Marine Environmental Monitoring Research Articles

Blind source separation algorithm for noisy hydroacoustic signals based on decoupled convolutional neural networks

Wireless communication technology has been widely used in marine engineering, marine ranching and Marine environmental monitoring. However, structural redundancy and functional confusion exist in applying neural networks in signal separation technology in underwater communication environments, which can result in a slower rate of signal separation and lead to confusion of parameters during transfer learning. Based on this, an end-to-end, internal functionally structured decoupled neural network (D-CNN) blind source separation (BSS) model is proposed in this paper, which can realize a neural network BSS algorithm with a well-defined structure and function. The one-dimensional convolutional neural network layer is used in algorithm to automatically extract observed signal's features, based on the features, and there are two generation modules of separation matrix and scaling coefficients. Then the two modules can be used to separate the observed signal and adjust the signal coefficients to obtain the separated signal. Finally the transfer learning technique is used to generalize the model, which reduces the transfer cost of the model in different application scenarios. Experimental results show that when the communication distance is set to 0.02 km–2 km, the MSE of independent signal and related signal can be reduced by 14.24% and 14.95% respectively compared with the nearest Neural FCA algorithm. The results prove that the proposed algorithm can accurately estimate the source signal and improve the signal reception quality.

Wave retrieval for Sentinel-1 synthetic aperture radar under complex sea state

ABSTRACT In this paper, sea surface waves during complex sea state (i.e. tropical cyclones (TCs) and Kuroshio current) are inverted from dual-polarized (vertical – vertical (VV) and vertical – horizontal (VH)) synthetic aperture radar (SAR) images using an existing algorithm, namely, the parameterized first-guess spectrum method (PFSM). In particular, the wind term is included in two modulation transfer functions (MTFs), i.e. tilt modulation and velocity bunching. More than 3000 Sentinel (S-1) images acquired in interferometric wide (IW) mode and extra wide (EW) mode are collected, and the operational CyclObs wind product provided by the French Research Institute for Exploitation of the Oceans (IFREMER) is collected corresponding to the TC images. Note that these images are located within 500 km away from TC eyes. The wave spectra during the TCs are then inverted using the PFSM algorithm and the SAR-derived wind product. Simultaneously, the 0.05° gridded wave spectra collocated with these images are simulated using a third-generation numeric wave model, called WAVEWATCH-III (WW3), in which the reconstructed wind based on the European Centre for Medium-Range Weather Forecasts (ECMWF), the Copernicus Marine Environment Monitoring Service (CMEMS) current, and the CMEMS sea level are treated as the forcing field. Comparison of the SAR wave retrievals and simulations reveals that the root mean square errors (RMSEs) of the significant wave height (SWH) and mean wave period (MWP) are 0.55 m and 0.43 s, their correlation coefficients (COR) are 0.94 and 0.92, and their scatter index (SI) values are 0.17 and 0.07. These results demonstrate the better performance of the PFSM algorithm using theoretical MTFs, i.e. an RMSE of 0.68 m and COR of 0.90 for the SWH and an RMSE of 0.60 s and a COR of 0.87 for the MWP. Although the accuracy of the wave retrieval from the SAR image under complex sea state is improved when implementing the tilt and velocity bunching MTFs with the strong wind term, SWH retrievals by algorithm PFSM suffer saturation problem at SWH > 8 m.

