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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 39461

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Special Issue Editors

Dr. Sang-Hoon Hong

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Guest Editor

Department of Geological Sciences, Pusan National University, Pusan 46241, Republic of Korea
Interests: synthetic aperture radar; radar interferometry; surface displacement; glacier; sea ice; wetlands; geodesy; hydrology
Special Issues, Collections and Topics in MDPI journals

Dr. Hyun-Cheol Kim

E-Mail Website
Guest Editor

Center of Remote Sensing and GIS, Korea Polar Research Institute, Incheon 21990, Korea
Interests: sea ice; ocean colour; climate change; UAV
Special Issues, Collections and Topics in MDPI journals

Dr. Sang-Wan Kim

E-Mail Website
Guest Editor

Dept. of Energy and Mineral Resources Engineering, Sejong University, Seoul 05006, Korea
Interests: geohazard; SAR interferometry; Persistent Scatterer; subsidence; water body; glacier; target recognition

Dr. Batuhan Osmanoglu

E-Mail Website
Co-Guest Editor

Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, 8800 Greenbelt Rd, Greenbelt, MD 20771, USA
Interests: synthetic aperture radar remote sensing; climate change; glaciers; snow
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to invite you to submit manuscripts to a Special Issue of “Remote Sensing in Sea Ice” in the journal of Remote Sensing.

With the great increase in interest about global warming, the understanding of the rapid changes in sea ice in the Arctic and Antarctic Oceans has become one of the most important factors for providing new insights into the relationship between the atmosphere, ocean, and Earth. Over the past decades, remotely sensed information from satellite, airborne, and unmanned aerial vehicles (UAV) has been greatly utilized in order to provide the characteristics of the sea ice. Furthermore, we already are or will be in the golden age for Earth observation, in that new satellite missions adopting new payloads that will be launched that will enable us to investigate sea ice with advanced methodologies.

For this Special Issue, we welcome the submission of manuscripts related to recent research and activities about sea ice using airborne, space-based, and UAV remote sensing, in situ measurements, and integration and modeling based on those observations. Especially, we invite contributions of recently launched SAR satellites and historical imaging radar data. However, the topic of this Special Issue will include active and passive microwave remote sensing, optical and hyperspectral observations, calibration and validation with in situ observations and sea ice model, and data fusion and assimilation approaches of remotely sensed imagery. We thank you in advance for your consideration to submit manuscripts to this Special Issue on sea ice remote sensing, and encourage you to share this announcement with your colleagues.

Dr. Sang-Hoon Hong
Dr. Hyun-cheol Kim
Dr. Sang-Wan Kim
Dr. Batuhan Osmanoglu
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
sea ice
Arctic
Antarctic
SAR
active microwave
passive microwave
altimetry
optical
hyperspectral

Published Papers (14 papers)

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20 pages, 56046 KiB

Open AccessArticle

Sea Ice–Water Classification of RADARSAT-2 Imagery Based on Residual Neural Networks (ResNet) with Regional Pooling

by Mingzhe Jiang, Linlin Xu and David A. Clausi

Remote Sens. 2022, 14(13), 3025; https://doi.org/10.3390/rs14133025 - 24 Jun 2022

Cited by 7 | Viewed by 2221

Abstract

Sea ice mapping plays an integral role in ship navigation and meteorological modeling in the polar regions. Numerous published studies in sea ice classification using synthetic aperture radar (SAR) have reported high classification rates. However, many of these focus on numerical results based on sample points and ignore the quality of the inferred sea ice maps. We have designed and implemented a novel SAR sea ice classification algorithm where the spatial context, obtained by the unsupervised IRGS segmentation algorithm, is integrated with texture features extracted by a residual neural network (ResNet) and, using regional pooling, classifies ice and water. This algorithm is trained and tested on a published dataset and cross-validated using leave-one-out (LOO) strategy, obtaining an overall accuracy of 99.67% and outperforming several existing algorithms. In addition, visual results show that this new method produces sea ice maps with natural ice–water boundaries and fewer ice and water errors. Full article

(This article belongs to the Special Issue Remote Sensing in Sea Ice)

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17 pages, 8537 KiB

Open AccessArticle

Evaluation of Sea Ice Radiative Forcing according to Surface Albedo and Skin Temperature over the Arctic from 1982–2015

by Noh-Hun Seong, Hyun-Cheol Kim, Sungwon Choi, Donghyun Jin, Daeseong Jung, Suyoung Sim, Jongho Woo, Nayeon Kim, Minji Seo, Kyeong-Sang Lee and Kyung-Soo Han

