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Remote Sensing
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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 7436

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

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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 178
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Map of the Sea of Okhotsk. The 200 m and 1000 m isobaths are indicated by red and blue dashed lines. The red box delineates the specific region where the PV Soya Icebreaker predominantly operated between 2002 and 2015 (see <a href="#sec2dot2-remotesensing-17-00772" class="html-sec">Section 2.2</a>). The orange box (i) represents the Tartar Strait region, and the green box (ii) corresponds to the Northern Polynya, indicating areas selected for specific studies (see <a href="#sec5dot1-remotesensing-17-00772" class="html-sec">Section 5.1</a>, <a href="#sec5dot2-remotesensing-17-00772" class="html-sec">Section 5.2</a> and <a href="#sec5dot3-remotesensing-17-00772" class="html-sec">Section 5.3</a>).</p> Full article ">Figure 2
<p>(<b>a</b>) Close-up map of the region in the Sea of Okhotsk (the red box in <a href="#remotesensing-17-00772-f001" class="html-fig">Figure 1</a>). The trace colors indicate the different navigational paths of the Soya Icebreaker in the years between 2002 and 2015, with the corresponding voyage years indicated in the colorbar placed at the bottom. (<b>b</b>) A detailed view of the PV Soya navigating through the Sea of Okhotsk, surrounded by sea ice and polynyas. The red circle on the tip of the ship highlights the location of the EKO MR-40 pyranometer that was used for the irradiance measurements.</p> Full article ">Figure 3
<p>Correlation between shortwave albedo measurements from the Soya Icebreaker and RTM-SciML retrievals. Panels (<b>a</b>,<b>b</b>) display the results with maximum time differences of three hours and one hour, respectively, between the measurement time and the MODIS overpass time. The color indicates the time interval between pyranometer measurements and satellite overpass. On the top left, the correlation equation (<math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>a</mi> <mo>·</mo> <mi>x</mi> <mo>+</mo> <mi>b</mi> </mrow> </semantics></math>), Pearson <span class="html-italic">r</span> coefficient, root mean square error (RMSE), and the number of pixels (<span class="html-italic">N</span>) used to calculate the statistics are provided. The solid black lines represent (0,0)–(1,1), and the dashed black lines represent the 15% error range.</p> Full article ">Figure 4
<p>Representative visualizations from 10 February 2004 (<b>a</b>) and 9 February 2008 (<b>b</b>). From top to bottom, each column displays: (<b>1</b>) true color RGB maps constructed using MODIS channels 645 nm, 555 nm, and 469 nm as the (R, G, B) bands, respectively; (<b>2</b>) surface classification maps with the spatial overlay of the Soya voyage shown in purple; (<b>3</b>) albedo retrieval maps with the spatial overlay of pyranometer measured values on RTM-SciML retrieved albedo; and (<b>4</b>) scatter–dot comparisons between the measurements and retrievals. On (<b>a-3</b>,<b>b-3</b>), the boxed A and B indicate the start and end points of the ship.</p> Full article ">Figure 5
<p>Representative visualizations from 14 February 2002 (<b>a</b>) and 11 February 2008 (<b>b</b>). From top to bottom, each column displays: (<b>1</b>) true color RGB maps constructed using MODIS channels 645 nm, 555 nm, and 469 nm as the (R, G, B) bands, respectively; (<b>2</b>) surface classification maps with the spatial overlay of the Soya voyage shown in purple; (<b>3</b>) albedo retrieval maps with the spatial overlay of pyranometer measured values on RTM-SciML retrieved albedo; and (<b>4</b>) scatter–dot comparisons between the measurements and retrievals. On (<b>a-3</b>,<b>b-3</b>), the boxed A and B indicate the start and end points of the ship.</p> Full article ">Figure 6
<p>Comparison of shortwave albedo measurements between the Soya Icebreaker and RTM-SciML retrievals represented as scatter plots. Each panel indicates the Pearson <span class="html-italic">r</span> coefficient and the number of pixels (<span class="html-italic">N</span>) at the top left. The dotted black lines delineate the MODIS overpass time. The <span class="html-italic">x</span>-axis across all panels displays the pyranometer measurement time (UTC).</p> Full article ">Figure 7
<p>Density plots illustrating the correlation between MODIS and SGLI albedo retrievals. Top two rows (subfigures 1): Bare sea ice; bottom two rows (subfigures 2): Melt-water/Water. (<b>a</b>–<b>h</b>) represents the eight time periods discussed in the main text. Each plot provides the Pearson correlation coefficient (r) and the root mean square error (RMSE) for the respective time periods on the top left.</p> Full article ">Figure 8
