CR2.1

The Hindu Kush Himalaya (HKH), the worlds ‘water tower’, contains the largest volume of snow and ice outside of the polar ice sheets and is the headwater area of Asia’s largest rivers. Due to the complex topography and its great spatial extent the HKH is characterised by variable temperature and precipitation pattern and thus exhibits large heterogeneity in the presence of seasonal snow cover (SSC). Previous studies usually focused on regional studies of snow cover area percentage or the influence of snow melt on the local hydrological system. Here we present a systematic overview of spatio-temporal SSC variability of the entire HKH region on a climate relevant time scale (four decades).

Our results are based on Advanced Very High Resolution (AVHRR) data, collected onboard the polar orbiting satellites NOAA-7 to -19, providing daily, global imagery at a spatial resolution of 5 km since 1982 up to today. This unique dataset is exceptionally valuable to derive pixel-based SSC information using a Normalised Difference Snow Cover (NDSI) approach including additional thresholds related to topography and land cover, and developed in the frame of ESA CCI+ snow.  Calibrated and geocoded reflectance data and a consistent cloud mask, derived in the ESA CCI cloud project, are used. A temporal gap-filling was applied to mitigate the influence of clouds. Reference snow maps from high-resolution optical satellite data as well as in-situ station data were used to validate the time series.

The dataset allows analysis of the state and trends of SSC at regional and sub-regional level. We thus investigated spatio-temporal evolution and long-term variability of SSC for the entire HKH as well as for 14 hydrological basins. We find large spatial difference in the amount of SSC depending on the regional elevation and precipitation characteristics. Furthermore, we investigate SSC phenology, which is directly linked to climate change and thus of high relevance for seasonal water storage and mountain streamflow. Our analysis indicates a significant decline in snow cover area percentage (SCA %) during warm and dry summer month and a decreasing tendency from high winter through spring to early summer. At the hydrological basin level, no significant long-term trend was detected, however, both western and central basins indicate a decrease in SCA % and generally the latest years are strongly negative. Moreover, we examine SCA % anomalies at the highest available temporal frequency (daily information) and reveal an overall shortening of the SSC occurrence and a general decrease of SSC extent in the HKH region.

How to cite: Naegeli, K., Rietze, N., Franke, J., Stengel, M., Neuhaus, C., Wu, X., Marin, C., Premier, V., Schwaizer, G., and Wunderle, S.: Long-term spatio-temporal seasonal snow cover variability in the Hindu Kush Himalaya, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14693, https://doi.org/10.5194/egusphere-egu21-14693, 2021.

The fractional snow cover (FSC) product H35 is a daily operational product based on multi-channel analysis of AVHRR onboard to NOAA and MetOp satellites. H35 is supplied by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (HSAF). The “traditional” H35 FSC product is generated at pixel resolution by exploiting the brightness intensity, which is the convolution of the snow signal and the fraction of snow within the pixel and the sampling is carried out at 1-km intervals. The product for flat/forested regions is generated by Finnish Meteorological Institute (FMI) and the product for mountainous areas is generated by Turkish State Meteorological Service (TSMS). Both products, thereafter, are merged at FMI. This presentation aims to represent the latest findings of our efforts in developing an “alternative” H35 FSC product for the mountainous part by using two data-driven machine learning methodologies, namely, multivariate adaptive regression splines (MARS) and random forests (RFs). In total, 332 Sentinel 2 images over Alps, Tatra Mountains and Turkey acquired between November 2018 and April 2019 are used in order to generate the necessary reference FSC maps for the training of the MARS and RF models. AVHRR bands 1-5, NDSI and NDVI are used as predictor variables. Binary classified Sentinel 2 snow maps, ERA5 snow depth and MODIS MOD10A1 NDSI data are employed in the validation of the models. The results show that both MARS- and RF-based H35 product are i) in good agreement with reference FSC maps (as indicated by low RMSE and relatively high R values) and ii) able to capture the spatial variability of the snow extend. However, MARS-based H35 is preferred for an operational FSC product generation due to the high computational cost required in RF model.

