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Sea Ice Remote Sensing and Analysis

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

Deadline for manuscript submissions: closed (30 April 2016) | Viewed by 139226

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Special Issue Editors

Dr. Walt Meier

E-Mail Website
Guest Editor

NASA Goddard Space Flight Center, Cryospheric Sciences Lab, Greenbelt, MD 20771, USA
Interests: passive microwave remote sensing; sea ice; Arctic climate change

Dr. Mark Tschudi

E-Mail Website
Co-Guest Editor

CCAR, Dept. of Aerospace Engr., University of Colorado at Boulder, UCB 431, Boulder, CO 80309, USA
Interests: satellite and airborne remote sensing of Arctic sea ice; sea ice motion; evolution of sea ice characteristics during summer melt; trends in Arctic sea ice age and thickness

Special Issue Information

Dear Colleagues,

Our capabilities to remotely gather information on Arctic and Antarctic sea ice are increasing greatly. New satellite and airborne technologies are being deployed and even further improvements are under development. Additionally, new methods are being developed to maximize the effectiveness of current and historical data. These new methods and new technologies promise to greatly increase our understanding of sea ice. However, can our understanding keep up with the rapid changes in the sea ice itself? Prospective authors are invited to contribute to this Special Issue of Remote Sensing by submitting an original manuscript of their latest research results on advances in sea ice remote sensing.

We welcome submissions on all aspects of sea ice remote sensing, airborne and satellite, including integration of remotely sensed data with in situ observations, model outputs, and in data assimilation contexts, including:

Products from new satellite instruments such as: AMSR2, CryoSat-2, SMOS, SMAP, Aquarius, VIIRS
New approaches and analyses from historical time series such as: ESMR, SMMR, SSMI-SSMIS, ICESat, ERS-1/2, RADARSAT, AVHRR, MODIS
Aircraft campaigns, including: IceBridge, CryoSat-2 validation, etc.
Validation of sea ice remote sensing observations including comparison/integration with in situ data
Data fusion (combined sensor products) and data assimilation techniques for remotely-sensed sea ice data
Use of remote sensing fields for operational applications of sea ice analysis and forecasting
Remote sensing products for comparison with and validation of sea ice models

Walt Meier
Dr. Mark Tschudi
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

sea ice
remote sensing
Arctic
Antarctic
sea ice thickness
snow on sea ice
altimetry
SAR
passive microwave
visible
infrared
data fusion
data assimilation
operational analysis

Published Papers (17 papers)

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Research

6121 KiB

Open AccessArticle

Comparison of Arctic Sea Ice Thickness from Satellites, Aircraft, and PIOMAS Data

by Xuanji Wang, Jeffrey Key, Ron Kwok and Jinlun Zhang

Remote Sens. 2016, 8(9), 713; https://doi.org/10.3390/rs8090713 - 30 Aug 2016

Cited by 68 | Viewed by 12577

Abstract

In this study, six Arctic sea ice thickness products are compared: the AVHRR Polar Pathfinder-extended (APP-x), ICESat, CryoSat-2, SMOS, NASA IceBridge aircraft flights, and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). The satellite products are based on three different retrieval methods: an energy budget approach, measurements of ice freeboard, and the relationship between passive microwave brightness temperatures and thin ice thickness. Inter-comparisons are done for the periods of overlap from 2003 to 2013. Results show that ICESat sea ice is thicker than APP-x and PIOMAS overall, particularly along the north coast of Greenland and Canadian Archipelago. The relative differences of APP-x and PIOMAS with ICESat are −0.48 m and −0.31 m, respectively. APP-x underestimates thickness relative to CryoSat-2, with a mean difference of −0.19 m. The biases for APP-x, PIOMAS, and CryoSat-2 relative to IceBridge thicknesses are 0.18 m, 0.18 m, and 0.29 m. The mean difference between SMOS and CryoSat-2 for 0~1 m thick ice is 0.13 m in March and −0.24 m in October. All satellite-retrieved ice thickness products and PIOMAS overestimate the thickness of thin ice (1 m or less) compared to IceBridge for which SMOS has the smallest bias (0.26 m). The spatial correlation between the datasets indicates that APP-x and PIOMAS are the most similar, followed by APP-x and CryoSat-2. Full article

