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Remote Sensing of Polar Sea Ice

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

Deadline for manuscript submissions: closed (19 January 2024) | Viewed by 14930

Special Issue Editors


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Guest Editor
Environment and Climate Change Canada, Meteorological Research Division, Toronto, ON, Canada
Interests: cryosphere; sea ice; remote sensing; polar climate
Special Issues, Collections and Topics in MDPI journals
School of Geospatial Engineering and Science, Sun Yat-sen University, Guangzhou, China
Interests: cryosphere; sea ice; polar climate; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Polar sea ice has gone through significant changes over the past five decades in terms of extent, concentration, thickness, mobility, deformation and snow cover. This has impacted the climate not only in the polar regions but also in mid-latitude regions. A few scientific questions are still open for research. Why is Arctic warming at a rate of about 2.5 higher than the globe? Why is Antarctic sea ice not responding to climate change as Arctic ice? What are the meteorological and oceanic factors that impact and are affected by sea ice in both regions? How remote sensing can be used to generate useful sea ice information for both marine operation and climate studies in the polar regions? What is the role of ice shelves and icebergs on the dynamics of sea ice in the Antarctic region?

The Special Issue aims at addressing those and more relevant questions. It is intended to show how advanced spaceborne remote sensing tools can be used to improve monitoring polar sea ice during the past few decades, with particular focus on showing seasonal and regional variabilities. Modelling sea ice to demonstrate the future of the ice cover is another objective. Articles may address, but not limited to, the following topics:

  • Recent improvement of sea ice parameters retrieval using remote sensing;
  • Monitoring sea ice in different regimes such as marginal ice zones;
  • Assimilation of remote sensing data in numerical simulations;
  • Snow on sea ice;
  • Sea ice dynamics in relation to climate change in the Arctic;
  • Machine learning applications to remote sensing data of polar sea ice;
  • Impacts of change of Arctic sea ice on Arctic and midlatitude environment;
  • Role of meteorological and oceanic factors on the sea ice cover;
  • Geophysical processes of polar sea ice;
  • Differences of impacts of climate change on the two polar sea ice covers;
  • Role of ice shelves and icebergs on Antarctic sea ice.

Dr. Mohammed Shokr
Dr. Yufang Ye
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.

Keywords

  • polar environment
  • sea ice
  • arctic sea ice retreat
  • remote sensing
  • climate chang
  • retreat of Arctic sea ice
  • ice shelves and iceberg
  • Arctic marine navigation
  • Antarctic icebergs
  • sea ice physics

Published Papers (13 papers)

