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The outbreaks of green tide have caused severe harm to the marine environment and human society. Synthetic Aperture Radar (SAR) plays an important role in green tide monitoring by virtue of its high resolution and cloud-free nature. The existing green tide extraction methods still face challenges in identifying multi-scale green tide patches due to noise interference, uneven greyscale and blurred boundaries in SAR images. Meanwhile, the practical application of deep learning methods with high precision is limited due to the complexity of the model and the large amount of computation. Therefore, we propose a multi-scale context-aware and batch-independent lightweight green tide extraction network called MBL-Net. A novel lightweight heterogeneous backbone is designed to extract multi-scale discriminative features and improve segmentation efficiency by using multi-scale selection kernel (MSK) modules and lightweight stages. Meanwhile, Triplet attention module is introduced to improve the internal consistency of the green tide region and suppress the effect of speckle noise. Then, the mixed pooling-based channel prior module (MCPM) is used to expand the receptive field of the network and extract the fine green tide structure by fusing multi-scale features. In addition, Filter Response Normalisation (FRN) is innovatively applied for feature normalization in the decoding stage, eliminating batch dependency. In order to verify the effectiveness of the proposed method, a dataset is built using the Sentinel-1 images of the Yellow Sea, China, from 2019 to 2021. The experimental results show that the proposed method achieves an overall accuracy of 98.59% with 0.970 G FLOPs and 3.525 M parameters, which ensures high precision and improves green tide detection efficiency. Compared with several representative networks, this method can capture more details of green tide with fewer parameters and faster calculation speed. [ABSTRACT FROM AUTHOR]

China's first dynamic environment satellite constellation includes the HY-2B, HY-2C, and HY-2D satellites. In this study, the along track SLA, SWH, and SSWS of this satellite constellation were evaluated. SLA parameters are evaluated using self-crossing and dual-crossing methods. The SSWS and SWH data were evaluated by comparing with NDBC buoy and other available satellites' data. The evaluation revealed that the standard deviation of the SLA from the HY-2B/C/D satellites' single mission crossovers was 3.29 cm, 3.51 cm, and 3.72 cm, respectively. In addition, at the dual-crossovers of the Jason-3 satellite and the HY-2B satellite, the HY-2B satellite, and the HY-2C/D satellites, the standard deviation was determined to be 3.40 cm, 3.48 cm, and 4.25 cm, respectively. The accuracy of the SWH products of the HY-2B/C/D satellite radar altimeters was observed to be 0.23 m, 0.25 m, and 0.26 m, respectively. The accuracy of the SSWS data of the HY-2B/C/D satellite radar altimeters was observed to be 1.48 m/s, 1.59 m/s, and 1.35 m/s, respectively. In addition, this study also analyzed and compared the observation efficiency of the dynamic environment satellite constellation with the following six satellites: Sentinel-3(A, B), Jason-3, Sentinel-6A, Saral, and Cryosat-2. Observation efficiency refers to selection of any point on the globe to find a minimum radius of at least one observation point within a circle in a 14-day period. The analysis results demonstrated that observation efficiency of China's first dynamic environment satellite constellation was comparable to that of the six satellites. [ABSTRACT FROM AUTHOR]

Snow depth is an essential meteorological indicator for monitoring snow disasters. The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has been proven to be a practical approach to retrieving snow depth. This study presents a case study to explore utilizing the GNSS-IR-derived snow depth to monitor the 2022 early February snowstorm over southern China. A snow depth retrieval framework considering data quality control and specific ground surface substances was developed using 8-day data from 13 operational GNSS/Meteorology stations. The daily snow depths retrieved from different ground surfaces, i.e., dry grass, wet grass, and concrete, agreed well with the measured snow depth, with Mean Absolute Error (MAE) of 2.79 cm, 3.36 cm, and 2.53 cm, respectively. The percentage MAE when snow depths > 5 cm for the three ground surface substances was 26.8%, 53.7%, and 35.0%, respectively. The 6 h snow depth results also showed a swift and significant response to the snowfall event. This study proves the potential of GNSS-IR, used as a new operational tool in the automatic meteorological system, to monitor snow disasters over southern China, particularly as an efficient and cost-effective framework for real-time and accurate monitoring. [ABSTRACT FROM AUTHOR]

The South China Sea is China's largest marginal sea area, and it is rich in oil and gas mineral resources; thus, estimating its sea level changes is of practical significance. Based on linear and nonlinear sea level change characteristics, this paper decomposes 1992–2019 monthly mean sea level anomaly time series in the South China Sea into trend, seasonal, and random terms. This paper compares the seasonal autoregressive integrated moving average (SARIMA) and Prophet models for estimating the trend and seasonal terms and the long short-term memory (LSTM) and radial basis function (RBF) models for estimating random terms, and the more suitable models were selected. A Prophet-LSTM combined model was developed based on the accuracy results. This paper uses the combined model to study the effect of known data length on the experimental results and determines the best prediction duration. The results show that the combined model is suitable for short-term and medium-term estimations of 12–36 months. The accuracy at 36 months is 0.962 cm, which proves that the combined model has high application value for estimating sea level changes in the South China Sea. [ABSTRACT FROM AUTHOR]

