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Monitoring Sea Ice Loss with Remote Sensing Techniques
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Monitoring Sea Ice Loss with Remote Sensing Techniques

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

Deadline for manuscript submissions: closed (15 February 2025) | Viewed by 8345

Special Issue Editors

Prof. Dr. Kohei Cho

E-Mail Website
Guest Editor
Research & Information Center, Tokai University, Tokyo 108-8619, Japan
Interests: remote sensing on sea ice; environmental change
Dr. Josefino Comiso

E-Mail Website
Guest Editor
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Interests: climate change; remote sensing on sea ice

Special Issue Information

Dear Colleagues,

Global warming is one of the most serious problems we are facing in the 21st century. Sea ice plays an important role in reflecting solar radiation back into space. The reduction in sea ice has increased the ocean’s absorption of solar radiation, enhancing global warming in what has been regarded as “ice albedo feedback”. Time series of microwave observations from space since the late 1970s have revealed a drastic reduction in the Arctic perennial ice cover, which is now recognized as an indicator of global warming in the IPCC reports. Continued monitoring of changes in the global sea ice cover from space is important because of the expected impacts on the rest of the cryosphere and other regions.

The aim of this special Issue is to focus on techniques for monitoring sea ice extent and thickness using various sensors onboard Earth observation satellites.  The sensors could include, but are not limited to, optical sensors, passive microwave sensors, SAR, and Lidar.  The articles of this Special Issue are expected to be of interest not only to the readers of the journal, but also to scientists who are involved in using remote sensing data in the study of climate and associated environmental changes.

The themes will include “Developing sophisticated techniques for monitoring sea ice loss using various sensors onboard satellites and gaining insights into the causes and potential impacts”. Article types could be original research articles, case reports, and technical notes.

Prof. Dr. Kohei Cho
Dr. Josefino Comiso
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

  • remote sensing
  • glaciology
  • sea ice
  • ice sheet
  • microwave radiometer
  • optical sensor
  • SAR

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Published Papers (6 papers)

