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Remote Sensing
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Combining Different Data Sources for Environmental and Operational Satellite Monitoring...
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Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions

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

Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 47239

Special Issue Editors

Prof. Dr. Wolfgang Dierking

E-Mail Website1 Website2
Guest Editor
1. Center for Integrated Remote Sensing and Forecasting for Arctic Operations (CIRFA), UiT The Arctic University of Norway, Tromsø, Norway
2. Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Bremerhaven, Germany
Interests: remote sensing of the Polar Regions; sensor technologies; field and airborne measurement techniques; image processing methods; parameter retrieval algorithms
Dr. Marko Makynen

E-Mail
Guest Editor
Finnish Meteorological Institute, Erik Palmenin aukio 1, FI-00560 Helsinki, Finland
Interests: sea ice; Arctic environments; ship-based observations
Special Issues, Collections and Topics in MDPI journals
Mr. Markku Similä

E-Mail Website
Guest Editor
Marine Research Unit, Finnish Meteorological Institute, Helsinki, Finland
Interests: Imaging and non-imaging microwave remote sensing of sea ice focusing on the Baltic and Arctic; statistical analysis of single and combined data sets; the development of operational marine services

Special Issue Information

Dear Colleagues,

Satellite remote sensing is an important tool for monitoring the state of and changes in the sea ice cover in the Arctic, Antarctic, and other regions such as, for example, the Baltic and the Bohai Sea. Information on daily and weekly changes—provided by operational ice services—is essential for marine traffic and operations in ice-infested waters, and improves the understanding and forecasting of short-term interactions between atmosphere, ice, and ocean. When focusing on regional and local sea ice conditions, the synthetic aperture radar (SAR) is one of the most useful sensors. However, the interpretation and analysis of SAR images may be prone to ambiguities. Since we are dependent on operational or scientific applications, it is therefore beneficial to combine SAR images with data obtained from other types of satellite sensors (e.g., optical and thermal spectrometers, microwave radiometers, altimeters, scatterometers) and/or to link them with results from airborne and ground measurements when available. Examples for applications are ice type mapping, ice thickness retrieval, detection of ice drift and deformation, studies of lead or polynya dynamics, monitoring of sea ice thermodynamic state (e.g. melting conditions), or detection of ice areas most suitable for navigation. The retrieval of sea ice conditions and parameters does not only benefit from the combination of different data sources but also from linking such retrievals with results from modeling sea ice thermodynamics and dynamics, or interpreting remote sensing data based on simulations of the interaction between electromagnetic radiation and sea ice.

This planned issue of Remote Sensing shall specifically address the potential of combining SAR with different complementary data sources (satellite, airborne, field, modeling) in science studies and for operational applications, considering the most advanced technologies, for enhancing the sea ice monitoring capabilities and reducing ambiguities in data analysis. Also, studies of suitable methods for analyzing merged data sets are welcome.

Examples are:

  • multi-polarization and multi-frequency SAR for ice classification;
  • different combinations of SAR, laser and radar altimeter, and radiometer and spectrometer for ice thickness retrieval (both thin (<0.5 m) and thick ice);
  • mixing of image sequences obtained at different SAR frequencies, polarizations, and/or imaging modes, or from SAR and complementary sensor types, for improving temporal resolution of ice drift/deformation retrievals;
  • using remotely sensed data from different sensor types (including SAR) as input or for validation of models simulating, e.g., evolution of polynyas or sea ice deformation;
  • sea ice thermodynamic stages (e.g., melt ponding and its evolution) determined from combinations of SAR and complementary sensor data and thermodynamic modeling;
  • comparison between observed radar signatures and calculations using scattering models with realistic ranges of input parameters;
  • examples of applications of interferometric SAR and complementary data for sea ice studies;

Other topics in line with the general idea of the special issue are, of course, also very welcome.

In the hope of receiving many exciting contributions.

Prof. Dr. Wolfgang Dierking
Adjunct Prof. Dr. Marko Mäkynen
Mr. Markku Similä
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.

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Published Papers (11 papers)

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Editorial

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4 pages, 186 KiB  
Editorial
Editorial for the Special Issue “Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions”
by Wolfgang Dierking, Marko Mäkynen and Markku Similä
Remote Sens. 2020, 12(4), 606; https://doi.org/10.3390/rs12040606 - 12 Feb 2020
Cited by 2 | Viewed by 1830
Abstract
Satellite remote sensing is an important tool for continuous monitoring of sea ice covered ocean regions and spatial and temporal variations of their geophysical characteristics [...] Full article
(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)

