for estimating such seasonal cycles. Surprisingly, the limitations of these low frequency samplings do not arise from the high frequency variability of ocean variables but from the shape of the seasonal cycles. Both high and low frequency sampling designs could be suitable for detecting long-linear trends, depending on the variance of the noise and that of the trend. In the case of quarterly sampling, we show that some statistics improve with the length of the time series, whereas others do not. Although some results may be related to the dynamics of this region, the results are generally applicable to any other marine monitoring system. Full article

Shark bycatch represents a substantial issue in the management of oceanic fisheries. Utilizing data on shark bycatch from the longline fishery, as released by the Western and Central Pacific Fisheries Commission, this study applied the boosted regression tree model to examine the impact of environmental factors on the bycatch per unit effort (BPUE) of key bycatch species, as well as to predict the spatial distribution dynamics of both BPUE and bycatch risk (BR). The findings emphasize that the oxygen concentration, sea surface temperature, and chlorophyll-a concentration are paramount to sharks’ BPUE. Furthermore, the study compared the variations in environmental preferences across diverse shark species, pinpointing key environmental attributes defining the ecological niches of distinct shark populations. The spatial predictions identified the hotspots of BPUE and BR for the bigeye thresher shark (Alopias superciliosus), longfin mako (Isurus paucus), silky shark (Carcharhinus falciformis), and oceanic whitetip shark (Carcharhinus longimanus) in tropical latitudes (10° S to 15° N), and for the blue shark (Prionace glauca) and shortfin mako (Isurus oxyrinchus) in temperate zones (south of 30° S or north of 30° N). The geometric center analysis indicated that all shark species exhibited large annual fluctuations in BPUE and BR, and most populations displayed significant shifting trends. Several grids (5° × 5°) were identified as high-risk areas due to their considerable contribution to bycatch. Furthermore, the geometric centers of BR were observed to shift eastward towards equatorial waters, compared to the geometric centers of BPUE. This underscores the necessity of considering factors beyond BPUE when identifying critical areas for the implementation of area-specific bycatch mitigation measures. The insights derived from this study can enhance and support the development and enforcement of targeted area-based fishery management initiatives. Full article