Search Results (193)

The presence of sea ice threatens low ice-class vessels’ navigation safety in the Arctic, and traditional Navigation Risk Assessment Models based on sea ice parameters have been widely used to guide safe passages for ships operating in ice regions. However, these models mainly rely on empirical coefficients, and the accuracy of these models in identifying sea ice navigation risk remains insufficiently validated. Therefore, under the binary classification framework, this study used Automatic Identification System (AIS) data along the Northeast Passage (NEP) as positive samples, manual interpretation non-navigable data as negative samples, a total of 10 machine learning (ML) models were employed to capture the complex relationships between ice conditions and navigation risk for Polar Class (PC) 6 and Open Water (OW) vessels. The results showed that compared to traditional Navigation Risk Assessment Models, most of the 10 ML models exhibited significantly improved classification accuracy, which was especially pronounced when classifying samples of PC6 vessel. This study also revealed that the navigability of the East Siberian Sea (ESS) and the Vilkitsky Strait along the NEP is relatively poor, particularly during the month when sea ice melts and reforms, requiring special attention. The navigation risk output by ML models is strongly determined by sea ice thickness. These findings offer valuable insights for enhancing the safety and efficiency of Arctic maritime transport. Full article

In recent years, spaceborne Global Navigation Satellite System reflectometry (GNSS-R) technology has made significant progress in the fields of Earth observation and remote sensing, with a wide range of applications, important research value, and broad development prospects. However, despite existing research focusing on the application of spaceborne GNSS-R L1-level data, the potential value of raw intermediate-frequency (IF) signals has not been fully explored for special applications that require a high accuracy and spatiotemporal resolution. This article provides a comprehensive overview of the current status of the measurement of raw IF signals from spaceborne GNSS-R in multiple application fields. Firstly, the development of spaceborne GNSS-R microsatellites launch technology is introduced, including the ability of microsatellites to receive GNSS signals and receiver technique, as well as related frequency bands and technological advancements. Secondly, the key role of coherence detection in spaceborne GNSS-R is discussed. By analyzing the phase and amplitude information of the reflected signals, parameters such as scattering characteristics, roughness, and the shape of surface features are extracted. Then, the application of spaceborne GNSS-R in inland water monitoring is explored, including inland water detection and the measurement of the surface height of inland (or lake) water bodies. In addition, the widespread application of group delay sea surface height measurement and carrier-phase sea surface height measurement technology in the marine field are also discussed. Further research is conducted on the progress of spaceborne GNSS-R in the retrieval of ice height or ice sheet height, as well as tropospheric parameter monitoring and the study of atmospheric parameters. Finally, the existing research results are summarized, and suggestions for future prospects are put forward, including improving the accuracy of signal processing and reflection signal analysis, developing more advanced algorithms and technologies, and so on, to achieve more accurate and reliable Earth observation and remote sensing applications. These research results have important application potential in fields such as environmental monitoring, climate change research, and weather prediction, and are expected to provide new technological means for global geophysical parameter retrieval. Full article

Understanding the climate–weathering coupling mechanisms remains pivotal for interpreting global glacial–interglacial cycles, yet advancements have been constrained by the limited high-resolution sedimentary archives. The newly acquired BXZK2017-2 borehole (30.5 m core) from Bohai Bay provides an exceptional sedimentary sequence to investigate the Late Quaternary climate–weathering interactions. Through an integrated high-resolution chronostratigraphic framework (AMS ¹⁴C and OSL dating) coupled with multi-proxy sedimentological analyses (major element geochemistry and granulometric parameters), we reconstructed the chemical–weathering dynamics in the Bohai coastal region since the Late Pleistocene. Our findings revealed four distinct climate-weathering phases that correlate with the regional paleoenvironmental evolution and global climate perturbations: (1) enhanced weathering during mid-MIS3 to ~37.5 cal kyr BP (Chemical Index of Alteration (CIA): 55.9–62.2), corresponding to regional warming and strengthened summer monsoon circulation; (2) weathering minimum in late MIS3 through early–mid-MIS2 (37.5–14.8 cal kyr BP, CIA < 55), marking the peak aridity before the Last Glacial Maximum; (3) maximum weathering intensity from mid-MIS2 to early MIS1 (14.8–3.34 cal kyr BP, CIA: 65–68), documenting the postglacial humidification driven by the intensified East Asian Summer Monsoon; (4) renewed weathering decline during the Neoglacial (3.34 cal kyr BP-present, CIA: 59–63), coinciding with the late Holocene cooling events. Remarkably, this study identifies a striking synchronicity between the CIA in marine drill cores and δ¹⁸O records derived from Greenland ice cores. Our results indicate that chemical weathering proxies from marginal sea sediments can serve as robust recorders of post-Late Pleistocene climate variability, establishing a new proxy framework for global paleoclimate comparative research. Full article

