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Analysis of high-resolution imagery acquired by the Digital Mapping System during annual, late-winter NASA Operation IceBridge surveys of Arctic sea ice between 2010 and 2018 reveals that pressure ridge sail heights ( ${H} _{\mathbf {S}}$ ) vary regionally and interannually. We find distinct differences in ${H} _{\mathbf {S}}$ distributions between the central Arctic (CA) and the Beaufort/Chukchi Seas region. Our results show that differences with respect to ice type occur within the tails of the ${H} _{\mathbf {S}}$ distributions and that the 95th and 99th percentiles of ${H} _{\mathbf {S}}$ are strong indicators of the predominant ice type in which the pressure ridge formed. During the first part of the study period ${H} _{\mathbf {S}}$ increased, with the largest sails observed in the winters of 2015 and 2016, after which ${H} _{\mathbf {S}}$ declined, suggesting that the most heavily deformed sea ice may have drifted beyond the area surveyed and exited the CA. Our analysis of the interannual and regional variability in sea ice deformation in the western Arctic during the last decade provides an improved understanding of sail height that will help advance ridge parameterizations in sea ice models.

Long-term quantification of sea ice production in coastal polynyas (thin sea ice areas) is an important issue to understand the global overturning circulation and its changes. The Special Sensor Microwave Imager (SSM/I), which has nearly 30 years of observation, is a powerful tool for that purpose owing to its ability to detect thin ice areas. However, previous SSM/I thin ice thickness algorithms differ between regions, probably due to the difference in dominant type of thin sea ice in each region. In this study, we developed an SSM/I thin ice thickness algorithm that accounts for three types of thin sea ice (active frazil, thin solid ice, and a mixture of two types), using the polarization and gradient ratios. The algorithm is based on comparison with the ice thickness derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) for 22 polynya events off the Ross Ice Shelf, off Cape Darnley, and off the Ronne Ice Shelf in the Southern Ocean. The algorithm can properly discriminate the ice type in coastal polynyas and estimate the thickness of thin sea ice (≤20 cm) with an error range of less than 6 cm. We also confirmed that the algorithm can be applied to other passive microwave radiometers with higher spatial resolution to obtain more accurate and detailed distributions of ice type and thickness. The validation of this algorithm in the Arctic Ocean suggests its applicability to the global oceans.

Automatic and visual sea ice classification of SAR imagery is impeded by the incidence angle dependence of backscatter intensities. Knowledge of the angular dependence of different ice types is therefore necessary to account for this effect. While consistent estimates exist for HH polarization for different ice types, they are lacking HV polarization data, especially for multiyear sea ice. Here we investigate the incidence angle dependence of smooth and rough/deformed first-year and multiyear ice of different ages for wintertime dual-polarization Sentinel-1 C-band SAR imagery in the Beaufort Sea. Assuming a linear relationship, this dependence is determined using the difference in incidence angle and backscatter intensities from ascending and descending images of the same area. At cross-polarization rough/deformed first-year sea ice shows the strongest angular dependence with $-\text{0.11}$ dB/ $1^{\circ }$ followed by multiyear sea ice with $-\text{0.07}$ dB/ $\text{1}^{\circ }$ , and old multiyear ice (older than three years) with $-\text{0.04}$ dB/ $\text{1}^{\circ }$ . The noise floor is found to have a strong impact on smooth first-year ice and estimated slopes are therefore not fully reliable. At co-polarization, we obtained slope values of $-0.24$ , $-0.20$ , $-\text{0.15},$ and $-\text{0.10}$ dB/ $\text{1}^{\circ }$ for smooth first-year, rough/deformed first-year, multiyear, and old multiyear sea ice, respectively. Furthermore, we show that imperfect noise correction of the first subswath influences the obtained slopes for multiyear sea ice. We demonstrate that incidence angle normalization should not only be applied to co-polarization but should also be considered for cross-polarization images to minimize intra ice type variation in backscatter intensity throughout the entire image swath.

