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Remote Sensing of Sea Ice and Icebergs

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (10 September 2021) | Viewed by 35570

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Special Issue Editors

Dr. Juha Karvonen

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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

Dr. Anton Korosov

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Nansen Environmental and Remote Sensing Center, Thormøhlens Gate 47, N-5006 Bergen, Norway
Interests: sea ice and land ice remote sensing oceanography; remote sensing; SAR

Special Issue Information

Dear Colleagues,

Climate change and changing local ice conditions have increased the need for reliable, accurate, and timely ice information as well as the need for time series (history) of the ice conditions. The time series information is useful and necessary, e.g., for designing and building coastal and off-shore constructions and ships for ice conditions. Longer-term ice information can also be used in the optimization and planning of ship routing and ice breaking operations. Accurate ice data can also be used in assimilation for the models forecasting weather and ice conditions, both of which are extremely necessary for all off-shore and coastal activities in ice-infected areas.

Today, a wide range of earth observation (EO) instruments capable of measuring different parameters (e.g., sea ice concentration, sea ice type, sea ice thickness, sea ice drift, and icebergs) related to sea ice in all weather and lighting conditions exist. Examples of instruments well suited to sea ice monitoring are, e.g., SAR, microwave radiometers, and altimeters. Every day, a huge amount of data is measured, and the full potential of these data is not being exploited. There is continuous need for temporal and spatial high-resolution accurate sea ice information in many sectors. To meet these needs, new algorithms and methods efficiently utilizing the available and coming EO data are required. Data fusion from multiple EO instruments and methods related to ice dynamics and multi-temporal analysis are currently being explored. Additionally, distinguishing between snow cover on sea ice and sea ice to yield more accurate sea ice thickness estimates is an important topic requiring a fresh perspective.

We would like to invite you to contribute to this Special Issue of Remote Sensing by submitting original manuscripts, experimental work, and/or reviews in the field of remote sensing of sea ice (the estimation of sea ice parameters based on EO data and sea ice classification) and the detection and classification icebergs. The main goal of this Special Issue is to increase the level of knowledge on these topics, and thus enable more accurate and reliable automated (operational) sea ice observations based on EO data. Especially, such topics as data fusion in sea ice remote sensing; ice dynamics and the derivation of ice parameters (such as ice deformation, openings of ice) from ice dynamics; more reliable detection and classification of icebergs, especially within sea ice; and the estimation of snow cover on sea ice are of special interest to improve current sea ice observation systems.

Dr. Juha Karvonen
Dr. Anton Korosov

Guest Editor

Manuscript Submission Information

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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.

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Keywords

Sea ice
Icebergs
Data fusion
Snow on sea ice
Ice dynamics
Multitemporal analysis
Sea ice parameter estimation
Sea ice classification

Published Papers (12 papers)

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Research

18 pages, 3339 KiB

Open AccessArticle

Physiographic Controls on Landfast Ice Variability from 20 Years of Maximum Extents across the Northwest Canadian Arctic

by Eleanor E. Wratten, Sarah W. Cooley, Paul J. Mann, Dustin Whalen, Paul Fraser and Michael Lim