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Research

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10 pages, 3758 KiB  
Communication
Changes in the Arctic Traffic Occupancy and Their Connection to Sea Ice Conditions from 2015 to 2020
by Yihan Liu, Hao Luo, Chao Min, Qiong Chen and Qinghua Yang
Remote Sens. 2024, 16(7), 1157; https://doi.org/10.3390/rs16071157 - 26 Mar 2024
Viewed by 335
Abstract
Arctic shipping activities are increasing in the context of sea ice decline. However, research gaps persist in studying recent Arctic shipping activities across various vessel types and their connection with sea ice conditions. Utilizing Automatic Identification System (AIS) data and sea ice satellite [...] Read more.
Arctic shipping activities are increasing in the context of sea ice decline. However, research gaps persist in studying recent Arctic shipping activities across various vessel types and their connection with sea ice conditions. Utilizing Automatic Identification System (AIS) data and sea ice satellite observations between 2015 and 2020, these matters are delved into this study. A discernible overall growth trend in Arctic traffic occupancy occurs from 2015 to 2020 during summer and autumn. Excluding passenger ships, the traffic occupancy trend for each ship type closely parallels that for all ships. Variations in traffic occupancy along the Northeast Passage dominate that in the entire Arctic. As sea ice diminishes, both Arctic traffic occupancy and its variability noticeably increase. Further examination of the relationship between shipping activities and ice conditions reveals that increased traffic occupancy corresponds significantly to diminishing sea ice extent, and the constraint imposed by sea ice on Arctic traffic occupancy weakens, while the 6-year AIS data could lead to uncertainties. In summary, as the Arctic sea ice declines continuously, not only sea ice but also additional social, military, and environmental factors constraining marine activities should be considered in the future operation of Arctic shipping. Full article
(This article belongs to the Special Issue Remote Sensing of Polar Sea Ice)
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21 pages, 11869 KiB  
Article
Estimating Winter Arctic Sea Ice Motion Based on Random Forest Models
by Linxin Zhang, Qian Shi, Matti Leppäranta, Jiping Liu and Qinghua Yang
Remote Sens. 2024, 16(3), 581; https://doi.org/10.3390/rs16030581 - 03 Feb 2024
Viewed by 842
Abstract
Sea ice motion (SIM) plays a crucial role in setting the distribution of the ice cover in the Arctic. Limited by images’ spatial resolution and tracking algorithms, challenges exist in obtaining coastal sea ice motion (SIM) based on passive microwave satellite sensors. In [...] Read more.
Sea ice motion (SIM) plays a crucial role in setting the distribution of the ice cover in the Arctic. Limited by images’ spatial resolution and tracking algorithms, challenges exist in obtaining coastal sea ice motion (SIM) based on passive microwave satellite sensors. In this study, we developed a method based on random forest (RF) models to obtain Arctic SIM in winter by incorporating wind field and coastal geographic location information. These random forest models were trained using Synthetic Aperture Radar (SAR) SIM data. Our results show good consistency with SIM data retrieved from satellite imagery and buoy observations. With respect to the SAR data, compared with SIM estimated with RF model training using reanalysis surface wind, the results by additional coastal information input had a lower root mean square error (RMSE) and a higher correlation coefficient by 31% and 14% relative improvement, respectively. The latter SIM result also showed a better performance for magnitude, especially within 100 km of the coastline in the north of the Canadian Arctic Archipelago. In addition, the influence of coastline on SIM is quantified through variable importance calculation, at 22% and 28% importance of all RF variables for east and north SIM components, respectively. These results indicate the great potential of RF models for estimating SIM over the whole Arctic Ocean in winter. Full article
(This article belongs to the Special Issue Remote Sensing of Polar Sea Ice)
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19 pages, 13668 KiB  
Article
Intercomparisons and Evaluations of Satellite-Derived Arctic Sea Ice Thickness Products
by Feifan Chen, Deshuai Wang, Yu Zhang, Yi Zhou and Changsheng Chen
Remote Sens. 2024, 16(3), 508; https://doi.org/10.3390/rs16030508 - 29 Jan 2024
Viewed by 738
Abstract
Currently, Arctic sea ice thickness (SIT) data with extensive spatiotemporal coverage primarily comes from satellite observations, including CryoSat-2, Soil Moisture and Ocean Salinity (SMOS), and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). The studies of the intercomparison and evaluation of multi-source satellite [...] Read more.