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

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9596 KiB

Open AccessArticle

Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

by Sanggyun Lee, Jungho Im, Jinwoo Kim, Miae Kim, Minso Shin, Hyun-cheol Kim and Lindi J. Quackenbush

Remote Sens. 2016, 8(9), 698; https://doi.org/10.3390/rs8090698 - 24 Aug 2016

Cited by 55 | Viewed by 11159

Abstract

Satellite altimeters have been used to monitor Arctic sea ice thickness since the early 2000s. In order to estimate sea ice thickness from satellite altimeter data, leads (i.e., cracks between ice floes) should first be identified for the calculation of sea ice freeboard. In this study, we proposed novel approaches for lead detection using two machine learning algorithms: decision trees and random forest. CryoSat-2 satellite data collected in March and April of 2011–2014 over the Arctic region were used to extract waveform parameters that show the characteristics of leads, ice floes and ocean, including stack standard deviation, stack skewness, stack kurtosis, pulse peakiness and backscatter sigma-0. The parameters were used to identify leads in the machine learning models. Results show that the proposed approaches, with overall accuracy >90%, produced much better performance than existing lead detection methods based on simple thresholding approaches. Sea ice thickness estimated based on the machine learning-detected leads was compared to the averaged Airborne Electromagnetic (AEM)-bird data collected over two days during the CryoSat Validation experiment (CryoVex) field campaign in April 2011. This comparison showed that the proposed machine learning methods had better performance (up to r = 0.83 and Root Mean Square Error (RMSE) = 0.29 m) compared to thickness estimation based on existing lead detection methods (RMSE = 0.86–0.93 m). Sea ice thickness based on the machine learning approaches showed a consistent decline from 2011–2013 and rebounded in 2014. Full article

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

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Open AccessArticle

Polar Sea Ice Monitoring Using HY-2A Scatterometer Measurements

by Mingming Li, Chaofang Zhao, Yong Zhao, Zhixiong Wang and Lijian Shi

Remote Sens. 2016, 8(8), 688; https://doi.org/10.3390/rs8080688 - 22 Aug 2016

Cited by 17 | Viewed by 7566

Abstract

A sea ice detection algorithm based on Fisher’s linear discriminant analysis is developed to segment sea ice and open water for the Ku-band scatterometer onboard the China’s Hai Yang 2A Satellite (HY-2A/SCAT). Residual classification errors are reduced through image erosion/dilation techniques and sea ice growth/retreat constraint methods. The arctic sea-ice-type classification is estimated via a time-dependent threshold derived from the annual backscatter trends based on previous HY-2A/SCAT derived sea ice extent. The extent and edge of the sea ice obtained in this study is compared with the Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration data and the Sentinel-1 SAR imagery for verification, respectively. Meanwhile, the classified sea ice type is compared with a multi-sensor sea ice type product based on data from the Advanced Scatterometer (ASCAT) and SSMIS. Results show that HY-2A/SCAT is powerful in providing sea ice extent and type information, while differences in the sensitivities of active/passive products are found. In addition, HY-2A/SCAT derived sea ice products are also proved to be valuable complements for existing polar sea ice data products. Full article

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

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21285 KiB

Open AccessArticle

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

by Ute C. Herzfeld, Scott Williams, John Heinrichs, James Maslanik and Steven Sucht