Global navigation satellite system reflectometry (GNSS-R) uses the reflection characteristics of navigation satellite signals reflected from the earth’s surface to provide an innovative tool for remote sensing, especially for monitoring surface and atmospheric environmental variables, such as wind speed, soil moisture, vegetation, and sea ice parameters. This paper focuses on the current application and future potential of spaceborne GNSS-R in vegetation remote sensing and the retrieval of inland water environmental and physical parameters. This paper reviews the technical progress of GNSS-R in detail, from early feasibility studies to multiple application examples at this stage, from the United Kingdom Disaster Monitoring Constellation (UK-DMC) satellite in 2003 to other recent GNSS-R missions. These cases demonstrate the unique advantages of GNSS-R in terms of global coverage, low cost, and real-time monitoring. This paper explores the application of GNSS-R technology in vegetation parameters and inland water monitoring, especially its potential in vegetation parameters and surface water monitoring applications. The article also mentioned that the accuracy and efficiency of parameter retrieval can be significantly improved by improving models and algorithms, such as using neural networks and data fusion technology. Finally, the article points out the future direction of spaceborne GNSS-R technology in vegetation remote sensing and the retrieval of inland water environment and physical parameters, including expanding its application areas to a broader range of environmental monitoring and resource management. It emphasized its essential role in monitoring the global ecosystem and monitoring water resources. Full article

The Laptev Sea, as a marginal sea and a key source of sea ice for the Arctic Ocean, has a profound influence on the dynamic processes of sea ice evolution. Under a 2 °C global warming scenario, the accelerated ablation of Arctic sea ice is projected to greatly impact Arctic warming. The ocean regulates global climate through its interactions with the atmosphere, where sea surface temperature (SST) serves as a crucial parameter in exchanging energy, momentum, and gases. SST is also a key driver of sea ice concentration (SIC). In this paper, we analyze the spatiotemporal variability of SST and SIC, along with their interrelationships in the Laptev Sea, using daily optimum interpolation SST datasets from NCEI and daily SIC datasets from the University of Bremen for the period 2004–2023. The results show that: (1) Seasonal variations are observed in the influence of SST on SIC. SIC exhibited a decreasing trend in both summer and fall with pronounced interannual variability as ice conditions shifted from heavy to light. (2) The highest monthly averages of SST and SIC were in July and September, respectively, while the lowest values occurred in August and November. (3) The most pronounced trends for SST and SIC appeared both in summer, with rates of +0.154 °C/year and −0.095%/year, respectively. Additionally, a pronounced inverse relationship was observed between SST and SIC across the majority of the Laptev Sea with correlation coefficients ranging from −1 to 0.83. Full article

Antarctic true-color imagery synthesized using multispectral remote sensing data is effective in reflecting sea ice conditions, which is crucial for monitoring. Deep learning has been explored for sea ice extraction, but traditional convolutional neural network models are constrained by a limited perceptual field, making it difficult to obtain global contextual information from remote sensing images. A novel model named GEFU-Net, a modification of U-Net, is presented. The self-established graph reconstruction module is employed to convert features into graph data and construct the adjacency matrix using a global adaptive average similarity threshold. Graph convolutional networks are utilized to aggregate the features at each pixel, enabling the rapid capture of global context, enhancing the semantic richness of the features, and improving the accuracy of sea ice extraction through graph reconstruction. Experimental results using the sea ice dataset of the Ross Sea in the Antarctic, produced by Sentinel-2, demonstrate that our GEFU-Net achieves the best performance compared to other commonly used segmentation models. Specifically, it achieves an accuracy of 97.52%, an Intersection over Union of 95.66%, and an F1-Score of 97.78%. Additionally, fewer model parameters and good inference speed are demonstrated, indicating strong potential for practical ice mapping applications. Full article