Remote Sens. 2022, 14(9), 2175; https://doi.org/10.3390/rs14092175 - 30 Apr 2022

Cited by 2 | Viewed by 2627

Abstract

Landfast ice is a defining feature among Arctic coasts, providing a critical transport route for communities and exerting control over the exposure of Arctic coasts to marine erosion processes. Despite its significance, there remains a paucity of data on the spatial variability of landfast ice and limited understanding of the environmental processes’ controls since the beginning of the 21st century. We present a new high spatiotemporal record (2000–2019) across the Northwest Canadian Arctic, using MODIS Terra satellite imagery to determine maximum landfast ice extent (MLIE) at the start of each melt season. Average MLIE across the Northwest Canadian Arctic declined by 73% in a direct comparison between the first and last year of the study period, but this was highly variable across regional to community scales, ranging from 14% around North Banks Island to 81% in the Amundsen Gulf. The variability was largely a reflection of 5–8-year cycles between landfast ice rich and poor periods with no discernible trend in MLIE. Interannual variability over the 20-year record of MLIE extent was more constrained across open, relatively uniform, and shallower sloping coastlines such as West Banks Island, in contrast with a more varied pattern across the numerous bays, headlands, and straits enclosed within the deep Amundsen Gulf. Static physiographic controls (namely, topography and bathymetry) were found to influence MLIE change across regional sites, but no association was found with dynamic environmental controls (storm duration, mean air temperature, and freezing and thawing degree day occurrence). For example, despite an exponential increase in storm duration from 2014 to 2019 (from 30 h to 140 h or a 350% increase) across the Mackenzie Delta, MLIE extents remained relatively consistent. Mean air temperatures and freezing and thawing degree day occurrences (over 1, 3, and 12-month periods) also reflected progressive northwards warming influences over the last two decades, but none showed a statistically significant relationship with MLIE interannual variability. These results indicate inferences of landfast ice variations commonly taken from wider sea ice trends may misrepresent more complex and variable sensitivity to process controls. The influences of different physiographic coastal settings need to be considered at process level scales to adequately account for community impacts and decision making or coastal erosion exposure. Full article

(This article belongs to the Special Issue Remote Sensing of Sea Ice and Icebergs)

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18 pages, 4457 KiB

Open AccessArticle

Long-Term Ice Conditions in Yingkou, a Coastal Region Northeast of the Bohai Sea, between 1951/1952 and 2017/2018: Modeling and Observations

by Yuxian Ma, Bin Cheng, Ning Xu, Shuai Yuan, Honghua Shi and Wenqi Shi

Remote Sens. 2022, 14(1), 182; https://doi.org/10.3390/rs14010182 - 1 Jan 2022

Cited by 10 | Viewed by 1584

Abstract

Bohai Sea ice creates obstacles for maritime navigation and offshore activities. A better understanding of ice conditions is valuable for sea-ice management. The evolution of 67 years of seasonal ice thickness in a coastal region (Yingkou) in the Northeast Bohai Sea was simulated by using a snow/ice thermodynamic model, using local weather-station data. The model was first validated by using seasonal ice observations from field campaigns and a coastal radar (the season of 2017/2018). The model simulated seasonal ice evolution well, particularly ice growth. We found that the winter seasonal mean air temperature in Yingkou increased by 0.33 °C/decade slightly higher than air temperature increase (0.27 °C/decade) around Bohai Sea. The decreasing wind-speed trend (0.05 m/s perdecade) was a lot weaker than that averaged (0.3 m/s per decade) between the early 1970s and 2010s around the entire Bohai Sea. The multi-decadal ice-mass balance revealed decreasing trends of the maximum and average ice thickness of 2.6 and 0.8 cm/decade, respectively. The length of the ice season was shortened by 3.7 days/decade, and ice breakup dates were advanced by 2.3 days/decade. All trends were statistically significant. The modeled seasonal maximum ice thickness is highly correlated (0.83, p < 0.001) with the Bohai Sea Ice Index (BoSI) used to quantify the severity of the Bohai Sea ice condition. The freezing-up date, however, showed a large interannual variation without a clear trend. The simulations indicated that Bohai ice thickness has grown continuously thinner since 1951/1952. The time to reach 0.15 m level ice was delayed from 3 January to 21 January, and the ending time advanced from 6 March to 19 February. There was a significant weakening of ice conditions in the 1990s, followed by some recovery in 2000s. The relationship between large-scale climate indices and ice condition suggested that the AO and NAO are strongly correlated with interannual changes in sea-ice thickness in the Yingkou region. Full article

(This article belongs to the Special Issue Remote Sensing of Sea Ice and Icebergs)

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51 pages, 15859 KiB

Open AccessFeature PaperArticle

Remote Sensing of the Polar Ice Zones with HF Radar

by Stuart Anderson

Remote Sens. 2021, 13(21), 4398; https://doi.org/10.3390/rs13214398 - 31 Oct 2021