As a first step towards a new combined product for sea ice classification based on optical/thermal data collected by Sentinel-3 satellites and SAR data from Sentinel-1 satellites, which can be used as an appropriate support for navigation in Arctic and sub-Arctic waters, two existing classification algorithms are adapted to these data. The classification based on optical data has improved, so it is expected that the results will be ideally suited to be processed together with SAR data into significantly improved sea ice information products to support marine navigation. The usefulness of the combined processing is demonstrated by means of two simple algorithms and a more sophisticated approach is outlined, which will be realized in the future in order to form the basis for an integration into an operational service with the involvement of further partners and users.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, 資料番号: PA1910066000

The importance of the area of active frazil and associated high sea-ice production in Antarctic coastal polynyas has been increasingly recognized in recent in situ and high-resolution satellite observations. However, the occurrence and spatial distribution of active-frazil area are not well understood. We present the first mapping of active-frazil area for Antarctic coastal polynyas, based on the thin ice algorithm of AMSR-E that discriminates active-frazil area. Mapping of sea-ice production is presented by taking account of active-frazil area. Active-frazil area is predominant along East Antarctica, particularly very close to the coast, locally leading to very high ice-production rates exceeding 20 m/yr. For frazil-dominant polynyas, estimated ice production is similar to 20%-50% higher compared with previous studies. Analyses of all the major polynyas suggest that active-frazil extent depends on offshore wind and air temperature, while ice production is determined by offshore wind only.

Accurate maps of ice concentration and ice type are needed to address increased interest in commercial marine transportation through the Arctic. RADARSAT-2 SAR imagery is the primary source of data used by expert ice analysts at the Canadian Ice Service (CIS) to produce sea ice maps over the Canadian territory. This study serves as a proof of concept that neural networks can be used to accurately predict ice type from SAR data. Datasets of SAR images served as inputs, and CIS ice charts served as labelled outputs to train a neural network to classify sea ice type. Our results show that DenseNet achieves the highest overall classification accuracy of 94.0% including water and the highest ice classification accuracy of 91.8% on a three class dataset using a fusion of HH and HV SAR polarizations for the input samples. The 91.8% ice classification accuracy validates the premise that a neural network can be used to effectively categorize different ice types based on SAR data.

Sea ice has been monitored in terms of concentration and types with microwave satellite observations since the late 1970s. However, it remains an open question as to which sea ice type concentration (SITC) method is most appropriate for ice type distribution and hence climate monitoring. This paper presents key results of inter-comparison and evaluation for eight SITC methods. The SITC methods were inter-compared with two sea ice age (SIA) and three sea ice type (SIT) products using microwave radiometer and scatterometer data from 2000 to 2015. Their performances were evaluated quantitatively with samples that are used for generating tie points, and qualitatively with the RADARSAT imagery. The methods that combined scatterometer and radiometer data have overall better performances on ice type discrimination. The best methods are ECICE-QSCAT-2 for the years 2000–2009 and ECICE-ASCAT for 2009–2015, both using scatterometer data along with radiometer data. Although the SIA and SIT products are fairly good datasets for delineating ice type distributions, the SITC methods are better on preserving details like varied concentration of different ice types and work better under specific sea ice conditions, for instance, homogeneous sea ice regions with little artifact for SIA algorithms to track.

A new Sentinel-1 image-based sea ice classification algorithm is proposed to support automated ice charting. Previous studies mostly rely on manual work in selecting training and validation data. We show that the use of readily available ice charts from an operational ice services allow to automate selection of large amount of training/testing data void of biased, subjective decisions. The proposed scheme has two phases: training and operational. Both phases start from removal of thermal, scalloping and textural noise from Sentinel-1 data and calculation of gray level co-occurrence matrix and Haralick texture features in a sliding window. In the training phase, the weekly ice charts are reprojected into the SAR image geometry. A random forest classifier is trained with the texture features on input and labels from the rasterized ice charts on output. Then the trained classifier is directly applied to the texture features from Sentinel-1 images in operational manner. Test results from two winter season dataset acquired over the Fram Strait and Barents Sea area showed that the classifier is capable of retrieving 3 generalized cover types (ice free, integrated first-year ice, old ice) with overall accuracies of 85 % and 5 cover types (ice free, new ice, young ice, first-year ice, old ice) with accuracy of 58 %. The errors are attributed both to incorrect manual classification on the ice charts and to the automated algorithm. We demonstrate the potential for near-real time service of ice type classification through an example of ice maps made from daily mosaiced images.

