895 research outputs found

    Satellite remote sensing of surface winds, waves, and currents: Where are we now?

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    This review paper reports on the state-of-the-art concerning observations of surface winds, waves, and currents from space and their use for scientific research and subsequent applications. The development of observations of sea state parameters from space dates back to the 1970s, with a significant increase in the number and diversity of space missions since the 1990s. Sensors used to monitor the sea-state parameters from space are mainly based on microwave techniques. They are either specifically designed to monitor surface parameters or are used for their abilities to provide opportunistic measurements complementary to their primary purpose. The principles on which is based on the estimation of the sea surface parameters are first described, including the performance and limitations of each method. Numerous examples and references on the use of these observations for scientific and operational applications are then given. The richness and diversity of these applications are linked to the importance of knowledge of the sea state in many fields. Firstly, surface wind, waves, and currents are significant factors influencing exchanges at the air/sea interface, impacting oceanic and atmospheric boundary layers, contributing to sea level rise at the coasts, and interacting with the sea-ice formation or destruction in the polar zones. Secondly, ocean surface currents combined with wind- and wave- induced drift contribute to the transport of heat, salt, and pollutants. Waves and surface currents also impact sediment transport and erosion in coastal areas. For operational applications, observations of surface parameters are necessary on the one hand to constrain the numerical solutions of predictive models (numerical wave, oceanic, or atmospheric models), and on the other hand to validate their results. In turn, these predictive models are used to guarantee safe, efficient, and successful offshore operations, including the commercial shipping and energy sector, as well as tourism and coastal activities. Long-time series of global sea-state observations are also becoming increasingly important to analyze the impact of climate change on our environment. All these aspects are recalled in the article, relating to both historical and contemporary activities in these fields

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

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    The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.Comment: 24 pages, 6 figure

    The Expedition PS129 of the Research Vessel POLARSTERN to the Weddell Sea in 2022

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    Improving satellite-based monitoring of the Arctic polar regions: identification of research and capacity gaps

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    We present a comprehensive review of the current status of remotely sensed and in situ sea ice, ocean, and land parameters acquired over the Arctic and Antarctic and identify current data gaps through comparison with the portfolio of products provided by Copernicus services. While we include several land parameters, the focus of our review is on the marine sector. The analysis is facilitated by the outputs of the KEPLER H2020 project. This project developed a road map for Copernicus to deliver an improved European capacity for monitoring and forecasting of the Polar Regions, including recommendations and lessons learnt, and the role citizen science can play in supporting Copernicus’ capabilities and giving users ownership in the system. In addition to summarising this information we also provide an assessment of future satellite missions (in particular the Copernicus Sentinel Expansion Missions), in terms of the potential enhancements they can provide for environmental monitoring and integration/assimilation into modelling/forecast products. We identify possible synergies between parameters obtained from different satellite missions to increase the information content and the robustness of specific data products considering the end-users requirements, in particular maritime safety. We analyse the potential of new variables and new techniques relevant for assimilation into simulations and forecasts of environmental conditions and changes in the Polar Regions at various spatial and temporal scales. This work concludes with several specific recommendations to the EU for improving the satellite-based monitoring of the Polar Regions

    investigating ice microphysical processes by combining multi-frequency and polarimetric Doppler radar observations with Lagrangian Monte-Carlo particle modelling

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    Clouds and precipitation strongly impact society and the earth system by influencing the water cycle, determining fresh water availability or causing natural disasters such as floods or droughts. However, many aspects of precipitation formation are still poorly understood, causing large uncertainties in the prediction of precipitation. Especially the microphysical processes, which describe the nucleation of cloud particle and their growth into precipitation lack understanding. As globally 63% of precipitation originates from the ice phase, increasing the understanding of ice microphysical processes is crucial to improve precipitation forecast. The dendritic growth layer (DGL), located at temperatures between −20 and −10 ° C, plays an important role in the formation of precipitation. Previous studies have found an in particle size and number concentration through depositional growth, aggregation and secondary ice processes. This dissertation investigates ice microphysical processes in the DGL by combining polarimetric and multi-frequency Doppler cloud radar observations with Monte-Carlo Lagrangian particle modelling. Study I presents a statistical analysis of a three-month polarimetric and multi-frequency Doppler radar dataset. This combination of radar measurements allows to observe the full evolution of ice particle growth, as the polarimetric measurements are indicators of depositional growth and possible secondary ice processes, while the multi-frequency approach gives an indication of the increase particle in size through aggregation and riming. The statistical analysis revealed an increase of aggregate size at −15 ° C. The mean size of aggregates is found to be correlated to an updraft with a maximum of approximately 0.1 m s −1 at −14 ° C. The radar observations further indicate the growth of plate-like ice crystals at −15 ° C. Unexpectedly, aggregation is found to increase in the DGL alongside an increase in ice particle number concentration. This simultaneous increase necessitates a source of new ice particles, as aggregation is expected to decrease the total number of ice particles. Secondary ice processes, such as collisional fragmentation provide one explanation for this increase in ice particle size. Another possible explanation might be that small ice particles sediment from colder temperatures into the DGL and enhance the number concentration locally. The third explanation is linked to the observed updraft, as this updraft might increase the super-saturation with respect to ice at −15 ° C, leading to the activation of ice nucleating particles and a subsequent increase in ice particle number and growth of plate-like particles. Unfortunately, radar observations do not observe the formation of particles directly, it is difficult to predict the origin of the particles responsible for the increase in particle concentration and observed polarimetric signatures further. With the observational dataset as a constrain, Study II uses the Monte-Carlo Lagrangian particle model McSnow to investigate the origin of the increase in ice particle number concentration in the DGL further. The comparison of the observations and McSnow simulations indicate that the particles responsible for the polarimetric signatures and increase in number concentration need to be nucleated at temperatures close to −15 ° C. This might indicate that in the observed clouds, sedimenting ice particles into the DGL play a lesser role. The McSnow simulations further indicate that neither collisional fragmentation nor new ice particles due to activation of ice nucleating particles can explain the observed multi-frequency and polarimetric observations. A combination of both processes might explain the observed signatures. This dissertation shows the potential of a combination of radar observations and modelling for increasing the understanding of microphysical processes in clouds. However, further laboratory studies are needed in order to further constrain the processes in the DGL and validate the findings of this dissertation