<p>Density plots comparing albedo retrievals from MODIS and SGLI sensors for different surface types over the total observation period from January to May 2021. (<b>a</b>) from bare ice surface, (<b>b</b>) from snow-covered sea ice surface, (<b>c</b>) from meltwater or open water and (<b>d</b>) from all valid sea-ice/water surfaces combined.</p> Full article ">Figure 9
<p>Comprehensive visualization of various parameters over the Sea of Okhotsk. From top to bottom, the rows depict (<b>a</b>) shortwave albedo, (<b>b</b>) surface classification, (<b>c</b>) brightness temperature as captured by SGLI’s <math display="inline"><semantics> <mrow> <mn>10.8</mn> <mspace width="0.166667em"/> <mi mathvariant="sans-serif">μ</mi> </mrow> </semantics></math>m channel, and (<b>d</b>) high-resolution sea ice concentration from AMSR-2. Each column (numbers 1–7) corresponds to distinct retrieval periods as detailed in <a href="#remotesensing-17-00772-t004" class="html-table">Table 4</a>, showcasing the evolution of sea ice conditions.</p> Full article ">Figure 10
<p>Average surface albedo of sea ice (left axis) and pixel percentage of the sea ice with different subtypes (right axis) during 1 April 2021∼7 April 2021. The colored bar plots and the labelled texts show the composition of sea ice. The black line is the relation between the average albedo (of all subtypes) and the SIC level. Error bars are the standard deviations of albedo.</p> Full article ">Figure 11
<p>(<b>a</b>) Snow pixel albedo values and their coverage percentages. (<b>b</b>) Sea ice (bare ice and ice with melt-water coverage) pixel albedo values and their percentages. The blue line and shadings are the relation and RMSE of 0.062 derived by [<a href="#B4-remotesensing-17-00772" class="html-bibr">4</a>].</p> Full article ">Figure 12
<p>Probability density curves of albedo in (<b>a</b>) Tatar Strait and the NWP adjoining the Japan Sea and (<b>b</b>) northern polynya. The number of pixels used to generate the curves in each panel are labelled at the top.</p> Full article ">Figure 13
<p>A detailed examination of the Tatar Strait region, derived from the broader overview provided in <a href="#remotesensing-17-00772-f009" class="html-fig">Figure 9</a>. The rows from top to bottom feature (<b>a</b>–<b>f</b>) shortwave albedo, (<b>g</b>–<b>l</b>) surface classification, and (<b>m</b>–<b>r</b>) AMSR-2 sea ice concentration at 15 km resolution, alongside (<b>s</b>–<b>x</b>) brightness temperature data from SGLI’s 10.8 µm channel. Color-bars for each parameter are included for reference at the bottom of their respective rows.</p> Full article ">
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 521
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Example sea motion fields for 8 March 2023. Black vectors are motions from AMSR2 (<b>a</b>) AU 36 GHz, (<b>b</b>) rSIR 36 GHz, (<b>c</b>) AU 89 GHz, (<b>d</b>) rSIR 89 GHz. Red vectors are buoy motions. The convention for the <span class="html-italic">u</span> and <span class="html-italic">v</span> motion components is illustrated in yellow in (<b>a</b>). The sea ice-covered area is denoted by the white background area. The North Pole is in the center of the image, with the Prime Meridian straight down toward the bottom of the image.</p> Full article ">Figure 2
<p>Time series of daily mean and RMS differences between buoy and AMSR2 36 GHz (<b>a</b>) u component and (<b>b</b>) v component motions. The line plot indicates daily RMSd values, and the thin bars represent the mean difference (bias).</p> Full article ">Figure 3
<p>Time series of daily mean and RMS differences between buoy and AMSR2 89 GHz (<b>a</b>) u component and (<b>b</b>) v component motions. The line plot indicates daily RMSd values, and the thin bars represent the mean difference (bias).</p> Full article ">
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 656
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Locations of four Sentinel-1 images acquired at Terpenia Bay, Anadyl Bay, St. Lawrence Island, the Chukchi Sea (top left panel), Mackenzie Bay, Shackleton polynya, Vincennes Bay, and the Ross Sea (top right panel), indicated by blue rectangles. The red cross marks the location of the ADCP observation off Cape Darnley (68.84°E, 67.63°S), which is utilized in the analysis in <a href="#sec4-remotesensing-17-00171" class="html-sec">Section 4</a>. Main large panel: a Sentinel-1 SAR image of the Shackleton polynya. The regions enclosed by the red and blue lines represent areas of active frazil and solid ice, respectively, identified by visual inspection.</p> Full article ">Figure 2
<p>(<b>a</b>,<b>c</b>) Frequency map of all MODIS validation data points, and (<b>b</b>,<b>d</b>) frequency map of MODIS data points with ice thickness of &lt;30 cm for the Northern (<b>a</b>,<b>b</b>) and Southern (<b>c</b>,<b>d</b>) Hemispheres.</p> Full article ">Figure 3