Sea fog is a disastrous marine phenomenon for ship navigation. Sea fog reduces visibility at sea and has a great impact on the safety of ship navigation, which may lead to catastrophic accidents. Geostationary orbit satellites such as Himawari-8 make it possible to monitor sea fog over large areas of the sea. In this paper, a framework for marine navigation risk evaluation in fog seasons is developed based on Himawari-8 satellite data, which includes: (1) a sea fog identification method for Himawari-8 satellite data based on multilayer perceptron; (2) a navigation risk evaluation model based on the CRITIC objective weighting method, which, along with the sea fog identification method, allows us to obtain historical sea fog data and marine environmental data, such as properties related to wind, waves, ocean currents, and water depth to evaluate navigation risks; and (3) a way to determine shipping routes based on the Delaunay triangulation method to carry out risk analyses of specific navigation areas. This paper uses global information system mapping technology to get navigation risk maps in different seasons in Bohai Sea and its surrounding waters. The proposed sea fog identification method is verified by CALIPSO vertical feature mask data, and the navigation risk evaluation model is verified by historical accident data. The probability of detection is 81.48% for sea fog identification, and the accident matching rate of the navigation risk evaluation model is 80% in fog seasons. [ABSTRACT FROM AUTHOR]

Bar top hollows (BTHs) are morphological elements recognized in modern braided rivers; however, information regarding their depositional features and types of filling units in ancient strata is unclear. This is an important reason behind why it is difficult to identify BTH units in subsurface reservoirs. A Middle Jurassic dryland sandy braided river outcrop in northwestern China is characterized in this study through the application of an unmanned aerial vehicle (UAV) surveying and mapping, and ground penetrating radar (GPR). A workflow of UAV data processing has been established, and a 3D digital outcrop model has been built. By observation and measurement of the outcrop model and GPR profiles, two types of BTH filled units were found: (a) sandstone-filled, and (b) mudstone-filled hollows. Both of these units were located between two adjacent bar units in an area that is limited to a compound bar, and were developed in the upper part of a braided bar depositional sequence. The ellipse-shaped, sandstone-filled unit measures 10 m × 27 m in map view and reaches a maximum thickness of 5 m. The transversal cross-section across the BTHs displays a concave upward basal surface, while the angle of the inclined structures infilling the BTHs decreases up-section. The GPR data show that, in the longitudinal profile, the basal surface is relatively flat, and low-angle, inclined layers can be observed in the lower- and middle part of the sandstone-filled BTHs. In contrast, no obvious depositional structures were observed in the mudstone-filled BTH in outcrop. The new understanding of BTH has a wide application, including the optimization of CO2 storage sites, fresh water aquifers exploration, and oil and gas reservoir characterization. [ABSTRACT FROM AUTHOR]

The risk caused by different environmental factors in the navigation of ships is not uniform. The univariant (e.g., sea fog), covariant (e.g., wind and waves), and spatially restricted (e.g., offshore distance) risk factors present a complex non-linearity impact on the navigation of ships. This means that a comprehensive evaluation of the environmental risk for navigation should consider the risk characteristics of different factors. In this paper, a non-linear spatial multi-criteria decision (SMCD) method-based evaluation framework was developed to evaluate the potential environmental risk of navigating ships. (1) Non-linear weight adjustment and risk decision functions were proposed to spatially evaluate the comprehensive risk and avoid diluting the critical impact of high-risk factors. (2) The double thresholds standardization approach was developed to construct the risk association between different tonnage ships and environmental factors. (3) The developed methods were used in the Bohai and Yellow Seas as a case study to verify the validity and practicability. The quantitative analysis, historical accident matching (82% accident matching rate), and a real accident case proved the validity of the developed model. • A nonlinear spatial multi-criteria decision model is proposed for maritime safety. • The potential risk of marine environment factors on ships is assessed spatially. • The spatial risk with different risk-causing features are fairly emphasized. • The risk tolerance of different ships is analyzed and considered in the evaluation. [ABSTRACT FROM AUTHOR]

The deep-water area in the central Bohai Sea (BS) serves as a spawning ground and nursery for fish, shrimp, and crabs. Frequent hypoxia will affect the ecological environment in the central BS. Data from an on-site investigation of the central BS in the spring and summer of 2022 were used to analyze the relevant factors generating the occurrence of hypoxia in the central BS through the eutrophication index E, apparent oxygen consumption (AOU), and Spearman correlation. The hypoxia area was largely distributed in the study area's deep water section, and stratification was the main cause of hypoxia at the bottom. Organic matter mineralization, degradation, and biological respiration further exacerbated the hypoxia. In the summer of 2022, temperature stratification was the dominant factor influencing hypoxia., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The Sargassum bloom has severely impacted the ecological environment of the East China Sea and the Yellow Sea, causing significant economic losses. In recent years, deep learning has seen extensive development due to its outstanding feature extraction capabilities. However, the deep learning process typically involves a large number of parameters and computations. To address this issue, this paper proposes a lightweight deep learning network based on the U-Net framework, called SLWE-NET, which uses lightweight modules to replace the feature extraction modules in U-Net. In this experiment, SLWE-Net performed the best in both extraction accuracy and model lightweight. Compared to the formal U-Net, the number of parameters decreased by 65.83 %, the model size reduced from 94.97 MB to 32.51 MB, and the mIoU increased to 93.81 %. Therefore, the method proposed in this paper is beneficial for Sargassum extraction and provides a basis for operational monitoring., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)