Bidirectional Rotating Turbine Hybrid Triboelectric‐Electromagnetic Wave Energy Harvester for Marine Environment Monitoring

AbstractHydrokinetic energy, a vital renewable resource, holds promise for addressing fossil fuel shortages to ensure a sustainable future. This study proposes an efficient and stable ocean wave energy harvesting system that combines a triboelectric nanogenerator (TENG), an electromagnetic generator (EMG) with a planetary gear system, and a driving turbine. The turbine transforms the up‐and‐down motion in water into continuous, unidirectional rotational motion. An EMG device accelerated by a planetary gear system further ensures sustained output at very low frequencies (0.25 Hz) and boosts the upper limit of power generation at medium to high frequencies. Under water wave conditions with a 1 Hz frequency, the TENG component yields an output of up to 2200 V and 122 µA, while the average EMG output is 15 V and 80 mA. Remarkably, the entire system maintains a steady and uninterrupted output even at extremely low frequencies (0.25 Hz). The peak power outputs for the TENG and EMG components are 115 and 350 mW, respectively, with power densities of 32.55 and 329.78 W m−3, surpassing prior achievements. This research demonstrates self‐powered applications and provides an efficient method to amplify water wave forces, enhancing energy harvesting capabilities for practical marine environmental monitoring.

A broadband hybrid blue energy nanogenerator for smart ocean IoT network

Blue energy represents a crucial renewable energy resource; however, its development has been limited by the low and variable frequency characteristics of sea waves. In this study, we present a novel approach, wherein a spring pendulum coupled hybrid energy harvester (SPC-HEH) is employed to address these limitations. The SPC-HEH integrates two electromagnetic generators (EMGs), two piezoelectric nanogenerators (PENGs), and two multilayer-structured triboelectric nanogenerators (TENGs) into a vessel, enabling efficient wave energy harvesting. Notably, the integration of the lightweight PENG and TENG with the heavy EMG not only enhances the water wave energy harvesting capability of SPC-HEH but also optimizes its spatial utilization efficiency. Additionally, owing to the spring pendulum, the SPC-HEH is capable of simultaneously harnessing the wave kinetic energy of horizontal and vertical directions and realize the decoupling of vertical energy capture from horizontal energy capture. Remarkably, owing to its precise geometric design and efficient space utilization, the SPC-HEH achieves an impressive peak power density of 82.4 Wm−3. Finally, we demonstrate the practical applicability of the SPC-HEH by integrating it with a circuit as a self-powered ocean buoy for real-time monitoring of ocean temperature, wave frequency, ocean turbidity and other information. The remarkable potential of this system for integration into self-powered smart ocean IoT networks holds significant promise for advancing marine research and environmental monitoring capabilities.

Assessing the potential of nematode metabarcoding for benthic monitoring of offshore oil platforms

Environmental DNA metabarcoding is gaining momentum as a time and cost-effective tool for biomonitoring and environmental impact assessment. Yet, its use as a replacement for the conventional marine benthic monitoring based on morphological analysis of macrofauna is still challenging. Here we propose to study the meiofauna, which is much better represented in sediment DNA samples. We focus on nematodes, which are the most numerous and diverse group of meiofauna. Our aim is to assess the potential of nematode metabarcoding to monitor impacts associated with offshore oil platform activities. To achieve this goal, we used nematode-optimized marker (18S V1V2-Nema) and universal eukaryotic marker (18S V9) region to analyse 252 sediment DNA samples collected near three offshore oil platforms in the North Sea. For both markers, we analysed changes in alpha and beta diversity in relation to distance from the platforms and environmental variables. We also defined three impact classes based on selected environmental variables that are associated with oil extraction activities and used random forest classifiers to compare the predictive performance of both datasets. Our results show that alpha- and beta-diversity of nematodes varies with the increasing distance from the platforms. The variables directly related to platform activity, such as Ba and THC, strongly influence the nematode community. The nematode metabarcoding data provide more robust predictive models than eukaryotic data. Furthermore, the nematode community appears more stable in time and space, as illustrated by the overlap of nematode datasets obtained from the same platform three years apart. A significative negative correlation between distance and Shannon diversity also advocates for higher performance of the V1V2-Nema over the V9. Overall, these results suggest that the sensitivity of nematodes is higher compared to the eukaryotic community. Hence, nematode metabarcoding has the potential to become an effective tool for benthic monitoring in marine environment.