Icebergs pose significant risks to shipping, offshore oil exploration, and underwater pipelines. Detecting and monitoring icebergs in the North Atlantic Ocean, where darkness and cloud cover are frequent, is particularly challenging. Synthetic aperture radar (SAR) serves as a powerful tool to overcome these difficulties. In this paper, we propose a method for automatically detecting and classifying icebergs in various sea conditions using C-band dual-polarimetric images from the RADARSAT Constellation Mission (RCM) collected throughout 2022 and 2023 across different seasons from the east coast of Canada. This method classifies SAR imagery into four distinct classes: open water (OW), which represents areas of water free of icebergs; open water with target (OWT), where icebergs are present within open water; sea ice (SI), consisting of ice-covered regions without any icebergs; and sea ice with target (SIT), where icebergs are embedded within sea ice. Our approach integrates statistical features capturing subtle patterns in RCM imagery with high-dimensional features extracted using a pre-trained Vision Transformer (ViT), further augmented by climate parameters. These features are classified using XGBoost to achieve precise differentiation between these classes. The proposed method achieves a low false positive rate of 1% for each class and a missed detection rate ranging from 0.02% for OWT to 0.04% for SI and SIT, along with an overall accuracy of 96.5% and an area under curve (AUC) value close to 1. Additionally, when the classes were merged for target detection (combining SI with OW and SIT with OWT), the model demonstrated an even higher accuracy of 98.9%. These results highlight the robustness and reliability of our method for large-scale iceberg detection along the east coast of Canada. Full article

Precambrian tropical glaciation is an enigma of Earth’s climate. Overlooking fundamental difference of land/sea icelines, it was equated with a global frozen ocean, which is at odds with the sedimentary evidence of an active hydrological cycle, and its genesis via the runaway ice–albedo feedback conflicts with the mostly ice-free Proterozoic when its trigger threshold was well exceeded by the dimmer sun. In view of these shortfalls, I put forth two key hypotheses of the tropical glaciation: first, if seeded by mountain glaciers, the land ice would advance on sea level to be halted by above-freezing summer temperature, which thus abuts an open cozonal ocean; second, a tropical supercontinent would block the brighter tropical sun to cause the required cooling. To test these hypotheses, I formulate a minimal tropical/polar box model to examine the temperature response to a varying tropical land area and show that tropical glaciation is indeed plausible when the landmass is concentrated in the tropics despite uncertain model parameters. In addition, given the chronology of paleogeography, the model may explain the observed deep time climate to provide a unified account of the faint young Sun paradox, Precambrian tropical glaciations, and Phanerozoic glacio-epochs, reinforcing, therefore, the uniformitarian principle. Full article

Arctic sea-ice surface temperature (IST) is an important environmental and climatic parameter. Currently, wide-swath sea-ice surface temperature products have a spatial resolution of approximately 1000 m. The Medium Resolution Spectral Imager (MERSI-I) offers a thermal infrared channel with a wide-swath width of 2900 km and a high spatial resolution of 250 m. In this study, we developed an applicable single-channel algorithm to retrieve ISTs from MERSI-I data. The algorithm accounts for the following challenges: (1) the wide range of incidence angle; (2) the unstable snow-covered ice surface; (3) the variation in atmospheric water vapor content; and (4) the unique spectral response function of MERSI-I. We reduced the impact of using a constant emissivity on the IST retrieval accuracy by simulating the directional emissivity. Different ice surface types were used in the simulation, and we recommend the sun crust type as the most suitable for IST retrieval. We estimated the real-time water vapor content using a band ratio method from the MERSI-I near-infrared data. The results show that the retrieved IST was lower than the buoy measurements, with a mean bias and root-mean-square error (RMSE) of −1.928 K and 2.616 K. The retrieved IST is higher than the IceBridge measurements, with a mean bias and RMSE of 1.056 K and 1.760 K. Compared with the original algorithm, the developed algorithm has higher accuracy and reliability. The sensitivity analysis shows that the atmospheric water vapor content with an error of 20% may lead to an IST retrieval error of less than 1.01 K. Full article