    Synoptic Variability in Satellite Altimeter-Derived Radar Freeboard of Arctic Sea Ice

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    Satellite observations of sea ice freeboard are integral to the estimation of sea ice thickness. It is commonly assumed that radar pulses from satellite-mounted Ku-band altimeters penetrate through the snow and reflect from the snow-ice interface. We would therefore expect a negative correlation between snow accumulation and radar freeboard measurements, as increased snow loading weighs the ice floe down. In this study we produce daily resolution radar freeboard products from the CryoSat-2 and Sentinel-3 altimeters via a recently developed optimal interpolation scheme. We find statistically significant (p < 0.05) positive correlations between radar freeboard anomalies and modeled snow accumulation. This suggests that, in the period after snowfall, radar pulses are not scattering from the snow-ice interface as commonly assumed. Our results offer satellite-based evidence of winter Ku-band radar scattering above the snow-ice interface, violating a key assumption in sea ice thickness retrievals

    Improving satellite-based monitoring of the polar regions: Identification of research and capacity gaps

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    We present a comprehensive review of the current status of remotely sensed and in situ sea ice, ocean, and land parameters acquired over the Arctic and Antarctic and identify current data gaps through comparison with the portfolio of products provided by Copernicus services. While we include several land parameters, the focus of our review is on the marine sector. The analysis is facilitated by the outputs of the KEPLER H2020 project. This project developed a road map for Copernicus to deliver an improved European capacity for monitoring and forecasting of the Polar Regions, including recommendations and lessons learnt, and the role citizen science can play in supporting Copernicus’ capabilities and giving users ownership in the system. In addition to summarising this information we also provide an assessment of future satellite missions (in particular the Copernicus Sentinel Expansion Missions), in terms of the potential enhancements they can provide for environmental monitoring and integration/assimilation into modelling/forecast products. We identify possible synergies between parameters obtained from different satellite missions to increase the information content and the robustness of specific data products considering the end-users requirements, in particular maritime safety. We analyse the potential of new variables and new techniques relevant for assimilation into simulations and forecasts of environmental conditions and changes in the Polar Regions at various spatial and temporal scales. This work concludes with several specific recommendations to the EU for improving the satellite-based monitoring of the Polar Regions

    Selected Problems of High-Resolution Automotive Imaging Radar

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    This thesis aims at two selected problems in the development of high-resolution au- tomotive imaging radar: 1) The feasibility of using sub-THz for the next generation of automotive radar; 2) The development of the physics-based image segmentation approach on the automotive radar imagery. The wide range of feasibility studies on the use of sub-THz frequencies for auto- motive radar have been undertaken in the Microwave Integrated Systems Laboratory (MISL) at the University of Birmingham, and the candidate is in charge of the included study on the theoretical modelling and experimental verification of the attenuation through the vehicle infrastructures which is the first part of this thesis. The importance of this work is related to the fact that automotive radar is placed within the car infras- tructure. Therefore, it would be a potential show-stopper in the development of this innovation if attenuation within the car bumper or badge is prohibitively high. Both theoretical modelling and experimental measurement are conducted by considering the impact factors on the propagation properties of the sub-THz signal such as the incident angle, frequency, characteristic parameters of materials, and the thicknesses of infrastructure layers. The transmissivity of multilayered structure has been modelled and good agreement with the results of measurements was demonstrated, so that the developed approach can be used in further studies on propagation through car infrastruc- ture. The published results on transmissivity and complex permittivity of automotive paints are valuable for researchers in either field of THz technology or automotive radar. The image segmentation on automotive radar maps aims at identifying the passable and impassable areas for path planning in autonomous driving. Contrary to traditional radar, radar clutter is regarded as the physical meaningful information, which can deliver valuable feature information for surface characterization, and enable the full scene reconstruction of automotive radar maps. The proposed novel segmentation algorithm is a hybrid method composed of pre-segmentation based on image processing methods, and the region classification using the multivariate Gaussian distribution (MGD) classifier developed based on the statistical distribution feature parameters of radar returns of various areas. Moving target indication (MTI) is implemented for the first time based on frame-to-frame context association. The end-to-end segmentation framework is therefore achieved robustly with good segmentation performance, and automatically without human intervention
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