<p>Scatterplots of (<b>a</b>) PR versus GR and (<b>b</b>) PR versus MODIS thickness. Red and blue marks indicate the data for active frazil and solid ice, respectively, in both panels (<b>a</b>,<b>b</b>). The ice types in <a href="#remotesensing-17-00171-f003" class="html-fig">Figure 3</a>a,b are identified based on visual inspections of Sentinel-1 and Equation (2), respectively. The background colors in <a href="#remotesensing-17-00171-f003" class="html-fig">Figure 3</a>a indicate the classification range of active frazil (red) and solid ice (blue) using the AMSR-E algorithm of Nakata et al. [<a href="#B25-remotesensing-17-00171" class="html-bibr">25</a>]. The solid and dashed lines in <a href="#remotesensing-17-00171-f003" class="html-fig">Figure 3</a>b show the fitted exponential curves for active frazil (yellow) and thin solid ice (light blue) derived in this study and by Nakata et al. [<a href="#B25-remotesensing-17-00171" class="html-bibr">25</a>], respectively.</p> Full article ">Figure 4
<p>Two-dimensional histograms of AMSR2 versus MODIS ice thickness for the (<b>a</b>) Northern Hemisphere, (<b>b</b>) Southern Hemisphere, and (<b>c</b>) global ocean.</p> Full article ">Figure 5
<p>(<b>a</b>) MODIS visible image, and spatial distributions of (<b>b</b>) MODIS surface temperature, (<b>c</b>) Sentinel-1 SAR backscatter, (<b>d</b>) MODIS thin-ice thickness, (<b>e</b>) AMSR2 ice type (AF: active frazil, TN: thin solid ice, TK: thick solid ice), and (<b>f</b>) AMSR2 thin-ice thickness in Vincennes Bay, Antarctica, on 9 August 2016.</p> Full article ">Figure 6
<p>(<b>a</b>) MODIS visible image, and spatial distributions of (<b>b</b>) MODIS surface temperature, (<b>c</b>) Sentinel-1 SAR backscatter, (<b>d</b>) MODIS thin-ice thickness, (<b>e</b>) AMSR2 ice type (AF: active frazil, TN: thin solid ice, TK: thick solid ice), and (<b>f</b>) AMSR2 thin-ice thickness in Anadyr Bay, the Bering Sea, on 3 February 2022.</p> Full article ">Figure 7
<p>Time series of (<b>a</b>) ADCP backscatter strength (SV) at the uppermost bin (0–5 m depth) (green lines) and AMSR-E active frazil detection (yellow bar), (<b>b</b>) vertical profile of ADCP SV, and (<b>c</b>) penetration depth of frazil ice extracted from our method, from March to November 2010 off Cape Darnley, Antarctica. (<b>d</b>) Enlarged (July) time series of vertical profile and (<b>e</b>) surface (uppermost bin) ADCP SV. The dashed lines in (<b>e</b>) indicate the ASAR-interpreted sea-ice types (red: active frazil, blue: solid ice, green: boundary). Scale marks with month labels indicate the first day of each month.</p> Full article ">Figure 8
<p>(<b>a</b>) Histogram of the ADCP SV for active frazil (orange) and solid ice (blue) detected by the AMSR algorithm, based on hourly averaged SV data from April to October. The red and blue lines indicate the kernel density estimates of the probability density distributions for the active frazil and solid ice. (<b>b</b>) “Recall”, “Precision”, and “Accuracy” of active frazil detection as a function of the threshold value of ADCP SV. The gray shading and black lines in (<b>b</b>) indicate the optimal threshold range and the boundary between active frazil and solid ice, respectively, inferred from the SAR images.</p> Full article ">Figure 9
<p>Scatterplots of PR versus GR colored by the penetration depth of frazil ice estimated from the vertical profile of the ADCP SV. The background colors indicate the classification ranges of active frazil (orange) and solid ice (purple) using the AMSR algorithm.</p> Full article ">
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 1275
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)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Sea ice concentration from NSIDC for Antarctica in January 2022 with solid dark blue depicting ice concentrations smaller than 15% and white indicating 100% ice. The study area in the Weddell Sea is indicated by the yellow polygon. (<b>b</b>) Expanded study area map showing the location of ICESat-2 tracks for September 2022. Also shown are the 45°W meridian, which divides the study area into eastern and western sectors, and the 68°S parallel, which further divides the western sector into northwestern and southwestern regions, for the purpose of this study. The solid blue dots indicate the approximate locations of the field observations used in this study, which were obtained from 2019 to 2022.</p> Full article ">Figure 2
<p>(<b>a</b>–<b>p</b>) Multi-panel maps of the study area showing ICESat-2 total freeboard tracks from 2019 to 2022 for different seasons. The bottom two panels show: (<b>q</b>) the modal values of freeboard in meters for the western Weddell (solid color circles) with second modal values shown by the color crosses, 2019 (red), 2020 (black), 2021 (green), and 2022 (blue); and (<b>r</b>) the same as (<b>q</b>) but for the eastern Weddell.</p> Full article ">Figure 2 Cont.
