2,207 resources related to Sea Ice
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The IEEE International Microwave Symposium (IMS) is the world s foremost conference covering the UHF, RF, wireless, microwave, millimeter-wave, terahertz, and optical frequencies; encompassing everything from basic technologies to components to systems including the latest RFIC, MIC, MEMS and filter technologies, advances in CAD, modeling, EM simulation and more. The IMS includes technical and interactive sessions, exhibits, student competitions, panels, workshops, tutorials, and networking events.
All fields of satellite, airborne and ground remote sensing.
OCEANS 2020 - SINGAPORE
An OCEANS conference is a major forum for scientists, engineers, and end-users throughout the world to present and discuss the latest research results, ideas, developments, and applications in all areas of oceanic science and engineering. Each conference has a specific theme chosen by the conference technical program committee. All papers presented at the conference are subsequently archived in the IEEE Xplore online database. The OCEANS conference comprises a scientific program with oral and poster presentations, and a state of the art exhibition in the field of ocean engineering and marine technology. In addition, each conference can have tutorials, workshops, panel discussions, technical tours, awards ceremonies, receptions, and other professional and social activities.
To promote awareness, understanding, advancement and application of ocean engineering and marine technology. This includes all aspects of science, engineering, and technology that address research, development, and operations pertaining to all bodies of water. This includes the creation of new capabilities and technologies from concept design through prototypes, testing, and operational systems to sense, explore, understand, develop, use, and responsibly manage natural resources.
ANTEM's technical sessions will provide a comprehensive and well-balanced program and are intended to provide an international forum for the exchange of information on state-of-the-art research in antennas, propagation, and electromagnetic engineering. Authors are invited to submit contributions for review and possible presentation during the symposium on topics of interest to ANTEM. In addition to regularly scheduled sessions for oral presentations, there will be distinguished lecturers and special sessions. There will be a Student Paper Competition as well as a Technical Exhibition.
Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.
IEEE Antennas and Wireless Propagation Letters (AWP Letters) will be devoted to the rapid electronic publication of short manuscripts in the technical areas of Antennas and Wireless Propagation.
Physics, medicine, astronomy—these and other hard sciences share a common need for efficient algorithms, system software, and computer architecture to address large computational problems. And yet, useful advances in computational techniques that could benefit many researchers are rarely shared. To meet that need, Computing in Science & Engineering (CiSE) presents scientific and computational contributions in a clear and accessible format. ...
EMC standards; measurement technology; undesired sources; cable/grounding; filters/shielding; equipment EMC; systems EMC; antennas and propagation; spectrum utilization; electromagnetic pulses; lightning; radiation hazards; and Walsh functions
It is expected that GRS Letters will apply to a wide range of remote sensing activities looking to publish shorter, high-impact papers. Topics covered will remain within the IEEE Geoscience and Remote Sensing Societys field of interest: the theory, concepts, and techniques of science and engineering as they apply to the sensing of the earth, oceans, atmosphere, and space; and ...
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
Bias assessment of sea ice concentration (SIC) derived from passive microwave sensors is very important because it has been used as an important parameter for climate change research. In this study, we analyzed biases in SICs retrieved from NASA Team (NT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithm implemented for the Special Sensor Microwave Imager/Sounder ...
IEEE Transactions on Geoscience and Remote Sensing, 2014
Eleven algorithms for the passive-microwave measurement of sea-ice were implemented and inter-compared. Daily, monthly and annual Arctic sea-ice concentration, area and extent were calculated by the algorithms using daily microwave brightness temperatures from SMMR, SSM/I, and SSMIS for the period 1979-2012. The differences between the 11 sea-ice concentration estimates- structural uncertainties-were quantified and analyzed spatially and seasonally. The algorithms differ ...
IEEE Transactions on Geoscience and Remote Sensing, 2017
In this paper, the surface emissivity is retrieved over the Arctic sea ice/open seas using observations from the advanced microwave sounding unit window channels during the year 2009. The emissivity computation is performed using two contrasted surface assumptions: specular and Lambertian assumptions. The obtained sea ice surface emissivities are studied in this paper with a focus on the effect of ...
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014
In this study, a new method, entitled as the multi-modality guided variational (MGV) method, is proposed, in which the data from a passive microwave sensor is used jointly with the data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the sea ice surface temperature (IST). The method augments existing sea IST values from the MODIS IST map, while filling ...