Cited by 4 | Viewed by 2879

Abstract

Radars operating in the HF band are widely used for over-the-horizon remote sensing of ocean surface conditions, ionospheric studies and the monitoring of ship and aircraft traffic. Several hundreds of such radars are in operation, yet only a handful of experiments have been conducted to assess the prospect of utilizing this technology for the remote sensing of sea ice. Even then, the measurements carried out have addressed only the most basic questions: is there ice present, and can we measure its drift? Recently the theory that describes HF scattering from the dynamic sea surface was extended to handle situations where an ice cover is present. With this new tool, it becomes feasible to interpret the corresponding radar echoes in terms of the structural, mechanical, and electrical properties of the ice field. In this paper we look briefly at ice sensing from space-borne sensors before showing how the persistent and synoptic wide area surveillance capabilities of HF radar offer an alternative. The dispersion relations of different forms of sea ice are examined and used in a modified implementation of the electromagnetic scattering theory employed in HF radar oceanography to compute the corresponding radar signatures. Previous and present-day HF radar deployments at high latitudes are reviewed, noting the physical and technical challenges that confront the implementation of an operational HF radar in its ice monitoring capability. Full article

(This article belongs to the Special Issue Remote Sensing of Sea Ice and Icebergs)

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20 pages, 7058 KiB

Open AccessArticle

Two-Stream Convolutional Long- and Short-Term Memory Model Using Perceptual Loss for Sequence-to-Sequence Arctic Sea Ice Prediction

by Junhwa Chi, Jihyun Bae and Young-Joo Kwon

Remote Sens. 2021, 13(17), 3413; https://doi.org/10.3390/rs13173413 - 27 Aug 2021

Cited by 11 | Viewed by 2643

Abstract

Arctic sea ice plays a significant role in climate systems, and its prediction is important for coping with global warming. Artificial intelligence (AI) has gained recent attention in various disciplines with the increasing use of big data. In recent years, the use of AI-based sea ice prediction, along with conventional prediction models, has drawn attention. This study proposes a new deep learning (DL)-based Arctic sea ice prediction model with a new perceptual loss function to improve both statistical and visual accuracy. The proposed DL model learned spatiotemporal characteristics of Arctic sea ice for sequence-to-sequence predictions. The convolutional neural network-based perceptual loss function successfully captured unique sea ice patterns, and the widely used loss functions could not use various feature maps. Furthermore, the input variables that are essential to accurately predict Arctic sea ice using various combinations of input variables were identified. The proposed approaches produced statistical outcomes with better accuracy and qualitative agreements with the observed data. Full article

(This article belongs to the Special Issue Remote Sensing of Sea Ice and Icebergs)

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27 pages, 7322 KiB

Open AccessArticle

Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKa

by Renée Mie Fredensborg Hansen, Eero Rinne and Henriette Skourup

Remote Sens. 2021, 13(16), 3183; https://doi.org/10.3390/rs13163183 - 11 Aug 2021

Cited by 6 | Viewed by 2746

Abstract

An important step in the sea ice freeboard to thickness conversion is the classification of sea ice types, since the ice type affects the snow depth and ice density. Studies using Ku-band CryoSat-2 have shown promise in distinguishing FYI and MYI based on the parametrisation of the radar echo. Here, we investigate applying the same classification algorithms that have shown success for Ku-band measurements to measurements acquired by SARAL/AltiKa at the Ka-band. Four different classifiers are investigated, i.e., the threshold-based, Bayesian, Random Forest (RF) and k-nearest neighbour (KNN), by using data from five 35 day cycles during Arctic mid-winter in 2014–2018. The overall classification performance shows the highest accuracy of 93% for FYI (Bayesian classifier) and 39% for MYI (threshold-based classifier). For all classification algorithms, more than half of the MYI cover falsely classifies as FYI, showing the difference in the surface characteristics attainable by Ka-band compared to Ku-band due to different scattering mechanisms. However, high overall classification performance (above 90%) is estimated for FYI for three supervised classifiers (KNN, RF and Bayesian). Furthermore, the leading-edge width parameter shows potential in discriminating open water (ocean) and sea ice when visually compared with reference data. Our results encourage the use of waveform parameters in the further validation of sea ice/open water edges and discrimination of sea ice types combining Ka- and Ku-band, especially with the planned launch of the dual-frequency altimeter mission Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) in 2027. Full article