Remote Sens. 2016, 8(8), 616; https://doi.org/10.3390/rs8080616 - 26 Jul 2016

Cited by 5 | Viewed by 6138

Abstract

Recent drastic reductions in the Arctic sea-ice cover have raised an interest in understanding the role of sea ice in the global system as well as pointed out a need to understand the physical processes that lead to such changes. Satellite remote-sensing data provide important information about remote ice areas, and Synthetic Aperture Radar (SAR) data have the advantages of penetration of the omnipresent cloud cover and of high spatial resolution. A challenge addressed in this paper is how to extract information on sea-ice types and sea-ice processes from SAR data. We introduce, validate and apply geostatistical and statistical approaches to automated classification of sea ice from SAR data, to be used as individual tools for mapping sea-ice properties and provinces or in combination. A key concept of the geostatistical classification method is the analysis of spatial surface structures and their anisotropies, more generally, of spatial surface roughness, at variable, intermediate-sized scales. The geostatistical approach utilizes vario parameters extracted from directional vario functions, the parameters can be mapped or combined into feature vectors for classification. The method is flexible with respect to window sizes and parameter types and detects anisotropies. In two applications to RADARSAT and ERS-2 SAR data from the area near Point Barrow, Alaska, it is demonstrated that vario-parameter maps may be utilized to distinguish regions of different sea-ice characteristics in the Beaufort Sea, the Chukchi Sea and in Elson Lagoon. In a third and a fourth case study the analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification. Field measurements and high-resolution aerial observations serve as basis for validation of the geostatistical-statistical classification methods. A combination of supervised classification and vario-parameter mapping yields best results, correctly identifying several sea-ice provinces in the shore-fast ice and the pack ice. Notably, sea ice does not have to be static to be classifiable with respect to spatial structures. In consequence, the geostatistical-statistical classification may be applied to detect changes in ice dynamics, kinematics or environmental changes, such as increased melt ponding, increased snowfall or changes in the equilibrium line. Full article

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

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6723 KiB

Open AccessArticle

Antarctic Sea-Ice Thickness Retrieval from ICESat: Inter-Comparison of Different Approaches

by Stefan Kern, Burcu Ozsoy-Çiçek and Anthony P. Worby

Remote Sens. 2016, 8(7), 538; https://doi.org/10.3390/rs8070538 - 24 Jun 2016

Cited by 33 | Viewed by 8587

Abstract

Accurate circum-Antarctic sea-ice thickness is urgently required to better understand the different sea-ice cover evolution in both polar regions. Satellite radar and laser altimetry are currently the most promising tools for sea-ice thickness retrieval. We present qualitative inter-comparisons of winter and spring circum-Antarctic sea-ice thickness computed with different approaches from Ice Cloud and land Elevation Satellite (ICESat) laser altimeter total (sea ice plus snow) freeboard estimates. We find that approach A, which assumes total freeboard equals snow depth, and approach B, which uses empirical linear relationships between freeboard and thickness, provide the lowest sea-ice thickness and the smallest winter-to-spring increase in seasonal average modal and mean sea-ice thickness: A: 0.0 m and 0.04 m, B: 0.17 and 0.16 m, respectively. Approach C uses contemporary snow depth from satellite microwave radiometry, and we derive comparably large sea-ice thickness. Here we observe an unrealistically large winter-to-spring increase in seasonal average modal and mean sea-ice thickness of 0.68 m and 0.65 m, respectively, which we attribute to biases in the snow depth. We present a conceptually new approach D. It assumes that the two-layer system (sea ice, snow) can be represented by one layer. This layer has a modified density, which takes into account the influence of the snow on sea-ice buoyancy. With approach D we obtain thickness values and a winter-to-spring increase in average modal and mean sea-ice thickness of 0.17 m and 0.23 m, respectively, which lay between those of approaches B and C. We discuss retrieval uncertainty, systematic uncertainty sources, and the impact of grid resolution. We find that sea-ice thickness obtained with approaches C and D agrees best with independent sea-ice thickness information—if we take into account the potential bias of in situ and ship-based observations. Full article

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

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5978 KiB

Open AccessArticle

Sea and Freshwater Ice Concentration from VIIRS on Suomi NPP and the Future JPSS Satellites