<p>(<b>a</b>–<b>p</b>) Multi-panel maps of the study area showing ICESat-2 total freeboard tracks from 2019 to 2022 for different seasons. The bottom two panels show: (<b>q</b>) the modal values of freeboard in meters for the western Weddell (solid color circles) with second modal values shown by the color crosses, 2019 (red), 2020 (black), 2021 (green), and 2022 (blue); and (<b>r</b>) the same as (<b>q</b>) but for the eastern Weddell.</p> Full article ">Figure 2 Cont.
<p>(<b>a</b>–<b>p</b>) Multi-panel maps of the study area showing ICESat-2 total freeboard tracks from 2019 to 2022 for different seasons. The bottom two panels show: (<b>q</b>) the modal values of freeboard in meters for the western Weddell (solid color circles) with second modal values shown by the color crosses, 2019 (red), 2020 (black), 2021 (green), and 2022 (blue); and (<b>r</b>) the same as (<b>q</b>) but for the eastern Weddell.</p> Full article ">Figure 3
<p>Thickness estimates of the western Weddell (<b>left</b>—<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) and eastern Weddell (<b>right</b>—<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) regions from 2019 to 2022. Mean, mode, and Standard Deviation (SD) in meters. Bimodal mode values in the western Weddell are depicted in parenthesis.</p> Full article ">Figure 4
<p>Location of ICESat-2 tracks in red with field data in blue. Field data from 2019 and 2021 are point measurements. The data from 2022 are thickness measurements on board Endurance 22 ship EM data.</p> Full article ">Figure 5
<p>Mean thickness from (<b>a</b>) northwestern and (<b>b</b>) southwestern Weddell Sea.</p> Full article ">Figure 6
<p>ERA5 monthly averaged 2 m air temperature in Kelvin for the (<b>a</b>) western and (<b>b</b>) eastern Weddell, 2019–2022.</p> Full article ">Figure 7
<p>Comparison of ICESat and IceBridge mean thickness results in blue from [<a href="#B7-remotesensing-16-03909" class="html-bibr">7</a>] using the same method, with ICESat-2 results from this study in orange.</p> Full article ">
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 833
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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Figure 1

Figure 1
<p>Schematic of the AMSR-E bootstrap algorithm. Gray circles represent sea ice areas and blue triangles correspond to open water areas or &lt;10% sea ice concentration (SIC). The thin solid line is the SIC 100% line. The red triangle (O) is the open water tie-point (SIC 0% point), and the red circle is the SIC 100% point (point A). Point B is one of the observation points. Point I is the intersection of the SIC 100% line and the extension of the OB line. The SIC at point B is the ratio of OB to OI.</p> Full article ">Figure 2
<p>Open Ocean mask lines for SSM/I. Blue points correspond to ice-free or less than 10% SIC and those in gray correspond to SIC 0–100% data (all valid data). Scatter plots for (<b>a</b>) 36 V versus 18 V (36 V 18 V). The black line in (<b>a</b>) is the open ocean mask line for 36 V 18 V. Scatter plots for (<b>b</b>) 23 V against 18 V (black line) and difference in thresholds of 23 V and 18 V (red line). The black line for 23 V 18 V in (<b>b</b>) is the regression line of the residual blue points over the 36 V 18 V line. The red line at 23 V 18 V has a slope of 1.0.</p> Full article ">Figure 3
<p>Comparison of the AMSR-E and MODIS SICs. The AMSR-E SIC was validated using the Aqua/MODIS sea ice/cloud flag (MYD29) throughout the Northern and Southern Hemispheres. The figure represents a sample of the validated area. (<b>a</b>) Validation area map (26 June 2006, 14:10 UTC). (<b>b</b>) Aqua/MODIS RGB (R: 7 ch G: 2 ch B: 1 ch). Pink and white are clouds, blue is sea ice, black is open water, and gray is no observation. (<b>c</b>) SIC differences between AMSR-E and MODIS (AMSR-E minus MODIS equals difference). To validate the AMSR-E SIC, clear-sky pixels (80% cloud-free) were selected. MODIS SIC was derived as a fraction of the MYD29 sea ice flag (spatial resolution of 1 km) within the AMSR-E footprint size (14.4 × 8.2 km).</p> Full article ">Figure 4