2012 IEEE International Geoscience and Remote Sensing Symposium, 2012
We compare passive microwave-derived sea ice concentrations with ship-based observations of sea ice concentration (OBS). We focus on different retrieval algorithms that are based on Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) measurements. OBS are collected according to the Antarctic Sea Ice Processes and Climate (ASPeCt) program. We assess the quality of the satellite data by calculating correlation coefficient, root- ...
Power: A Fundamental Ingredient of Advanced Science and Applied Technology - Adam Hamilton, APEC 2018
Geoff Mulligan: Welcome Address: WF IoT 2016
IEEE in the North and South Poles (INSP) - Tony Milne - Ignite: Sections Congress 2017
2011 IEEE Dennis J. Picard Medal for Radar Technologies and Applications - James M. Headrick
The ALMA Array: An IMS 2013 Closing Keynote
Bias assessment of sea ice concentration (SIC) derived from passive microwave sensors is very important because it has been used as an important parameter for climate change research. In this study, we analyzed biases in SICs retrieved from NASA Team (NT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithm implemented for the Special Sensor Microwave Imager/Sounder (SSMIS) and Advanced Microwave Scanning Radiometer-2 (AMSR2), respectively, in the Chukchi Sea in summer using the Korea Multi- Purpose SATellite-5 (KOMPSAT-5) synthetic aperture radar images. The root mean square error of ASI SIC was smaller than that of NT SIC. The SIC values from the sea ice algorithms showed different biases by the KOMPSAT-5 SIC range due to different sensitivities to atmospheric effects and ice surface melting conditions.
Eleven algorithms for the passive-microwave measurement of sea-ice were implemented and inter-compared. Daily, monthly and annual Arctic sea-ice concentration, area and extent were calculated by the algorithms using daily microwave brightness temperatures from SMMR, SSM/I, and SSMIS for the period 1979-2012. The differences between the 11 sea-ice concentration estimates- structural uncertainties-were quantified and analyzed spatially and seasonally. The algorithms differ in annual sea-ice area by 0.0-1.3 million km2 and in extent by 0.0-0.6 million km2. Linear trends for 34- and 21-year periods were calculated and compared for sea-ice concentration, area and extent. Low-frequency algorithms obtained annual Arctic sea-ice area decrease of 0.534-0.573 million km2 per decade (0.439-0.491 million km2 per decade for the extent) over the period 1979 to 2012, and a decrease of 0.866-0.975 million km2 per decade (0.767-0.812 million km2 per decade for the extent) for the 1992-2012 period. High-frequency algorithms obtained a decrease of 0.766-0.978 million km2 per decade in the area and 0.758-0.814 million km2 per decade in the extent over the period 1992-2012. Results for all the algorithms have close agreement on the strength of the negative trend in Arctic sea-ice area and extent, but are individually biased from the mean. The algorithms' ensemble mean and standard deviation in sea-ice concentration, describing part of the uncertainty, are presented to provide users with more insight into the uncertainties and potential biases of sea-ice concentration data.
In this paper, the surface emissivity is retrieved over the Arctic sea ice/open seas using observations from the advanced microwave sounding unit window channels during the year 2009. The emissivity computation is performed using two contrasted surface assumptions: specular and Lambertian assumptions. The obtained sea ice surface emissivities are studied in this paper with a focus on the effect of the surface assumption. Some factors of variability of the obtained emissivities are analyzed: variability in space, in time, with the zenith angle, and with respect to the frequency. We show that the near- nadir surface emissivity and emissivity difference (obtained using two contrasted surface assumptions) could be used as an excellent proxy to detect ice/no ice regions. We also show that near-nadir sea ice emissivity at some selected frequencies and the combination of both high and low window frequencies could also be very useful to better characterize sea ice surface physical properties and provide additional information for existing sea ice classifications, as they bring relevant information about first year and multiyear sea ice properties and their seasonal evolution.
In this study, a new method, entitled as the multi-modality guided variational (MGV) method, is proposed, in which the data from a passive microwave sensor is used jointly with the data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the sea ice surface temperature (IST). The method augments existing sea IST values from the MODIS IST map, while filling in areas in the MODIS image that may be sparsely sampled due to the cloud cover, or due to increased spacing between the pixels at the swath edges. The former issue is particularly problematic in the marginal ice zone, where the atmospheric conditions often lead to persistent cloud cover. The sea IST is of interest because it can be used to estimate the sea ice thickness, an important parameter for shipping, climate change, and weather forecasting applications. The impact of the MGV method is checked through a comparison between the sea ice thickness calculated using the swath surface temperature and that calculated using the surface temperature from MGV. Using the operational ice charts as a guideline, it is found that the sea ice thickness values calculated using the MGV surface temperature are realistic, and there is a 16% increase in the number of sea ice thickness data points available when the MGV method is used as compared to when the swath data are used.