by Yinghui Liu, Jeffrey Key and Robert Mahoney

Remote Sens. 2016, 8(6), 523; https://doi.org/10.3390/rs8060523 - 22 Jun 2016

Cited by 39 | Viewed by 9197

Abstract

Information on ice is important for shipping, weather forecasting, and climate monitoring. Historically, ice cover has been detected and ice concentration has been measured using relatively low-resolution space-based passive microwave data. This study presents an algorithm to detect ice and estimate ice concentration in clear-sky areas over the ocean and inland lakes and rivers using high-resolution data from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar Orbiting Partnership (S-NPP) and on future Joint Polar Satellite System (JPSS) satellites, providing spatial detail that cannot be obtained with passive microwave data. A threshold method is employed with visible and infrared observations to identify ice, then a tie-point algorithm is used to determine the representative reflectance/temperature of pure ice, estimate the ice concentration, and refine the ice cover mask. The VIIRS ice concentration is validated using observations from Landsat 8. Results show that VIIRS has an overall bias of −0.3% compared to Landsat 8 ice concentration, with a precision (uncertainty) of 9.5%. Biases and precision values for different ice concentration subranges from 0% to 100% can be larger. Full article

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

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9613 KiB

Open AccessArticle

Automatic Detection of the Ice Edge in SAR Imagery Using Curvelet Transform and Active Contour

by Jiange Liu, K. Andrea Scott, Ahmed Gawish and Paul Fieguth

Remote Sens. 2016, 8(6), 480; https://doi.org/10.3390/rs8060480 - 08 Jun 2016

Cited by 24 | Viewed by 6619

Abstract

A novel method based on the curvelet transform and active contour method to automatically detect the ice edge in Synthetic Aperture Radar (SAR) imagery is proposed. The method utilizes the location of high curvelet coefficients to determine regions in the image likely to contain the ice edge. Using an ice edge from passive microwave sea ice concentration for initialization, these regions are then joined using the active contour method to obtain the final ice edge. The method is evaluated on four dual polarization SAR scenes of the Labrador sea. Through comparison of the ice edge with that from image analysis charts, it is demonstrated that the proposed method can detect the ice edge effectively in SAR images. This is particularly relevant when the marginal ice zone is diffuse or the ice is thin, and using the definition of ice edge from the passive microwave ice concentration would underestimate the ice edge location. It is expected that the method may be useful for operations in marginal ice zones, such as offshore drilling, where a high resolution estimate of the ice edge location is required. It could also be useful as a first guess for an ice analyst, or for the assimilation of SAR data. Full article

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

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1500 KiB

Open AccessArticle

Relating the Age of Arctic Sea Ice to its Thickness, as Measured during NASA’s ICESat and IceBridge Campaigns

by Mark A. Tschudi, Julienne C. Stroeve and J. Scott Stewart

Remote Sens. 2016, 8(6), 457; https://doi.org/10.3390/rs8060457 - 27 May 2016

Cited by 61 | Viewed by 9633

Abstract

Recent satellite observations yield estimates of the distribution of sea ice thickness across the entire Arctic Ocean. While these sensors were only placed in operation within the last few years, information from other sensors may assist us with estimating the distribution of sea ice thickness in the Arctic beginning in the 1980s. A previous study found that the age of sea ice is correlated to sea ice thickness from 2003 to 2006, but an extension of the temporal analysis is needed to better quantify this relationship and its variability from year to year. Estimates of the ice age/thickness relationship may allow the thickness record to be extended back to 1985, the beginning of our ice age dataset. Comparisons of ice age and thickness estimates derived from both ICESat (2004–2008) and IceBridge (2009–2015) reveal that the relationship between age and thickness differs between these two campaigns, due in part to the difference in area of coverage. Nonetheless, sea ice thickness and age exhibit a direct relationship when compared on pan-Arctic or regional spatial scales. Full article

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

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22032 KiB

Open AccessArticle

Satellite Remote Sensing of Snow Depth on Antarctic Sea Ice: An Inter-Comparison of Two Empirical Approaches