Remote Sens. 2022, 14(11), 2512; https://doi.org/10.3390/rs14112512 - 24 May 2022

Cited by 4 | Viewed by 2266

Abstract

Rapid warming of the Arctic has resulted in widespread sea ice loss. Sea ice radiative forcing (SIRF) is the instantaneous perturbation of Earth’s radiation at the top of the atmosphere (TOA) caused by sea ice. Previous studies focused only on the role of albedo on SIRF. Skin temperature is also closely related to sea ice changes and is one of the main factors in Arctic amplification. In this study, we estimated SIRF considering both surface albedo and skin temperature using radiative kernels. The annual average net-SIRF, which consists of the sum of albedo-SIRF and temperature-SIRF, was calculated as −54.57 ± 3.84 W/m² for the period 1982–2015. In the net-SIRF calculation, albedo-SIRF and temperature-SIRF made similar contributions. However, the albedo-SIRF changed over the study period by 0.12 ± 0.07 W/m² per year, while the temperature-SIRF changed by 0.22 ± 0.07 W/m² per year. The SIRFs for each factor had different patterns depending on the season and region. In summer, rapid changes in the albedo-SIRF occurred in the Kara and Barents regions. In winter, only a temperature-SIRF was observed, and there was little difference between regions compared to the variations in albedo-SIRF. Based on the results of the study, it was concluded that the overall temperature-SIRF is changing more rapidly than the albedo-SIRF. This study indicates that skin temperatures may have a greater impact on the Arctic than albedo in terms of sea ice surface changes. Full article

(This article belongs to the Special Issue Remote Sensing in Sea Ice)

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25 pages, 14269 KiB

Open AccessArticle

Sea Ice Monitoring with CFOSAT Scatterometer Measurements Using Random Forest Classifier

by Xiaochun Zhai, Zhixiong Wang, Zhaojun Zheng, Rui Xu, Fangli Dou, Na Xu and Xingying Zhang

Remote Sens. 2021, 13(22), 4686; https://doi.org/10.3390/rs13224686 - 19 Nov 2021

Cited by 9 | Viewed by 2435

Abstract

The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). This paper performs sea ice monitoring with the CSCAT backscatter measurements in polar areas. The CSCAT measurements have the characteristics of diverse incidence and azimuth angles and separation between open water and sea ice. Hence, five microwave feature parameters, which show different sensitivity to ice or water, are defined and derived from the CSCAT measurements firstly. Then the random forest classifier is selected for sea ice monitoring because of its high overall accuracy of 99.66% and 93.31% in the Arctic and Antarctic, respectively. The difference of features ranked by importance in different seasons and regions shows that the combination of these parameters is effective in discriminating sea ice from water under various conditions. The performance of the algorithm is validated against the sea ice edge data from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) on a global scale in a period from 1 January 2019 to 10 May 2021. The mean sea ice area differences between CSCAT and OSI SAF product in the Arctic and Antarctic are 0.2673 million km² and −0.4446 million km², respectively, and the sea ice area relative errors of CSCAT are less than 10% except for summer season in both poles. However, the overall sea ice area derived from CSCAT is lower than the OSI SAF sea ice area in summer. This may be because the CSCAT is trained by radiometer sea ice concentration data while the radiometer measurement of sea ice is significantly affected by melting in the summer season. In conclusion, this research verifies the capability of CSCAT in monitoring polar sea ice using a machine learning-aided random forest classifier. This presented work can give guidance to sea ice monitoring with radar backscatter measurements from other spaceborne scatterometers, particular for the recently launched FY-3E scatterometer (called WindRad). Full article

(This article belongs to the Special Issue Remote Sensing in Sea Ice)

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17 pages, 12571 KiB

Open AccessEditor’s ChoiceArticle

Application of a Convolutional Neural Network for the Detection of Sea Ice Leads

by Jay P. Hoffman, Steven A. Ackerman, Yinghui Liu, Jeffrey R. Key and Iain L. McConnell

Remote Sens. 2021, 13(22), 4571; https://doi.org/10.3390/rs13224571 - 13 Nov 2021

