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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 19891

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Dr. Linlin Xu

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Guest Editor

Vision and Image Processing Laboratory, University of Waterloo, Waterloo, ON, Canada
Interests: computer vision; image segmentation/classification; remote sensing; stochastic models; sea ice
Special Issues, Collections and Topics in MDPI journals

Dr. Xinwei Chen

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Guest Editor

School of Marine Science and Engineering, South China University of Technology, 777 Xingyedadao East Rd, Guangzhou 511400, China
Interests: remote sensing; synthetic aperture radar; ocean remote sensing; image processing; machine learnig; computer vision
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Dr. Qingyun Yan

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Guest Editor

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
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Dr. Juha Karvonen

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Guest Editor

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.

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Related Special Issue

Recent Advances in Sea Ice Research Using Satellite Data (Second Edition) in Remote Sensing (1 article)

Published Papers (11 papers)

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17 pages, 3336 KiB

Open AccessArticle

Sea Ice Detection from GNSS-R Data Based on Local Linear Embedding

by Yuan Hu, Xifan Hua, Qingyun Yan, Wei Liu, Zhihao Jiang and Jens Wickert

Remote Sens. 2024, 16(14), 2621; https://doi.org/10.3390/rs16142621 - 17 Jul 2024

Cited by 2 | Viewed by 1245

Abstract

Sea ice plays a critical role in the Earth’s climate system, and its variations affect ecosystem stability. This study introduces a novel method for detecting sea ice in the Arctic Ocean using bidirectional radar reflections from the Global Navigation Satellite System (GNSS). Utilizing delay-Doppler maps (DDM) from the UK TechDemoSat-1 (TDS-1) satellite mission and surface data from the U.S. National Oceanic and Atmospheric Administration (NOAA), we employ the local linear embedding (LLE) algorithm for feature extraction. This approach notably reduces training costs and enhances real-time performance, while maintaining a high accuracy and robust noise immunity level. Focusing on the region above 70° north latitude throughout 2018, we aimed to distinguish between sea ice and seawater. The extracted DDM features via LLE are input into a support vector machine (SVM) for classification. The results indicate that our method achieves an accuracy of over 99% for selected low-noise data and a monthly average accuracy of 92.74% for data containing noise, while the CNN method has a monthly average accuracy of only 77.31% for noisy data. A comparative analysis between the LLE-SVM approach and the convolutional neural network (CNN) method demonstrated the superior anti-interference capabilities of the former. Additionally, the impact of the sea ice melting period on detection accuracy was analyzed. Full article

(This article belongs to the Special Issue Recent Advances in Sea Ice Research Using Satellite Data)

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24 pages, 15151 KiB

Open AccessArticle

Polar Sea Ice Monitoring Using HY-2B Satellite Scatterometer and Scanning Microwave Radiometer Measurements

by Tao Zeng, Lijian Shi, Yingni Shi, Dunwang Lu and Qimao Wang

Remote Sens. 2024, 16(13), 2486; https://doi.org/10.3390/rs16132486 - 6 Jul 2024

Viewed by 1492

Abstract

The Ku band microwave scatterometer (SCA) and scanning microwave radiometer (SMR) onboard HaiYang-2B (HY-2B) can simultaneously supply active and passive microwave observations over the polar region. In this paper, a polar ice water discrimination model and Arctic sea-ice-type classification model based on the support vector machine (SVM) method were established and used to produce a daily sea ice extent dataset from 2019 to 2021 with data from SCA and SMR. First, suitable scattering and radiation parameters are chosen as input data for the discriminant model. Then, the sea ice extent was obtained based on the monthly ice water discrimination model, and finally, the ice over the Arctic was classified into multiyear ice (MYI) and first-year ice (FYI). The 3-year ice extent and MYI extent products were consistent with the similar results of the National Snow and Ice Data Center (NSIDC) and Ocean and Sea Ice Satellite Application Facility (OSISAF). Using the OSISAF similar product as validation data, the overall accuracies (OAs) of ice/water discrimination and FYI/MYI discrimination are 99% and 97%, respectively. Compared with the high spatial resolution classification results of the Moderate Resolution Imaging Spectroradiometer (MODIS) and SAR, the OAs of ice/water discrimination and FYI/MYI discrimination are 96% and 86%, respectively. In conclusion, the SAC and SMR of HY-2B have been verified for monitoring polar sea ice, and the sea ice extent and sea-ice-type products are promising for integration into long-term sea ice records. Full article