We construct and present the first baseline, time series evolution of L-band SAR microwave backscatter (σ0HH) for landfast Arctic first-year (FYI) and multi-year (MYI) sea ice from ALOS PALSAR. We compare the winter to freeze-up seasonal L-band σ0HH signatures with C-band RADARSAT-2 SAR imagery and Ku-band QuikSCAT scatterometer data. We also explored the utility of L-band σ0HH to detect FYI and MYI melt onset dates. For both ice types, L-band exhibited a similar σ0HH evolution to C- and Ku-bands. A rise in L-band σ0HH was observed from winter to melt onset, however, with a lower magnitude attributed to the larger penetration of L-band microwaves through the snow-covered sea ice medium. Over FYI, L-band σ0HH exhibited the most robust response and greater diversity in melt onset signature, compared to C- and Ku-bands. Over MYI, L-band σ0HH declined during the winter to melt onset transition contrasting the C- and Ku-band evolution of σ0HH during melt onset. Although, a distinct L-band σ0HH response was not found during pond onset, a very strong σ0HH response was found during sea ice break-up, which could operationally be utilized to identify sea ice break-up timing. During freeze-up, L-band σ0HH showed greater diversity compared to C- and Ku-bands. Our study also found that, L-band detected melt onset dates were up to 6 days later than C- and Ku-band detected dates, for both ice types. The melt response was the strongest at L-band for FYI, where C-band detected a robust melt onset date for MYI. Therefore, for operational sea ice monitoring, L-band suits better for FYI, whereas C-band is still a better choice for MYI. In the warming Arctic, where FYI has become the dominant ice type, L-band demonstrates promise to be the optimal choice for sea ice operational monitoring with high spatiotemporal data acquisition from existing, upcoming and proposed L-band SAR missions.

Space-borne Synthetic Aperture Radar (SAR) images have proven to be very useful tool in challenging Polar environment - due to its active radar antenna which can acquire valuable data in all weathers, though clouds and darkness. In several polar expeditions, German Aerospace Center (DLR) acquired high-resolution SAR images from the satellite mission TerraSAR-X/TanDEM-X over the planned course, and provided them to navigators on board the ship in near real-time. It has been shown that these targeted acquisitions helped the onboard navigators to identify cost effective routes. During the course of MOSAiC campaign German Aerospace Center (DLR) plans to provide SAR derived sea ice related products such as sea ice type, sea ice dirft information within Near Real Time through AWI’s MapViewer. The sea ice type product is based on dual polarized SAR acquisitions and capable to discriminate between open water and different types of sea ice (e.g. First Year Ice, Multi Year Ice). The sea ice drift algorithm is based on two consecutive SAR acquisition can capable to provide high resolution drift information within Near Real Time. During the Potsdam workshop we will show examples of above mentioned products which is not only helpful for campaign planning but also might provide useful information to scientists across different scientific domains.

Compact-polarimetry (CP) synthetic aperture radar (SAR) observations are presented for major sea ice types in each ice season. CP data for three wide-swath Radarsat Constellation Mission (RCM) modes were simulated and evaluated. Regression models and statistical distances as functions of incidence angles were calculated for 26 CP parameters, based on 969 samples of user-selected homogeneous regions of sea ice. CP parameters, best able to discriminate sea ice types and open water, were quantitatively identified in three incidence angle ranges (19–29°, 30–39°, 40–49°). These parameters will likely provide discrimination of sea ice types and open water for both visual interpretation and automated classification. Several parameter–ice type combinations exhibit novel scattering responses, which present new opportunities for ice type discrimination and for inferring scattering mechanisms. Specifically, phase-related parameters with early-stage ice types provide discrimination for ice type pairings tha...