Biomarkers: Their Vital Role in Understanding the Complexity of Marine Pollution Monitoring

The monitoring of marine pollution plays a pivotal role in our efforts to comprehend and mitigate its detrimental effects on marine ecosystems. Biomarkers, quantifiable biological indicators of exposure or effect, have emerged as indispensable tools in gauging the magnitude of pollution. This review delves into the multifaceted challenges associated with the use of biomarkers in marine pollution monitoring, including species-specific responses, sensitivity and specificity concerns, extraneous factors, species variability, an exclusive focus on single stressors, and limited knowledge. Discerning between pollution-induced impacts and natural fluctuations is made challenging by the inherent natural variability and confounding factors. Differences in sensitivity and specificity add complexity to the precision of pollution detection. Species-specific variations and the presence of multiple contaminants add layers of complexity to biomarker examination. Additionally, the lack of standardized operating procedures and insufficient information hinder the widespread adoption of biomarkers. Limited studies on specific stressors restrict our comprehensive understanding of pollution effects particularly in the Philippines. Further research, collaborative efforts, and the establishment of standardized protocols are imperative to enhance the precision and efficacy of biomarker-based monitoring in marine environments.

An Underwater Source Location Privacy Protection Scheme Based on Game Theory in a Multi-Attacker Cooperation Scenario.

Ensuring source location privacy is crucial for the security of underwater acoustic sensor networks amid the growing use of marine environmental monitoring. However, the traditional source location privacy scheme overlooks multi-attacker cooperation strategies and also has the problem of high communication overhead. This paper addresses the aforementioned limitations by proposing an underwater source location privacy protection scheme based on game theory under the scenario of multiple cooperating attackers (SLP-MACGT). First, a transformation method of a virtual coordinate system is proposed to conceal the real position of nodes to a certain extent. Second, through using the relay node selection strategy, the diversity of transmission paths is increased, passive attacks by adversaries are resisted, and the privacy of source nodes is protected. Additionally, a secure data transmission technique utilizing fountain codes is employed to resist active attacks by adversaries, ensuring data integrity and enhancing data transmission stability. Finally, Nash equilibrium could be achieved after the multi-round evolutionary game theory of source node and multiple attackers adopting their respective strategies. Simulation experiments and performance evaluation verify the effectiveness and reliability of SLP-MACGT regarding aspects of the packet forwarding success rate, security time, delay and energy consumption: the packet delivery rate average increases by 30%, security time is extended by at least 85%, and the delay is reduced by at least 90% compared with SSLP, PP-LSPP, and MRGSLP.

Numerical Model Test of Spilled Oil Transport Near the Korean Coasts Using Various Input Parametric Models

Oil spills pose significant threats to marine ecosystems, human health, socioeconomic aspects, and coastal communities. Accurate real-time predictions of oil slick transport along coastlines are paramount for quick preparedness and response efforts. This study used an open-source OpenOil numerical model to simulate the fate and trajectories of oil slicks released during the 2007 Hebei Spirit accident along the Korean coasts. Six combinations of input parameters, derived from a five-day met-ocean dataset incorporating various hydrodynamic, meteorological, and wave models, were investigated to determine the input variables that lead to the most reasonable results. The predictive performance of each combination was evaluated quantitatively by comparing the dimensions and matching rates between the simulated and observed oil slicks extracted from synthetic aperture radar (SAR) data on the ocean surface. The results show that the combination incorporating the Hybrid Coordinate Ocean Model (HYCOM) for hydrodynamic parameters exhibited more substantial agreement with the observed spill areas than Copernicus Marine Environment Monitoring Service (CMEMS), yielding up to 88% and 53% similarity, respectively, during a more than four-day oil transportation near Taean coasts. This study underscores the importance of integrating high-resolution met-ocean models into oil spill modeling efforts to enhance the predictive accuracy regarding oil spill dynamics and weathering processes.