<p>Differences in the SIC between AMSR-E and MODIS (AMSR-E minus MODIS equals difference) were plotted in the (<b>a</b>) Northern and (<b>b</b>) Southern Hemispheres in 2006.</p> Full article ">Figure 5
<p>Root-mean-square error (RMSE) and bias (AMSR-E minus MODIS) of AMSR-E compared with those of MODIS in the entire (<b>a</b>) Northern and (<b>b</b>) Southern Hemispheres in 2006. MODIS SIC = 0, 20, 40, 60, 80, and 100% plot indicates the average RMSE and bias of MODIS SIC = 0%, 0% &lt; SIC ≤ 30%, 30% &lt; SIC ≤ 50%, 50% &lt; SIC ≤ 70%, 70% &lt; SIC ≤ 90%, and 90% &lt; SIC ≤ 100%, respectively. The horizontal solid lines show −15% and 15% bias.</p> Full article ">Figure 6
<p>Time-series of sea ice extent (<b>a</b>) before adjusting the SIC threshold value (SSMI SIC &gt; 15% (blue line) and AMSR-E &gt; 15% (red line)) and (<b>b</b>) after adjusting the SIE of SSM/I to that of AMSR-E (SSMI SIC &gt; 21% (blue line) and AMSR-E &gt; 15% (red line)), and (<b>c</b>) time-series of sea ice extent difference of SSM/I and AMSR-E before adjusting (blue line) and after adjusting (red line) in the Northern Hemisphere.</p> Full article ">Figure 7
<p>AMSR-E-based daily sea ice extent (12.5 km resolution) trends in (<b>a</b>) the Northern Hemisphere; (<b>b</b>) the Southern Hemisphere; and (<b>c</b>) both hemispheres for 45 years, i.e., from 1 November 1978, to 31 December 2023. The red lines are the sea ice extent trend per year.</p> Full article ">Figure 8
<p>AMSR-E-based global yearly sea ice extent trends. The red, orange, green, and blue lines show the first, second, third, and fourth lowest SIE from November 1978 to December 2023, respectively. The first, second, third, and fourth lowest SIE were reached in 2023, 2018, 2017, and 2006, respectively. The lightest gray, light gray, and gray dotted lines show the average SIE in the 1980s, 1990s, and 2000s, respectively.</p> Full article ">Figure 9
<p>(<b>a</b>) Daily sea ice extent (SIE) trends of JAXA, OSISAF, BOOT, and NASA from October 2002 to September 2003 in the Northern Hemisphere (lines with increasing to decreasing curves) and Southern (lines with decreasing to increasing curves) Hemisphere. “JAXA” is the dataset in this study. “BOOT” is the Goddard bootstrap product at NSIDC (NSIDC-0192 in <a href="#remotesensing-16-03549-t002" class="html-table">Table 2</a>). “NASA” means NASA Team product (G0192 in <a href="#remotesensing-16-03549-t002" class="html-table">Table 2</a>). “OSISAF” is OSI-SAF (Bristol/Bootstrap) product (OSI-420 in <a href="#remotesensing-16-03549-t002" class="html-table">Table 2</a>) at EUMETSAT. The black solid line and lightest gray, light gray, and gray dotted lines show the SIE of JAXA, OSISAF, BOOT, and NASA, respectively. (<b>b</b>) Difference of daily sea ice extent among the JAXA, OSISAF, BOOT, and NASA in the Northern Hemisphere (solid line) and Southern Hemisphere (dashed line). The differences of BOOT–JAXA, NASA–JAXA, and OSISAF–JAXA are the red, blue, and black lines, respectively.</p> Full article ">Figure 10
<p>(<b>a</b>) AMSR-E daily sea ice extent (SIE) trends derived from different land–ocean flags from October 2002 to September 2003 in Northern (lines with increasing to decreasing curves) and Southern (lines with decreasing to increasing curves) Hemispheres. The solid line represents new land, and the dashed line indicates old land. The new land is AMSR-E-based, and the old land is SSM/I-based. (<b>b</b>) Difference of sea ice extent with new and old land in Northern (solid line) and Southern (dashed line) Hemispheres.</p> Full article ">Figure 11
<p>(<b>a</b>) Effect of land filter on the AMSR-E daily sea ice extent (SIE) trends from October 2002 to September 2003 in Northern (lines with increasing to decreasing curves) and Southern (lines with decreasing to increasing curves) Hemispheres. The solid line represents the SIE applied to the land filter, and the dashed line indicates the SIE of the no land filter. (<b>b</b>) Difference of applying land filter and no land filter in Northern (solid line) and Southern Hemispheres (dashed line).</p> Full article ">