Research

Jump to: Editorial, Other

17 pages, 5488 KiB  
Article
Sensitivity of Radar Altimeter Waveform to Changes in Sea Ice Type at Resolution of Synthetic Aperture Radar
by Wiebke Aldenhoff, Céline Heuzé and Leif E. B. Eriksson
Remote Sens. 2019, 11(22), 2602; https://doi.org/10.3390/rs11222602 - 6 Nov 2019
Cited by 11 | Viewed by 3724
Abstract
Radar altimetry in the context of sea ice has mostly been exploited to retrieve basin-scale information about sea ice thickness. In this paper, we investigate the sensitivity of altimetric waveforms to small-scale changes (a few hundred meters to about 10 km) of the [...] Read more.
Radar altimetry in the context of sea ice has mostly been exploited to retrieve basin-scale information about sea ice thickness. In this paper, we investigate the sensitivity of altimetric waveforms to small-scale changes (a few hundred meters to about 10 km) of the sea ice surface. Near-coincidental synthetic aperture radar (SAR) imagery and CryoSat-2 altimetric data in the Beaufort Sea are used to identify and study the spatial evolution of altimeter waveforms over these features. Open water and thin ice features are easily identified because of their high peak power waveforms. Thicker ice features such as ridges and multiyear ice floes of a few hundred meters cause a response in the waveform. However, these changes are not reflected in freeboard estimates. Retrieval of robust freeboard estimates requires homogeneous floes in the order of 10 km along-track and a few kilometers to both sides across-track. We conclude that the combination of SAR imagery and altimeter data could improve the local sea ice picture by extending spatially scarce freeboard estimates to regions of similar SAR signature. Full article
(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Beaufort Sea study area: Sentinel-1 images are shown in blue and the red lines represent the CryoSat-2 altimeter ground tracks. The inset in the upper left corner shows the location of the study area in the Arctic.</p> Full article ">Figure 2
<p>Example waveforms for different surface types. (<b>a</b>) Lead, (<b>b</b>) first-year and (<b>c</b>) multiyear sea ice. Note the difference in the scaling of the y-axis.</p> Full article ">Figure 3
<p>Example of waveform evolution over a multiyear sea ice floe 2018-02-18. (<b>a</b>) HV SAR image with altimeter footprint in blue rectangles [Contains Copernicus Sentinel data 2018] and (<b>b</b>) waveforms in different colors for distinction and freeboard as a solid black line.</p> Full article ">Figure 4
<p>Waveform parameters of different ice types covering all winter seasons. (<b>a</b>) Pulse peakiness, (<b>b</b>) scaled mean power, (<b>c</b>) stack standard deviation and (<b>d</b>) mean HV backscatter intensities of altimeter footprint. Solid lines represent a kernel density estimate from measured histograms.</p> Full article ">Figure 5
<p>Waveform parameters of FYI for the different winter seasons. (<b>a</b>) Pulse peakiness, (<b>b</b>) scaled mean power, (<b>c</b>) stack standard deviation and (<b>d</b>) mean HV backscatter intensities of altimeter footprint. Solid lines represent a kernel density estimate from measured histograms.</p> Full article ">Figure 6
<p>Waveform parameters of MYI for the different winter seasons. (<b>a</b>) Pulse peakiness, (<b>b</b>) scaled mean power, (<b>c</b>) stack standard deviation and (<b>d</b>) mean HV backscatter intensities of altimeter footprint. Solid lines represent a kernel density estimate from measured histograms.</p> Full article ">Figure 7
<p>Freeboard distributions for (<b>a</b>) FYI and MYI of all winter seasons, (<b>b</b>) FYI and (<b>c</b>) MYI for the three winter seasons. Solid lines are kernel density estimates of the histograms.</p> Full article ">Figure 8
<p>Waveform evolution and SAR imagery over a large ridge feature on 2016-03-15 with time separation less than five minutes. (<b>a</b>) SAR HV image with blue rectangles marking the altimeter footprints, (<b>b</b>,<b>c</b>) HH and HV mean backscatter with standard deviation interval in light blue and minimum and maximum values in grey, (<b>d</b>) waveforms, (<b>e</b>) mean inverse power and freeboard and (<b>f</b>) SSD and PP. [Contains Copernicus Sentinel data 2016]</p> Full article ">Figure 9
<p>Evolution of waveforms over the ridge in vicinity of the large negative freeboard value.</p> Full article ">Figure 10
<p>Waveform evolution and SAR imagery over a large MYI floe on 22 February 2018 with time separation of about 20 min. (<b>a</b>) SAR HH image with blue rectangles marking the altimeter footprints, (<b>b</b>,<b>c</b>) HH and HV mean backscatter with standard deviation interval in light blue and minimum and maximum values in grey, (<b>d</b>) waveforms, (<b>e</b>) mean inverse power and freeboard and (<b>f</b>) SSD and PP. [Contains Copernicus Sentinel data 2018]</p> Full article ">Figure 11
<p>Waveform evolution and SAR imagery over a small MYI floe embedded in FYI on 2018-02-24 with time separation of about 10 min. The small MYI floe is split in two by a high backscatter FYI feature. (<b>a</b>) SAR HV image with blue rectangles marking the altimeter footprints, (<b>b</b>,<b>c</b>) HH and HV mean backscatter with standard deviation interval in light blue and minimum and maximum values in grey, (<b>d</b>) waveforms, (<b>e</b>) mean inverse power and freeboard and (<b>f</b>) SSD and PP. [Contains Copernicus Sentinel data 2018]</p> Full article ">
31 pages, 23899 KiB  
Article
Comparison of Arctic Sea Ice Concentrations from the NASA Team, ASI, and VASIA2 Algorithms with Summer and Winter Ship Data
by Tatiana Alekseeva, Vasiliy Tikhonov, Sergei Frolov, Irina Repina, Mikhael Raev, Julia Sokolova, Evgeniy Sharkov, Ekaterina Afanasieva and Sergei Serovetnikov
Remote Sens. 2019, 11(21), 2481; https://doi.org/10.3390/rs11212481 - 24 Oct 2019
Cited by 27 | Viewed by 4376
Abstract
The paper presents a comparison of sea ice concentration (SIC) derived from satellite microwave radiometry data and dedicated ship observations. For the purpose, the NASA Team (NT), Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI), and Variation Arctic/Antarctic Sea Ice Algorithm [...] Read more.
The paper presents a comparison of sea ice concentration (SIC) derived from satellite microwave radiometry data and dedicated ship observations. For the purpose, the NASA Team (NT), Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI), and Variation Arctic/Antarctic Sea Ice Algorithm 2 (VASIA2) algorithms were used as well as the database of visual ice observations accumulated in the course of 15 Arctic expeditions. The comparison was performed in line with the SIC gradation (in tenths) into very open (1–3), open (4–6), close (7–8), very close and compact (9–10,10) ice, separately for summer and winter seasons. On average, in summer NT underestimates SIC by 0.4 tenth as compared to ship observations, while ASI and VASIA2 by 0.3 tenth. All three algorithms overestimate total SIC in regions of very open ice and underestimate it in regions of close, very close, and compact ice. The maximum average errors are typical of open ice regions that are most common in marginal ice zones. In winter, NT and ASI also underestimate SIC on average by 0.4 and 0.8 tenths, respectively, while VASIA2, on the contrary, overestimates by 0.2 tenth against the ship data, however, for open and close ice the average errors are significantly higher than in summer. In the paper, we also estimate the impact of ice melt stage and presence of new ice and nilas on SIC derived from NT, ASI, and VASIA2. Full article
(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Dependencies of tangents <span class="html-italic">tg</span>(85–19 V) (<b>A</b>), <span class="html-italic">tg</span>(85–37 H) (<b>B</b>), and <span class="html-italic">tg</span>(37–19 V) (<b>C</b>) on sea ice concentration (SIC). Open region (1) between the two straight lines corresponds to ice cover with and without snow on top, shaded triangle (2) to ice cover with snow-water mixture (SWM). Dash-dotted and dashed lines denote the medians of the regions.</p> Full article ">Figure 2
