Antarctic sea ice is generally considered to be free from surface melt, as opposed to the Arctic sea ice where surface melt is prevalent during summer. In this work, we present recent high-resolution Sentinel-2 images which confirm the presence of melt ponds also on the Antarctic sea ice. We employ an optical melt pond fraction retrieval applied to Sentinel-3 data to investigate occurrences of surface melt in the vicinity of Lützow-Holm Bay where sea ice ponding has been confirmed in situ. Sea ice surface melt has the potential to disrupt passive microwave sea ice concentration product. We focus on an area of land fast ice with confirmed 100% ice concentration to highlight this effect on real data.

Contrary to the sea ice surface melt during Arctic summer, melt ponds on top of the Antarctic sea ice during austral summer are thought to be rare. Their observations are limited to few occasional sightings and no regular dataset is published till now. In this work, we employ an existing remote sensing method which was already applied in the Arctic and for the first time detect potential surface melt areas on top of the Antarctic sea ice at a regional to global scale using Sentinel-3 data. We confirm the occurrence of melt ponds in Lützow-Holm Bay in the vicinity of the Shōwa Station observed in situ in January 2017 and detect surface melt in the following years 2018–2020. We also perform global processing of the Antarctic sea ice to detect further regions of potential surface melt.

We present new high-resolution snow depth data on Arctic sea ice derived from airborne microwave radar measurements from the IceBird campaigns of the Alfred Wegener Institute (AWI) together with a new retrieval method using signal peakiness based on an intercomparison exercise of colocated data at different altitudes. We aim to demonstrate the capabilities and potential improvements of radar data, which were acquired at a lower altitude (200 ft) and slower speed (110 kn) and had a smaller radar footprint size (2-m diameter) than previous airborne snow radar data. So far, AWI Snow Radar data have been derived using a 2–18-GHz ultrawideband frequency-modulated continuous-wave (FMCW) radar in 2017–2019. Our results show that our method in combination with thorough calibration through coherent noise removal and system response deconvolution significantly improves the quality of the radar-derived snow depth data. The validation against a 2-D grid ofin situsnow depth measurements on level landfast first-year ice indicates a mean bias of only 0.86 cm between radar and ground truth. Comparison between the radar-derived snow depth estimates from different altitudes shows good consistency. We conclude that the AWI Snow Radar aboard the IceBird campaigns is able to measure the snow depth on Arctic sea ice accurately at higher spatial resolution than but consistent with the existing airborne snow radar data of NASA Operation IceBridge. Together with the simultaneous measurements of the total ice thickness and surface freeboard, the IceBird campaign data will be able to describe the whole sea-ice column on regional scales.

Remote sensing of Antarctic sea ice is required to characterize properties of the vast sea ice cover to understand its long-term increase in contrast to the decrease of Arctic sea ice. For this objective, the OIB/TanDEM-X Coordinated Science Campaign (OTASC) was successfully conducted in 2017 to obtain contemporaneous and collocated remote sensing data from NASA's Operation IceBridge (OIB) and the German Aerospace Center (DLR) TanDEM-X Synthetic Aperture Radar (SAR) system at X band together with Sentinel-1 and RADARSAT-2 SARs at C band in conjunction with WorldView satellite spectral sensors, surface measurements, and field observations. The Weddell Sea and the Ross Sea were two primary regions while SAR data were also collected over six other regions in the Southern Ocean. Satellite SAR data included both polarimetric and interferometric capabilities to infer snow and sea ice information in three dimensions (3D), while OIB/P-3 aircraft data include snow radar together with altimeter data for snow and sea ice observations in 3D over the Weddell Sea. Across the Ross Sea, IcePOD and AntNZ/York-University flights were carried out together with satellite SAR data acquisitions.

Satellite observations have revealed decreases in the duration of the seasonal snow and ice coverage of Great Slave Lake (GSL) and Great Bear Lake (GBL), large freshwater lakes in Northern Canada. However, limited information is available about ice thickness changes. Here, we present and validate a method to retrieve lake ice thickness using the CryoSat-2 (CS2) radar altimeter. These are the first satellite altimeter retrievals of lake ice thickness. Under optimal conditions, the CS2 signal is scattered from both the snow-ice and the ice-water interfaces, with returns from each interface being of sufficient power to be identified in the radar waveform. The distance between the scattering horizons is used to determine the ice thickness, similar to traditional ground penetrating radar measurements. The seasonal evolution of ice thickness of GBL and GSL is compared with in situ measurements, modeled ice thicknesses, and previous studies. The impact of ice and snow properties on signal penetration and the thickness retrieval are examined with synthetic aperture radar imagery. The CS2 ice thickness retrievals are able to observe the seasonal thickening of the lake ice and closely match the in situ measurements over both lakes (R > 0.65 , RMSE <; 0.33 m). Thickness retrievals of thin ice are limited by a minimum waveform peak separation of 2 range bins, approximately 0.26 m in ice. Although not designed for lake ice studies, CS2 and future SAR satellite altimeter missions offer new possibilities to monitor the ice and water levels of climatically sensitive and influential lakes.