Cited by 10 | Viewed by 3075

Abstract

Despite accounting for a small fraction of the surface area in the Arctic, long and narrow sea ice fractures, known as “leads”, play a critical role in the energy flux between the ocean and atmosphere. As the volume of sea ice in the Arctic has declined over the past few decades, it is increasingly important to monitor the corresponding changes in sea ice leads. A novel approach has been developed using artificial intelligence (AI) to detect sea ice leads using satellite thermal infrared window data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS). In this new approach, a particular type of convolutional neural network, a U-Net, replaces a series of conventional image processing tests from our legacy algorithm. Results show the new approach has a high detection accuracy with F1 Scores on the order of 0.7. Compared to the legacy algorithm, the new algorithm shows improvement, with more true positives, fewer false positives, fewer false negatives, and better agreement between satellite instruments. Full article

(This article belongs to the Special Issue Remote Sensing in Sea Ice)

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19 pages, 7011 KiB

Open AccessArticle

Weekly Mapping of Sea Ice Freeboard in the Ross Sea from ICESat-2

by YoungHyun Koo, Hongjie Xie, Nathan T. Kurtz, Stephen F. Ackley and Alberto M. Mestas-Nuñez

Remote Sens. 2021, 13(16), 3277; https://doi.org/10.3390/rs13163277 - 19 Aug 2021

Cited by 5 | Viewed by 2681

Abstract

NASA’s ICESat-2 has been providing sea ice freeboard measurements across the polar regions since October 2018. In spite of the outstanding spatial resolution and precision of ICESat-2, the spatial sparsity of the data can be a critical issue for sea ice monitoring. This study employs a geostatistical approach (i.e., ordinary kriging) to characterize the spatial autocorrelation of the ICESat-2 freeboard measurements (ATL10) to estimate weekly freeboard variations in 2019 for the entire Ross Sea area, including where ICESat-2 tracks are not directly available. Three variogram models (exponential, Gaussian, and spherical) are compared in this study. According to the cross-validation results, the kriging-estimated freeboards show correlation coefficients of 0.56–0.57, root mean square error (RMSE) of ~0.12 m, and mean absolute error (MAE) of ~0.07 m with the actual ATL10 freeboard measurements. In addition, the estimated errors of the kriging interpolation are low in autumn and high in winter to spring, and low in southern regions and high in northern regions of the Ross Sea. The effective ranges of the variograms are 5–10 km and the results from the three variogram models do not show significant differences with each other. The southwest (SW) sector of the Ross Sea shows low and consistent freeboard over the entire year because of the frequent opening of wide polynya areas generating new ice in this sector. However, the southeast (SE) sector shows large variations in freeboard, which demonstrates the advection of thick multiyear ice from the Amundsen Sea into the Ross Sea. Thus, this kriging-based interpolation of ICESat-2 freeboard can be used in the future to estimate accurate sea ice production over the Ross Sea by incorporating other remote sensing data. Full article

(This article belongs to the Special Issue Remote Sensing in Sea Ice)

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15 pages, 5499 KiB

Open AccessArticle

Assessing Scale Dependence on Local Sea Level Retrievals from Laser Altimetry Data over Sea Ice

by Liuxi Tian, Hongjie Xie, Stephen F. Ackley and Alberto M. Mestas-Nuñez

Remote Sens. 2020, 12(22), 3732; https://doi.org/10.3390/rs12223732 - 13 Nov 2020

Viewed by 1988

Abstract

The measurement of sea ice elevation above sea level or the “freeboard” depends upon an accurate retrieval of the local sea level. The local sea level has been previously retrieved from altimetry data alone by the lowest elevation method, where the percentage of the lowest elevations over a particular segment length scale was used. Here, we provide an evaluation of the scale dependence on these local sea level retrievals using data from NASA Operation IceBridge (OIB) which took place in the Ross Sea in 2013. This is a unique dataset of laser altimeter measurements over five tracks from the Airborne Topographic Mapper (ATM), with coincidently high-spatial resolution images from the Digital Mapping System (DMS), that allows for an independent sea level validation. The local sea level is first calculated by using the mean elevation of ATM L1B data over leads identified by using the corresponding DMS imagery. The resulting local sea level reference is then used as ground truth to validate the local sea levels retrieved from ATM L2 by using nine different percentages of the lowest elevation (0.1%, 0.5%, 1%, 1.5%, 2%, 2.5%, 3%, 3.5%, and 4%) at seven different segment length scales (1, 5, 10, 15, 20, 25, and 50 km) for each of the five ATM tracks. The closeness to the 1:1 line, R², and root mean square error (RMSE) is used to quantify the accuracy of the retrievals. It is found that all linear least square fits are statistically significant (p < 0.05) using an F test at every scale for all tested data. In general, the sea level retrievals are farther away from the 1:1 line when the segment length scale increases from 1 or 5 to 50 km. We find that the retrieval accuracy is affected more by the segment length scale than the percentage scale. Based on our results, most retrievals underestimate the local sea level; the longer the segment length (from 1 to 50 km) used, especially at small percentage scales, the larger the error tends to be. The best local sea level based on a higher R² and smaller RMSE for all the tracks combined is retrieved by using 0.1–2% of the lowest elevations at the 1–5 km segment lengths. Full article