Benthic foraminiferal community structure and its response to environmental factors revealed using high-throughput sequencing in the Zhoushan Fishing Ground, East China Sea

Benthic foraminifera are excellent tools for monitoring marine environments and reconstructing paleoenvironments. This study investigated the structure and diversity of benthic foraminiferal communities in 20 superficial sediment samples obtained from the Zhoushan Fishing Ground (ZFG) using high-throughput sequencing based on small subunit ribosomal DNA and RNA amplification. The results revealed Rotaliida as the most dominant group, with spatial heterogeneity in foraminiferal distribution. Total benthic foraminiferal communities exhibited higher species richness and diversity compared to active communities. While heavy metal pollution in the ZFG was moderate, areas with elevated concentrations of heavy metals exhibited low diversity and richness in foraminiferal communities. Total foraminiferal community structure was primarily influenced by factors such as water depth and Hg, Pb, Cd, and Zn levels. Notably, Hg levels emerged as a critical factor impacting the structure and diversity of the active foraminiferal community. The dominant species, Operculina, exhibited tolerance toward heavy metal pollution.

Enhanced triboelectric nanogenerators in saline environments and their applications in the ocean

Blue energy resources hold immense potential in alleviating global energy pressures, with the Triboelectric Nanogenerator (TENG) gaining attention for its unique ability to harvest ocean energy. However, applying TENG technology in the ocean still faces challenges due to its limited performance in hypersaline environments. In this work, a solid-liquid contact-based TENG is proposed for harvesting ocean wave energy. The TENG generates a significant charge output reaching up to 2.5 μC in saline water and excels in producing substantial instantaneous currents of up to 2 mA. This TENG ensures a relatively stable output in hypersaline environments. Furthermore, the TENG demonstrates the capability to degrade 98.83 % of organic dye Rhodamine B in 360 min by ionizing sodium chloride solution. Finally, a self-powered system for monitoring environmental parameters and detecting water levels is developed. This work showcases the high efficiency of TENG in harvesting energy from the ocean environment and demonstrates its immense potential for applications in marine environmental monitoring and seawater purification.

A Compact-Sized Fully Self-Powered Wireless Flowmeter Based on Triboelectric Discharge.

Flow sensing exhibits significant potential for monitoring, controlling, and optimizing processes in industries, resource management, and environmental protection. However, achieving wireless real-time and omnidirectional sensing of gas/liquid flow on a simple, self-contained device without external power support has remained a formidable challenge. In this study, a compact-sized, fully self-powered wireless sensing flowmeter (CSWF) is introduced with a small size diameter of down to less than 50mm, which can transmit real-time and omnidirectional wireless signals, as driven by a rotating triboelectric nanogenerator (R-TENG). The R-TENG triggers the breakdown discharge of a gas discharge tube (GDT), which enables flow rate wireless sensing through emitted electromagnetic waves. Importantly, the performance of the CSWF is not affected by the R-TENG's varied output, while the transmission distance is greater than 10m. Real-time wireless remote monitoring of wind speed and water flow rate is successfully demonstrated. This research introduces an approach to achieve a wireless, self-powered environmental monitoring system with a diverse range of potential applications, including prolonged meteorological observations, marine environment monitoring, early warning systems for natural disasters, and remote ecosystem monitoring.