The spatial and temporal dynamics of melt ponds and sea ice albedo contain information on the current state and the trend of the climate of the Arctic region. This publication presents a study on melt pond fraction (MPF) and sea ice albedo spatial and temporal dynamics obtained with the Melt Pond Detection (MPD) retrieval scheme for the Medium Resolution Imaging Spectrometer (MERIS) satellite data. This study compares sea ice albedo and MPF to surface air temperature reanalysis data, compares MPF retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS), and examines albedo and MPF trends. Weekly averages of MPF for 2007 and 2011 showed different MPF dynamics while summer sea ice minimum was similar for both years. The gridded MPF and albedo products compare well to independent reanalysis temperature data and show melt onset when the temperature gets above zero; however MPD shows an offset at low MPFs of about 10 % most probably due to unscreened high clouds. Weekly averaged trends show pronounced dynamics of both, MPF and albedo: a negative MPF trend in the East Siberian Sea and a positive MPF trend around the Queen Elizabeth Islands. The negative MPF trend appears due to a change of the absolute MPF value in its peak, whereas the positive MPF trend is created by the earlier melt onset, with the peak MPF values unchanged. The MPF dynamics in the East Siberian Sea could indicate a temporal change of ice type prevailing in the region, as opposed to the Queen Elizabeth Islands, where MPF dynamics react to an earlier seasonal onset of melt.

The polar ocean’s sea ice cover is an unconventional and challenging geophysical target. Helicopter electromagnetic (HEM) sea-ice thickness mapping is currently limited to 1D interpretation due to traditional procedures and systems. These systems are mainly sensitive to layered structures, ideally set for the widespread flat (level) ice type. Because deformed sea ice (e.g., pressure ridges) is 3D and usually also heterogeneous, ice thickness errors up to 50% can be observed for pressure ridges using 1D approximations for the interpretation of HEM data. We researched a new generation multisensor, airborne sea ice explorer (MAiSIE) to overcome these limitations. Three-dimensional finite-element modeling enabled us to determine that more than one frequency is needed, ideally in the range 1–8 kHz, to improve thickness estimates of grounded sea-ice pressure ridges that are typical of 3D sea ice structures. With the MAiSIE system, we found a new electromagnetic concept based on one multifrequency transmitter loop and a 3C receiver coil triplet with active digital bucking. The relatively small weight of the EM components freed enough payload to include additional scientific sensors, including a cross-track lidar scanner and high-accuracy inertial-navigation system combined with dual-antenna differential GPS. Integrating the 3D ice-surface topography obtained from the lidar with the EM data at frequencies from 500 Hz to 8 kHz in [Formula: see text]-, [Formula: see text]-, and [Formula: see text]-directions, significantly increased the accuracy of sea-ice pressure-ridge geometry derived from HEM data. Initial test flight results over open water showed the proof-of-concept with acceptable sensor drift and receiver sensitivity. Noise levels were relatively high (20–250 parts-per-million) due to unwanted interference, leaving room for optimization. The 20 ppm noise level at 4.1 kHz is sufficient to map level ice thickness with 10 cm precision for sensor altitudes below 13 m.

To evaluate sea ice concentrations (SICs) from the special sensor microwave/imager (SSM/I) and advanced microwave scanning radiometer-EOS (AMSR-E), we observed sea ice with the 6-m-resolution panchromatic electronic optical camera (EOC) sensor onboard the Korea Multi-Purpose Satellite-1 (KOMPSAT-1). A total of 68 cloud-free EOC images were obtained across the Antarctic continental edges from September to November 2005. Sea ice types in the EOC images were classified into white ice (W), gray ice (G), and dark-gray ice (D) and then compared with SSM/I and AMSR-E SICs. Spatiotemporal standard deviation of passive microwave SIC proved useful in selecting temporally stable and spatially homogeneous SICs to overcome the diurnal variation of sea ice in the analysis of data from multiple satellites. In the Antarctic spring, the EOC SIC of W + G showed the best fit to SSM/I SIC calculated by the NASA Team (NT) algorithm (mean difference of -2.3% and rmse of 3.2%), whereas that of W + G + D showed the best fit to AMSR-E SIC calculated by the NT2 algorithm (mean difference of 0.3% and rmse of 1.4%). It is concluded that the SSM/I NT algorithm responds to young ice in addition to the ice types A and B, whereas the AMSR-E NT2 algorithm detects ice type C and thin ice as well. The 4.7% difference of SICs between AMSR-E and SSM/I was attributed to the enhanced detection of ice type C (2.1%) and thin ice (2.6%) of the AMSR-E NT2 algorithm.