(This article belongs to the Special Issue Remote Sensing in Sea Ice)

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15 pages, 2425 KiB

Open AccessArticle

Towards the Sea Ice and Wind Measurement by a C-Band Scatterometer at Dual VV/HH Polarization: A Prospective Appraisal

by Alexey Nekrasov, Alena Khachaturian, Ján Labun, Pavol Kurdel and Mikhail Bogachev

Remote Sens. 2020, 12(20), 3382; https://doi.org/10.3390/rs12203382 - 16 Oct 2020

Cited by 3 | Viewed by 1836

Abstract

Following the mission science plan of EPS/Metop-SG C-band scatterometer for 2023–2044, we consider the potential application of the sea ice/water discrimination method based on the minimum statistical distance of the measured normalized radar cross sections (NRCS) to the geophysical model functions (GMF) of the sea ice and water, respectively. The application of the method is considered for the classical spacecraft scatterometer geometry with three fixed fan-beam antennas and the hypothetical prospective scatterometer geometry with the five fixed fan-beam antennas. Joint vertical (VV) and horizontal (HH) transmit and receive polarization are considered for the spaceborne scatterometer geometries. We show explicitly that the hypothetical five fixed fan-beam antenna geometry combined with the dual VV and HH polarization for all antennas provides better estimates of the sea wind speed and direction as well as sea ice/water discrimination during single spacecraft pass. The sea ice/water discrimination algorithms developed for each scatterometer geometry and dual VV/HH polarization are presented. The obtained results can be used to optimize the design of new spaceborne scatterometers and will be beneficial to the forthcoming satellite missions. Full article

(This article belongs to the Special Issue Remote Sensing in Sea Ice)

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16 pages, 8487 KiB

Open AccessArticle

An Examination of the Non-Formation of the North Water Polynya Ice Arch

by Ron F. Vincent

Remote Sens. 2020, 12(17), 2712; https://doi.org/10.3390/rs12172712 - 21 Aug 2020

Cited by 9 | Viewed by 3221

Abstract

The North Water (NOW), situated between Ellesmere Island and Greenland in northern Baffin Bay, is the largest recurring polynya in the Canadian Arctic. Historically, the northern border of the NOW is defined by an ice arch that forms annually in Kane Basin, which is part of the Nares Strait system. In 2007 the NOW ice arch failed to consolidate for the first time since observations began in the 1950s. The non-formation of the NOW ice arch occurred again in 2009, 2010, 2017 and 2019. Satellite Advanced Very High Resolution Radiometry data shows that large floes broke off from the normally stable landfast ice in Kane Basin for each of these years, impeding ice arch formation. A closer analysis of a 2019 event, in which 2500 km² of ice sheared away from Kane Basin, indicates that significant tidal forces played a role. The evidence suggests that thinning ice from a warming climate combined with large amplitude tides is a key factor in the changing ice dynamics of the NOW region. The non-formation of the NOW ice arch results in an increased loss of multiyear ice through Nares Strait. Full article

(This article belongs to the Special Issue Remote Sensing in Sea Ice)

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23 pages, 19416 KiB

Open AccessArticle

Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery

by Marcel König, Gerit Birnbaum and Natascha Oppelt

Remote Sens. 2020, 12(16), 2623; https://doi.org/10.3390/rs12162623 - 14 Aug 2020

Cited by 6 | Viewed by 3816

Abstract

Hyperspectral remote-sensing instruments on unmanned aerial vehicles (UAVs), aircraft and satellites offer new opportunities for sea ice observations. We present the first study using airborne hyperspectral imagery of Arctic sea ice and evaluate two atmospheric correction approaches (ATCOR-4 (Atmospheric and Topographic Correction version 4; v7.0.0) and empirical line calibration). We apply an existing, field data-based model to derive the depth of melt ponds, to airborne hyperspectral AisaEAGLE imagery and validate results with in situ measurements. ATCOR-4 results roughly match the shape of field spectra but overestimate reflectance resulting in high root-mean-square error (