How to cite: Kuter, S., Aksu, C., Bolat, K., and Akyurek, Z.: An Alternative Machine Learning-Based Methodology for H-SAF H35 Fractional Snow Cover Product, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6038, https://doi.org/10.5194/egusphere-egu21-6038, 2021.

The western Nyainqêntanglha Range on the Tibetan Plateau (TP) reaches an elevation of 7162 m and is characterized by an extensive periglacial environment. Here, we present the first rock glacier inventory of the central TP containing 1433 rock glaciers over an area of 4622 km². The rock glaciers are identified based on their surface velocity. The surface velocity is derived from Sentinel-1 satellite data of 2016 to 2019 via InSAR time series analysis. 16.4 % of the inventoried rock glaciers are classified as active with a surface velocity above 10 cmyr^-1 and 80.0 % are classified as transitional with 1 to 10 cmyr^-1. The western Nyainqêntanglha Range forms a climate divide between the dry continental climate brought by the Westerlies from the north-west and the Indian Summer Monsoon to the south. 89.7 % of all active rock glaciers and 74 % of the free ice glacial area are located on the southern side. The higher moisture availability on the southern (windward) side of the mountain range is likely the cause of a higher rock glacier occurrence and the greater activity.

Manually identifying and outlining rock glaciers is time consuming and subjective. To ensure a high reliability and comparability of our inventory, we therefore combined a manual approach with an automated classification. Three analysts worked in tandem to generate the manual outlines according to the guidelines of the IPA action group on ‘Rock glacier inventories and kinematics’. A subset of these outlines acted as training areas for a pixel-based maximum likelihood classification. Both the manual and the automated classification were performed based on DEM parameters (elevation, slope etc.), optical datasets (Sentinel-2 and NDVI) and surface velocity (generated with InSAR). 87.8 % of all manually outlined rock glaciers were identified successfully at a true positive rate of 69.5 %. 18 additional rock glaciers were added to the inventory based on the automated classification. This combined approach is therefore beneficial to generate a complete inventory. The automated classification can, however, not replace the expertise of an analyst as it greatly overestimates the actual rock glacier area.

How to cite: Reinosch, E., Gerke, M., Riedel, B., Schwalb, A., Ye, Q., and Buckel, J.: Inventory of active rock glaciers in the western Nyainqêntanglha Range, Tibetan Plateau, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2461, https://doi.org/10.5194/egusphere-egu21-2461, 2021.

Documenting ground deformation is important for a range of areas in Earth and environmental sci-
ences (such as earthquake, volcanoes, landslides and glaciers/ice sheets monitoring). In particular
monitoring the deformation of the cryosphere is key to understand its evolution in a context of
global changes, through the creation of long-term ice velocity datasets, but also possibly detect
failure onsets. The availability of optical satellite constellations with a frequent revisit time at medi-
um to high spatial resolution and an open access policy (e.g. Sentinel 2, Landsat 7/8) provides the
potential to contribute to ice monitoring on a global basis. However, this observational capability
also represents a challenge in term of storage capacity and computing resources which together
with the complexity of the tuning of the different parameters, may prevent users from exploiting the
data.

Here we propose a new version of the Multi-Pairwise Image Correlation for OPTical images
(MPIC-OPT) algorithm. The new version of the algorithm offers a complete chain to process optical
images including data download, image pairs creation and advanced analysis of the displacement
field. It offers the choice to compute the ground displacement associated to image pairs with two
correlation techniques (MicMac, developed by IGN; GéFolki developed by ONERA). Finally, the
Time-Series Inversion for Opical image (TIO) algorithm is integrated to provide displacement time
series.

The processing chain is accessible through the Geohazards Exploitation Platform (GEP) in the
framework of the Thematic Exploitation Platform initiative of the European Space Agency and the
runs are performed using the High Performance Computing facility at the A2S/Mesocentre of Uni-
versity of Strasbourg.