Currently, Arctic sea ice thickness (SIT) data with extensive spatiotemporal coverage primarily comes from satellite observations, including CryoSat-2, Soil Moisture and Ocean Salinity (SMOS), and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). The studies of the intercomparison and evaluation of multi-source satellite products in recent years are limited. In this study, three latest version products of ICESat-2, CryoSat-2, and CS2SMOS (a merged product of CryoSat-2 and SMOS) were examined from October to April, between 2018 and 2022. Three types of observation including airborne data from the Operation IceBridge (OIB) and IceBird, and in situ data from Beaufort Gyre Exploration Project (BGEP) are selected as the reference in the evaluation. The intercomparison results show that the mean SIT is generally largest in ICESat-2, second largest in CryoSat-2, and smallest in CS2SMOS. The SIT in CryoSat-2 is closer to the SIT in ICESat-2. The thickness displayed by the three satellite products starts to increase at different freezing months, varying between October and November. The three satellite products demonstrated the strongest agreements in SIT in the Beaufort Sea and Central Arctic regions, and exhibited the most distinct differences in the Barents Sea. In the evaluation with OIB data, three satellite-derived SIT were generally underestimated and CS2SMOS demonstrates the closest match. The evaluation using IceBird data indicates an underestimation for all satellites, with CryoSat-2 showing the best agreement. In the assessment with BGEP data, ICESat-2 displayed a more pronounced degree of overestimation or underestimation compared to the other two satellites, and CS2SMOS exhibited the optimal agreement. Based on the comprehensive consideration, CS2SMOS demonstrated the best performance with the airborne and in situ observational data, followed by CryoSat-2 and ICESat-2. The intercomparison and evaluation results of satellite products can contribute to a further understanding of the accuracies and uncertainties of the latest version SIT retrieval and the appropriate selection and utilization of satellite products. Full article
(This article belongs to the Special Issue Remote Sensing of Polar Sea Ice)
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18 pages, 6201 KiB  
Article
Distribution Characteristics and Influencing Factors of Sea Ice Leads in the Weddell Sea, Antarctica
by Yueyun Wang, Qing Ji, Xiaoping Pang, Meng Qu, Mingxing Cha, Fanyi Zhang, Zhongnan Yan and Bin He
Remote Sens. 2023, 15(23), 5568; https://doi.org/10.3390/rs15235568 - 30 Nov 2023
Viewed by 648
Abstract
The characteristics of sea ice leads (SILs) in the Weddell Sea are an important basis for understanding the mechanism of the atmosphere–ocean system in the Southern Ocean. In this study, we derived the sea ice surface temperature (IST) of the Weddell Sea from [...] Read more.
The characteristics of sea ice leads (SILs) in the Weddell Sea are an important basis for understanding the mechanism of the atmosphere–ocean system in the Southern Ocean. In this study, we derived the sea ice surface temperature (IST) of the Weddell Sea from MODIS thermal images and then generated a daily SIL map for 2015 and 2022 by utilizing the iterative threshold method on the optimised MOD35 cloud-masked IST. The results showed that SIL variations in the Weddell Sea presented remarkable seasonal characteristics. The trend of the SIL area exhibited an initial rise followed by a decline from January to December, characterised by lower values in spring and summer and higher values in fall and winter. SILs in the Weddell Sea were predominantly concentrated between 70~78°S and 60~30°W. The coastal spatial distribution density of the SILs exceeded that of offshore regions, peaking near the Antarctic Peninsula and then near Queen Maud Land. The SIL variation was mainly influenced by dynamical factors, and there were strong positive correlations between the wind field, ocean currents, and sea-ice motion. Full article
(This article belongs to the Special Issue Remote Sensing of Polar Sea Ice)
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22 pages, 17520 KiB  
Article
Retrieval of Arctic Sea Ice Motion from FY-3D/MWRI Brightness Temperature Data
by Haihua Chen, Kun Ni, Jun Liu and Lele Li
Remote Sens. 2023, 15(17), 4191; https://doi.org/10.3390/rs15174191 - 25 Aug 2023
Cited by 1 | Viewed by 714
Abstract
Sea ice motion (SIM) has significant implications for sea–air interactions, thermohaline circulation, and the development of the Arctic passage. This research proposes an improved SIM retrieval method from Fengyun-3D’s (FY-3D) microwave radiometer imager’s (MWRI) brightness temperature (Tb) data based on the [...] Read more.