by Stefan Kern and Burcu Ozsoy-Çiçek

Remote Sens. 2016, 8(6), 450; https://doi.org/10.3390/rs8060450 - 26 May 2016

Cited by 22 | Viewed by 7326

Abstract

Snow on Antarctic sea ice plays a key role for sea ice physical processes and complicates retrieval of sea ice thickness using altimetry. Current methods of snow depth retrieval are based on satellite microwave radiometry, which perform best for dry, homogeneous snow packs on level sea ice. We introduce an alternative approach based on in-situ measurements of total (sea ice plus snow) freeboard and snow depth, which we use to compute snow depth on sea ice from Ice, Cloud, and land Elevation Satellite (ICESat) total freeboard observations. We compare ICESat snow depth for early winter and spring of the years 2004 through 2006 with the Advanced Scanning Microwave Radiometer aboard EOS (AMSR-E) snow depth product. We find ICESat snow depths agree more closely with ship-based visual and air-borne snow radar observations than AMSR-E snow depths. We obtain average modal and mean ICESat snow depths, which exceed AMSR-E snow depths by 5–10 cm in winter and 10–15 cm in spring. We observe an increase in ICESat snow depth from winter to spring for most Antarctic regions in accordance with ground-based observations, in contrast to AMSR-E snow depths, which we find to stay constant or to decrease. We suggest satellite laser altimetry as an alternative method to derive snow depth on Antarctic sea ice, which is independent of snow physical properties. Full article

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

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2346 KiB

Open AccessArticle

Filling the Polar Data Gap in Sea Ice Concentration Fields Using Partial Differential Equations

by Courtenay Strong and Kenneth M. Golden

Remote Sens. 2016, 8(6), 442; https://doi.org/10.3390/rs8060442 - 24 May 2016

Cited by 14 | Viewed by 5863

Abstract

The “polar data gap” is a region around the North Pole where satellite orbit inclination and instrument swath for SMMR and SSM/I-SSMIS satellites preclude retrieval of sea ice concentrations. Data providers make the irregularly shaped data gap round by centering a circular “pole hole mask” over the North Pole. The area within the pole hole mask has conventionally been assumed to be ice-covered for the purpose of sea ice extent calculations, but recent conditions around the perimeter of the mask indicate that this assumption may already be invalid. Here we propose an objective, partial differential equation based model for estimating sea ice concentrations within the area of the pole hole mask. In particular, the sea ice concentration field is assumed to satisfy Laplace’s equation with boundary conditions determined by observed sea ice concentrations on the perimeter of the gap region. This type of idealization in the concentration field has already proved to be quite useful in establishing an objective method for measuring the “width” of the marginal ice zone—a highly irregular, annular-shaped region of the ice pack that interacts with the ocean, and typically surrounds the inner core of most densely packed sea ice. Realistic spatial heterogeneity in the idealized concentration field is achieved by adding a spatially autocorrelated stochastic field with temporally varying standard deviation derived from the variability of observations around the mask. To test the model, we examined composite annual cycles of observation-model agreement for three circular regions adjacent to the pole hole mask. The composite annual cycle of observation-model correlation ranged from approximately 0.6 to 0.7, and sea ice concentration mean absolute deviations were of order

10^{- 2}

or smaller. The model thus provides a computationally simple approach to solving the increasingly important problem of how to fill the polar data gap. Moreover, this approach based on solving an elliptic partial differential equation with given boundary conditions has sufficient generality to also provide more sophisticated models which could potentially be more accurate than the Laplace equation version. Such generalizations and potential validation opportunities are discussed. Full article

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

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8769 KiB

Open AccessArticle

Improving Multiyear Sea Ice Concentration Estimates with Sea Ice Drift

by Yufang Ye, Mohammed Shokr, Georg Heygster and Gunnar Spreen

Remote Sens. 2016, 8(5), 397; https://doi.org/10.3390/rs8050397 - 10 May 2016

Cited by 36 | Viewed by 6714

Abstract

Multiyear ice (MYI) concentration can be retrieved from passive or active microwave remote sensing observations. One of the algorithms that combines both observations is the Environmental Canada Ice Concentration Extractor (ECICE). However, factors such as ridging, snow wetness and metamorphism can cause significant changes in brightness temperature and backscatter, leading to misidentification of FYI as MYI, hence increasing the estimated MYI concentrations suddenly. This study introduces a correction scheme to restore the MYI concentrations under these conditions. The correction utilizes ice drift records to constrain the MYI changes and uses two thresholds of passive microwave radiometric parameters to account for snow wetness and metamorphism. The correction is applied to MYI concentration retrievals from ECICE with inputs from QuikSCAT and AMSR-E observations, acquired over the Arctic region in a series of winter seasons (October to May) from 2002 to 2009. Qualitative comparison with the Radarsat-1 SAR images and quantitative comparison against results from previous studies show that the correction works well by removing the anomalous high MYI concentrations. On average, the correction reduces the estimated MYI area by 5.2 × 10⁵ km² (14.3%) except for the April–May time frame, when the reduction is larger as the warmer weather prompts the condition of the anomalous snow radiometric signature. Due to the long-lasting (i.e., from one to several weeks) effect of the warm spells on FYI, the correction could be important in climatological research and operational applications. Full article