This study presents the development of a conceptual model for an autonomous underwater vehicle (AUV) information and control system (ICS) tailored for the mineral and raw materials complex (MRMC). To address the challenges of underwater mineral exploration, such as harsh conditions, high costs, and personnel risks, a comprehensive model was designed. This model was built using correlation analysis and expert evaluations to identify critical parameters affecting AUV efficiency and reliability. Key elements, including pressure resistance, communication stability, energy efficiency, and maneuverability, were prioritized. The results indicate that enhancing these elements can significantly improve AUV performance in deep-sea environments. The proposed model optimizes the ICS, providing a foundation for designing advanced AUVs capable of efficiently executing complex underwater tasks. By integrating these innovations, the model aims to boost operational productivity, ensure safety, and open new avenues for mineral resource exploration. This study’s findings highlight the importance of focusing on critical AUV parameters for developing effective and reliable solutions, thus addressing the pressing needs of the MRMC while promoting sustainable resource management. Full article

Ongoing warming in the Arctic has led to significant sea-ice loss and alterations in primary production, affecting all components of the marine food web. The considerable spatial variability of near-bottom environments around the Svalbard Archipelago renders the local fjords promising sites for revealing responses of benthic organisms to different environmental conditions. We investigated spatial variations in abundance, biomass, and growth parameters of the common bivalve Macoma calcarea in waters off western Spitsbergen and identified two distinct groups of this species: one composed mainly of cold-water stations from Storfjorden (Group I) and the other comprising warmer-water stations from Grønfjorden and Coles Bay (Group II). Within these groups, the mean abundance, biomass, production, and mortality accounted for 0.2 and 429 ind. m⁻², 20 and 179 g m⁻², 18.5 and 314 g m⁻² year⁻¹, and 0.22 and 0.10 year⁻¹ respectively. The size–frequency and age–frequency distributions were biased towards smaller and younger specimens in Group I, while Group II displayed more even distributions. The maximum ages were 11 and 21 years, respectively. The mollusks from cold water were significantly smaller than their same-aged counterparts from warmer water. Two groups of Macoma were identified: slow-growing individuals with a rate of 1.4 mm and fast-growing individuals with a growth rate of 1.8 mm. Most population parameters were higher than those observed in the Pechora, Kara, and Greenland Seas. Redundancy analysis indicated water temperature as the main driving factor of abundance and biomass, while the latter was also influenced by the presence of pebbles. Our findings provide new insights into the growth patterns and spatial distribution of Macoma at high latitudes and confirm that this species can serve as a reliable indicator of environmental conditions. Full article

A machine learning model for prediction of icing on vessels and offshore structures, Spice, was recently developed by Deshpande 2023. Some variables required for the prediction of icing rates in most prediction models, including Spice, such as the spray flux, cannot be easily measured. Existing models estimate these using empirical formulations that have been heavily criticized. Most existing models are also incapable of providing the distribution of icing on the structure. The current study demonstrates a method to estimate the local wind speeds, along with spray duration, spray period, and spray flux at different locations on the surface of a moving vessel. These, along with other easily measurable values of air temperature, water temperature, and salinity, are used to predict the icing rates. The result is a model, dubbed Spice2—an upgrade of the existing Spice model—that is able to provide the icing rates and the distribution of icing on the surface of vessels and other offshore structures. The model was demonstrated with a case study of a totally enclosed lifeboat where icing rates were predicted at different locations on its surface. Successful implementation of a two-phase simulation with a coupled wind–wave domain and a moving vessel was demonstrated. Research into simplification of the currently computationally expensive method is suggested. Validation of the proposed Spice2 model against a full-scale measurement is covered in part 2 of the study. Full article