Sea ice motion (SIM) has significant implications for sea–air interactions, thermohaline circulation, and the development of the Arctic passage. This research proposes an improved SIM retrieval method from Fengyun-3D’s (FY-3D) microwave radiometer imager’s (MWRI) brightness temperature (Tb) data based on the modified classical maximum cross-correlation (MCC) method and the multisource data merging method. This study utilized buoy data to establish the search area range, applied distinct thresholds across various Arctic regions, and merged the buoy data, reanalysis wind data, and SIM retrieved from FY-3D/MWRI Tb data. In 2019, for the final Arctic SIM results retrieved from the MWRI 89 GHz and 36.5 GHz Tb data, the root-mean-square error (RMSE) and the mean average error (MAE) in the east–west direction were 2.07 cm/s and 1.38 cm/s and those in the north–south direction were 1.96 cm/s and 1.15 cm/s, compared to the ice-tethered profiler (ITP) data. Compared with the daily average data of the National Snow and Ice Data Center (NSIDC), the RMSE and MAE of the SIM results obtained in this study were 0.74 cm/s and 0.93 cm/s in the east–west direction, and 0.56 cm/s and 0.72 cm/s in the north–south direction, respectively. The monthly average of the SIM retrieved from the MWRI Tb data in this research also showed a good agreement with the monthly average of the NSIDC SIM product. The comparison showed that the MWRI Tb data could be used to retrieve the Arctic SIM, and the Arctic SIM retrieval method presented in this paper was accurate and general. Full article
(This article belongs to the Special Issue Remote Sensing of Polar Sea Ice)
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24 pages, 10588 KiB  
Article
Evaluation and Application of SMRT Model for L-Band Brightness Temperature Simulation in Arctic Sea Ice
by Yanfei Fan, Lele Li, Haihua Chen and Lei Guan
Remote Sens. 2023, 15(15), 3889; https://doi.org/10.3390/rs15153889 - 05 Aug 2023
Viewed by 895
Abstract
Using L-band microwave radiative transfer theory to retrieve ice and snow parameters is one of the focuses of Arctic research. At present, due to limitations of frequency and substrates, few operational microwave radiative transfer models can be used to simulate L-band brightness temperature [...] Read more.
Using L-band microwave radiative transfer theory to retrieve ice and snow parameters is one of the focuses of Arctic research. At present, due to limitations of frequency and substrates, few operational microwave radiative transfer models can be used to simulate L-band brightness temperature (TB) in Arctic sea ice. The snow microwave radiative transfer (SMRT) model, developed with the support of the European Space Agency in 2018, has been used to simulate high-frequency TB in polar regions and has obtained good results, but no studies have shown whether it can be used appropriately in the L-band. Therefore, in this study, we systematically evaluate the ability of the SMRT model to simulate L-band TB in the Arctic sea ice and snow environment, and we show that the results are significantly optimized by improving the simulation method. In this paper, we first consider the thermal insulation effect of snow by adding the thermodynamic equation, then use a reasonable salinity profile formula for multi-layer model simulation to solve the problem of excessive L-band penetration in the SMRT single-layer model, and finally add ice lead correction to resolve the large influence it has on the results. The improved SMRT model is evaluated using Operation IceBridge (OIB) data from 2012 to 2015 and compared with the snow-corrected classical L-band radiative transfer model for Arctic sea ice proposed in 2010 (KA2010). The results show that the SMRT model has better simulation results, and the correlation coefficient (R) between SMRT-simulated TB and Soil Moisture and Ocean Salinity (SMOS) satellite TB is 0.65, and the RMSE is 3.11 K. Finally, the SMRT model with the improved simulation method is applied to the whole Arctic from November 2014 to April 2015, and the simulated R is 0.63, and the RMSE is 5.22 K. The results show that the SMRT multi-layer model is feasible for simulating L-band TB in the Arctic sea ice and snow environment, which provides a basis for the retrieval of Arctic parameters. Full article
(This article belongs to the Special Issue Remote Sensing of Polar Sea Ice)
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46 pages, 7870 KiB  
Article
Examining the Consistency of Sea Surface Temperature and Sea Ice Concentration in Arctic Satellite Products
by Sandra L. Castro, Gary A. Wick, Steinar Eastwood, Michael A. Steele and Rasmus T. Tonboe
Remote Sens. 2023, 15(11), 2908; https://doi.org/10.3390/rs15112908 - 02 Jun 2023
Viewed by 1216
Abstract
Available observations and a theoretical simulation are used to explore the consistency and relationship between sea surface temperature (SST) and sea ice concentration (SIC) within open-ocean-sea ice mixed satellite pixels as a function of grid resolution. The maximum limiting SST value for a [...] Read more.