<p>Cruise tracks of the research vessels and icebreakers that hosted the dedicated summer (<b>A</b>) and winter (<b>B</b>) ice observations.</p> Full article ">Figure 3
<p>Schematic illustration of matching the ship and the satellite microwave SIC data.</p> Full article ">Figure 4
<p>Distribution of observed visibility in % of ship track length. Visibility was recalculated from nautical miles (units of observations) to kilometers.</p> Full article ">Figure 5
<p>Distribution of ship data coverage area in pixels of 25 km (<b>A</b>) and 12.5 km (<b>B</b>) resolution in summer and winter.</p> Full article ">Figure 6
<p>Ice cover in the east of the East Siberian Sea on 26 July 2017: (<b>a</b>) Terra MODIS (or Moderate Resolution Imaging Spectroradiometer), 00:30 UTC (Coordinated Universal Time), RGB (Red, Green, Blue) composite of bands 2-2-1; (<b>b</b>) Arctic and Antarctic Research Institute (AARI) overview SIC chart based on Terra MODIS image (<b>a</b>), (<b>c</b>) SIC derived from NASA Team (NT) algorithm, resolution 25 km (dataset Sea Ice Concentrations from Nimbus-7 Special Sensor Microwave Radiometer (SMMR) and Defence Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) - Special Sensor Microwave Imager Sounder (SSMIS) Passive Microwave Data, Version 1 (NSIDC-0051)); (<b>d</b>) SIC derived from Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) algorithm, resolution 6.25 km (version 5.4.). Both the radiometer-derived SIC charts are shown at the same scale as the AARI one. In (<b>b</b>–<b>d</b>), the SIC is in tenths: 0–1 corresponds to 0–9%, 1–3 to 10–30%, 4–6 to 31–60%, 7–8 to 61–80%, and 9–10 to 81–100%.</p> Full article ">Figure 7
<p>(<b>A</b>,<b>B</b>) Average error distribution of SIC derived from NT against the ship data in summer and winter seasons. Blue bars denote C<sub>smr</sub>–C<sub>so</sub>, green bars C<sub>smr</sub>–C<sub>so-ni</sub>. Pixel number is noted above each SIC range. Green numbers show the percentage of new ice and nilas in C<sub>so</sub> for each SIC range.</p> Full article ">Figure 8
<p>(<b>A</b>,<b>B</b>) Average error distribution of SIC derived from ASI against the ship data in summer and winter seasons. Blue bars denote <span class="html-italic">C<sub>smr</sub></span>–<span class="html-italic">C<sub>so</sub></span>, green bars <span class="html-italic">C<sub>smr</sub></span>–<span class="html-italic">C<sub>so-ni</sub></span>. Pixel number is noted above each SIC range. Green numbers show the percentage of new ice and nilas in C<sub>so</sub> for each SIC range.</p> Full article ">Figure 9
<p>(<b>A</b>,<b>B</b>) Average error distribution of SIC derived from VASIA2 against the ship data in summer and winter seasons. Blue bars denote C<sub>smr</sub>–C<sub>so</sub>, green bars C<sub>smr</sub>–C<sub>so-ni</sub>. Pixel number is noted above each SIC range. Green numbers show the percentage of new ice and nilas in C<sub>so</sub> for each SIC range.</p> Full article ">Figure 10
<p>(<b>A</b>–<b>F</b>) Observed C<span class="html-italic"><sub>so</sub></span> vs SIC derived by NT, ASI, and VASIA2 in summer and winter.</p> Full article ">Figure 11
<p>(<b>A</b>–<b>F</b>) Observed C<span class="html-italic"><sub>so-ni</sub></span> vs SIC derived by NT, ASI, and VASIA2 in summer and winter seasons.</p> Full article ">Figure 11 Cont.
<p>(<b>A</b>–<b>F</b>) Observed C<span class="html-italic"><sub>so-ni</sub></span> vs SIC derived by NT, ASI, and VASIA2 in summer and winter seasons.</p> Full article ">Figure 12
<p>Photo of 27 August 2016, taken on board RV Akademik Tryoshnikov in the Laptev Sea at 75.56 N, 126.84 E (by T.A. Alekseeva).</p> Full article ">Figure 13
<p>Photo of 20 April 2011, taken on board Kapitan Danilkin cargo vessel in the Kara Sea (east of Cape Gelaniya) by T.A. Alekseeva.</p> Full article ">Figure 14
<p>(<b>A</b>,<b>B</b>) The difference between NT (25 km) and ship observations depending on ice melt stage. The horizontal axis denotes the difference C<sub>smr</sub>–C<sub>so</sub> (tenths), the vertical axis the melt stage (points). Color intensity indicates the C<sub>smr</sub>–C<sub>so</sub> count for particular melt stage.</p> Full article ">Figure 15
<p>(<b>A</b>,<b>B</b>) The difference between ASI (12.5 km) and ship observations depending on ice melt stage. The horizontal axis denotes the difference C<sub>smr</sub>–C<sub>so</sub> (tenths), the vertical axis the melt stage (points). Color intensity indicates the C<sub>smr</sub>–C<sub>so</sub> count for particular melt stage.</p> Full article ">Figure 16
<p>(<b>A</b>,<b>B</b>) The difference between VASIA2 (12.5 km) and ship observations depending on ice melt stage. The horizontal axis denotes the difference C<sub>smr</sub>–C<sub>so</sub> (tenths), the vertical axis the melt stage (points). Color intensity indicates the C<sub>smr</sub>–C<sub>so</sub> count for particular melt stage.</p> Full article ">Figure A1
<p>Example (excerpt) of an ice journal compiled during visual ship observations (here from a 2004 cruise of RV Akademik Fedorov).</p> Full article ">
27 pages, 12037 KiB  
Article
Remote Sensing of Ice Phenology and Dynamics of Europe’s Largest Coastal Lagoon (The Curonian Lagoon)
by Rasa Idzelytė, Igor E. Kozlov and Georg Umgiesser
Remote Sens. 2019, 11(17), 2059; https://doi.org/10.3390/rs11172059 - 2 Sep 2019
Cited by 11 | Viewed by 3847
Abstract
A first-ever spatially detailed record of ice cover conditions in the Curonian Lagoon (CL), Europe’s largest coastal lagoon located in the southeastern Baltic Sea, is presented. The multi-mission synthetic aperture radar (SAR) measurements acquired in 2002–2017 by Envisat ASAR, RADARSAT-2, Sentinel-1 A/B, and [...] Read more.
A first-ever spatially detailed record of ice cover conditions in the Curonian Lagoon (CL), Europe’s largest coastal lagoon located in the southeastern Baltic Sea, is presented. The multi-mission synthetic aperture radar (SAR) measurements acquired in 2002–2017 by Envisat ASAR, RADARSAT-2, Sentinel-1 A/B, and supplemented by the cloud-free moderate imaging spectroradiometer (MODIS) data, are used to document the ice cover properties in the CL. As shown, satellite observations reveal a better performance over in situ records in defining the key stages of ice formation and decay in the CL. Using advantages of both data sources, an updated ice season duration (ISD) record is obtained to adequately describe the ice cover season in the CL. High-resolution ISD maps provide important spatial details of ice growth and decay in the CL. As found, ice cover resides longest in the south-eastern CL and along the eastern coast, including the Nemunas Delta, while the shortest ice season is observed in the northern CL. During the melting season, the ice melt pattern is clearly shaped by the direction of prevailing winds, and ice drift velocities obtained from a limited number of observations range within 0.03–0.14 m/s. The pronounced shortening of the ice season duration in the CL is observed at a rate of 1.6–2.3 days year‒1 during 2002–2017, which is much higher than reported for the nearby Baltic Sea regions. While the timing of the freeze onset and full freezing has not changed much, the dates of the final melt onset and last observation of ice have a clear decreasing pattern toward an earlier ice break-up and complete melt-off due to an increase of air temperature strongly linked to the North Atlantic Oscillation (NAO). Notably, the correlation between the ISD, air temperature, and winter NAO index is substantially higher when considering the lagoon-averaged ISD values derived from satellite observations compared to those derived from coastal records. The latter clearly demonstrated the richness of the satellite observations that should definitely be exploited in regional ice monitoring programs. Full article
(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)
►▼ Show Figures