(This article belongs to the Special Issue Recent Advances in Sea Ice Research Using Satellite Data)

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27 pages, 1138 KiB

Open AccessArticle

A Quantile-Conserving Ensemble Filter Based on Kernel-Density Estimation

by Ian Grooms and Christopher Riedel

Remote Sens. 2024, 16(13), 2377; https://doi.org/10.3390/rs16132377 - 28 Jun 2024

Cited by 1 | Viewed by 1046

Abstract

Ensemble Kalman filters are an efficient class of algorithms for large-scale ensemble data assimilation, but their performance is limited by their underlying Gaussian approximation. A two-step framework for ensemble data assimilation allows this approximation to be relaxed: The first step updates the ensemble in observation space, while the second step regresses the observation state update back to the state variables. This paper develops a new quantile-conserving ensemble filter based on kernel-density estimation and quadrature for the scalar first step of the two-step framework. It is shown to perform well in idealized non-Gaussian problems, as well as in an idealized model of assimilating observations of sea-ice concentration. Full article

(This article belongs to the Special Issue Recent Advances in Sea Ice Research Using Satellite Data)

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18 pages, 31707 KiB

Open AccessArticle

IceGCN: An Interactive Sea Ice Classification Pipeline for SAR Imagery Based on Graph Convolutional Network

by Mingzhe Jiang, Xinwei Chen, Linlin Xu and David A. Clausi

Remote Sens. 2024, 16(13), 2301; https://doi.org/10.3390/rs16132301 - 24 Jun 2024

Cited by 2 | Viewed by 1463

Abstract

Monitoring sea ice in the Arctic region is crucial for polar maritime activities. The Canadian Ice Service (CIS) wants to augment its manual interpretation with machine learning-based approaches due to the increasing data volume received from newly launched synthetic aperture radar (SAR) satellites. However, fully supervised machine learning models require large training datasets, which are usually limited in the sea ice classification field. To address this issue, we propose a semi-supervised interactive system to classify sea ice in dual-pol RADARSAT-2 imagery using limited training samples. First, the SAR image is oversegmented into homogeneous regions. Then, a graph is constructed based on the segmentation results, and the feature set of each node is characterized by a convolutional neural network. Finally, a graph convolutional network (GCN) is employed to classify the whole graph using limited labeled nodes automatically. The proposed method is evaluated on a published dataset. Compared with referenced algorithms, this new method outperforms in both qualitative and quantitative aspects. Full article

(This article belongs to the Special Issue Recent Advances in Sea Ice Research Using Satellite Data)

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19 pages, 2043 KiB

Open AccessArticle

Arctic Thin Ice Detection Using AMSR2 and FY-3C MWRI Radiometer Data

by Marko Mäkynen and Markku Similä

Remote Sens. 2024, 16(9), 1600; https://doi.org/10.3390/rs16091600 - 30 Apr 2024

Cited by 1 | Viewed by 1245

Abstract

Thin ice with a thickness of less than half a meter produces strong salt and heat fluxes which affect deep water circulation and weather in the polar oceans. The identification of thin ice areas is essential for ship navigation. We have developed thin ice detection algorithms for the AMSR2 and FY-3C MWRI radiometer data over the Arctic Ocean. Thin ice (<20 cm) is detected based on the classification of the H-polarization 89–36-GHz gradient ratio (GR8936H) and the 36-GHz polarization ratio (PR36) signatures with a linear discriminant analysis (LDA) and thick ice restoration with GR3610H. The brightness temperature (