Available observations and a theoretical simulation are used to explore the consistency and relationship between sea surface temperature (SST) and sea ice concentration (SIC) within open-ocean-sea ice mixed satellite pixels as a function of grid resolution. The maximum limiting SST value for a specified SIC and spatial resolution is first examined within collocated satellite-derived products contained within existing Level 4 SST analyses distributed using the data specification from the Group for High Resolution Sea Surface Temperature. The shape of the interdependence is further validated with manually quality-controlled buoy SST and SIC collocations. A parametric equation for the limiting SST value is derived from simulations of a mixed ocean/ice pixel with specified ice fraction and a linear SST gradient extending away from the ice edge. The exponential curve matching the observed interdependence suggests a maximum 5 km pixel-averaged SST at SIC values approaching zero between 6 and 8 °C. This maximum value is significantly greater than the previously assumed limiting values of ~3 °C and the corresponding SST gradient is larger than those typically observed with satellite SST products, but agrees well with recent Saildrone SST observations near ice. The curve provides a conservative limit with which inconsistent SST/SIC pairings can be identified, not only near the ice edge but at intermediate ice concentrations. Application of the filter improves the agreement between the SST/SIC relationship in satellite products and available Saildrone observations as well as the internal consistency of the different satellite products. Full article
(This article belongs to the Special Issue Remote Sensing of Polar Sea Ice)
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18 pages, 8486 KiB  
Article
The Identification of Ice Floes and Calculation of Sea Ice Concentration Based on a Deep Learning Method
by Li Zhou, Jinyan Cai and Shifeng Ding
Remote Sens. 2023, 15(10), 2663; https://doi.org/10.3390/rs15102663 - 19 May 2023
Cited by 2 | Viewed by 1212
Abstract
When navigating ships in cold regions, sea ice concentration plays a crucial role in determining a ship’s navigability. However, automatically extracting the sea ice concentration and floe size distribution remains challenging, due to the difficulty in detecting all the ice floes from the [...] Read more.
When navigating ships in cold regions, sea ice concentration plays a crucial role in determining a ship’s navigability. However, automatically extracting the sea ice concentration and floe size distribution remains challenging, due to the difficulty in detecting all the ice floes from the images captured in complex polar environments, particularly those that include both ships and sea ice. In this paper, we propose using the YOLACT network to address this issue. Cameras installed on the ship collect images during transit and an image dataset is constructed to train a model that can intelligently identify all the targets in the image and remove any noisy targets. To overcome the challenge of identifying seemingly connected ice floes, the non-maximum suppression (NMS) in YOLACT is improved. Binarization is then applied to process the detection results, with the aim of obtaining an accurate sea ice concentration. We present a color map and histogram of the associated floe size distribution based on the ice size. The speed of calculating the sea ice density of each image reaches 21 FPS and the results show that sea ice concentration and floe size distribution can be accurately measured. We provide a case study to demonstrate the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Remote Sensing of Polar Sea Ice)
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13 pages, 3423 KiB  
Communication
Automated Identification of Landfast Sea Ice in the Laptev Sea from the True-Color MODIS Images Using the Method of Deep Learning
by Cheng Wen, Mengxi Zhai, Ruibo Lei, Tao Xie and Jinshan Zhu
Remote Sens. 2023, 15(6), 1610; https://doi.org/10.3390/rs15061610 - 16 Mar 2023
Cited by 1 | Viewed by 1331
Abstract
Landfast sea ice (LFSI) refers to sea ice attached to the shoreline with little or no horizonal motion in contrast to drifting sea ice. The LFSI plays an important role in the Arctic marine environmental and biological systems. Therefore, it is crucial to [...] Read more.
