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Special Issue "Recent Advances in Sea Ice Research Using Satellite Data"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: 15 December 2023 | Viewed by 1102

Special Issue Editors

Vision and Image Processing Lab, University of Waterloo, Waterloo, ON, Canada
Interests: computer vision; image segmentation/classification; remote sensing; stochastic models; sea ice
Dr. Xinwei Chen
E-Mail Website
Guest Editor
Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
Interests: remote sensing; synthetic aperture radar; ocean remote sensing; image processing; machine learnig; computer vision
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: GNSS-R remote sensing; sea ice sensing; soil moisture retrieval; land cover mapping
Special Issues, Collections and Topics in MDPI journals
Finnish Meteorological Institute, PB 503, FI-00101 Helsinki, Finland
Interests: remote sensing; synthetic aperture radar; sea ice
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We warmly invite you to contribute manuscripts to a Special Issue, “Recent Advances in Sea Ice Research Using Satellite Data”, that will be published in the Remote Sensing journal. Given the escalating concerns surrounding global warming, understanding the swift transformations in sea ice across the Arctic and Antarctic Oceans holds paramount significance, particularly with regard to gaining new insights into the intricate relationship between the atmosphere, ocean, and Earth, as well as the emergence and implications of Arctic shipping routes.

The progressive utilization of remotely sensed data from satellites has played a pivotal role in advancing our comprehension of sea ice dynamics. Satellite-based observations have provided valuable insights into the characteristics and changes in sea ice cover, thickness, and concentration. In this Special Issue, we aim to showcase the recent strides made in sea ice research, with an emphasis on cutting-edge AI-based sea ice mapping methods, novel satellite sea ice datasets, and innovative processing techniques for satellite sensor data.

We wholeheartedly appreciate your consideration in submitting your manuscripts to this Special Issue on sea ice research using satellite data. We also kindly request your assistance in sharing this announcement with your esteemed colleagues, encouraging them to contribute their expertise to this important field of study.

Together, let us propel the advancements in sea ice research forward and contribute to a better understanding of the changing cryosphere and its implications for the Earth system.

Dr. Linlin Xu
Dr. Xinwei Chen
Dr. Qingyun Yan
Dr. Juha Karvonen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

Article
Sea Ice Detection from GNSS-R Data Based on Residual Network
Remote Sens. 2023, 15(18), 4477; https://doi.org/10.3390/rs15184477 - 12 Sep 2023
Viewed by 456
Abstract
Sea ice is an important component of the polar circle and influences atmospheric change. Global navigation satellite system reflectometry (GNSS-R) not only realizes time-continuous and wide-area sea ice detection, but also greatly reduces the cost of sea ice remote sensing research, which has [...] Read more.
Sea ice is an important component of the polar circle and influences atmospheric change. Global navigation satellite system reflectometry (GNSS-R) not only realizes time-continuous and wide-area sea ice detection, but also greatly reduces the cost of sea ice remote sensing research, which has been a hot topic in recent years. To tackle the challenges of noise interference and the reduced accuracy of sea ice detection during the melting period, this paper proposes a sea ice detection method based on a residual neural network (ResNet). ResNet addresses the issue of vanishing gradients in deep neural networks and introduces residual connections, which allows the network to reuse learned features from previous layers. Delay-Doppler maps (DDMs) collected from TechDemoSat-1 (TDS-1) are used as input, and National Oceanic and Atmospheric Administration (NOAA) surface-type data above 60°N are selected as the true values. Based on ResNet, the sea ice detection achieved an accuracy of 98.61%, demonstrating high robustness to noise and strong stability during the sea ice melting period (June to September). In comparison to other sea ice detection algorithms, it stands out with its advantages of high accuracy, stability, and insensitivity to noise. Full article
(This article belongs to the Special Issue Recent Advances in Sea Ice Research Using Satellite Data)
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Article
Satellite-Based Identification and Characterization of Extreme Ice Features: Hummocks and Ice Islands
Remote Sens. 2023, 15(16), 4065; https://doi.org/10.3390/rs15164065 - 17 Aug 2023
Viewed by 385
Abstract
The satellite-based techniques for the monitoring of extreme ice features (EIFs) in the Canadian Arctic were investigated and demonstrated using synthetic aperture radar (SAR) and electro-optical data sources. The main EIF types include large ice islands and ice-island fragments, multiyear hummock fields (MYHF) [...] Read more.
The satellite-based techniques for the monitoring of extreme ice features (EIFs) in the Canadian Arctic were investigated and demonstrated using synthetic aperture radar (SAR) and electro-optical data sources. The main EIF types include large ice islands and ice-island fragments, multiyear hummock fields (MYHF) and other EIFs, such as fragments of MYHF and large, newly formed hummock fields. The main objectives for the paper included demonstration of various satellite capabilities over specific regions in the Canadian Arctic to assess their utility to detect and characterize EIFs. Stereo pairs of very-high-resolution (VHR) imagery provided detailed measurements of sea ice topography and were used as validation information for evaluation of the applied techniques. Single-pass interferometric SAR (InSAR) data were used to extract ice topography including hummocks and ice islands. Shape from shading and height from shadow techniques enable us to extract ice topography relying on a single image. A new method for identification of EIFs in sea ice based on the thermal infrared band of Landsat 8 was introduced. The performance of the methods for ice feature height estimation was evaluated by comparing with a stereo or InSAR digital elevation models (DEMs). Full polarimetric RADARSAT-2 data were demonstrated to be useful for identification of ice islands. Full article
(This article belongs to the Special Issue Recent Advances in Sea Ice Research Using Satellite Data)
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