We present the results of the chain in various cryospheric areas: the European Alps glaciers
(France, Italy, Switzerland), the Astrolabe ice shelf (Antartica) and the Gangotri glacier (India). We
define some relevant strategies for an operational use of the service for regional monitoring of
land-ice from satellite images. We compare the results of the MPIC-OPT-ICE service to in-situ
dataset and/or results obtained with similar strategies (e.g. GoLive or ITS-LIVE products, etc.). We
discuss the influence of the pair network and the inversion strategy to retrieve short-term to long-
term kinematic regimes.

How to cite: Michea, D., Provost, F., Malet, J.-P., Doin, M.-P., Lacroix, P., Dehecq, A., Boissier, E., Pointal, E., and Bally, P.: The Multi-Pairwise Image Correlation (MPIC) processing chain, an end-to-end online service for ice motion monitoring using optical imagery, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15876, https://doi.org/10.5194/egusphere-egu21-15876, 2021.

Glacier surging is an unique dynamic pattern that involves a long term quiescent phase and a sudden surge phase. The surge causes abnormal glacier movement, such as high flow velocity, transportation of large amount of ice mass, and dramatic thickening and advancing of the glacier terminus. Glacier surge not only confound the understanding of regional glacier dynamics, but also pose threats to local residents by invoking glacier lake outburst floods. 

In this work, we reported the recent surge event of the South Rimo Glacier, one of the largest glaciers in Karakorum. The surge happened between 2018-2020 with very little terminus advancement, and thus it is difficult to interpret the dynamics of the event simply by visual inspections of satellite images. We studied both the topography evolution and the surface velocity change of the glacier before and during the surge. By differencing a series of digital elevation models (DEMs) produced from the TanDEM-x CoSSC data acquired between 2011 and 2017, we found that the South Rimo glacier started accumulating height in the middle stream since 2013. A bulge was built in the reservoir region since 2014 and reached its maximum height (27.51m higher than 2011) before the surge activation in 2017. Velocity maps between 2016-2020 were obtained from SAR offset tracking using Sentinel-1 images. It was shown that the surface velocity greatly increased in 2017 at areas around the bulge. The peak velocity was found in the mid of 2019 at about 10 m/day, which is of three magnitude higher than the velocity during the quiescent phase. Our work characterized the development of the recent surge of the South Rimo Glacier, and highlighted the value of high resolution DEM products and velocity maps in pre-identifying glacier surge and mitigating related hazards.

How to cite: Li, S., Bernhard, P., Hajnsek, I., and Leinss, S.: Recent surge of the South Rimo Glacier, Karakoram: Dynamics Characterization using SAR data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15560, https://doi.org/10.5194/egusphere-egu21-15560, 2021.

Climate induced glacier change has important implications for global sea level rise, freshwater availability and geomorphologic hazards. Changes in ice dynamics and mass flow can globally be observed by long- and short-term changes in ice surface velocity. Consistent and continuous data on glacier surface velocity are important inputs to time series analyses, numerical ice dynamic modelling and glacier mass balance calculations. Therefore, glacier surface velocities have been identified as an Essential Climate Variable (ECV) that should be monitored on a regular and global scale. Since 2014, repeat-pass Synthetic Aperture Radar (SAR) data, acquired by the Sentinel-1 constellation as part of ESA’s (European Space Agency) Copernicus program, enable global, near real time-like and fully automatic processing of glacier velocity fields at up to 6-day temporal resolution, independent of weather conditions, season and daylight.

We present a new near-global data set of Sentinel-1 glacier velocities that comprises continuously updated image pair velocity fields, as well as monthly and annually averaged velocity mosaics at 200 m spatial resolution, derived from applying intensity feature tracking on both archived and new acquisitions. The data set covers all major glaciated regions outside the polar ice sheets and is generated in an HPC (High Performance Computing) environment at the University of Erlangen-Nuremberg. By the beginning of January 2021, we processed more than 110.000 Sentinel-1 scenes, amounting to roughly 450 TB of data. The velocity products are freely accessible via an interactive web portal (http://retreat.geographie.uni-erlangen.de) that provides capabilities for download and simple online analyses. We give information on the procedures of data generation, as well as on how to access the data and demonstrate the capabilities of our products for velocity time series analyses at very high temporal resolution. We compare our data to velocity products generated from very high resolution TerraSAR-X SAR (Synthetic Aperture Radar) and Landsat-8 optical (ITS_LIVE, GoLIVE) data. For this comparison we selected Svalbard as an example region, as it includes glaciers of a broad variety of sizes, different velocitiy magnitudes and seasonal velocity patterns, as well as very fast flowing surging glaciers and almost featureless ice caps.