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Research

21 pages, 15325 KiB  
Article
Spatiotemporal Variations in Sea Ice Albedo: A Study of the Dynamics of Sea Ice Albedo in the Sea of Okhotsk
by Yingzhen Zhou, Wei Li, Nan Chen, Takenobu Toyota, Yongzhen Fan, Tomonori Tanikawa and Knut Stamnes
Remote Sens. 2025, 17(5), 772; https://doi.org/10.3390/rs17050772 - 23 Feb 2025
Viewed by 306
Abstract
This study utilizes a novel albedo retrieval framework combining radiative transfer modeling with scientific machine learning (RTM-SciML) to investigate sea ice dynamics in the Sea of Okhotsk. By validating albedo estimates derived from the MODIS sensor against in situ pyranometer measurements near the [...] Read more.
This study utilizes a novel albedo retrieval framework combining radiative transfer modeling with scientific machine learning (RTM-SciML) to investigate sea ice dynamics in the Sea of Okhotsk. By validating albedo estimates derived from the MODIS sensor against in situ pyranometer measurements near the Hokkaido coast, we achieved a robust Pearson coefficient of 0.86 and an RMSE of 0.089 for all sea ice types, with even higher correlations for specific surfaces like snow-covered ice (Pearson-r = 0.89) and meltwater/open water (Pearson-r = 0.90). This confirms the framework’s efficacy across varying surface conditions. Cross-sensor comparisons between MODIS and the Second-Generation Global Imager (SGLI) further demonstrated its consistency, achieving an overall Pearson-r of 0.883 and RMSE of 0.036. Integrating these albedo estimates with sea ice concentration data from the Advanced Microwave Scanning Radiometer 2 (AMSR-2), we analyzed the complex role of the Sea of Okhotsk’s polynya systems and ice interactions in regional climate processes. Our results highlight the synergistic advantage of pairing optical sensors, like MODIS and SGLI, with microwave sensors, offering a more comprehensive understanding of evolving sea ice conditions and paving the way for future climate and cryosphere studies. Full article
(This article belongs to the Special Issue Monitoring Sea Ice Loss with Remote Sensing Techniques)
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11 pages, 5357 KiB  
Communication
Evaluation of Sea Ice Motion Estimates from Enhanced Resolution Passive Microwave Brightness Temperatures
by Walter N. Meier and J. Scott Stewart
Remote Sens. 2025, 17(2), 259; https://doi.org/10.3390/rs17020259 - 13 Jan 2025
Viewed by 637
Abstract
Sea ice motion plays an important role in the seasonal and interannual evolution of the polar sea ice cover. Satellite imagery can be used to track the motion of sea ice via cross-correlation feature tracking algorithms. Such a method has been used for [...] Read more.
Sea ice motion plays an important role in the seasonal and interannual evolution of the polar sea ice cover. Satellite imagery can be used to track the motion of sea ice via cross-correlation feature tracking algorithms. Such a method has been used for the National Snow and Ice Data Center (NSIDC) sea ice motion product, based largely on passive microwave imagery. This study investigates the use of a new enhanced resolution passive microwave brightness temperature (TB) product to derive ice motion products. The results demonstrate that the new imagery source provides useful daily motion estimates that provide denser spatial coverage and reduced errors. The enhanced TBs yield motions that have a 30% lower Root Mean Square (RMS) difference with motion estimates from buoys. The enhanced resolution TBs will be used in the new version of the NSIDC motion product that is currently in development. Full article
(This article belongs to the Special Issue Monitoring Sea Ice Loss with Remote Sensing Techniques)
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22 pages, 10300 KiB  
Article
Validation of an AMSR2 Thin-Ice Thickness Algorithm for Global Sea-Ice-Covered Oceans Using Satellite and In Situ Observations
by Kazuki Nakata, Misako Kachi, Rigen Shimada, Eri Yoshizawa, Masato Ito and Kay I. Ohshima
Remote Sens. 2025, 17(1), 171; https://doi.org/10.3390/rs17010171 - 6 Jan 2025
Viewed by 815
Abstract
The detection of thin-ice thickness using satellite microwave radiometers is a strong tool for estimating sea-ice production in coastal polynyas, which leads to dense water formation driving ocean thermohaline circulation. Thin-ice areas are classified into two ice types: active frazil, comprising frazil ice [...] Read more.
The detection of thin-ice thickness using satellite microwave radiometers is a strong tool for estimating sea-ice production in coastal polynyas, which leads to dense water formation driving ocean thermohaline circulation. Thin-ice areas are classified into two ice types: active frazil, comprising frazil ice and open water, and thin solid ice, areas of relatively uniform thin ice. A thin-ice algorithm for AMSR-E has been developed to classify these two ice types and estimate ice thickness of <20 cm. In this study, we validate the applicability of the algorithm to the successor, AMSR2, using validation data of ice types identified from Sentinel-1 and ice thickness derived from MODIS. The validation results show an ice-type misclassification rate of ~3% and mean absolute errors in ice thickness of 2.0 cm and 5.0 cm for active frazil and thin solid ice, respectively. These values are similar to those for AMSR-E, indicating that the thin-ice algorithm can be applied to AMSR2. Further validations with the moored ADCP backscattering data capturing underwater frazil ice signals demonstrate that the algorithm can accurately distinguish between two ice types and effectively detect deep-penetrating frazil ice. The AMSR2 thin-ice thickness data has been released as a JAXA research product. Full article
(This article belongs to the Special Issue Monitoring Sea Ice Loss with Remote Sensing Techniques)
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16 pages, 12826 KiB  
Article
Seasonal and Interannual Variations in Sea Ice Thickness in the Weddell Sea, Antarctica (2019–2022) Using ICESat-2
by Mansi Joshi, Alberto M. Mestas-Nuñez, Stephen F. Ackley, Stefanie Arndt, Grant J. Macdonald and Christian Haas
Remote Sens. 2024, 16(20), 3909; https://doi.org/10.3390/rs16203909 - 21 Oct 2024
Viewed by 1389