Figure 1

Figure 1
<p>Location of the study area–the Curonian Lagoon, with respect to the Baltic Sea. The red frame in the smaller map shows the location of the lagoon in the southeastern part of the Baltic Sea, while the red points indicate the locations of the coastal stations. The contour lines inside the lagoon indicate the bathymetry.</p> Full article ">Figure 2
<p>Intercomparison of the (<b>a</b>) freeze onset (FO), (<b>b</b>) full freezing (FF), (<b>c</b>) melt onset (MO), (<b>d</b>) last observation of ice cover (LOI) dates, and (<b>e</b>) ice season duration (<math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>s</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>S</mi> <mi>A</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math> ) derived from the satellite observations (solid lines) and in situ records (dashed lines), and the time difference between them (black solid line).</p> Full article ">Figure 3
<p>Sentinel-1B image of ice outflow from the Curonian Lagoon to the Baltic Sea (taken on the January 6 2017). The light blue arrow indicates the magnitude of the ice outflow from the port gates.</p> Full article ">Figure 4
<p>MODIS images of the ice cover retreat in the northern Curonian Lagoon during the winter of 2002–2003. Labels and arrows indicate the wind speed and direction.</p> Full article ">Figure 5
<p>Examples of the ice drift in the Curonian Lagoon observed in the SAR images. Envisat ASAR images of the two ice floes in the Lithuanian part of the Curonian Lagoon taken on 4 April 2005 at 8:48:44 UTC (<b>a</b>) and at 20:13:50 UTC (<b>b</b>). Sentinel-1A images of the ice floes in the Russian part of the lagoon acquired on 28 February 2015 at 16:19:21 UTC (<b>c</b>) and 2 March at 04:51:06 UTC (<b>d</b>).</p> Full article ">Figure 6
<p>SAR-derived parameters of the ice drift in the Curonian Lagoon. (<b>a</b>) Drift of two ice floes observed on 4 April 2005; (<b>b</b>) drift of an ice floe on 28 March 2009; (<b>c</b>) drift of the first ice floe on 28 February and 2 March 2015; (<b>d</b>) drift of the second ice floe on 28 February and 2 March 2015. Labeled points indicate the polygon centroids, lines represent the linear distance between them, and the arrows indicate an average wind speed and direction.</p> Full article ">Figure 7
<p>Variations of the average air temperature (blue line) and percentage of the ice cover (gray bars) in the Curonian Lagoon during the shortest winter season of 2007–2008.</p> Full article ">Figure 8
<p>The ice cover retreat in the Curonian Lagoon in the winter of 2011–2012 under the prevalence of northwesterly winds. The red color indicates the ice cover boundary, the blue color shows water, the labels and arrows show the wind speed and direction.</p> Full article ">Figure 9
<p>The ice cover retreat in the Curonian Lagoon in the winter of 2013–2014 under the prevalence of easterly and northerly winds. The red color indicates the ice cover boundary, the blue color shows water, the labels and arrows show the wind speed and direction.</p> Full article ">Figure 10
<p>Yearly-mean ice season duration in the Curonian Lagoon as derived from the satellite data in 2002–2017.</p> Full article ">Figure 11
<p>Yearly maps of the ice season duration in the Curonian Lagoon obtained from satellite data in 2002–2017.</p> Full article ">Figure 12
<p>The 10-day averaged percentage of the ice cover extent in the Curonian Lagoon for the three winter categories.</p> Full article ">Figure 13
<p>The spatial variations of the ice cover extent during (<b>a</b>) ice formation and (<b>b</b>) ice melting periods. The colors represent the percentage of ice observations during these periods, i.e., blue color in (<b>a</b>) shows the areas where the ice starts to form first, while the red color in (<b>b</b>) shows the areas where the ice melts first.</p> Full article ">Figure 14
<p>The interannual variability of the ice freeze onset, full freezing, final melt onset, and last observation of ice dates determined from the satellite data (solid lines) and their trends (dashed lines).</p> Full article ">Figure 15
<p>The interannual variability of the various ice season duration types (<b>a</b>), NAO winter index, and cumulative negative air temperature during the 15 winter seasons (<b>b</b>) in 2002–2017.</p> Full article ">Figure 16
<p>Scatterplots of the ice season duration values derived from the coastal records (<math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>s</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math>, green points), joint use of the coastal and satellite data (<math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>r</mi> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math>, blue points), and spatially-averaged satellite data (<math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mrow> <mi>S</mi> <mi>A</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math>, red points) compard to the NAO winter index.</p> Full article ">Figure 17
<p>Scatterplots of the cumulative negative air temperature compared to the ice season duration (ISD) obtained from the coastal observations (<b>a</b>), maximum (<b>b</b>) and spatial-mean (<b>c</b>) ISD from the satellite data.</p> Full article ">
15 pages, 6497 KiB  
Article
An Optimal Decision-Tree Design Strategy and Its Application to Sea Ice Classification from SAR Imagery
by Johannes Lohse, Anthony P. Doulgeris and Wolfgang Dierking
Remote Sens. 2019, 11(13), 1574; https://doi.org/10.3390/rs11131574 - 3 Jul 2019
Cited by 33 | Viewed by 4631
Abstract
We introduce the fully automatic design of a numerically optimized decision-tree algorithm and demonstrate its application to sea ice classification from SAR data. In the decision tree, an initial multi-class classification problem is split up into a sequence of binary problems. Each branch [...] Read more.
We introduce the fully automatic design of a numerically optimized decision-tree algorithm and demonstrate its application to sea ice classification from SAR data. In the decision tree, an initial multi-class classification problem is split up into a sequence of binary problems. Each branch of the tree separates one single class from all other remaining classes, using a class-specific selected feature set. We optimize the order of classification steps and the feature sets by combining classification accuracy and sequential search algorithms, looping over all remaining features in each branch. The proposed strategy can be adapted to different types of classifiers and measures for the class separability. In this study, we use a Bayesian classifier with non-parametric kernel density estimation of the probability density functions. We test our algorithm on simulated data as well as airborne and spaceborne SAR data over sea ice. For the simulated cases, average per-class classification accuracy is improved between 0.5% and 4% compared to traditional all-at-once classification. Classification accuracy for the airborne and spaceborne SAR datasets was improved by 2.5% and 1%, respectively. In all cases, individual classes can show larger improvements up to 8%. Furthermore, the selection of individual feature sets for each single class can provide additional insights into physical interpretation of different features. The improvement in classification results comes at the cost of longer computation time, in particular during the design and training stage. The final choice of the optimal algorithm therefore depends on time constraints and application purpose. Full article
(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)
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<p>Traditional multi-class classification for a four-class problem. A feature vector <math display="inline"><semantics> <munder> <mi>x</mi> <mo>̲</mo> </munder> </semantics></math> is assigned to one of the four classes <math display="inline"><semantics> <msub> <mi>ω</mi> <mi>j</mi> </msub> </semantics></math> in a single decision, using the feature set <span class="html-italic">F</span>.</p> Full article ">Figure 2
<p>Decision-tree classification for a four-class problem. A feature vector <math display="inline"><semantics> <munder> <mi>x</mi> <mo>̲</mo> </munder> </semantics></math> is assigned to one of the four classes <math display="inline"><semantics> <msub> <mi>ω</mi> <mi>j</mi> </msub> </semantics></math> after a maximum of three binary decisions, using separate feature sets <math display="inline"><semantics> <msub> <mi>F</mi> <mi>i</mi> </msub> </semantics></math> for each individual decision.</p> Full article ">Figure 3
<p>Design stage of decision tree for a four-class problem. The optimal path through the tree is highlighted in red and may differ from the decision-tree (DT) architecture shown in <a href="#remotesensing-11-01574-f002" class="html-fig">Figure 2</a>. Sequential Forward Feature Selection (SFFS) is run at each black square to determine the feature set <math display="inline"><semantics> <msub> <mi>F</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </semantics></math> during the design stage.</p> Full article ">Figure 4