Landfast sea ice (LFSI) refers to sea ice attached to the shoreline with little or no horizonal motion in contrast to drifting sea ice. The LFSI plays an important role in the Arctic marine environmental and biological systems. Therefore, it is crucial to accurately monitor the spatiotemporal changes in the LFSI distribution. Here we present an automatic LFSI retrieval method for the Laptev Sea, eastern Arctic Ocean, based on a conditional generative adversarial network Pix2Pix using the true-color images of Moderate Resolution Imaging Spectroradiometer (MODIS). The spatial resolution of the derived product is 1.25 km, with a temporal interval of 7 days. Compared to the manually identified data from the true-color images of MODIS, the average precision of the LFSI area derived from LFSI mapping model reaches 91.4%, with the recall reaching 98.7% and F1-score reaching 94.5%. The LFSI coverage is consistent with the traditional large-scale LFSI products, but provides more details. Intraseasonal and interannual variations in LFSI area of the Laptev Sea in spring (March–May) during the period of 2002–2021 are investigated using the new product. The spring LFSI area in this region decreases at a rate of 0.67 × 103 km2 per year during this period (R2 = 0.117, p < 0.01). According to the spatial and temporal changes, we conclude that the LFSI is becoming more stable while the area is shrinking. The method is fully-automatic and computationally efficient, which can be further applied to the entire Arctic Ocean for LFSI identification and monitoring. Full article
(This article belongs to the Special Issue Remote Sensing of Polar Sea Ice)
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16 pages, 8623 KiB  
Article
A Simplified Coastline Inflection Method for Correcting Geolocation Errors in FengYun-3D Microwave Radiation Imager Images
by Zhuoqi Chen, Jin Xie, Georg Heygster, Zhaohui Chi, Lei Yang, Shengli Wu, Fengming Hui and Xiao Cheng
Remote Sens. 2023, 15(3), 813; https://doi.org/10.3390/rs15030813 - 31 Jan 2023
Cited by 1 | Viewed by 1179
Abstract
Passive microwave (PMW) sensors are popularly applied to Earth observations. However, the satellite PMW radiometer data sometimes have non-negligible errors in geolocation. Coastline inflection methods (CIMs) are widely used to improve geolocation errors of PMW images. However, they commonly require accuracy satellite flight [...] Read more.
Passive microwave (PMW) sensors are popularly applied to Earth observations. However, the satellite PMW radiometer data sometimes have non-negligible errors in geolocation. Coastline inflection methods (CIMs) are widely used to improve geolocation errors of PMW images. However, they commonly require accuracy satellite flight parameters, which are difficult to obtain by users. In this study, a simplified coastline inflection method (SCIM) is proposed to correct the geolocation errors without demanding for the satellite flight parameters. SCIM is applied to improve geolocation errors of FengYun-3D (FY-3D) Microwave Radiation Imager (MWRI) brightness temperature images from 2018 and 2019. It reduces the geolocation errors of MWRI images to 0.15 pixels in the along-track and cross-track direction. This means reductions of 75% and 86% of the geolocation errors, respectively. The mean brightness temperature differences between the ascending and descending MWRI images are reduced by 34%, demonstrating the improved geolocation accuracy of SCIM. The corrected images are also used to estimate Arctic sea ice concentration (SIC). By comparing with SICs retrieved from the un-corrected images, the root mean square error (RMSE) and mean absolute error (MAE) of the SICs from the corrected images are reduced from 13.7% to 10.2% and 8.9% to 6.9%, respectively. The mean correlation coefficient (R) increases from 0.91 to 0.95. All these results indicate that SCIM can reduce geolocation errors of satellite-based PMW images significantly. As SCIM is very simple and easy to be applied, it could be a useful method for users of PMW images. Full article
(This article belongs to the Special Issue Remote Sensing of Polar Sea Ice)
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22 pages, 7490 KiB  
Article
Multiple Sea Ice Type Retrieval Using the HaiYang-2B Scatterometer in the Arctic
by Lu Han, Haihua Chen, Lei Guan and Lele Li
Remote Sens. 2023, 15(3), 678; https://doi.org/10.3390/rs15030678 - 23 Jan 2023
Cited by 1 | Viewed by 1611
Abstract
Sea ice type classification is of great significance for the exploration of waterways, fisheries, and offshore operations in the Arctic. However, to date, there is no multiple remote sensing method to detect sea ice type in the Arctic. This study develops a multiple [...] Read more.
Sea ice type classification is of great significance for the exploration of waterways, fisheries, and offshore operations in the Arctic. However, to date, there is no multiple remote sensing method to detect sea ice type in the Arctic. This study develops a multiple sea ice type algorithm using the HaiYang-2B Scatterometer (HY-2B SCA). First, the parameters most applicable to classify sea ice type are selected through feature extraction, and a stacking model is established for the first time, which integrates decision tree and image segmentation algorithms. Finally, multiple sea ice types are classified in the Arctic, comprising Nilas, Young Ice, First Year Ice, Old Ice, and Fast Ice. Comparing the results with the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) Sea Ice Type dataset (SIT) indicates that the sea ice type classified by HY-2B SCA (Stacking-HY2B) is similar to OSI-SAF SIT with regard to the changing trends in extent of sea ice. We use the Copernicus Marine Environment Monitoring Service (CMEMS) high-resolution sea ice type data and EM-Bird ice thickness data to validate the result, and accuracies of 87% and 88% are obtained, respectively. This indicates that the algorithm in this work is comparable with the performance of OSI-SAF dataset, while providing information of multiple sea ice types. Full article