(This article belongs to the Special Issue Remote Sensing of Sea Ice and Icebergs)

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19 pages, 29320 KiB

Open AccessArticle

RUF: Effective Sea Ice Floe Segmentation Using End-to-End RES-UNET-CRF with Dual Loss

by Anmol Sharan Nagi, Devinder Kumar, Daniel Sola and K. Andrea Scott

Remote Sens. 2021, 13(13), 2460; https://doi.org/10.3390/rs13132460 - 24 Jun 2021

Cited by 21 | Viewed by 3216

Abstract

Sea ice observations through satellite imaging have led to advancements in environmental research, ship navigation, and ice hazard forecasting in cold regions. Machine learning and, recently, deep learning techniques are being explored by various researchers to process vast amounts of Synthetic Aperture Radar (SAR) data for detecting potential hazards in navigational routes. Detection of hazards such as sea ice floes in Marginal Ice Zones (MIZs) is quite challenging as the floes are often embedded in a multiscale ice cover composed of ice filaments and eddies in addition to floes. This study proposes a segmentation model tailored for detecting ice floes in SAR images. The model exploits the advantages of both convolutional neural networks and convolutional conditional random field (Conv-CRF) in a combined manner. The residual UNET (RES-UNET) computes expressive features to generate coarse segmentation maps while the Conv-CRF exploits the spatial co-occurrence pairwise potentials along with the RES-UNET unary/segmentation maps to generate final predictions. The whole pipeline is trained end-to-end using a dual loss function. This dual loss function is composed of a weighted average of binary cross entropy and soft dice loss. The comparison of experimental results with the conventional segmentation networks such as UNET, DeepLabV3, and FCN-8 demonstrates the effectiveness of the proposed architecture. Full article

(This article belongs to the Special Issue Remote Sensing of Sea Ice and Icebergs)

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21 pages, 3359 KiB

Open AccessArticle

Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with the PCA Network

by Yanling Han, Xi Shi, Shuhu Yang, Yun Zhang, Zhonghua Hong and Ruyan Zhou

Remote Sens. 2021, 13(12), 2253; https://doi.org/10.3390/rs13122253 - 9 Jun 2021

Cited by 10 | Viewed by 2095

Abstract

Sea ice is one of the most prominent causes of marine disasters occurring at high latitudes. The detection of sea ice is particularly important, and the classification of sea ice images is an important part of sea ice detection. Traditional sea ice classification based on optical remote sensing mostly uses spectral information only and does not fully extract rich spectral and spatial information from sea ice images. At the same time, it is difficult to obtain samples and the resulting small sample sizes used in sea ice classification has limited the improvement of classification accuracy to a certain extent. In response to the above problems, this paper proposes a hyperspectral sea ice image classification method involving spectral-spatial-joint features based on the principal component analysis (PCA) network. First, the method uses the gray-level co-occurrence matrix (GLCM) and Gabor filter to extract textural and spatial information about sea ice. Then, the optimal band combination is extracted with a band selection algorithm based on a hybrid strategy, and the information hidden in the sea ice image is deeply extracted through a fusion of spectral and spatial features. Then, the PCA network is designed based on principal component analysis filters in order to extract the depth features of sea ice more effectively, and hash binarization maps and block histograms are used to enhance the separation and reduce the dimensions of features. Finally, the low-level features in the data form more abstract and invariant high-level features for sea ice classification. In order to verify the effectiveness of the proposed method, we conducted experiments on two different data collection points in Bohai Bay and Baffin Bay. The experimental results show that, compared with other single feature and spectral-spatial-joint feature algorithms, the proposed method achieves better sea ice classification results (94.15% and 96.86%) by using fewer training samples and a shorter training time. Full article

(This article belongs to the Special Issue Remote Sensing of Sea Ice and Icebergs)

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16 pages, 36858 KiB

Open AccessArticle

Sea Ice Thickness Retrieval Based on GOCI Remote Sensing Data: A Case Study

by Fengguan Gu, Rui Zhang, Xiangshan Tian-Kunze, Bo Han, Lei Zhu, Tingwei Cui and Qinghua Yang