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

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Open AccessArticle

The Impact of Geophysical Corrections on Sea-Ice Freeboard Retrieved from Satellite Altimetry

by Robert Ricker, Stefan Hendricks and Justin F. Beckers

Remote Sens. 2016, 8(4), 317; https://doi.org/10.3390/rs8040317 - 09 Apr 2016

Cited by 20 | Viewed by 6826

Abstract

Satellite altimetry is the only method to monitor global changes in sea-ice thickness and volume over decades. Such missions (e.g., ERS, Envisat, ICESat, CryoSat-2) are based on the conversion of freeboard into thickness by assuming hydrostatic equilibrium. Freeboard, the height of the ice above the water level, is therefore a crucial parameter. Freeboard is a relative quantity, computed by subtracting the instantaneous sea surface height from the sea-ice surface elevations. Hence, the impact of geophysical range corrections depends on the performance of the interpolation between subsequent leads to retrieve the sea surface height, and the magnitude of the correction. In this study, we investigate this impact by considering CryoSat-2 sea-ice freeboard retrievals in autumn and spring. Our findings show that major parts of the Arctic are not noticeably affected by the corrections. However, we find areas with very low lead density like the multiyear ice north of Canada, and landfast ice zones, where the impact can be substantial. In March 2015, 7.17% and 2.69% of all valid CryoSat-2 freeboard grid cells are affected by the ocean tides and the inverse barometric correction by more than 1 cm. They represent by far the major contributions among the impacts of the individual corrections. Full article

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

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4758 KiB

Open AccessArticle

Multiyear Arctic Ice Classification Using ASCAT and SSMIS

by David B. Lindell and David G. Long

Remote Sens. 2016, 8(4), 294; https://doi.org/10.3390/rs8040294 - 30 Mar 2016

Cited by 31 | Viewed by 7168

Abstract

The concentration, type, and extent of sea ice in the Arctic can be estimated based on measurements from satellite active microwave sensors, passive microwave sensors, or both. Here, data from the Advanced Scatterometer (ASCAT) and the Special Sensor Microwave Imager/Sounder (SSMIS) are employed to broadly classify Arctic sea ice type as first-year (FY) or multiyear (MY). Combining data from both active and passive sensors can improve the performance of MY and FY ice classification. The classification method uses C-band σ⁰ measurements from ASCAT and 37 GHz brightness temperature measurements from SSMIS to derive a probabilistic model based on a multivariate Gaussian distribution. Using a Gaussian model, a Bayesian estimator selects between FY and MY ice to classify pixels in images of Arctic sea ice. The ASCAT/SSMIS classification results are compared with classifications using the Oceansat-2 scatterometer (OSCAT), the Equal-Area Scalable Earth Grid (EASE-Grid) Sea Ice Age dataset available from the National Snow and Ice Data Center (NSIDC), and the Canadian Ice Service (CIS) charts, also available from the NSIDC. The MY ice extent of the ASCAT/SSMIS classifications demonstrates an average difference of 282 thousand km - + from that of the OSCAT classifications from 2009 to 2014. The difference is an average of 13.6% of the OSCAT MY ice extent, which averaged 2.19 million km² over the same period. Compared to the ice classified as two years or older in the EASE-Grid Sea Ice Age dataset (EASE-2+) from 2009 to 2012, the average difference is 617 thousand km² . The difference is an average of 22.8% of the EASE-2+ MY ice extent, which averaged 2.79 million km² from 2009 to 2012. Comparison with the Canadian Ice Service (CIS) charts shows that most ASCAT/SSMIS classifications of MY ice correspond to a MY ice concentration of approximately 50% or greater in the CIS charts. The addition of the passive SSMIS data appears to improve classifications by mitigating misclassifications caused by ASCAT's sensitivity to rough patches of ice which can appear similar to, but are not, MY ice. Full article