A new method of ship detection in complex background of SAR images based on YOLO

Synthetic Aperture Radar (SAR) is a remote sensing technology capable of high-resolution imaging of the ground surface under any weather and light conditions. SAR image ship detection is a technique that uses the ship target features in SAR images to identify and locate ships, which is of great value for applications in the fields of maritime traffic management, marine environment monitoring, and maritime security and defence. However, SAR image ship detection also faces some challenges, such as complex sea surface background, low signal-to-noise ratio, and the diversity and density of ship targets, which lead to the shortcomings of traditional feature extraction and classifier-based detection methods in terms of accuracy and efficiency. To address these challenges, this paper proposes a new method for ship detection in SAR images based on YOLOv7. When dealing with SAR images containing clutter interference and complex backgrounds, many models face the dual challenge of insufficient real-time and accuracy. To address this problem, this study proposes a new PCAN aggregation network, which firstly introduces the SimAM attention mechanism, which significantly enhances the model's ability to identify the differences between ships and background clutter, further improves the ability of focusing on target features, and effectively It further improves the ability to focus on target features, effectively reduces the interference of background noise, and improves the detection accuracy in complex scenes. At the same time, the PConv strategy is added to further lighten the model structure, thus accelerating the computation process and improving the inference efficiency. We trained and tested the model on the SAR-Ship-Dataset dataset, and achieved an accuracy of 91.1%, an F1 score of 92.9, and an FPS of 46, demonstrating its excellent detection capability. By comparing and analysing the model with several classical and cutting-edge methods in the current field, the proposed model in this study demonstrates obvious performance advantages, and in addition, through ablation experiments, we further confirm the effectiveness and contribution of PCAN.

Retrieval of sea ice drift in the Fram Strait based on data from Chinese satellite HaiYang (HY-1D)

Abstract. Melting of sea ice in the Arctic has accelerated due to global warming. The Fram Strait (FS) serves as a crucial pathway for sea ice export from the Arctic to the North Atlantic Ocean. Monitoring sea ice drift (SID) in the FS provides insight into how Arctic sea ice responds to the climate change. The SID has been retrieved from Sentinel-1 synthetic aperture radar (SAR), Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and further exploration is needed for the retrieval of SID using optical imagery. In this paper, we retrieve SID in the FS using the Chinese HaiYang1-D (HY-1D) satellite equipped with the Coastal Zone Imager (CZI). A multi-template matching technique is employed to calculate the cross-correlation, and subpixel estimation is used to locate displacement vectors from the cross-correlation matrix. The dataset covering March to May 2021 was divided into hourly and daily intervals for analysis, and validation was performed using Copernicus Marine Environment Monitoring Service (CMEMS) SAR-based product and International Arctic Buoy Programme (IABP) buoy. A comparison with the CMEMS SID product revealed a high correlation with the daily interval dataset; however, due to the spatial and temporal variability in the sea ice motion, differences are observed with the hourly interval dataset. Additionally, validation with the IABP buoys yielded a velocity bias of −0.005 m s−1 and RMSE of 0.031 m s−1 for the daily interval dataset, along with a flow direction bias of 0.002 rad and RMSE of 0.009 rad, respectively. For the hourly interval dataset, the velocity bias was negligible (0 m s−1), with a RMSE of 0.036 m s−1, while the flow direction bias was 0.003 rad, with a RMSE of 0.010 rad. In addition, during the validation with buoys, we found that the accuracy of retrieving the SID flow direction is distinctly interrelated with the sea ice displacement.

A dual-branch joint learning network for underwater object detection

Underwater object detection (UOD) is crucial for developing marine resources, environmental monitoring, and ecological protection. However, the degradation of underwater images limits the performance of object detectors. Most existing schemes treat underwater image enhancement (UIE) and UOD as two independent tasks, which take UIE as a preprocessing step to reduce the degradation problem, thus being unable to improve the detection accuracy effectively. Therefore, in this paper, we propose a dual-branch joint learning network (DJL-Net) that combines image processing and object detection through multi-task joint learning to construct an end-to-end model for underwater detection. With the dual-branch structure, DJL-Net can use the enhanced images generated by the image-processing module to supplement the features lost due to the degradation of the original underwater images. Specifically, DJL-Net first employs an image decolorization module governed by the detection loss, generating gray images to eliminate color disturbances stemming from underwater light absorption and scattering effects. An improved edge enhancement module is utilized to enhance the shape and texture expression in gray images and improve the recognition of object boundary features. Then, the generated edge-enhanced gray images and their original underwater images are input into the two branches to learn different types of features. Finally, a tridimensional adaptive gated feature fusion module is proposed to effectively fuse the complementary features learned from the two branches. Comprehensive experiments on four UOD datasets, including some scenes with challenging underwater environments, demonstrate the effectiveness and robustness of the proposed DJL-Net.