In this paper, we explore the capabilities of an algorithm for ice type classification. Our main motivation and exemplary application was the recent incident of the research vessel Akademik Shokalskiy, which was trapped in pack ice for about two weeks. Strong winds had driven ice floes into a way, forming an area of pack ice, blocking the ship's advancement. High-resolution satellite images helped to assess the ice conditions at the location. To extract relevant information automatically from the images, we apply an algorithm that is aimed to generate an ice chart, outlining the different ice type zones such as pack ice, fast ice, open water. The algorithm is based on texture analysis. Textures are selected that allow recognition of different structures in ice. Subsequently, a neural network performs the classification. Since results are output in near real time, the algorithm offers new opportunities for ship routing in ice infested areas.

The mapping and monitoring of sea ice regions represents a key application area for spaceborne synthetic aperture radar (SAR) missions. The European Space Agency (ESA) is under-taking the development of Sentinel-1, an imaging radar mission at C-Band for the continuation of SAR operational applications. Monitoring sea ice zones and the arctic environment is one of the major application areas supported by Sentinel-1. Other spaceborne SAR missions are being developed or considered by a number of space organizations. The study described herein focuses on evaluating the contribution of SAR constellations to operational and scientific monitoring of sea ice conditions through the analysis of multi-parameter SAR datasets (airborne, spaceborne) and through the development and exploitation of a simulation tool able to predict sea ice radar signatures as a function of the ice type and condition and of sensor parameters.

This chapter presents the material structure of sea ice from local scale to large scale. Ice floes form granular drift ice fields, for which continuum approximations are used when the scale of interest is much larger than the floe size. These fields are char- acterized by their ice type, ice compactness, floe size and shape, and ice thickness, as shown in Sections 2.1 and 2.2. Section 2.3 is devoted to thermodynamics of sea ice, which is closely coupled with dynamics and which is a necessary element in sea ice dynamics models.

A sea ice classification technique is introduced. The classification was performed on Radarsat-2 dual polarization image (07.02.2009) and MODIS images (09.02.2009) that covered coastal area in the western part of Baltic Sea. The classification on SAR image was based on Shannon entropy differences that occur on various ice types. Analysis showed that four ice types (water, fast ice, ridged ice and deformed ice) were identifiable. Also two independent analysis were performed on MODIS image. The spectral classification enabled to separate ridged ice regions and fast ice areas. Using histogram analysis ice concentration map was created. The analysis of Modis image enabled to identify regions where water was covering ice surface. The same phenomena was also detected from SAR data.

Fully automatic algorithms have been developed which can produce sea ice type classification maps and sea ice concentration estimates of synthetic aperture radar (SAR) imagery. The sea ice type classification algorithm uses local statistics to determine ice type boundaries, and the ice concentration algorithm iteratively decomposes the histogram into ice and water histograms. algorithms have been used on both simulated imagery and actual SAR imagery gathered during the 1984 and

In April-May 1998 sea ice conditions in the Barents and Kara Seas were studied from synchronous RADARSAT, ERS and "Okean" radar images. SAR images were transmitted in quasi-real time onboard nuclear icebreaker "Sovetsky Soyuz" and signatures of the main sea ice types and features were retrieved and compared with subsatellite observations. RADARSAT and ERS signatures of first-year ice are rather similar, whereas for gray and new ice they can be different. Analysis of synchronous RADARSAT and "Okean" images proved, that large-scale ice features can be identified from both images, whereas navigationally important small-scale features-only from SAR.