<p>Single-feature histograms for selected example features of the training data from the simulated test image C4-F25.</p> Full article ">Figure 5
<p>ICESAR dataset. From left to right: C-band VH, C-band VV, L-band false-color (R-HV, G-HH, B-VV), optical scanner. Colored boxes in the L-band image indicate training regions for different classes.</p> Full article ">Figure 6
<p>Result of ICESAR ice type classification from numerically optimized DT.</p> Full article ">
20 pages, 2733 KiB  
Article
A New Retracking Algorithm for Retrieving Sea Ice Freeboard from CryoSat-2 Radar Altimeter Data during Winter–Spring Transition
by Xiaoyi Shen, Markku Similä, Wolfgang Dierking, Xi Zhang, Changqing Ke, Meijie Liu and Manman Wang
Remote Sens. 2019, 11(10), 1194; https://doi.org/10.3390/rs11101194 - 20 May 2019
Cited by 9 | Viewed by 4577
Abstract
A new method called Bézier curve fitting (BCF) for approximating CryoSat-2 (CS-2) SAR-mode waveform is developed to optimize the retrieval of surface elevation of both sea ice and leads for the period of late winter/early spring. We found that the best results are [...] Read more.
A new method called Bézier curve fitting (BCF) for approximating CryoSat-2 (CS-2) SAR-mode waveform is developed to optimize the retrieval of surface elevation of both sea ice and leads for the period of late winter/early spring. We found that the best results are achieved when the retracking points are fixed on positions at which the rise of the fitted Bézier curve reaches 70% of its peak in case of leads, and 50% in case of sea ice. In order to evaluate the proposed retracking algorithm, we compare it to other empirically-based methods currently reported in the literature, namely the threshold first-maximum retracker algorithm (TFMRA) and the European Space Agency (ESA) CS-2 in-depth Level-2 algorithm (L2I). The results of the retracking procedure for the different algorithms are validated using data of the Operation Ice Bridge (OIB) airborne mission. For two OIB campaign periods in March 2015 and April 2016, the mean absolute differences between freeboard values retrieved from CS-2 and OIB data were 9.22 and 7.79 cm when using the BCF method, 10.41 cm and 8.16 cm for TFMRA, and 10.01 cm and 8.42 cm for L2I. This suggests that the sea ice freeboard data can be obtained with a higher accuracy when using the proposed BCF method instead of the TFMRA or the CS-2 L2I algorithm. Full article
(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)
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<p>Map of OIB freeboard for March 2015 (<b>a</b>), April 2016 (<b>b</b>).</p> Full article ">Figure 2
<p>Comparisons of fitting performances using segment strategy a, strategy b, strategy c, and strategy d for typical lead (<b>a</b>) and ice (<b>b</b>) waveforms. Red points are the breakpoints for strategy d.</p> Full article ">Figure 3
<p>Flowchart of the CS-2 ice freeboard retrieving algorithm. Here, SSH is sea surface height and SSA is sea surface height anomaly, respectively.</p> Full article ">Figure 4
<p>Probability density function of sea ice freeboard from the BCF, TFMRA, CS-2 L2I products and OIB data in March 2015 (<b>a</b>) and April 2016 (<b>b</b>). Probability density function of ice freeboard difference between BCF, TFMRA, and CS-2 L2I products compared to OIB in March 2015 (<b>c</b>) and April 2016 (<b>d</b>).</p> Full article ">Figure 5
<p>Ice freeboard from BCF (<b>a</b> and <b>c</b>), probability density function of ice freeboard difference between BCF and TFMRA, BCF and CS-2 L2I products in March 2015 (<b>b</b>) and April 2016 (<b>d</b>). The black polygon defines the extent of the MYI zone.</p> Full article ">
24 pages, 14835 KiB  
Article
Automatic Detection of Small Icebergs in Fast Ice Using Satellite Wide-Swath SAR Images
by Ingri Halland Soldal, Wolfgang Dierking, Anton Korosov and Armando Marino
Remote Sens. 2019, 11(7), 806; https://doi.org/10.3390/rs11070806 - 3 Apr 2019
Cited by 26 | Viewed by 5976
Abstract
Automatic detection of icebergs in satellite images is regarded a useful tool to provide information necessary for safety in Arctic shipping or operations over large ocean areas in near-real time. In this work, we investigated the feasibility of automatic iceberg detection in Sentinel-1 [...] Read more.
Automatic detection of icebergs in satellite images is regarded a useful tool to provide information necessary for safety in Arctic shipping or operations over large ocean areas in near-real time. In this work, we investigated the feasibility of automatic iceberg detection in Sentinel-1 Extra Wide Swath (EWS) SAR images which follow the preferred image mode in operational ice charting. As test region, we selected the Barents Sea where the size of many icebergs is on the order of the spatial resolution of the EWS-mode. We tested a new approach for a detection scheme. It is based on a combination of a filter for enhancing the contrast between icebergs and background, subsequent blob detection, and final application of a Constant False Alarm Rate (CFAR) algorithm. The filter relies mainly on the HV-polarized intensity which often reveals a larger difference between icebergs and sea ice or open water. The blob detector identifies locations of potential icebergs and thus shortens computation time. The final detection is performed on the identified blobs using the CFAR algorithm. About 2000 icebergs captured in fast ice were visually identified in Sentinel-2 Multi Spectral Imager (MSI) data and exploited for an assessment of the detection scheme performance using confusion matrices. For our performance tests, we used four Sentinel-1 EWS images. For judging the effect of spatial resolution, we carried out an additional test with one Sentinel-1 Interferometric Wide Swath (IWS) mode image. Our results show that only 8–22 percent of the icebergs could be detected in the EWS images, and over 90 percent of all detections were false alarms. In IWS mode, the number of correctly identified icebergs increased to 38 percent. However, we obtained a larger number of false alarms in the IWS image than in the corresponding EWS image. We identified two problems for iceberg detection: 1) with the given frequency–polarization combination, not all icebergs are strong scatterers at HV-polarization, and (2) icebergs and deformation structures present on fast ice can often not be distinguished since both may reveal equally strong responses at HV-polarization. Full article
(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)
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<p>The Sentinel-1 image acquisitions used for automatic iceberg detection. The subimages with hatched outline are the regions used for testing and their colors match the corresponding acquisition.</p> Full article ">Figure 2
<p>RGB composite from B4, B3, B2 bands of Sentinel-2 MSI showing manually detected icebergs in (<b>a</b>) smooth fast ice at Franz Josef Land on 4 April 2017 (image size 2800 by 1950 m) and (<b>b</b>) rough fast ice at Nord-Austlandet, Svalbard, on 10 April 2017 (image size 1100 by 800 m).</p> Full article ">Figure 3
<p>A total of 2292 icebergs manually detected in Sentinel-2 image over Franz Josef Land on 4 April 2017. Grey areas are land. Iceberg positions located in the landmask may be due to geolocation errors or details such as bays that are not present in the landmask.</p> Full article ">Figure 4
<p>Schematic overview of the algorithm for automatic iceberg detection using Sentinel-1 SAR data, and the validation of the algorithm using manual detections from Sentinel-2 MSI.</p> Full article ">Figure 5
<p>Schematic representation of PDF fitting to the histogram of the data. The dark blue shaded area represents the TIP (see <a href="#sec3dot4dot4-remotesensing-11-00806" class="html-sec">Section 3.4.4</a>) and the hatched region represents the area corresponding to the PFA. <math display="inline"><semantics> <msub> <mi>T</mi> <mi mathvariant="sans-serif">Λ</mi> </msub> </semantics></math> is the threshold, <math display="inline"><semantics> <msub> <mi>I</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>I</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </semantics></math> are the lower and upper boundaries used to represent the background data respectively, and <math display="inline"><semantics> <msub> <mi>I</mi> <mrow> <mi>b</mi> <mi>l</mi> <mi>o</mi> <mi>b</mi> </mrow> </msub> </semantics></math> is the value of the blob-detected pixel.</p> Full article ">Figure 6
<p>(<b>a</b>) <math display="inline"><semantics> <msubsup> <mi>σ</mi> <mrow> <mi>H</mi> <mi>V</mi> </mrow> <mn>0</mn> </msubsup> </semantics></math>, (<b>b</b>) iDPolRAD filter. The scene is from Franz Josef Land on 4 April 2017, showing icebergs in frozen sea ice. The contrast between background intensity variations and icebergs is strongly enhanced after applying the iDPolRAD filter. The red hatched line shows the transect presented in <a href="#remotesensing-11-00806-f007" class="html-fig">Figure 7</a>.</p> Full article ">Figure 7