Edible Sea urchins Echinus esculentus from Norwegian waters- Effect of season on nutritional quality and chemical contaminants

This study aimed to characterize Echinus esculentus gonads in terms of biometric parameters and nutritional quality at two sites in Mid-Norway at four different seasons. The chemical contamination of the gonads was also investigated for the first time through the evaluation of 28 macro- and trace elements and 32 components from the emerging and persistent group per- and polyfluoroalkyl substances (PFAS). The spawning period was determined in summer, given that the gonad index was the lowest in this season for both sites. Protein concentrations were constant (8%–10%). However, lipid contents (1%–3%) were noticed to be higher in gonads during autumn and winter. The gonads had high contents of PUFA mainly EPA and DHA, followed by SFA, and MUFA year around for both locations. E. esculentus gonads constitute a good source of fatty acids, macro, and trace elements. This species could also be a bioindicator for the monitoring of marine environments.

Assessment of oceanographic services for the monitoring of highly anthropised coastal lagoons: The Mar Menor case study

Ocean monitoring systems are designed for continuous monitoring of the seas and oceans to track their evolution and thus anticipate environmental issues. However, they are often based on Internet of Things (IoT) systems that are expensive, hard to maintain, and offer little spatial coverage. An emerging alternative is Satellite Remote Sensing (SRS) systems that offer good geographical coverage but, as a reliable and real-time monitoring system, they also face several challenges such as environmental conditions, spatio-temporal granularity, terrain oleography, etc. This paper introduces an easy-to-use software tool to crawl water-quality data from up to 6 satellite instruments (i.e. Sentinel 3A, Sentinel-3B, NOAA VIIRS, SNPP VIIRS, Aqua MODIS, Terra MODIS) of the European Space Agency's (ESA) Coopernicus system and NASA's Marine Environmental Monitoring Service. We also provide an in-depth analysis in terms of reliability and data coverage for the chlorophyll-a (Chl-a) in a highly anthropised local/regional context like the Mar Menor lagoon (Murcia, Spain), where serious socio-environmental issues are arising due to the eutrophication process. Our results show a good linear correlation between in situ data, obtained by several Spanish public institutions since 2016, and data generated by the IRTM-NN (Inverse Radiative Transfer Model-Neural Network) algorithm of Sentinel 3 in general, reaching coefficient correlation values close to 0.9. They also show that organic matter inputs from ephemeral streams are relevant in determining Chl-a concentrations. Moreover, the temporal coverage using a single instrument is rather limited, as no data were recorded for 80% of the days studied. This figure decreases to 30% when data from the 6 satellite instruments are combined, increasing the temporal granularity of the measurements from approximately 5 to 1.5 days, suggesting the need for a combination of these systems for a robust SRS system.

Neural network approach for detecting spatial changes in catch probability of Engraulis ringens during El Niño‐Southern Oscillation events in northern Chile