We compare passive microwave-derived sea ice concentrations with ship-based observations of sea ice concentration (OBS). We focus on different retrieval algorithms that are based on Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) measurements. OBS are collected according to the Antarctic Sea Ice Processes and Climate (ASPeCt) program. We assess the quality of the satellite data by calculating correlation coefficient, root- mean-square-deviation (RMSD) and bias with respect to ASPeCt observations of sea ice concentration. All retrieval algorithms compared in this study, Comiso Bootstrap (BST), NASA-Team 2 (NT2) and Artist Sea Ice (ASI), show correlation coefficients above 0.8 for all dates considered (N=313). BST and ASI do not have a significant bias, but NT2 has a bias of 8 %. RMSD values are different for different seasons, but generally, Comiso BST has the lowest RMSD (<;13 %).
In the paper, we proposed a novel framework for the sea-ice images classification, which will be very helpful for the future channel navigation in polar applications. Firstly, the three different types of sea ice are defined in the paper based on the large amounts of data inspection, such as thick ice, thin ice and water. Further, we present to use relative radiometric normalization method to solve the grayscale difference of different temporal sea-ice images, which can affect the classification accuracy of sea ice. Finally, the SAE (sparse auto-encoder) classifier is employed for different types of sea-ice classification. We designed several experiments to discuss and analyze the influence factors on the classification accuracy of sea-ice images. The experiment results indicated the validity of the proposed method and the rationality of sea ice type definition.
MODIS data is the important data resource of acquiring sea ice parameters of Yellow and Bohai sea. In this paper, the spectral characteristic of Yellow and Bohai sea water is analyzed firstly, then an ice acquisition and thickness retrieval method are developed based on the analyzing result, finally the ice retrieval result during the winter time of the year from 2006 to 2007 is compared with the corresponding period surveillance result of the observation stations and airborne remote sensing and it indicates that the former can basically coincide with the latter.
SAR Polarimetry has become a valuable tool in spaceborne SAR based sea ice analysis. The two major objectives in SAR based remote sensing of sea ice is on the one hand to have a large coverage of the imaged ground area, and on the other hand to obtain a radar response that carries as much information as possible. Whereas single-polarimetric acquisitions of existing sensors offer a wide coverage on the ground, dual polarimetric, or even better fully polarimetric data offer a higher information content which allows for a more reliable automated sea ice analysis. In order to reconcile the advantages of fully polarimetric acquisitions with the higher ground coverage of acquisitions with fewer polarimetric channels, compact polarimetric acquisitions offer a trade-off between the mentioned objectives. With the advent of the RISAT-l satellite platform, we are able to explore the potential of compact polarimteric acquisitions for sea ice analysis and classification in operational environment. Our algorithmic approach for an automated sea ice classification consists of two steps. In the first step, we perform a feature extraction procedure. The resulting feature vectors are then ingested into a trained neural network classifier to arrive at a pixelwise supervised classification. We present our results on datasets acquired over both Arctic and Antarctic sea ice.
The overall reduction in sea ice thickness may have profound impact on the Earth's climate system, but currently predictive models do not have consistent sea ice thickness datasets available for improving their results through proper initialization and assimilation. We will present a novel approach to retrieve Arctic sea ice thickness from satellite observations. Our approach offers hemispheric scale sea ice thickness and volume at a temporal resolution shorter than a month (from days to weeks) by utilizing altimetry-derived thickness and passive microwave observations. Our method is validated against the airborne Operation IceBridge measuremnts. Moreover, as an application, we will show the gain obtained when assimilating our basin wide Arcitc sea ice thickness in the NASA Goddard Earth Observing System Model, version 5 (GEOS-5) ocean sea-ice data assimilation system.
Numerical simulation of brightness temperatures of the Advanced Microwave Scanning Radiometer 2 (AMSR2) over the sea ice-open ocean-atmosphere system is fulfilled for non-precipitating conditions using a database of atmospheric meteorological parameter profiles, model profiles of cloud liquid water and published experimental data for sea ice emissivities. The results of numerical experiment show that polarization difference (PD) at high frequency channels can help to discriminate between the sea ice and sea water. At that high wind speeds and optically thick atmospheres lead to misinterpretation of sea ice basing on the only PD values. This complicates sea ice concentration retrievals under bad weather conditions, associated with high winds and atmospheres with high values of total atmospheric absorption. In this study we use numerical simulation results to define the weather limits of applicability of the current sea ice concentration algorithms based on satellite passive microwave PD measurements.
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