T_{B}

) data are corrected for the atmospheric effects following an EUMETSAT OSI SAF correction method in sea ice concentration retrieval algorithms. The thin ice detection algorithms were trained and validated using MODIS ice thickness charts covering the Barents and Kara Seas. Thin ice detection is applied to swath

T_{B}

datasets and the swath charts are compiled into a daily thin ice chart using 10 km pixel size for AMSR2 and 20 km for MWRI. On average, the likelihood of misclassifying thick ice as thin in the ATIDA2 daily charts is 7.0% and 42% for reverse misclassification. For the MWRI chart, these accuracy figures are 4% and 53%. A comparison of the MWRI chart to the AMSR2 chart showed a very high match (98%) for the thick ice class with SIC > 90% but only a 53% match for the thin ice class. These accuracy disagreements are due to the much coarser resolution of MWRI, which gives larger spatial averaging of

T_{B}

signatures, and thus, less detection of thin ice. The comparison of the AMSR2 and MWRI charts with the SMOS sea ice thickness chart showed a rough match in the thin ice versus thick ice classification. The AMSR2 and MWRI daily thin ice charts aim to complement SAR data for various sea ice classification tasks. Full article

(This article belongs to the Special Issue Recent Advances in Sea Ice Research Using Satellite Data)

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25 pages, 4552 KiB

Open AccessArticle

Sea Ice Detection from RADARSAT-2 Quad-Polarization SAR Imagery Based on Co- and Cross-Polarization Ratio

by Li Zhao, Tao Xie, William Perrie and Jingsong Yang

Remote Sens. 2024, 16(3), 515; https://doi.org/10.3390/rs16030515 - 29 Jan 2024

Cited by 5 | Viewed by 2958

Abstract

Arctic sea ice detection is very important in global climate research, Arctic ecosystem protection, ship navigation and human activities. In this paper, by combining the co-pol ratio (HH/VV) and two kinds of cross-pol ratio (HV/VV, HV/HH), a novel sea ice detection method is proposed based on RADARSAT-2 quad-polarization synthetic aperture radar (SAR) images. Experimental results suggest that the co-pol ratio shows promising capability in sea ice detection at a wide range of incidence angles (25–50°), while the two kinds of cross-pol ratio are more applicable to sea ice detection at small incidence angles (20–35°). When incidence angles exceed 35°, wind conditions have a great effect on the performance of the cross-pol ratio. Our method is validated by comparison with the visual interpretation results. The overall accuracy is 96%, far higher than that of single polarization ratio (PR) parameter-based methods. Our method is suitable for sea ice detection in complex sea ice and wind conditions. Full article

(This article belongs to the Special Issue Recent Advances in Sea Ice Research Using Satellite Data)

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18 pages, 5749 KiB

Open AccessArticle

Self-Attention Convolutional Long Short-Term Memory for Short-Term Arctic Sea Ice Motion Prediction Using Advanced Microwave Scanning Radiometer Earth Observing System 36.5 GHz Data

by Dengyan Zhong, Na Liu, Lei Yang, Lina Lin and Hongxia Chen

Remote Sens. 2023, 15(23), 5437; https://doi.org/10.3390/rs15235437 - 21 Nov 2023

Cited by 1 | Viewed by 1883

Abstract

Over the past four decades, Arctic sea ice coverage has steadily declined. This loss of sea ice has amplified solar radiation and heat absorption from the ocean, exacerbating both polar ice loss and global warming. It has also accelerated changes in sea ice movement, posing safety risks for ship navigation. In recent years, numerical prediction models have dominated the field of sea ice movement prediction. However, these models often rely on extensive data sources, which can be limited in specific time periods or regions, reducing their applicability. This study introduces a novel approach for predicting Arctic sea ice motion within a 10-day window. We employ a Self-Attention ConvLSTM deep learning network based on single-source data, specifically optical flow derived from the Advanced Microwave Scanning Radiometer Earth Observing System 36.5 GHz data, covering the entire Arctic region. Upon verification, our method shows a reduction of 0.80 to 1.18 km in average mean absolute error over a 10-day period when compared to ConvLSTM, demonstrating its improved ability to capture the spatiotemporal correlation of sea ice motion vector fields and provide accurate predictions. Full article

