Sea ice monitoring by polar orbiting satellites has been developed over more than four decades and is today one of the most well-established applications of space observations. This article gives an overview of data product development from the first sensors to the state-of-the-art regarding retrieval methods, new products and operational data sets serving climate monitoring as well as daily operational services including ice charting and forecasting. Passive microwave data has the longest history and represents the backbone of global ice monitoring with already more than four decades of consistent observations of ice concentration and extent. Time series of passive microwave data is the primary climate data set to document the sea ice decline in the Arctic. Scatterometer data is a valuable supplement to the passive microwave data, in particular to retrieve ice displacement and distinguish between firstyear and multiyear ice. Radar and laser altimeter data has become the main method to estimate sea ice thickness and thereby fill a gap in the observation of sea ice as an essential climate variable. Data on ice thickness allows estimation of ice volume and masses as well as improvement of the ice forecasts. The use of different altimetric frequencies also makes it possible to measure the depth of the snow covering the ice. Synthetic Aperture Radar (SAR) has become the work horse in operational ice observation on regional scale because high-resolution radar images are delivered year-round in nearly all regions where national ice services produce ice charts. Synthetic Aperture Radar data are also important for sea ice research because the data can be used to observe a number of sea ice processes and phenomena, like ice type development and sea ice dynamics, and thereby contribute to new knowledge about sea ice. The use of sea ice data products in modelling and forecasting services as well as in ice navigation is discussed. Finally, the article describes future plans for new satellites and sensors to be used in sea ice observation.

An unusual, large, latent-heat polynya opened and then closed by freezing and convergence north of Greenland's coast in late winter 2018. The closing presented a natural but well-constrained full-scale ice deformation experiment. We observed the closing of and deformation within the polynya with satellite synthetic-aperture radar (SAR) imagery and measured the accumulated effects of dynamic and thermodynamic ice growth with an airborne electromagnetic (AEM) ice thickness survey 1 month after the closing began. During that time, strong ice convergence decreased the area of the refrozen polynya by a factor of 2.5. The AEM survey showed mean and modal thicknesses of the 1-month-old ice of 1.96 ± 1.5 m and 1.1 m, respectively. We show that this is in close agreement with modeled thermodynamic growth and with the dynamic thickening expected from the polynya area decrease during that time. We found significant differences in the shapes of ice thickness distributions (ITDs) in different regions of the refrozen polynya. These closely corresponded to different deformation histories of the surveyed ice that we reconstructed from Lagrangian ice drift trajectories in reverse chronological order. We constructed the ice drift trajectories from regularly gridded, high-resolution drift fields calculated from SAR imagery and extracted deformation derived from the drift fields along the trajectories. Results show a linear proportionality between convergence and thickness change that agrees well with the ice thickness redistribution theory. We found a proportionality between the e folding of the ITDs' tails and the total deformation experienced by the ice. Lastly, we developed a simple, volume-conserving model to derive dynamic ice thickness change from the combination of Lagrangian trajectories and high-resolution SAR drift and deformation fields. The model has a spatial resolution of 1.4 km and reconstructs thickness profiles in reasonable agreement with the AEM observations. The modeled ITD resembles the main characteristics of the observed ITD, including mode, e folding, and full width at half maximum. Thus, we demonstrate that high-resolution SAR deformation observations are capable of producing realistic ice thickness distributions.

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.

Quantitative parameters characterizing the sea ice surface topography are needed in geophysical investigations such as studies on atmosphere–ice interactions or sea ice mechanics. Recently, the use of space-borne single-pass interferometric synthetic aperture radar (InSAR) for retrieving the ice surface topography has attracted notice among geophysicists. In this paper the potential of InSAR measurements is examined for several satellite configurations and radar frequencies, considering statistics of heights and widths of ice ridges as well as possible magnitudes of ice drift. It is shown that, theoretically, surface height variations can be retrieved with relative errors ≤ 0.5 m. In practice, however, the sea ice drift and open water leads may contribute significantly to the measured interferometric phase. Another essential factor is the dependence of the achievable interferometric baseline on the satellite orbit configurations. Possibilities to assess the influence of different factors on the measurement accuracy are demonstrated: signal-to-noise ratio, presence of a snow layer, and the penetration depth into the ice. Practical examples of sea surface height retrievals from bistatic SAR images collected during the TanDEM-X Science Phase are presented.

Robust and reliable classification of sea ice types in synthetic aperture radar (SAR) images is needed for various operational and environmental applications. Previous studies have investigated the class-dependent decrease in SAR backscatter intensity with incident angle (IA); others have shown the potential of textural information to improve automated image classification. In this work, we investigate the inclusion of Sentinel-1 (S1) texture features into a Bayesian classifier that accounts for linear per-class variation of its features with IA. We use the S1 extra-wide swath (EW) product in ground-range detected format at medium resolution (GRDM), and we compute seven grey level co-occurrence matrix (GLCM) texture features from the HH and the HV backscatter intensity in the linear and logarithmic domain. While GLCM texture features obtained in the linear domain vary significantly with IA, the features computed from the logarithmic intensity do not depend on IA or reveal only a weak, approximately linear dependency. They can therefore be directly included in the IA-sensitive classifier that assumes a linear variation. The different number of looks in the first sub-swath (EW1) of the product causes a distinct offset in texture at the sub-swath boundary between EW1 and the second sub-swath (EW2). This offset must be considered when using texture in classification; we demonstrate a manual correction for the example of GLCM contrast. Based on the Jeffries–Matusita distance between class histograms, we perform a separability analysis for 57 different GLCM parameter settings. We select a suitable combination of features for the ice classes in our data set and classify several test images using a combination of intensity and texture features. We compare the results to a classifier using only intensity. Particular improvements are achieved for the generalized separation of ice and water, as well as the classification of young ice and multi-year ice.

The presented database contains time-referenced sea ice draft values from upward looking sonar (ULS) measurements in the Weddell Sea, Antarctica. The sea ice draft data can be used to infer the thickness of the ice. They were collected during the period 1990–2008. In total, the database includes measurements from 13 locations in the Weddell Sea and was generated from more than 3.7 million measurements of sea ice draft. The files contain uncorrected raw drafts, corrected drafts and the basic parameters measured by the ULS. The measurement principle, the data processing procedure and the quality control are described in detail. To account for the unknown speed of sound in the water column above the ULS, two correction methods were applied to the draft data. The first method is based on defining a reference level from the identification of open water leads. The second method uses a model of sound speed in the oceanic mixed layer and is applied to ice draft in austral winter. Both methods are discussed and their accuracy is estimated. Finally, selected results of the processing are presented. The data can be downloaded from doi:10.1594/PANGAEA.785565.