Remote Sens. 2021, 13(5), 936; https://doi.org/10.3390/rs13050936 - 3 Mar 2021

Cited by 2 | Viewed by 2474

Abstract

The accurate monitoring and measurement of sea ice thickness (SIT) is crucial for understanding climate change and preventing economic losses caused by sea ice disasters near coastal regions. In this study, a new method is developed to retrieve the SIT in Liaodong Bay (LDB) based on the Rayleigh-corrected reflectance from Geostationary Ocean Color Imager (GOCI) images in the winters of 2012 and 2013. Compared with previously developed SIT retrieval methods (e.g., the method based on the thermodynamic principle of sea ice) using remote sensing data, our method has significant advantages with respect to the inversion accuracy (achieving retrieval skill scores as high as 0.86) and spatiotemporal resolution. Moreover, there is no significant increase in the computational cost with this method, which makes the method suitable for operational SIT retrieval in the global ocean. Full article

(This article belongs to the Special Issue Remote Sensing of Sea Ice and Icebergs)

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20 pages, 5659 KiB

Open AccessArticle

Tenuous Correlation between Snow Depth or Sea Ice Thickness and C- or X-Band Backscattering in Nunavik Fjords of the Hudson Strait

by Sophie Dufour-Beauséjour, Monique Bernier, Jérome Simon, Saeid Homayouni, Véronique Gilbert, Yves Gauthier, Juupi Tuniq, Anna Wendleder and Achim Roth

Remote Sens. 2021, 13(4), 768; https://doi.org/10.3390/rs13040768 - 19 Feb 2021

Cited by 1 | Viewed by 3199

Abstract

Radar penetration in brine-wetted snow-covered sea ice is almost nil, yet reports exist of a correlation between snow depth or ice thickness and SAR parameters. This article presents a description of snow depth and first-year sea ice thickness distributions in three fjords of the Hudson Strait and of their tenuous correlation with SAR backscattering in the C- and X-band. Snow depth and ice thickness were directly measured in three fjords of the Hudson Strait from 2015 to 2018 in April or May. Bayesian linear regression analysis was used to investigate their relationship with RADARSAT-2 (C-band) or TerraSAR-X (X-band). Polarimetric ratios and the Cloude–Pottier decomposition parameters were explored along with the HH, HV and VV bands. Linear correlations were generally no higher than 0.3 except for a special case in May 2018. The co-polarization ratio did not perform better than the backscattering coefficients. Full article

(This article belongs to the Special Issue Remote Sensing of Sea Ice and Icebergs)

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17 pages, 3302 KiB

Open AccessArticle

Sea Ice Image Classification Based on Heterogeneous Data Fusion and Deep Learning

by Yanling Han, Yekun Liu, Zhonghua Hong, Yun Zhang, Shuhu Yang and Jing Wang

Remote Sens. 2021, 13(4), 592; https://doi.org/10.3390/rs13040592 - 7 Feb 2021

Cited by 35 | Viewed by 4178

Abstract

Sea ice is one of the typical causes of marine disasters. Sea ice image classification is an important component of sea ice detection. Optical data contain rich spectral information, but they do not allow one to easily distinguish between ground objects with a similar spectrum and foreign objects with the same spectrum. Synthetic aperture radar (SAR) data contain rich texture information, but the data usually have a single source. The limitation of single-source data is that they do not allow for further improvements of the accuracy of remote sensing sea ice classification. In this paper, we propose a method for sea ice image classification based on deep learning and heterogeneous data fusion. Utilizing the advantages of convolutional neural networks (CNNs) in terms of depth feature extraction, we designed a deep learning network structure for SAR and optical images and achieve sea ice image classification through feature extraction and a feature-level fusion of heterogeneous data. For the SAR images, the improved spatial pyramid pooling (SPP) network was used and texture information on sea ice at different scales was extracted by depth. For the optical data, multi-level feature information on sea ice such as spatial and spectral information on different types of sea ice was extracted through a path aggregation network (PANet), which enabled low-level features to be fully utilized due to the gradual feature extraction of the convolution neural network. In order to verify the effectiveness of the method, two sets of heterogeneous sentinel satellite data were used for sea ice classification in the Hudson Bay area. The experimental results show that compared with the typical image classification methods and other heterogeneous data fusion methods, the method proposed in this paper fully integrates multi-scale and multi-level texture and spectral information from heterogeneous data and achieves a better classification effect (96.61%, 95.69%). Full article