How to cite: Friedl, P., Seehaus, T., and Braun, M.: RETREAT: A new freely available data set of  Sentinel-1 glacier velocities in regions outside the polar ice sheets, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2740, https://doi.org/10.5194/egusphere-egu21-2740, 2021.

Glaciers retreat contributed to about 1/3 of the observed sea level rise since 1971 (IPCC). However, long term estimates of glaciers volume changes rely on sparse field observations and region-wide satellite observations are available mostly after 2000. The now declassified images from the American reconnaissance satellite series Hexagon (KH-9), that acquired 6 m resolution stereoscopic images from 1971 to 1986, open new possibilities for glaciers observation.

Based on recently published methodology (Dehecq et al., 2020, doi: 10.3389/feart.2020.566802), we process all available KH-9 images over the Arctic (Canadian arctic, Iceland, Svalbard, Russian arctic) to generate Digital Elevation Models (DEMs) and ortho-images for the period 1974-1980. We validate the KH-9 DEMs over Iceland against elevation derived from historical aerial images acquired within a month from the satellite acquisition.

Finally, we calculate the glacier elevation change between the historical DEMs and modern elevation obtained from a time series of ASTER stereo images and validated against ICESat-2 elevation. The geodetic glacier mass balance is calculated for all pan-Arctic regions and analyzed with reference to the last 20 years evolution.

How to cite: Dehecq, A., Gardner, A., Hugonnet, R., and Belart, J.: Pan-arctic glaciers volume changes over 1975-2019, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8606, https://doi.org/10.5194/egusphere-egu21-8606, 2021.

The potential of surface altimetry and photogrammetry for assessing the volume change of glaciers is tremendous and the scope of available data sets is increasing at a rapid pace. Surface elevation changes are now available for all glaciers globally and the time periods that can be resolved by these data are becoming shorter. However, most glaciological and hydrological studies rely on glacier mass change instead of volume change, thus necessitating a conversion accounting for the density of the gained or lost ice, firn or snow. While glaciers gain or lose volume, their firn coverage simultaneously changes, both in terms of extent, thickness and density, complicating the estimation of the conversion factor. Often, geodetic studies use a density of volume change equal to 850 kg m^-3 which has been found to be valid for a wide range of cases. Nevertheless, particular situations, e.g. changes in mass balance gradients related to abrupt accelerations or decelerations of local atmospheric warming might result in significant departures of the conversion factor from this reference value. This probably represents the most important uncertainty factor in regional to global-scale assessments of geodetic glacier mass change.

Here, we substantially update the assessment of the optimal conversion between volume and mass change by Huss (2013) and apply the same firn densification model to all roughly 200'000 glaciers globally. Local annual surface mass balance over the period 2000-2019 is prescribed by the global glacier model GloGEM. The model is driven by ERA5 climate re-analysis data, and cumulative modelled mass balance is constrained to match observations of geodetic elevation change for each individual glacier for 2000-2019. By comparing mass balance and computed glacier volume changes resulting from the firn density model, a volume-to-mass change conversion factor is derived for each glacier and any period over the last two decades. Our assessment thus accounts for local changes in climate and, hence, shifts in the properties of the firn coverage, as well as the observed changes of each individual glacier.

A considerable variance in the factors necessary to convert geodetic ice volume change to mass change is found, both at the regional scale but also for different time periods of the same region. For many regions, the estimate of 850±60 kg m^-3 for the density of ice volume change is valid, encompassing most of the investigated periods within 2000-2019. However, for some - mostly high-latitude - regions significantly lower and higher conversion factors have been found, related to particular long-term changes in firn density and thickness. Various assumptions and simplifications are involved in this global-scale assessment. Nevertheless, we consider our results as a helpful guideline for estimating volume-to-mass conversion factors in geodetic studies around the world over arbitrary time periods.