In situ and airborne observations of sea ice properties are compared to polarimetric C- and X-band synthetic aperture radar images acquired in the Lincoln Sea in 2012 and 2013. A decision-tree classification algorithm is developed to separate level and deformed ice, as well as first- and multi-year ice (MYI), using parameters of the Freeman-Durden Decomposition. Preliminary qualitative and quantitative evaluations of the algorithm indicate it has considerable promise for the separation of these ice types. For the MYI class, correlations between backscatter and ice thickness were moderate to strong for the 2012 field observations but were weak for the 2013 field observations. Further research is required to determine whether or not MYI thickness can be inverted from polarimetric C- and X-band SAR data.

CryoSat-2 Level 1B and Level 2 data are compared with coincident airborne laser scanner and airborne electromagnetic induction surveys conducted in the Baltic Sea in March 2011 and in the Lincoln Sea in March and April 2012. Across-track snagging caused range retrieval errors in all examined tracks. The L2 surface height profiles are very noisy due to poor retracker performance. Cryosat-2 L2 data are produced by tracking the strongest return in the Level 1B waveforms, not the first return. When a strong reflector is present off-nadir, the retracker incorrectly selects this surface as the surface location of the nadir point. The surface height profile is smoothed by tracking the first return point instead of the peak power return. Ice thickness data produced by retracking the CryoSat-2 L1B data showed good correlation with airborne electromagnetic induction measurements. CryoSat-2 and NASA Operation Ice Bridge data exhibited similar latitudinal freeboard gradients. Accurate measurements of sea ice freeboard or thickness changes can be achieved using appropriate spatial and temporal averaging.

First results are presented from a comparison of in situ and airborne measurements of sea ice thickness to C- and X-band polarimetric synthetic aperture radar images acquired over the Lincoln Sea in March-May 2012. Operation IceBridge data indicate a moderate correlation between ice surface roughness and thickness in this multi-year ice regime, motivating efforts to derive ice thickness from SAR data. C-band backscatter shows moderate correlations to in situ and airborne ice thicknesses (r ≈ 0.6). At X-band correlations with airborne ice thickness data are much lower. For all polarizations the relationship with ice thickness levels off at thicknesses greater than 5 m, where ice is heavily deformed. Current research efforts are focused on exploiting polarimetric parameters to discriminate thick deformed ice to support future efforts to develop a thickness retrieval algorithm for undeformed ice up to 5 m thick.

Results from two years of the CryoSat Validation Experiment (CryoVEx) over sea ice in the western Arctic Ocean are presented. The estimation of freeboard, the height of sea ice floating above the water level, is one the main goals of the CryoSat-2 mission of the European Space Agency (ESA) in order to investigate sea ice volume changes on an Arctic wide scale. Freeboard retrieval requires precise radar range measurements to the ice surface, therefore we investigate the penetration of the Ku-Band radar waves into the overlying snow cover as well as the effects of sub-footprint-scale surface roughness using airborne radar and laser altimeters. We find regional variable penetration of the radar signal at late spring conditions, where the difference of the radar and the reference laser range measurement never agrees with the expected snow thickness. In addition, a rough surface can lead to biases of the airborne validation dataset, since the radar overestimates the amount of open water and thin ice as well the freeboard of heavy ice deformation zones.

Snow is a critical component of the global water cycle and climate system, and a major source of water supply in many parts of the world. There is a lack of spatially distributed information on the accumulation of snow on land surfaces, glaciers, lake ice, and sea ice. Satellite missions for systematic and global snow observations will be essential to improve the representation of the cryosphere in climate models and to advance the knowledge and prediction of the water cycle variability and changes that depend on snow and ice resources. This paper describes the scientific drivers and technical approach of the proposed Cold Regions Hydrology High-Resolution Observatory (CoReH2O) satellite mission for snow and cold land processes. The sensor is a synthetic aperture radar operating at 17.2 and 9.6 GHz, VV and VH polarizations. The dual-frequency and dual-polarization design enables the decomposition of the scattering signal for retrieving snow mass and other physical properties of snow and ice.