<p>A transect from <a href="#remotesensing-11-00806-f006" class="html-fig">Figure 6</a> showing how the iDPolRAD filter enhances the difference between iceberg-value and background-values compared to HH and HV intensities.</p> Full article ">Figure 8
<p>Detected blobs in comparison to manually detected icebergs for <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mi>B</mi> </msub> <mo>=</mo> <mn>5</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>7</mn> </mrow> </msup> </mrow> </semantics></math> and <math display="inline"><semantics> <msub> <mi>S</mi> <mi>M</mi> </msub> </semantics></math> = 0.1 over FJL on 4 April 2017. Grey areas are land. Blobs are classified according to <a href="#remotesensing-11-00806-t003" class="html-table">Table 3</a>.</p> Full article ">Figure 9
<p>TIP values for all blob-detected pixels - both icebergs (orange) and non-icebergs (blue) for the Generalized Gamma distribution. These data includes both dates from the area of smooth ice (4 and 7 April 2017). Note that in (<b>a</b>) both axes are in log-scale, while in (<b>b</b>) only the <span class="html-italic">x</span>-axis is in logarithmic scale.</p> Full article ">Figure 10
<p>Detection results using (<b>a</b>) pixel-by-pixel and (<b>b</b>) blob-detection as a first step. Red circles are manually detected icebergs, blue circles are true positive detected icebergs, and yellow circles are false alarms. Landmasks are white. Small bright dots are due to high iDPolRAD values.</p> Full article ">Figure 11
<p>Relationships between backscattering coefficients at HH- and HV-polarization and the iDPolRAD for blob-detected icebergs and non-icebergs. Data are from (<b>a</b>) FJL, representing smooth fast ice, and (<b>b</b>) NA, representing rough fast ice.</p> Full article ">Figure 12
<p>Backscattering coefficients of icebergs and their corresponding background at HV-polarization. Data are from (<b>a</b>) FJL, representing smooth fast ice, and (<b>b</b>) NA, representing rough fast ice. The orange line indicates where the iceberg value equals the background value.</p> Full article ">Figure 13
<p>iDPolRAD values and backscattering coefficients at HH-polarization and HV-polarization vs incidence angle for blob detected pixels. Orange dots are true icebergs while blue stars are the corresponding background values. The images represents (<b>a</b>) smooth ice, and (<b>b</b>) rough ice, each containing both images for each test site. Note that the iDPolRAD values are a function of the background and we therefore only represent icebergs compared to the incidence angles. Note also that the iDPolRAD values are in log-scale.</p> Full article ">
17 pages, 6039 KiB  
Article
Snow Thickness Estimation on First-Year Sea Ice from Late Winter Spaceborne Scatterometer Backscatter Variance
by John Yackel, Torsten Geldsetzer, Mallik Mahmud, Vishnu Nandan, Stephen E. L. Howell, Randall K. Scharien and Hoi Ming Lam
Remote Sens. 2019, 11(4), 417; https://doi.org/10.3390/rs11040417 - 18 Feb 2019
Cited by 15 | Viewed by 5129
Abstract
Ku- and C-band spaceborne scatterometer sigma nought (σ°) backscatter data of snow covered landfast first-year sea ice from the Canadian Arctic Archipelago are acquired during the winter season with coincident in situ snow-thickness observations. Our objective is to describe a methodological framework for [...] Read more.
Ku- and C-band spaceborne scatterometer sigma nought (σ°) backscatter data of snow covered landfast first-year sea ice from the Canadian Arctic Archipelago are acquired during the winter season with coincident in situ snow-thickness observations. Our objective is to describe a methodological framework for estimating relative snow thickness on first-year sea ice based on the variance in σ° from daily time series ASCAT and QuikSCAT scatterometer measurements during the late winter season prior to melt onset. We first describe our theoretical basis for this approach, including assumptions and conditions under which the method is ideally suited and then present observational evidence from four independent case studies to support our hypothesis. Results suggest that the approach can provide a relative measure of snow thickness prior to σ° detected melt onset at both Ku- and C-band frequencies. We observe that, during the late winter season, a thinner snow cover displays a larger variance in daily σ° compared to a thicker snow cover on first-year sea ice. This is because for a given increase in air temperature, a thinner snow cover manifests a larger increase in basal snow layer brine volume owing to its higher thermal conductivity, a larger increase in the dielectric constant and a larger increase in σ° at both Ku- and C bands. The approach does not apply when snow thickness distributions on first-year sea ice being compared are statistically similar, indicating that similar late winter σ° variances likely indicate regions of similar snow thickness. Full article
(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)
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<p>Franklin Bay [2008] and Cambridge Bay [2014 and 2018] case study locations on landfast first-year sea ice in the western Canadian Arctic Archipelago.</p> Full article ">Figure 2
<p>RADARSAT-1 (<b>a</b>) and RADARSAT-2 (<b>b</b>,<b>c</b>) winter season synthetic aperture radar (SAR) images showing Franklin Bay, 2008 (<b>a</b>), Dease Strait near Cambridge Bay, 2014 (<b>b</b>) and 2018 (<b>c</b>) snow covered sea ice case study sites. In situ mean and standard deviation of snow thickness transect locations are indicated by the red squares inside the QuikSCAT/ASCAT (see <a href="#sec4dot3-remotesensing-11-00417" class="html-sec">Section 4.3</a>) (<b>a</b>) and ASCAT (<b>b</b>,<b>c</b>) 4.45 km ASCAT scatterometer sample locations (yellow squares) and QuikSCAT (blue squares). Note the tonal homogeneity within each of the yellow and blue squares.</p> Full article ">Figure 3
<p>Hourly air temperature and daily precipitation total for Franklin Bay, 2008 (measured at Cape Parry Environment and Climate Change station) (<b>a</b>), Cambridge Bay, 2014 (measured at Cambridge Bay EEEC station) (<b>b</b>) and 2018 (<b>c</b>) (measured at the on-ice station). Thin horizontal and vertical grey lines correspond to the freezing mark and date of melt onset (MO), respectively.</p> Full article ">Figure 4
<p>Time series evolution of daily mean air temperature and QuikSCAT σ° for the Franklin Bay (FB) snow thickness sites between 1 April and 20 May 2008 (during the winter season). The upturn in σ° at melt onset on ~13 May for each of the snow thickness sites is denoted by the vertical dashed line.</p> Full article ">Figure 5
<p>Time series evolution of daily mean air temperature and ASCAT σ° for the Franklin Bay (FB) snow thickness sites between 1 April and 20 May 2008 (during the winter season). The upturn in σ° at melt onset on ~14 May for each of the snow thickness sites is denoted by the vertical dashed line.</p> Full article ">Figure 6
<p>Time series evolution of daily mean air temperature and ASCAT σ° for the Cambridge Bay (CB) snow thickness sites between 1 April and 28 May 2014 (during the winter season). The upturn in σ° at melt onset on ~24 May for each of the snow thickness sites is denoted by the vertical dashed line.</p> Full article ">Figure 7
<p>Time series evolution of daily mean air temperature and ASCAT σ° for the Cambridge Bay (CB) snow thickness sites between 1 April and 15 June 2018 (during the winter season). The upturn in ASCAT σ° at melt onset on ~5 June for each of the snow thickness sites is denoted by the vertical dashed line.</p> Full article ">Figure 8
<p>Damping effect versus snow thickness for all ASCAT samples. The blue line is a linear regression and the dashed purple lines represent the standard error of the regression.</p> Full article ">
16 pages, 531 KiB  
Article
Numerical Analysis of Microwave Scattering from Layered Sea Ice Based on the Finite Element Method
by Xu Xu, Camilla Brekke, Anthony P. Doulgeris and Frank Melandsø
Remote Sens. 2018, 10(9), 1332; https://doi.org/10.3390/rs10091332 - 21 Aug 2018
Cited by 9 | Viewed by 3943
Abstract
A two-dimensional scattering model based on the Finite Element Method (FEM) is built for simulating the microwave scattering of sea ice, which is a layered medium. The scattering problem solved by the FEM is formulated following a total- and scattered-field decomposition strategy. The [...] Read more.