(This article belongs to the Special Issue Recent Advances in Sea Ice Research Using Satellite Data)

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17 pages, 5517 KiB

Open AccessArticle

Sea Ice Detection from GNSS-R Data Based on Residual Network

by Yuan Hu, Xifan Hua, Wei Liu and Jens Wickert

Remote Sens. 2023, 15(18), 4477; https://doi.org/10.3390/rs15184477 - 12 Sep 2023

Cited by 4 | Viewed by 2409

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 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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23 pages, 11802 KiB

Open AccessArticle

Satellite-Based Identification and Characterization of Extreme Ice Features: Hummocks and Ice Islands

by Igor Zakharov, Pradeep Bobby, Desmond Power, Sherry Warren and Mark Howell

Remote Sens. 2023, 15(16), 4065; https://doi.org/10.3390/rs15164065 - 17 Aug 2023

Cited by 3 | Viewed by 1634

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) 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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10 pages, 2076 KiB

Open AccessTechnical Note

Sea Ice Detection Method Using the Dependence of the Radar Cross-Section on the Incidence Angle

by Maria Panfilova and Vladimir Karaev

Remote Sens. 2024, 16(5), 859; https://doi.org/10.3390/rs16050859 - 29 Feb 2024

Cited by 1 | Viewed by 1113

Abstract

The method for sea ice detection using the data from the Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) satellite data is suggested. The approach is based on the analysis of the shape of normalized radar cross-section dependence on the incidence angle. The coefficient of kurtosis of surface slopes probability density function is introduced as a parameter to distinguish between open water and ice cover. The approach was validated using the data on sea ice concentration from the AMSR-2 radiometer in the Antarctic region. Full article

(This article belongs to the Special Issue Recent Advances in Sea Ice Research Using Satellite Data)

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14 pages, 2509 KiB

Open AccessTechnical Note

Estimating Early Summer Snow Depth on Sea Ice Using a Radiative Transfer Model and Optical Satellite Data

by Mingfeng Wang and Natascha Oppelt

Remote Sens. 2023, 15(20), 5016; https://doi.org/10.3390/rs15205016 - 18 Oct 2023

Cited by 1 | Viewed by 1766

Abstract

Sea ice regulates the overall energy exchange and radiation budget of the Arctic region, and understanding this relationship requires an accurate determination of snow depth. However, methods for deriving snow depth have a large error through the annual winter and early spring periods due to the potential complexity of surface melting during early summer. In this study, we explore the potential of retrieving snow depth during the early summer using optical satellite imagery of the sea-ice cover. Measurements using VIS/IR (visible and infrared) usually feature much higher spatial resolution than L-band satellite data and can provide additional surface melting and leads information; in addition, considering the snow grain size–snow surface temperature interaction, there is co-variability between the observed sea-ice surface broadband albedo using an optical satellite sensor, the sea-ice surface temperature, and the retrieval target of snow depth on the spatial scale of optical imagery samples. We applied a surface classification procedure to optical satellite imagery and introduce an approach to derive snow depth from optical satellite imagery and ice surface temperature data using two solar radiation transfer models: the Delta-Eddington solar radiation model, which is the shortwave radiative scheme of the Los Alamos sea-ice model, and a simplified snow albedo scheme, which is tuned to the observational data of buoys. The snow depth was inversed from the model simulation results using a lookup-table-based method. For comparison with the observational data, using the Delta-Eddington solar radiation model, about 55% of the differences are below 5 cm, and thicker snowpack has a larger bias; using the simplified snow albedo scheme, a mean difference of 4.1 cm between retrieval and measurements was found, with 93% of the differences being smaller than 5 cm. This approach can be applied to optical satellite imagery acquired under clear-sky conditions and can serve as an addition to overcome the limitations of existing methods. Full article

(This article belongs to the Special Issue Recent Advances in Sea Ice Research Using Satellite Data)

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