R M S E

) (between 0.08 and 0.16). Noisy reflectance spectra may be attributed to the low flight altitude of 200 ft and Arctic atmospheric conditions. Empirical line calibration resulted in smooth, accurate spectra (

R M S E

< 0.05) that enabled the assessment of melt pond bathymetry. Measured and modeled pond bathymetry are highly correlated (

r

= 0.86) and accurate (

R M S E

= 4.04 cm), and the model explains a large portion of the variability (

R^{2}

= 0.74). We conclude that an accurate assessment of melt pond bathymetry using airborne hyperspectral data is possible subject to accurate atmospheric correction. Furthermore, we see the necessity to improve existing approaches with Arctic-specific atmospheric profiles and aerosol models and/or by using multiple reference targets on the ground. Full article

(This article belongs to the Special Issue Remote Sensing in Sea Ice)

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11 pages, 2339 KiB

Open AccessArticle

Sea Ice Freeboard in the Ross Sea from Airborne Altimetry IcePod 2016–2017 and a Comparison with IceBridge 2013 and ICESat 2003–2008

by Liuxi Tian, Hongjie Xie, Stephen F. Ackley, Kirsty J. Tinto, Robin E. Bell, Christopher J. Zappa, Yongli Gao and Alberto M. Mestas-Nuñez

Remote Sens. 2020, 12(14), 2226; https://doi.org/10.3390/rs12142226 - 11 Jul 2020

Cited by 4 | Viewed by 2454

Abstract

As part of the Polynyas and Ice Production in the Ross Sea (PIPERS) project, the IcePod system onboard the LC-130 aircraft based at McMurdo Station was flown over the Ross Sea, Antarctica in November 2016 and 2017, with the purpose of repeating the same lines that NASA’s Operation IceBridge (OIB) aircraft flew over in 2013. We resampled the lidar data into 70 m pixels (similar to the footprint size of OIB L2 and ICESat data) and took the mean of the lowest 2% elevation values of 25 km (50 km) length along a flight track as the local sea level of the central 25 km (50 km). Most of the IcePod data were over the same flight lines taken by OIB in 2013, so the total freeboard changes from 2013 to 2016 and 2017 were examined. Combining with the ICESat (2003–2008), we obtained a better picture of total freeboard and its interannual variability in the Ross Sea. The pattern of the sea ice distribution supports that new ice produced in coastal polynyas was transported northward by katabatic winds off the ice shelf. Compared to ICESat years, sea ice near the coast was thicker, while sea ice offshore was thinner in the more recent OIB/IcePod years. The results also showed that, in general, sea ice was thicker in 2017 compared to 2013 or 2016—0.02–0.55 m thicker in total freeboard. Full article

(This article belongs to the Special Issue Remote Sensing in Sea Ice)

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17 pages, 5780 KiB

Open AccessArticle

Ice Production in Ross Ice Shelf Polynyas during 2017–2018 from Sentinel–1 SAR Images

by Liyun Dai, Hongjie Xie, Stephen F. Ackley and Alberto M. Mestas-Nuñez

Remote Sens. 2020, 12(9), 1484; https://doi.org/10.3390/rs12091484 - 7 May 2020

Cited by 16 | Viewed by 3717

Abstract

High sea ice production (SIP) generates high-salinity water, thus, influencing the global thermohaline circulation. Estimation from passive microwave data and heat flux models have indicated that the Ross Ice Shelf polynya (RISP) may be the highest SIP region in the Southern Oceans. However, the coarse spatial resolution of passive microwave data limited the accuracy of these estimates. The Sentinel-1 Synthetic Aperture Radar dataset with high spatial and temporal resolution provides an unprecedented opportunity to more accurately distinguish both polynya area/extent and occurrence. In this study, the SIPs of RISP and McMurdo Sound polynya (MSP) from 1 March–30 November 2017 and 2018 are calculated based on Sentinel-1 SAR data (for area/extent) and AMSR2 data (for ice thickness). The results show that the wind-driven polynyas in these two years occurred from the middle of March to the middle of November, and the occurrence frequency in 2017 was 90, less than 114 in 2018. However, the annual mean cumulative SIP area and volume in 2017 were similar to (or slightly larger than) those in 2018. The average annual cumulative polynya area and ice volume of these two years were 1,040,213 km² and 184 km³ for the RSIP, and 90,505 km² and 16 km³ for the MSP, respectively. This annual cumulative SIP (volume) is only 1/3–2/3 of those obtained using the previous methods, implying that ice production in the Ross Sea might have been significantly overestimated in the past and deserves further investigations. Full article