A hybrid CFD–ML model for the prediction of sea spray icing, SPICE2, was developed in Part 1 of this study in Deshpande et al., 2024. The SPICE2 model is an extension of the ML model, SPICE, where some of the variables required for icing rate predictions: local wind speed, spray duration, spray period, and spray flux, are computed from CFD simulations. These, along with the air and water temperatures, and the salinity from the metocean data are used for the prediction of icing rates at different locations on a moving vessel. The existing full-scale icing measurements proved to be not detailed enough for the purpose of the verification of sea spray icing prediction models and the verification of the SPICE2 required distribution of sea spray icing data on the vessel surface in addition to the vessel design for simulation. A full-scale sea spray icing test was conducted in 2018 by Sundsbø et al. on a fully enclosed lifeboat equipped for the Goliat field in the Barents Sea. The 3D design of the same lifeboat, together with the corresponding metocean conditions and ship characteristics was used for the simulation of the vessel-specific parameters required for the verification of the icing rate and distribution prediction from the SPICE2 model against the measured distribution of sea spray icing rates on the lifeboat surface. The availability of the 3D model of this lifeboat, in addition to the fact that the icing measurements from this test were detailed enough to attempt a model verification served the purpose of validating the SPICE2 model. The icing rates measured on this lifeboat are used for the full-scale validation of the SPICE2 model that is proposed in Part 1 of this study. It was seen that the icing rates predicted by SPICE2 concurred with 9 of 13 selected locations on the lifeboat. The ones which did not showed very little deviation from the measurements. The icing rate and distribution prediction with SPICE2 were satisfactorily validated against full-scale icing measurements. This is a first attempt in modelling sea spray generation using CFD and further research into CFD for the estimation of spray flux is suggested. Full article

The Cenozoic glaciation of Antarctica proceeded through two distinct steps around 35 and 15 million years ago. The first icing was attributed to thermal isolation due to the opening of the Drake/Tasman passages and the development of the Antarctic circumpolar current. I also subscribe to this “thermal isolation” but posit that, although the snowline was lowered below the Antarctic plateau for it to be iced over, the glacial line remains above sea level to confine the ice sheet to the plateau, a “partial” glaciation that would be sustained over time. The origin of the second icing remains unknown, but based on the sedimentary evidence, I posit that it was triggered when the isostatic rebound of West Antarctica caused by heightened erosion rose above the glacial line to be iced over by the expanding plateau ice, and the ensuing cooling lowered the glacial line to sea level to cause the “full” glaciation of Antarctica. To test these hypotheses, I formulate a minimal box model, which is nonetheless subjected to thermodynamic closure that allows a prognosis of the Miocene climate. Applying representative parameter values, the model reproduces the observed two-step icing followed by the stabilized temperature level, in support of the model physics. Full article

Surface downward longwave radiation (SDLR) is crucial for maintaining the global radiative budget balance. Due to their ease of practicality, SDLR parameterization models are widely used, making their objective evaluation essential. In this study, against comprehensive ground measurements collected from more than 300 globally distributed sites, four SDLR parameterization models, including three popular existing ones and a newly proposed model, were evaluated under clear- and cloudy-sky conditions at hourly (daytime and nighttime) and daily scales, respectively. The validation results indicated that the new model, namely the Peng model, originally proposed for SDLR estimation at the sea surface and applied for the first time to the land surface, outperformed all three existing models in nearly all cases, especially under cloudy-sky conditions. Moreover, the Peng model demonstrated robustness across various land cover types, elevation zones, and seasons. All four SDLR models outperformed the Global Land Surface Satellite product from Advanced Very High-Resolution Radiometer Data (GLASS-AVHRR), ERA5, and CERES_SYN1de-g_Ed4A products. The Peng model achieved the highest accuracy, with validated RMSE values of 13.552 and 14.055 W/m² and biases of −0.25 and −0.025 W/m² under clear- and cloudy-sky conditions at daily scale, respectively. Its superior performance can be attributed to the inclusion of two cloud parameters, total column cloud liquid water and ice water, besides the cloud fraction. However, the optimal combination of these three parameters may vary depending on specific cases. In addition, all SDLR models require improvements for wetlands, bare soil, ice-covered surfaces, and high-elevation regions. Overall, the Peng model demonstrates significant potential for widespread use in SDLR estimation for both land and sea surfaces. Full article