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

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48286 KiB

Open AccessArticle

Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification

by Rudolf Ressel and Suman Singha

Remote Sens. 2016, 8(3), 198; https://doi.org/10.3390/rs8030198 - 29 Feb 2016

Cited by 38 | Viewed by 6702

Abstract

This work compares the polarimetric backscatter behavior of sea ice in spaceborne X-band and C-band Synthetic Aperture Radar (SAR) imagery. Two spatially and temporally coincident pairs of fully polarimetric acquisitions from the TerraSAR-X/TanDEM-X and RADARSAT-2 satellites are investigated. Proposed supervised classification algorithm consists of two steps: The first step comprises a feature extraction, the results of which are ingested into a neural network classifier in the second step. Based on the common coherency and covariance matrix, we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification. Coherency matrix based features which require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix based features, which makes coherency matrix based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant based features (Geometric Intensity, Scattering Diversity, Surface Scattering Fraction). This analysis reveals analogous results for all four acquisitions, in both X-band and C-band frequencies. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected ice types. Full article

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

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8119 KiB

Open AccessArticle

Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

by Hyangsun Han, Jungho Im, Miae Kim, Seongmun Sim, Jinwoo Kim, Duk-jin Kim and Sung-Ho Kang

Remote Sens. 2016, 8(1), 57; https://doi.org/10.3390/rs8010057 - 12 Jan 2016

Cited by 29 | Viewed by 8050

Abstract

Melt ponds, a common feature on Arctic sea ice, absorb most of the incoming solar radiation and have a large effect on the melting rate of sea ice, which significantly influences climate change. Therefore, it is very important to monitor melt ponds in order to better understand the sea ice-climate interaction. In this study, melt pond retrieval models were developed using the TerraSAR-X dual-polarization synthetic aperture radar (SAR) data with mid-incidence angle obtained in a summer multiyear sea ice area in the Chukchi Sea, the Western Arctic, based on two rule-based machine learning approaches—decision trees (DT) and random forest (RF)—in order to derive melt pond statistics at high spatial resolution and to identify key polarimetric parameters for melt pond detection. Melt ponds, sea ice and open water were delineated from the airborne SAR images (0.3-m resolution), which were used as a reference dataset. A total of eight polarimetric parameters (HH and VV backscattering coefficients, co-polarization ratio, co-polarization phase difference, co-polarization correlation coefficient, alpha angle, entropy and anisotropy) were derived from the TerraSAR-X dual-polarization data and then used as input variables for the machine learning models. The DT and RF models could not effectively discriminate melt ponds from open water when using only the polarimetric parameters. This is because melt ponds showed similar polarimetric signatures to open water. The average and standard deviation of the polarimetric parameters based on a 15 × 15 pixel window were supplemented to the input variables in order to consider the difference between the spatial texture of melt ponds and open water. Both the DT and RF models using the polarimetric parameters and their texture features produced improved performance for the retrieval of melt ponds, and RF was superior to DT. The HH backscattering coefficient was identified as the variable contributing the most, and its spatial standard deviation was the next most contributing one to the classification of open water, sea ice and melt ponds in the RF model. The average of the co-polarization phase difference and the alpha angle in a mid-incidence angle were also identified as the important variables in the RF model. The melt pond fraction and sea ice concentration retrieved from the RF-derived melt pond map showed root mean square deviations of 2.4% and 4.9%, respectively, compared to those from the reference melt pond maps. This indicates that there is potential to accurately monitor melt ponds on multiyear sea ice in the summer season at a local scale using high-resolution dual-polarization SAR data. Full article