Abstract
The sea ice extent in the Weddell Sea exhibited a positive trend from the start of satellite observations in 1978 until 2016 but has shown a decreasing trend since then. This study analyzes seasonal and interannual variations in sea ice thickness using ICESat-2 [...] Read more.
The sea ice extent in the Weddell Sea exhibited a positive trend from the start of satellite observations in 1978 until 2016 but has shown a decreasing trend since then. This study analyzes seasonal and interannual variations in sea ice thickness using ICESat-2 laser altimetry data over the Weddell Sea from 2019 to 2022. Sea ice thickness was calculated from ICESat-2’s ATL10 freeboard product using the Improved Buoyancy Equation. Seasonal variability in ice thickness, characterized by an increase from February to September, is more pronounced in the eastern Weddell sector, while interannual variability is more evident in the western Weddell sector. The results were compared with field data obtained between 2019 and 2022, showing a general agreement in ice thickness distributions around predominantly level ice. A decreasing trend in sea ice thickness was observed when compared to measurements from 2003 to 2017. Notably, the spring of 2021 and summer of 2022 saw significant decreases in Sea Ice Extent (SIE). Although the overall mean sea ice thickness remained unchanged, the northwestern Weddell region experienced a noticeable decrease in ice thickness. Full article
(This article belongs to the Special Issue Monitoring Sea Ice Loss with Remote Sensing Techniques)
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17 pages, 4989 KiB  
Article
Intersensor Calibration of Spaceborne Passive Microwave Radiometers and Algorithm Tuning for Long-Term Sea Ice Trend Analysis Based on AMSR-E Observations
by Mieko Seki, Masahiro Hori, Kazuhiro Naoki, Misako Kachi and Keiji Imaoka
Remote Sens. 2024, 16(19), 3549; https://doi.org/10.3390/rs16193549 - 24 Sep 2024
Viewed by 899
Abstract
Sea ice monitoring is key to analyzing the Earth’s climate system. Long-term sea ice extent (SIE) has been continuously monitored using various spaceborne passive microwave radiometers (PMRs) since November 1978. As the lifetime of a satellite is usually approximately 5 years, bias caused [...] Read more.
Sea ice monitoring is key to analyzing the Earth’s climate system. Long-term sea ice extent (SIE) has been continuously monitored using various spaceborne passive microwave radiometers (PMRs) since November 1978. As the lifetime of a satellite is usually approximately 5 years, bias caused by differences in PMRs should be eliminated to obtain objective SIE trends. Most sea ice products have been analyzed for long-term trends with a bias adjustment based on the coarse resolution special sensor microwave imager (SSM/I) in operation for the longest period. However, since 2002, Japanese microwave radiometers of the Advanced Microwave Scanning Radiometer (AMSR) series, which have the highest spatial resolution in PMR, have been available. In this study, we developed standardization techniques for processing SIE including calibration of the brightness temperature (TB), tuning the sea ice concentration (SIC) algorithm, and adjusting the SIC threshold to retrieve a consistent SIE trend based on the AMSR for the Earth Observing System (AMSR-E, one of the AMSR that operated from May 2002 to October 2011). Analysis results showed that the root-mean-square error between AMSR-E SICs and those of moderate resolution imaging spectroradiometer (MODIS) was 15%. In this study, SIE was defined as the sum of the areas where the AMSR-E SIC was >15%. When retrieving SIE, we adjusted the SIC threshold for each PMR to be consistent with the SIE calculated based on the 15% SIC threshold for AMSR-E. We then calculated a time-series of the SIE trends over approximately 45 years using the adjusted SIE data. Therefore, we revealed the dramatic decrease in global sea ice extent since 1978. This technique enables retrieval of more accurate long-term sea ice trends for more than half a century in the future. Full article
(This article belongs to the Special Issue Monitoring Sea Ice Loss with Remote Sensing Techniques)
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21 pages, 4101 KiB  
Article
Two Decades of Arctic Sea-Ice Thickness from Satellite Altimeters: Retrieval Approaches and Record of Changes (2003–2023)
by Sahra Kacimi and Ron Kwok
Remote Sens. 2024, 16(16), 2983; https://doi.org/10.3390/rs16162983 - 14 Aug 2024
Cited by 1 | Viewed by 3125
Abstract
There now exists two decades of basin-wide coverage of Arctic sea ice from three dedicated polar-orbiting altimetry missions (ICESat, CryoSat-2, and ICESat-2) launched by NASA and ESA. Here, we review our retrieval approaches and discuss the composite record of Arctic ice thickness (2003–2023) [...] Read more.
There now exists two decades of basin-wide coverage of Arctic sea ice from three dedicated polar-orbiting altimetry missions (ICESat, CryoSat-2, and ICESat-2) launched by NASA and ESA. Here, we review our retrieval approaches and discuss the composite record of Arctic ice thickness (2003–2023) after appending two more years (2022–2023) to our earlier records. The present availability of five years of snow depth estimates—from differencing lidar (ICESat-2) and radar (CryoSat-2) freeboards—have benefited from the concurrent operation of two altimetry missions. Broadly, the dramatic volume loss (5500 km3) and Arctic-wide thinning (0.6 m) captured by ICESat (2003–2009), primarily due to the decline in old ice coverage between 2003 and 2007, has slowed. In the central Arctic, away from the coasts, the CryoSat-2 and shorter ICESat-2 records show near-negligible thickness trends since 2007, where the winter and fall ice thicknesses now hover around 2 m and 1.3 m, from a peak of 3.6 m and 2.7 m in 1980. Ice volume production has doubled between the fall and winter with the faster-growing seasonal ice cover occupying more than half of the Arctic Ocean at the end of summer. Seasonal ice behavior dominates the Arctic Sea ice’s interannual thickness and volume signatures. Full article
(This article belongs to the Special Issue Monitoring Sea Ice Loss with Remote Sensing Techniques)
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