In this paper first results of a sensitivity study using dual polarimetric TerraSAR-X data for ice thickness estimation are presented. The sea ice thickness reference data set was measured, coincident to the SAR data take, by means of a helicopter-borne EM sounding device on April 28, 2008 in the Russian Arctic. For some of the signatures, namely the complex correlation coefficient, a relation to ice thickness could be found that is theoretically predicted for L-band SAR. The first results show, that the new generation of polarimetric space borne SAR sensors like TerraSAR-X may open a new opportunity for thin sea ice thickness monitoring from space.

The COld REgions Hydrology High-resolution Observatory (CoRe-H2O) satellite mission has been selected for scientific and technical studies within the ESA Earth Explorer Programme. The mission addresses the need for spatially detailed snow and ice observations in order to improve the representation of the cryosphere in climate models and to improve the knowledge and prediction of water cycle variability and changes. CoRe-H2O will observe the extent, water equivalent and melting state of the snow cover, accumulation and diagenetic facies of glaciers, and properties of sea ice and lake ice. The sensor is a dual frequency SAR, operating at 17 GHz and 9.6 GHz, VV and VH polarizations. This configuration enables the decomposition of the scattering signal for retrieving physical properties of snow and ice. Scientific preparation activities include experimental field campaigns, improvement of radar backscatter models, and the development of inversion algorithms.

These level ice charts are automatically derived from man- made ice thickness charts using novel SAR information [1]. The aim of the present study has been to include some navigationally meaningful information about the degree of ice deformation into these level ice thickness charts. To this end, we propose a procedure where the SAR image is first segmented, and then an ice thickness distribution is assigned to each segment. For this purpose ice thickness measurements based on a helicopter-borne electromagnetic induction sensor (EM) have been made in the Baltic Sea. The critical phase in the proposed approach is to establish a statistical relationships between the C-band SAR measurements and the EM-based ice thickness measurements. This analysis is fundamentally complicated by the location inaccuracies between SAR and EM data sets.

In the Baltic Sea the annual maximum ice extent varies strongly, from the minimum (12 %) to an almost total ice cover over the Baltic Sea, the average being 40 % during the last 30 years. The occurrence of such large annual ice cover has guided the SAR based sea ice mapping work done at the Finnish Institute of Marine Research (FIMR). During the winters 2003-2005 ice thickness measurement campaigns with a simultaneous acquisition of satellite-borne SAR data have been performed. Using these data sets we can address the problem how well one can estimate ice thickness or some other quantity related to it using solely SAR data. In this paper some estimation results for a 1-dimensional data set are presented. In our other paper [1] an approach to the estimation in the two dimensional setting, a much harder problem, is described. Here we will examine the applicability of two different quantities describing the total sea ice thickness. The quantities are: total ice thickness (T ) and equivalent deformed ice thickness (Tdef ). The proposed methods use either a classification or a regression approach. The surface scattering dominates the backscattering at Cband [2]. The link between the ice thickness and the backscattering strength is surface roughness. Typically the surface of level ice becomes more rough when aging due to weathering. Hence, the radar response is, on average, stronger from older (and thicker) level ice than from newer (and thinner) level ice. A major exception for this correspondence is the fast ice area where the thickest level ice fields occur. For many reasons, e.g. for snow ice, the surface roughness and ice thickness in the fast ice field are so weakly correlated that there does not exist any functional relationship between them. Another major exception is the marginal ice zone. In the sequel we restrict our analysis only to the estimation of ice thickness in drift ice areas, excluding fast ice and marginal ice zones, see [1]. The most drastic changes in the ice thickness are associated with the deformation of ice fields, i.e. the formation of ice ridges or hummocks. Both these deformation types significantly modify the ice surface characteristics. Hence, these deformed areas can be detected by SAR, the detection accuracy being dependent on the resolution of the sensor.

In February 2003, sea ice thickness measurements using an electromagnetic induction (EM) instrument were made in the Gulf of Bothnia and Gulf of Finland. We have made comparisons between the EM measurements and Radarsat-1 ScanSAR Wide mode SAR data, and also between our operational sea ice products (digitized ice thickness charts, and ice thickness charts refined by the latest Radarsat-1 image). The SAR images are in 100 m resolution, and the other products are in 500 m resolution