(This article belongs to the Special Issue Remote Sensing in Sea Ice)

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15 pages, 2590 KiB

Open AccessArticle

On Sea Ice Measurement by a C-Band Scatterometer at VV Polarization: Methodology Optimization Based on Computer Simulations

by Alexey Nekrasov, Alena Khachaturian, Evgeny Abramov, Oleg Markelov and Mikhail Bogachev

Remote Sens. 2019, 11(21), 2518; https://doi.org/10.3390/rs11212518 - 28 Oct 2019

Cited by 3 | Viewed by 2666

Abstract

We consider sea ice and water microwave backscatter features at the C-band with vertical transmit and receive polarization and present a method for sea ice/water discrimination using a multiple fixed fan-beam satellite scatterometer. The method is based on the criterion of the minimum statistical distance of measured backscatter values to the sea ice and water (CMOD7) geophysical model functions. Implementation of the method is considered both for a typical three fan-beam geometry as well as for a potential five fan-beam geometry of a satellite scatterometer. By using computer simulations, we show explicitly that the number of looks at the same cell from different azimuthal directions needs to be increased to provide better (unambiguous) retrieval of the wind vector and sea ice/water discrimination. The algorithms for sea ice/water discrimination are described, and the results obtained are also discussed along with recommendations for the number of different azimuthal looks (beams) at the same cell from the point of view of sea ice/water discrimination as well as unambiguous wind direction retrieval during the satellite’s single pass. Full article

(This article belongs to the Special Issue Remote Sensing in Sea Ice)

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15 pages, 6411 KiB

Open AccessLetter

Assessment of AMSR2 Ice Extent and Ice Edge in the Arctic Using IMS

by Yinghui Liu, Sean Helfrich, Walter N. Meier and Richard Dworak

Remote Sens. 2020, 12(10), 1582; https://doi.org/10.3390/rs12101582 - 16 May 2020

Cited by 4 | Viewed by 2589

Abstract

This work assesses the AMSR2 (the Advanced Microwave Scanning Radiometer 2) ice extent and ice edge in the Arctic using the ice extent products of NOAA’s Interactive Multisensor Snow and Ice Mapping System (IMS) from the period of July 2015 to July 2019. Daily values and monthly means of four statistical scores (hit rate, false alarm ratio, false alarm rate, and Hanssen-Kuiper Skill Score) over the Arctic Ocean show distinct annual cycles. IMS ice edges often extend further south compared to those from AMSR2, with up to 100 km differences over the Beaufort, Chukchi, and East Siberian Seas in August and September. Full article

(This article belongs to the Special Issue Remote Sensing in Sea Ice)

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15 pages, 7853 KiB

Open AccessLetter

Evolution of Backscattering Coefficients of Drifting Multi-Year Sea Ice during End of Melting and Onset of Freeze-up in the Western Beaufort Sea

by Seung Hee Kim, Hyun-Cheol Kim, Chang-Uk Hyun, Sungjae Lee, Jung-Seok Ha, Joo-Hong Kim, Young-Joo Kwon, Jeong-Won Park, Hyangsun Han, Seong-Yeob Jeong and Duk-jin Kim

Remote Sens. 2020, 12(9), 1378; https://doi.org/10.3390/rs12091378 - 27 Apr 2020

Viewed by 2892

Abstract

Backscattering coefficients of Sentinel-1 synthetic aperture radar (SAR) data of drifting multi-year sea ice in the western Beaufort Sea during the transition period between the end of melting and onset of freeze-up are analyzed, in terms of the incidence angle dependence and temporal variation. The mobile sea ice surface is tracked down in a 1 km by 1 km region centered at a GPS tracker, which was installed during a field campaign in August 2019. A total of 24 Sentinel-1 images spanning 17 days are used and the incidence angle dependence in HH- and HV-polarization are −0.24 dB/deg and −0.10 dB/deg, respectively. Hummocks and recently frozen melt ponds seem to cause the mixture behavior of surface and volume scattering. The normalized backscattering coefficients in HH polarization gradually increased in time at a rate of 0.15 dB/day, whereas the HV-polarization was relatively flat. The air temperature from the ERA5 hourly reanalysis data has a strong negative relation with the increasing trend of the normalized backscattering coefficients in HH-polarization. The result of this study is expected to complement other previous studies which focused on winter or summer seasons in other regions of the Arctic Ocean. Full article

(This article belongs to the Special Issue Remote Sensing in Sea Ice)

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