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

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Open AccessArticle

Sea-Ice Wintertime Lead Frequencies and Regional Characteristics in the Arctic, 2003–2015

by Sascha Willmes and Günther Heinemann

Remote Sens. 2016, 8(1), 4; https://doi.org/10.3390/rs8010004 - 22 Dec 2015

Cited by 74 | Viewed by 9242

Abstract

The presence of sea-ice leads represents a key feature of the Arctic sea ice cover. Leads promote the flux of sensible and latent heat from the ocean to the cold winter atmosphere and are thereby crucial for air-sea-ice-ocean interactions. We here apply a binary segmentation procedure to identify leads from MODIS thermal infrared imagery on a daily time scale. The method separates identified leads into two uncertainty categories, with the high uncertainty being attributed to artifacts that arise from warm signatures of unrecognized clouds. Based on the obtained lead detections, we compute quasi-daily pan-Arctic lead maps for the months of January to April, 2003–2015. Our results highlight the marginal ice zone in the Fram Strait and Barents Sea as the primary region for lead activity. The spatial distribution of the average pan-Arctic lead frequencies reveals, moreover, distinct patterns of predominant fracture zones in the Beaufort Sea and along the shelf-breaks, mainly in the Siberian sector of the Arctic Ocean as well as the well-known polynya and fast-ice locations. Additionally, a substantial inter-annual variability of lead occurrences in the Arctic is indicated. Full article

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

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Multi-Decadal Variability of Polynya Characteristics and Ice Production in the North Water Polynya by Means of Passive Microwave and Thermal Infrared Satellite Imagery

by Andreas Preußer, Günther Heinemann, Sascha Willmes and Stephan Paul

Remote Sens. 2015, 7(12), 15844-15867; https://doi.org/10.3390/rs71215807 - 27 Nov 2015

Cited by 28 | Viewed by 7418

Abstract

The North Water (NOW) Polynya is a regularly-forming area of open-water and thin-ice, located between northwestern Greenland and Ellesmere Island (Canada) at the northern tip of Baffin Bay. Due to its large spatial extent, it is of high importance for a variety of physical and biological processes, especially in wintertime. Here, we present a long-term remote sensing study for the winter seasons 1978/1979 to 2014/2015. Polynya characteristics are inferred from (1) sea ice concentrations and brightness temperatures from passive microwave satellite sensors (Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2), Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager/Sounder (SSM/I-SSMIS)) and (2) thin-ice thickness distributions, which are calculated using MODIS ice-surface temperatures and European Center for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis data in a 1D thermodynamic energy-balance model. Daily ice production rates are retrieved for each winter season from 2002/2003 to 2014/2015, assuming that all heat loss at the ice surface is balanced by ice growth. Two different cloud-cover correction schemes are applied on daily polynya area and ice production values to account for cloud gaps in the MODIS composites. Our results indicate that the NOW polynya experienced significant seasonal changes over the last three decades considering the overall frequency of polynya occurrences, as well as their spatial extent. In the 1980s, there were prolonged periods of a more or less closed ice cover in northern Baffin Bay in winter. This changed towards an average opening on more than 85% of the days between November and March during the last decade. Noticeably, the sea ice cover in the NOW polynya region shows signs of a later-appearing fall freeze-up, starting in the late 1990s. Different methods to obtain daily polynya area using passive microwave AMSR-E/AMSR2 data and SSM/I-SSMIS data were applied. A comparison with MODIS data (thin-ice thickness ≤ 20 cm) shows that the wintertime polynya area estimates derived by MODIS are about 30 to 40% higher than those derived using the polynya signature simulation method (PSSM) with AMSR-E data. In turn, the difference in polynya area between PSSM and a sea ice concentration (SIC) threshold of 70% is fairly low (approximately 10%) when applied to AMSR-E data. For the coarse-resolution SSM/I-SSMIS data, this difference is much larger, particularly in November and December. Instead of a sea ice concentration threshold, the PSSM method should be used for SSM/I-SSMIS data. Depending on the type of cloud-cover correction, the calculated ice production based on MODIS data reaches an average value of 264.4 ± 65.1 km 3 to 275.7 ± 67.4 km 3 (2002/2003 to 2014/2015) and shows a high interannual variability. Our achieved long-term results underline the major importance of the NOW polynya considering its influence on Arctic ice production and associated atmosphere/ocean processes. Full article

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

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