Coincident ship based X-, C-, and L-band scatterometer data and measurements of physical ice and snow properties were collected during ERS-1 synthetic aperture radar (SAR) overflights in the Barents Sea in March of 1992. One aspect of this Seasonal Ice Zone Experiment (SIZEX) concentrated on the marginal ice zone (MIZ), where a variety of sea ice types and open ocean conditions were encountered. A specific objective of SIZEX was to determine which MIZ ice types can be clearly discerned using ERS-1 SAR C-band imagery. First- and second-order statistics were generated to characterize ERS-1 ice signature. Comparing these statistics for each ice type, reveals that many of the ice types observed in the MIZ have comparable tones, as indicated by their means (i.e., multiyear and pancakes). However, the use of the SAR image texture may be used in conjunction with the tonal properties to successfully discern the various ice types and open water (all wind regimes) found in the MIZ. >

During a field expedition with the Russian nuclear icebreaker "Sovetsky Soyuz" to the Laptev Sea in September 1997, RADARSAT ScanSAR images were used to investigate late summer ice conditions and support ice navigation. The predominant ice types were first year and newly formed ice, primarily nilas. The in situ observations made from the icebreaker and its helicopter were used to validate the ScanSAR observations and assess the capability of RADARSAT data to discriminate open water, first year and new ice. Also SSM/I and ERS SAR were compared with the ScanSAR data. To observe first year ice at concentrations above 10% seemed to be straight forward, while nilas was difficult to discriminate from open water during low wind conditions. Use of RADARSAT ScanSAR data represents a significant improvement in ice monitoring and navigation in the Northern Sea Route.

ERS-1 Synthetic Aperture Radar (SAR) imagery from the Alaska SAR facility is available to operational ice analysts at the Joint Ice Center. Analyst can use the imagery directly through manual interpretation. One such use would be to provide information on ice conditions for a field expedition. In addition, analysts can run automated algorithms which produce estimates of ice type and ice motion from SAR images. Such automated analysis will become increasingly important if SAR is to be used to improve climatologies of ice characteristics and to improve the performance of dynamic ice models. Here the ice classification and ice motion algorithms developed by the Jet Propulsion Laboratory for ERS-1 SAR imagery are evaluated and results presented. Remote sensing, Synthetic aperture radar, Sea ice.

This paper presents an overview of recent remote-sensing techniques as applied to geophysical studies of floating ice. The current increase in scientific interest in floating ice has occurred during a time of rapid evolution of both remote-sensing platforms and sensors. Mesoscale and macroscale studies of floating ice are discussed under three sensor categories: visual, passive microwave, and active microwave. The specific studies that are reviewed primarily investigate ice drift and deformation, and ice type and ice roughness identification and distribution.

The development of strong preferred c-axis alignments in sea ice has been a subject of debate since its initial discovery in 1959. Although numerous theories have been proposed for its development, none has been able to account for all of its field characteristics.A detailed field investigation was carried out at Pond Inlet, N.W.T., to describe further the occurrence and structure of this ice type. Data collected over the winter of 1983–84 suggest two possible mechanisms of development. The first involves the selective growth of crystals during the formation of the initial ice skim, while the second requires the solid-state rotation of slip planes during constrained plastic deformation.

A computer simulation program which depicts the behaviour and distribution of oil spilled in or under sea ice has been developed. The program combines mathematical models developed in previous studies to describe the motion of oil in/under landfast, first-year and multi-year ice. By inputting or estimating such key parameters as oil type, spill type, spill duration, flow rate, ice type, relative under ice current speed and direction, under ice roughness and distinct ice features, a time series of “maps” is generated showing the distribution of the spilled oil. At present, the simulation does not include the movement of oil contaminated ice but future development plans include incorporation of the program into a Beaufort Sea ice motion model. The program is a tool which can be used to assist on - scene commanders during spill response and to plan counter-measures for oil spills in/under sea ice.