(This article belongs to the Special Issue Remote Sensing of Polar Sea Ice)
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23 pages, 7691 KiB  
Article
Quality Assessment of FY-3D/MERSI-II Thermal Infrared Brightness Temperature Data from the Arctic Region: Application to Ice Surface Temperature Inversion
by Haihua Chen, Xin Meng, Lele Li and Kun Ni
Remote Sens. 2022, 14(24), 6392; https://doi.org/10.3390/rs14246392 - 18 Dec 2022
Cited by 3 | Viewed by 1948
Abstract
The Arctic region plays an important role in the global climate system. To promote the application of Medium Resolution Spectral Imager-II (MERSI-II) data in the ice surface temperature (IST) inversion, we used the thermal infrared channels (channels 24 and 25) of the MERSI-II [...] Read more.
The Arctic region plays an important role in the global climate system. To promote the application of Medium Resolution Spectral Imager-II (MERSI-II) data in the ice surface temperature (IST) inversion, we used the thermal infrared channels (channels 24 and 25) of the MERSI-II onboard Chinese FY-3D satellite and the thermal infrared channels (channels 31 and 32) of the Earth Observing System (EOS) Moderate-Resolution Imaging Spectroradiometer (MODIS) onboard the National Aeronautical and Space Administration (NASA) Aqua satellite for data analysis. Using the Observation–Observation cross-calibration algorithm to cross-calibrate the MERSI and MODIS thermal infrared brightness temperature (Tb) data in the Arctic, channel 24 and 25 data from the FY-3D/MERSI-II on Arctic ice were evaluated. The thermal infrared Tb data of the MERSI-II were used to retrieve the IST via the split-window algorithm. In this study, the correlation coefficients of the thermal infrared channel Tb data between the MERSI and MODIS were >0.95, the mean bias was −0.5501–0.1262 K, and the standard deviation (Std) was <1.3582 K. After linear fitting, the MERSI-II thermal infrared Tb data were closer to the MODIS data, and the bias range of the 11 μm and 12 μm channels was −0.0214–0.0119 K and the Std was <1.2987 K. These results indicate that the quality of the MERSI-II data is comparable to that of the MODIS data, so that can be used for application to IST inversion. When using the MERSI thermal infrared Tb data after calibration to retrieve the IST, the results of the MERSI and MODIS IST were more consistent. By comparing the IST retrieved from the MERSI thermal infrared calibrated Tb data with MODIS MYD29 product, the mean bias was −0.0612–0.0423 °C and the Std was <1.3988 °C. Using the MERSI thermal infrared Tb data after calibration is better than that before calibration for retrieving the IST. When comparing the Arctic ocean sea and ice surface temperature reprocessed data (L4 SST/IST) with the IST data retrieved from MERSI, the bias was 0.9891–2.7510 °C, and the Std was <3.5774 °C. Full article
(This article belongs to the Special Issue Remote Sensing of Polar Sea Ice)
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Review

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37 pages, 32881 KiB  
Review
Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges
by Wenjun Huang, Anzhu Yu, Qing Xu, Qun Sun, Wenyue Guo, Song Ji, Bowei Wen and Chunping Qiu
Remote Sens. 2024, 16(5), 842; https://doi.org/10.3390/rs16050842 - 28 Feb 2024
Cited by 3 | Viewed by 990
Abstract
Deep learning, which is a dominating technique in artificial intelligence, has completely changed 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 [...] Read more.
Deep learning, which is a dominating technique in artificial intelligence, has completely changed 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 future trends. Our review focuses on research published from 2016 to the present, with a specific focus on deep-learning-based approaches in the last five years. We divided all related algorithms into three categories, including the conventional image classification approach, the 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 four aspects including climate research, navigation, geographic information systems (GIS) production and others. This paper also provides insightful observations and inspiring future research directions. Full article
(This article belongs to the Special Issue Remote Sensing of Polar Sea Ice)
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