(This article belongs to the Special Issue Remote Sensing of Sea Ice and Icebergs)

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19 pages, 1741 KiB

Open AccessArticle

Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification

by Johannes Lohse, Anthony P. Doulgeris and Wolfgang Dierking

Remote Sens. 2021, 13(4), 552; https://doi.org/10.3390/rs13040552 - 4 Feb 2021

Cited by 15 | Viewed by 3794

Abstract

Robust and reliable classification of sea ice types in synthetic aperture radar (SAR) images is needed for various operational and environmental applications. Previous studies have investigated the class-dependent decrease in SAR backscatter intensity with incident angle (IA); others have shown the potential of textural information to improve automated image classification. In this work, we investigate the inclusion of Sentinel-1 (S1) texture features into a Bayesian classifier that accounts for linear per-class variation of its features with IA. We use the S1 extra-wide swath (EW) product in ground-range detected format at medium resolution (GRDM), and we compute seven grey level co-occurrence matrix (GLCM) texture features from the HH and the HV backscatter intensity in the linear and logarithmic domain. While GLCM texture features obtained in the linear domain vary significantly with IA, the features computed from the logarithmic intensity do not depend on IA or reveal only a weak, approximately linear dependency. They can therefore be directly included in the IA-sensitive classifier that assumes a linear variation. The different number of looks in the first sub-swath (EW1) of the product causes a distinct offset in texture at the sub-swath boundary between EW1 and the second sub-swath (EW2). This offset must be considered when using texture in classification; we demonstrate a manual correction for the example of GLCM contrast. Based on the Jeffries–Matusita distance between class histograms, we perform a separability analysis for 57 different GLCM parameter settings. We select a suitable combination of features for the ice classes in our data set and classify several test images using a combination of intensity and texture features. We compare the results to a classifier using only intensity. Particular improvements are achieved for the generalized separation of ice and water, as well as the classification of young ice and multi-year ice. Full article

(This article belongs to the Special Issue Remote Sensing of Sea Ice and Icebergs)

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28 pages, 7554 KiB

Open AccessFeature PaperArticle

Operational Service for Mapping the Baltic Sea Landfast Ice Properties

by Marko Mäkynen, Juha Karvonen, Bin Cheng, Mwaba Hiltunen and Patrick B. Eriksson

Remote Sens. 2020, 12(24), 4032; https://doi.org/10.3390/rs12244032 - 9 Dec 2020

Cited by 9 | Viewed by 2560

Abstract

The Baltic Sea is partly covered by sea ice in every winter season. Landfast ice (LFI) on the Baltic Sea is a place for recreational activities such as skiing and ice fishing. Over thick LFI ice roads can be established between mainland and islands to speed up transportation compared to the use of ferries. LFI also allows transportation of material to or from islands without piers for large ships. For all these activities, information on LFI extent and sea ice thickness, snow thickness and degree of ice deformation on LFI is very important. We generated new operational products for these LFI parameters based on synthetic aperture radar (SAR) imagery and existing products and prediction models on the Baltic Sea ice properties. The products are generated daily and have a 500 m pixel size. They are visualized in a web-portal titled “Baltic Sea landfast ice extent and thickness (BALFI)” which has free access. The BALFI service was started in February 2019. Before the BALFI service, information on the LFI properties in fine scale (<1 km) was not available from any single source or product. We studied the accuracy and quality of the BALFI products for the ice season 2019–2020 using ice charts and in-situ coastal ice station data. We suggest that the current products give usable information on the Baltic LFI properties for various end-users. We also identify some topics for the further development of the BALFI products. Full article

(This article belongs to the Special Issue Remote Sensing of Sea Ice and Icebergs)

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