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Special Issue "Remote Sensing of Polar Sea Ice"

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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: 30 June 2023 | Viewed by 4367

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Special Issue Editors

Dr. Mohammed Shokr

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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

Dr. Yufang Ye

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Guest Editor

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 2500 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 (6 papers)

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Research

Open AccessArticle

Examining the Consistency of Sea Surface Temperature and Sea Ice Concentration in Arctic Satellite Products

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Remote Sens. 2023, 15(11), 2908; https://doi.org/10.3390/rs15112908 - 02 Jun 2023

Viewed by 147

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 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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Open AccessArticle

The Identification of Ice Floes and Calculation of Sea Ice Concentration Based on a Deep Learning Method

Li Zhou

Jinyan Cai

and

Shifeng Ding

Remote Sens. 2023, 15(10), 2663; https://doi.org/10.3390/rs15102663 - 19 May 2023

Viewed by 395

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 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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Open AccessCommunication

Automated Identification of Landfast Sea Ice in the Laptev Sea from the True-Color MODIS Images Using the Method of Deep Learning

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Remote Sens. 2023, 15(6), 1610; https://doi.org/10.3390/rs15061610 - 16 Mar 2023

Viewed by 642

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 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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Open AccessArticle

A Simplified Coastline Inflection Method for Correcting Geolocation Errors in FengYun-3D Microwave Radiation Imager Images

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Remote Sens. 2023, 15(3), 813; https://doi.org/10.3390/rs15030813 - 31 Jan 2023

Cited by 1 | Viewed by 591

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 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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Open AccessArticle

Multiple Sea Ice Type Retrieval Using the HaiYang-2B Scatterometer in the Arctic

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Remote Sens. 2023, 15(3), 678; https://doi.org/10.3390/rs15030678 - 23 Jan 2023

Viewed by 874

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 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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Open AccessArticle

Quality Assessment of FY-3D/MERSI-II Thermal Infrared Brightness Temperature Data from the Arctic Region: Application to Ice Surface Temperature Inversion

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Remote Sens. 2022, 14(24), 6392; https://doi.org/10.3390/rs14246392 - 18 Dec 2022

Cited by 1 | Viewed by 1202

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 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 (T_b) data in the Arctic, channel 24 and 25 data from the FY-3D/MERSI-II on Arctic ice were evaluated. The thermal infrared T_b 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 T_b 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 T_b 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 T_b 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 T_b 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 T_b 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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