How to cite: Huss, M., Hugonnet, R., Compagno, L., and Farinotti, D.: Converting geodetic ice volume to mass change: a global-scale assessment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2094, https://doi.org/10.5194/egusphere-egu21-2094, 2021.

The roughness of a natural surface is an important parameter in atmospheric models, as it determines the intensity of turbulent transfer between the atmosphere and the surface. Unfortunately, this parameter is often poorly known, especially in remote areas where neither high-resolution elevation models nor eddy-covariance measurements are available.

In this study, we take advantage of the measurements of the ICESat-2 satellite laser altimeter. We use the geolocated photons product (ATL03) to retrieve a 1-m resolution surface elevation product over the K-transect (West Greenland ice sheet). In combination with a bulk drag partitioning model, the retrieved surface elevation is used to estimate the aerodynamic roughness length (z_0m) of the surface.

We demonstrate the high precision of the retrieved ICESat-2 elevation using co-located UAV photogrammetry, and then evaluate the modelled aerodynamic roughness against multiple in situ eddy-covariance observations. The results point out the importance to use a bulk drag model over a more empirical formulation.

The currently available ATL03 geolocated photons are used to map the aerodynamic roughness along the K-transect (2018-2020). We find a considerable spatiotemporal variability in z_0m, ranging between 10⁻⁴ m for a smooth snow surface to more than 10⁻¹ m for rough crevassed areas, which confirms the need to incorporate a variable aerodynamic roughness in atmospheric models over ice sheets.

How to cite: van Tiggelen, M., Smeets, P. C. J. P., Reijmer, C. H., Wouters, B., Steiner, J. F., Nieuwstraten, E. J., Immerzeel, W. W., and van den Broeke, M. R.: Mapping the aerodynamic roughness of the Greenland ice sheet surface using ICESat-2, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2136, https://doi.org/10.5194/egusphere-egu21-2136, 2021.

River ice is a major contributor to flood risk in cold regions due to the physical impediment of flow caused by ice jamming. Although a variety of classifiers have been developed to distinguish ice types using HH or VV intensity of SAR data, mostly based on data from RADARSAT-1 and -2, these classifiers still experience problems with breakup classification, because meltwater development causes overlap in co-polarization backscatter intensities of open water and sheet ice pixels.

In this study, we develop a Random Forest classifier based on multiple features of Sentinel-1 data for three main classes generally present during breakup: rubble ice, sheet ice and open water, in a case study over the Athabasca River in Canada. For each ice stage, intensity of the VV and VH backscatter, pseudo-polarimetric decomposition parameters and Grey Level Co-occurrence Matrix texture features were computed for 70 verified sample areas. Several classifiers were developed, based on i) solely intensity features or on ii) a combination of intensity, pseudo-polarimetric and texture features and each classifier was evaluated based on Recursive Feature Elimination with Cross-Validation and pair-wise correlation of the studied features.

Results show improved classifier performance when including GLCM mean of VV intensity, and VH intensity features instead of the conventional classifier based solely on intensity. This highlights the importance of texture and intensity features when classifying river ice. GLCM mean incorporates spatial patterns of the co-polarized intensity and sensitivity to context, while VH intensity introduces cross-polarized surface and volume scattering signals, in contrast to the commonly used co-polarized intensity.

We conclude that the proposed method based on the combination of texture and intensity features is suitable for and performs well in physically complex situations such as breakup, which are hard to classify otherwise. This method has a high potential for classifying river ice operationally, also for data from other SAR missions. Since it is a generic approach, it also has potential to classify river ice along other rivers globally.  

How to cite: de Roda Husman, S., van der Sanden, J. J., Lhermitte, S., and Eleveld, M. A.: Integrating intensity and context for improved supervised river ice classification from dual-pol Sentinel-1 SAR data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3393, https://doi.org/10.5194/egusphere-egu21-3393, 2021.

The high latitudes of the Northern Hemisphere have experienced the largest regional warming over the last decades. On the Greenland ice sheet, rapid changes are observed in response to temperature increase, with the amount of liquid water at the surface particularly increasing. Understanding Greenland’s ice sheet hydrology is essential to assess  its contribution to global sea-level rise in a future warming climate.