A two-dimensional scattering model based on the Finite Element Method (FEM) is built for simulating the microwave scattering of sea ice, which is a layered medium. The scattering problem solved by the FEM is formulated following a total- and scattered-field decomposition strategy. The model set-up is first validated with good agreements by comparing the results of the FEM with those of the small perturbation method and the method of moment. Subsequently, the model is applied to two cases of layered sea ice to study the effect of subsurface scattering. The first case is newly formed sea ice which has scattering from both air–ice and ice–water interfaces. It is found that the backscattering has a strong oscillation with the variation of sea ice thickness. The found oscillation effects can increase the difficulty of retrieving the thickness of newly formed sea ice from the backscattering data. The second case is first-year sea ice with C-shaped salinity profiles. The scattering model accounts for the variations in the salinity profile by approximating the profile as consisting of a number of homogeneous layers. It is found that the salinity profile variations have very little influence on the backscattering for both C- and L-bands. The results show that the sea ice can be considered to be homogeneous with a constant salinity value in modelling the backscattering and it is difficult to sense the salinity profile of sea ice from the backscattering data, because the backscattering is insensitive to the salinity profile. Full article
(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)
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<p>Geometry illustrating the computational domain for the sea ice scattering.</p> Full article ">Figure 2
<p>Bistatic radar cross section (RCS) of (<b>a</b>) HH and (<b>b</b>) VV polarizations simulated by the Finite Element Method (FEM) and the small perturbation method (SPM) for incidence angle <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mi>i</mi> </msub> <mo>=</mo> <msup> <mn>40</mn> <mo>°</mo> </msup> </mrow> </semantics></math> at C-band.</p> Full article ">Figure 3
<p>Bistatic RCS of (<b>a</b>) HH and (<b>b</b>) VV polarizations simulated by FEM and SPM for incidence angle <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mi>i</mi> </msub> <mo>=</mo> <msup> <mn>40</mn> <mo>°</mo> </msup> </mrow> </semantics></math> at L-band.</p> Full article ">Figure 4
<p>Bistatic RCS for a two-layer structure with (<b>a</b>) slightly rough surfaces and (<b>b</b>) moderately rough surfaces simulated by the FEM and method of moment (MoM) at C-band. The incidence angle was <math display="inline"><semantics> <msup> <mn>45</mn> <mo>°</mo> </msup> </semantics></math> and the polarization was HH.</p> Full article ">Figure 5
<p>Bistatic RCS for the slush-covered sea ice simulated by the FEM and MoM at C-band. The incidence angle was <math display="inline"><semantics> <msup> <mn>40</mn> <mo>°</mo> </msup> </semantics></math> and the polarization was HH.</p> Full article ">Figure 6
<p>Bistatic RCS for the HH polarization simulated by the SPM and the FEM under different surface sizes.</p> Full article ">Figure 7
<p>Geometry illustrating the newly formed sea ice considered for the scattering simulation.</p> Full article ">Figure 8
<p>Backscattering RCS with respect to the thickness of the newly formed sea ice at an incidence angle of <math display="inline"><semantics> <msup> <mn>40</mn> <mo>°</mo> </msup> </semantics></math> for (<b>a</b>) C-band and (<b>b</b>) L-band. The HH and VV results were simulated for both smooth and rough ice–water interfaces.</p> Full article ">Figure 9
<p>Co-polarization ratio with respect to the thickness of the newly formed sea ice at an incidence angle of <math display="inline"><semantics> <msup> <mn>40</mn> <mo>°</mo> </msup> </semantics></math> for (<b>a</b>) C-band and (<b>b</b>) L-band.</p> Full article ">Figure 10
<p>Backscattering ratio of HH and VV polarizations with respect to the thickness of the newly formed sea ice at an incidence angle of <math display="inline"><semantics> <msup> <mn>40</mn> <mo>°</mo> </msup> </semantics></math> for (<b>a</b>) C-band and (<b>b</b>) L-band. The backscattering ratio was defined to describe the scattering difference between smooth and rough ice–water interfaces.</p> Full article ">Figure 11
<p>Graphical representation of six salinity profiles. The top and bottom salinity of first-year sea ice are 6 ppt. The salinity at the middle depth ranges from 1 ppt to 6 ppt.</p> Full article ">Figure 12
<p>Backscattering RCS of HH polarization for six different salinity profiles at (<b>a</b>) C-band and (<b>b</b>) L-band. The results using the assumption that sea ice is a homogeneous medium with a constant salinity are also plotted by using the average salinity of the profile and the salinity in the middle of the sea ice.</p> Full article ">
23 pages, 1309 KiB  
Article
Estimating the Speed of Ice-Going Ships by Integrating SAR Imagery and Ship Data from an Automatic Identification System
by Markku Similä and Mikko Lensu
Remote Sens. 2018, 10(7), 1132; https://doi.org/10.3390/rs10071132 - 18 Jul 2018
Cited by 20 | Viewed by 4259
Abstract
The automatic identification system (AIS) was developed to support the safety of marine traffic. In ice-covered seas, the ship speeds extracted from AIS data vary with ice conditions that are simultaneously reflected by features in synthetic aperture radar (SAR) images. In this study, [...] Read more.
The automatic identification system (AIS) was developed to support the safety of marine traffic. In ice-covered seas, the ship speeds extracted from AIS data vary with ice conditions that are simultaneously reflected by features in synthetic aperture radar (SAR) images. In this study, the speed variation was related to the SAR features and the results were applied to generate a chart of expected speeds from the SAR image. The study was done in the Gulf of Bothnia in March 2013 for ships with ice class IA Super that are able to navigate without icebreaker assistance. The speeds were normalized to dimensionless units ranging from 0 to 10 for each ship. As the matching between AIS and SAR was complicated by ice drift during the time gap (from hours to two days), we calculated a set of local statistical SAR features over several scales. Random forest tree regression was used to estimate the speed. The accuracy was quantified by mean squared error and by the fraction of estimates close to the actual speeds. These depended strongly on the route and the day. The error varied from 0.4 to 2.7 units2 for daily routes. Sixty-five percent of the estimates deviated by less than one speed unit and 82% by less than 1.5 speed units from the AIS speeds. The estimated daily mean speeds were close to the observations. The largest speed decreases were provided by the estimator in a dampened form or not at all. This improved when the ice chart thickness was included as a predictor. Full article
(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)
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<p>Mean ice thickness and maximum degree of ridging in March 2013 (<b>upper panel</b>, <b>lower panel</b>). The maps are based on daily ice charts.</p> Full article ">Figure 2
<p>Intensity of automatic identification system (AIS messages) and average measured speeds of IA Super ships in March 2013 (<b>left</b>, <b>right</b>).</p> Full article ">Figure 3
<p>The AIS (blue line) and estimated (red line) speed distributions.</p> Full article ">Figure 4
<p>The pointwise differences in the 0.5 km scale between AIS speeds and estimates for the whole test data set.</p> Full article ">Figure 5
<p>The AIS and estimated mean speeds for the daily routes. The blue line is the identity line. The parallel green lines deviate from the central line by one unit.</p> Full article ">Figure 6
<p>The AIS and estimated mean speeds for each ice class. The blue line is the identity line. The parallel green lines deviate from the central line by one unit.</p> Full article ">Figure 7
<p>The routewise std of the AIS speeds and MAPE (panel (<b>a</b>)). The KL-distance and MAPE (panel (<b>b</b>)).</p> Full article ">Figure 8
<p>Results from 8 March 2013. (<b>Upper right</b>): SAR image; (<b>upper left</b>) histograms (estimated and AIS speeds); (<b>lower left</b>) estimated speed chart and AIS speeds for the daily route; (<b>lower right</b>) estimated speed chart and estimation accuracy for the daily route. All speeds are in units.</p> Full article ">Figure 9
<p>The daily route recorded on 8 March 2013. Each panel (<b>a</b>–<b>e</b>) comprises a transect of 250 km. For an explanation of the used coloring, see the text.</p> Full article ">Figure 10
<p>Results from 20 March 2013. (<b>Upper right</b>) SAR image; (<b>upper left</b>) histograms (estimated and AIS speeds); (<b>lower left</b>) estimated speed chart and AIS speeds for the daily route; (<b>lower right</b>) estimated speed chart and estimation accuracy for the daily route. All speeds are in units.</p> Full article ">Figure 11
<p>The daily route recorded on 20 March 2013. Each panel (<b>a</b>–<b>e</b>) comprises a transect of 250 km. The coloring and lines are as described in <a href="#remotesensing-10-01132-f009" class="html-fig">Figure 9</a>.</p> Full article ">