AbstractEngraulis ringens (anchovy) is a small pelagic fish of the Family Engraulidae that inhabits the neretic‐coastal zone from northern Peru to south‐central Chile. It is the main resource species of industrial fishing of northern Chile, representing 80% of the annual landings of the purse seine fleet. The history of this fishery (1985–2023) shows a strong decrease in annual industrial landings, especially during extreme El Niño–Southern Oscillation (ENSO) events. The greatest decrease in landings in two decades occurred in 2020, coinciding with a cold La Niña event, which had not been observed in previous La Niña events. We evaluated whether the current decrease in annual landings of E. ringens is associated with oceanographic changes in northern Chile during El Niño or La Niña events. We applied a neuronal network model to identify the spatial and temporal distribution of E. ringens using the catch probability of each boat of the industrial purse seine fleet. The selected oceanographic variables (sea surface temperature, salinity, depth of the mixed layer, sea height and currents) for the 2003–2020 period were obtained from the Copernicus Marine Environment Monitoring Service (CMEMS program) and used as predictor variables of the monthly landings of E. ringens. The neural network model explained 97% of the monthly variability of catch probability of E. ringens by the industrial purse seine fleet. The spatial distribution of catch probability of E. ringens was analyzed independently for El Niño (2015), La Niña (2007, 2013, and 2020), and Neutral (2004) years. We found that catch probability extended further west during La Niña events (except for 2020), occupying a greater area, but were limited to a 10 nautical mile coastal strip during the El Niño event. The spatial distribution of catch probability in the Neutral condition was near the coast, although not as restricted as during the El Niño event. The higher catch probabilities in the La Niña event of 2020 were near the coast, in contrast to the previous La Niña events of 2007 and 2013, due to the restriction of the optimal habitat of E. ringens in response to changes in oceanographic conditions. The application of the results of this study will allow understanding and probably anticipating the consequences that extreme ENSO events could have on the catch probability of the industrial anchovy purse seine fleet in northern Chile.

Spatial-temporal characteristics of temperature in Indonesian Sea based on a high-resolution reanalysis data

Influenced by both the Pacific and Indian Oceans, the temperature field in the Indonesian Sea are complicated. In order to reveal the spatial-temporal variability, the surface, thermocline and intermediate layers of the temperature field are studied based on the global ocean reanalysis data of Copernicus Marine Environment Monitoring Service (CMEMS) from 1993 to 2019 and the Empirical Orthogonal Function (EOF) analysis method. The results show that there is a warming trend of 1.36×10−2 °C /year in the surface layer. The first two modes both show reverse changes close to the Pacific and Indian Oceans. The correlation coefficients between the first and second modal time coefficients and the Niño 3.4 index are 0.62 and 0.48, respectively. And the correlation coefficient between the second modal time coefficient and the ITF inflow is -0.45. In the thermocline, there is a warmer trend, which is 4.78×10−2 °C /year. The correlation index of the first and second modal time coefficient with the Niño 3.4 and DMI indices are 0.87 and 0.43, respectively. The correlation index between the first and second modal time coefficient and the ITF inflow are -0.60 and 0.38, respectively. In the intermediate layer, the warming trend is 2.18×10−2 °C /year. From 1993 to 1999, from 2000 to 2016 and from 2017 to 2019, the Sulu Sea and northern Halmahera Sea experienced three periods of warming, cooling and warming, respectively. The study is helpful for further understanding the variation of the temperature field in the Indonesian Sea.

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A gap-filling method for satellite-derived chlorophyll-a time series based on neighborhood spatiotemporal information

Satellite-derived Chlorophyll-a concentration (Chla) time series products are essential for large-scale marine environmental monitoring. However, the plenty of missing pixels in current satellite Chla products severely hinder their applications for marine research, due to cloud contamination, solar glint, and unfavorable observation conditions. This study proposed a Chla time series gap-filling method for MODIS 8-day composite Chla product by integrating spatiotemporal information (STGF). This method employed spatially neighboring pixels with similar temporal variation to fill the missing values in time series, without involving training or auxiliary data. The performance of the STGF is assessed quantitatively and qualitatively. The correlation coefficients (CC) between gap-filled data and actual observations for years of 2004, 2010, 2016, and 2022 across the entire study area are all greater than 0.97. The mean absolute percentage error (MAPE) and root mean square error (RMSE) are less than 16.1 % and 0.233 mg/m3, respectively. The proposed STGF outperformed linear interpolation and the DINEOF algorithm from both spatial and temporal perspectives, suggesting the effectiveness of STGF in handling continuous data gaps and capturing detailed Chla variation patterns, especially in regions with significant variability. The findings suggest that the proposed STGF method offers a viable alternative for filling missing values in Chla time series data. This supports the demand for long-term, large-scale, and high-coverage ocean color remote sensing data in marine environmental studies.