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 2844
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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<p>Two-layer model of sea ice assumed in thickness calculations.</p> Full article ">Figure 2
<p>Arctic sea ice thickness composites from ICESat (IS), CryoSat-2 (CS-2) and ICESat-2 between 2003 and 2023. These 25 km-gridded composites are February and March averages of thickness estimates in SSM/I polar stereographic projection. For comparison, the 2-month averages are aligned with the winter campaigns (durations of ~33 days) of IS, which was not operated continuously as the altimeters on CS-2 and IS-2 observatories. There is a gap of about a year between the completion of the IS mission and the launch of CS-2. The third and fourth rows show the separate thickness retrievals in a 5-year overlap (2018–2023) between CS-2 and IS-2, highlighting the spatial differences between retrievals using snow depth from two approaches: modified climatology for CS-2 and snow depth calculated using differences between radar (CS-2) and lidar (IS-2) freeboards. Thicknesses are calculated within the Arctic basin (of ~7 × 10<sup>6</sup> km<sup>2</sup>) bounded by the gateways into the Pacific (Bering Strait), the Canadian Arctic Archipelago (CAA), and the Greenland (Fram Strait) and Barents Seas.</p> Full article ">Figure 3
<p>Decline in ice sea thickness and multiyear ice (MYI) coverage. (<b>a</b>) Changes in area-averaged basin-wide, multiyear ice and first-year ice thickness in winter between 2003 and 2023 from IS, CS-2, and IS-2. (<b>b</b>) Declines in MYI coverage and September sea-ice extent and increases in first-year ice (FYI) coverage over the same period. Area and thickness computed within the same bounds as in <a href="#remotesensing-16-02983-f002" class="html-fig">Figure 2</a>. The corresponding September ice extent behavior is for comparison.</p> Full article ">Figure 4
<p>Arctic sea ice volume and ice production since ICESat. (<b>a</b>) Decline in sea ice volume calculated from IS, CS-2 and IS-2 thickness fields. Volume is computed within the same bounds as in <a href="#remotesensing-16-02983-f002" class="html-fig">Figure 2</a>. (<b>b</b>) Increase in ice production between the fall (Oct-Nov) and winter (Feb-Mar) calculated by differencing the winter and fall ice volume. Note that ice volume export is not accounted for here.</p> Full article ">Figure 5
<p>Seasonal (October-April) evolution of snow depth over the Arctic ice cover from (<b>a</b>) <span class="html-italic">mW99</span> (dashed line) and satellite-derived snow depths (solid line with symbols). (<b>b</b>) Monthly differences between the <span class="html-italic">mW99</span> and satellite-derived snow depths. Their impact on ice thickness and volume can be seen in earlier figures.</p> Full article ">Figure 6
<p>Interannual changes in mean winter and fall ice thickness (1975–2003), within the data release area, from regression analysis of the submarine record, ICESat, CryoSat-2, and ICESat-2 retrievals. Inset shows the data release area (irregular polygon) of submarine data from U.S. Navy cruises, which covers ~38% of the Arctic Ocean. Sampling of winter and summer are centered on the dates of the ICESat campaigns. Shadings (blue and red) show expected residuals in the regression analysis. Thickness estimates from more localized airborne and ground EM surveys near the North Pole (diamonds) and from Operation IceBridge (circles) are shown within the context of the larger-scale changes in the submarine and satellite records. The corresponding September ice extent behavior is shown as a backdrop.</p> Full article ">
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