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11 pages, 4796 KiB  
Letter
SAR Pancake Ice Thickness Retrieval in the Terra Nova Bay (Antarctica) during the PIPERS Expedition in Winter 2017
by Giuseppe Aulicino, Peter Wadhams and Flavio Parmiggiani
Remote Sens. 2019, 11(21), 2510; https://doi.org/10.3390/rs11212510 - 26 Oct 2019
Cited by 15 | Viewed by 3953
Abstract
Pancake and frazil ice represent an important component of the Arctic and Antarctic cryosphere, especially in marginal ice zones. The retrieval of their thickness by remote sensing is, in general, a difficult task. A processing system was developed and refined by the present [...] Read more.
Pancake and frazil ice represent an important component of the Arctic and Antarctic cryosphere, especially in marginal ice zones. The retrieval of their thickness by remote sensing is, in general, a difficult task. A processing system was developed and refined by the present authors in the framework of the EU SPICES project; it is meant for routinely deriving ice thickness in frazil-pancake regions using the spectral changes in wave spectra from imagery provided by space-borne Synthetic Aperture Radar (SAR) systems. This methodology was successfully tested in the Beaufort Sea through comparison with ground truth collected during the cruise of the “Sikuliaq” in the fall of 2015. In the present study, this technique has been adapted and applied to Antarctic frazil/pancake icefields using COSMO-SkyMed satellite images. Our retrievals were analyzed and validated through a comparison with co-located in situ observations collected during the 2017 PIPERS cruise in Terra Nova Bay polynya. A broad agreement was found between measured thicknesses and those retrieved from the SAR analysis. Results and statistics presented and discussed in detail in this study represent a step towards the autonomous measurement of pancake icefields in remote areas such as Antarctic coastal polynyas and marginal ice zones. Full article
(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>PIPERS cruise track map in the Ross Sea. Courtesy of the University of Texas at San Antonio (<a href="http://www.utsa.edu/signl/PIPERS/gallery.html" target="_blank">http://www.utsa.edu/signl/PIPERS/gallery.html</a>). A stereographic map of Antarctica (top-left box) and a zoom on the Terra Nova Bay (TNB) study area (bottom-left box) are also presented. NIS and DIT acronyms identify Nansen Ice Shelf and Drygalski Ice Tongue, respectively.</p> Full article ">Figure 2
<p>Terra Nova Bay seen from CSK SAR-X on (<b>a</b>) 6 May 2017 03:06:45 UTC; (<b>b</b>) 10 May 2017 03:30:40 UTC; (<b>c</b>) 12 May 2017 06:31:28 UTC. The locations of the stripmaps in the imagettes, as well as PIPERS ice core (red circles) and visual observations (yellow circles) sites, are reported in white.</p> Full article ">Figure 3
<p>Observed SAR spectra for stripmap 1 imagettes extracted from 6 May 2017 CSK SAR-X acquisition. Units are wavenumbers Kx/ΔK and Ky/ΔK, with ΔK = π/8 10<sup>−2</sup> rad m<sup>−1</sup>. The top of the spectra is not due north but in the direction of right angles to the incoming main wave vector.</p> Full article ">Figure 4
<p>Scatterplot of SAR retrieved and ASPeCt in situ pancake ice thicknesses co-located in space and time, collected during the 6 May (red circles) and 10 May (blue squares) experiments. Linear fitting between observed and retrieved data (black line) does not include the value on the top right of the figure which does not represent pancake ice.</p> Full article ">Figure 5
<p>Terra Nova Bay in situ pancake ice thickness collected by PIPERS scientists through ASPeCt visual observations on 5–7 May (red circles), 9–10 May (blue squares), 11–12 May (black triangles); and ice cores (black dots).</p> Full article ">
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