With the objective of maximizing the use of Earth Observation (EO) data, the European Space Agency (ESA) has funded the 2-year project 4DGreenland (https://4dgreenland.eo4cryo.dk/) to assess and quantify the hydrology of the Greenland ice sheet. The project is focused on dynamic variations in the hydrological components of the ice sheet, and on quantifying the water fluxes between reservoirs including surface melt, supraglacial lakes and rivers, and subglacial melt and lakes. Efforts will focus on a thorough analysis of various components of the hydrological network in selected test regions and their impact on ice sheet flow. 4DGreenland started in September 2020. Here, we will present the project objectives, methods, and show initial results obtained within the project such as a comparison of supraglacial lake depths from optical imagery and ICESat-2 altimetry data, estimation of basal melt water production, and identification and mapping of surface meltwater presence and subglacial lakes from EO data.

How to cite: Sandberg Sørensen, L. and the 4DGreenland team: The 4DGreenland project: Greenland hydrology assessment from remote sensing, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10728, https://doi.org/10.5194/egusphere-egu21-10728, 2021.

Amery Ice Shelf is  the largest ice shelf in East Antarctica. Large calving event occurs every thirty to forty years as recorded. The latest calving event happened in September 2019, leading to the birth of a giant tabular iceberg. We used satellite imagery and altimeter data from multiple sources to monitor the evolution of the iceberg from October 2019 to October 2020. The evolution of iceberg area is measured with Sentinel-1 images,  and the change of freeboards was derived from CryoSat-2, Sentinel-3 SRAL, and ICESat-2 profiles. Compared with topography of Amery Front before calving, we found the temporal freeboards of the iceberg show a trend of descending after calved from Amery Ice Shelf, which indicates overall basal melting process. While the freeboards of Amery Front remain stable within a year before calving. We also calculated the freeboard changes of  30 footstep intersections from different altimeter profiles on the iceberg. The results show different changing patterns of freeboards, varying from  4.72m to -3.1m, which indicates there is basal re-freezing process as well as basal melting at the bottom of the iceberg. Furthermore, we studied the correlation between freeboard change and sea surface temperature. This study reveals that the use of different remote sensing data can provide more detailed observations on Antarctic icebergs.

How to cite: Liu, X.: Observation of a giant tabular iceberg calved from Amery Ice shelf based on multiple remote sensing data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14121, https://doi.org/10.5194/egusphere-egu21-14121, 2021.

Detecting iceberg calving events and subsequently tracking their movement is important because large icebergs can create problem in shipping and navigation. This study discusses two calving events that took place at 1) Amery ice shelf (East Antarctica) in September 2019 and 2) Pine Island Glacier’s floating ice shelf (West Antarctica) in February 2020. Though the calving that occurred in September 2019 does not have any impact on climate change, it is considered to be the most significant calving event on Amery ice shelf since 1963-64. The gigantic tabular iceberg officially named D-28 measures more than 600 square-miles. On the other hand, Pine Island is considered as the fastest retreating glaciers in Antarctica. This calving event gave rise to smaller icebergs, the largest of which was 120 square-miles, big enough to earn it a name: B-49. Though ice calving is a normal phenomenon at the ice shelves, the front of the glacier is stable if the rate of calving is in synchronization with the glacier’s forward flow. But, at Pine Island, the rate of disintegration has increased more than the glacier's speed to push the inland ice into Pine Island Bay. On-screen digitization approach of analysing time series dataset of glacier front positions is conventional, time consuming and subjective. To track the movement of icebergs D-28 and B-49, present study has detected rifts using canny edge detection filter and textural measures. We have utilized the Sentinel 1A SAR C-band (GRD) EW mode (Resolution (Rg x Az): 93 x 87 m and pixel spacing 40 x 40 m) images pertaining to the Amery ice shelf for Sep 2020-Mar 2020 and Pine Island Glacier with Pine Island Bay for Dec 2019-Mar 2020. All the images were processed for calibration (sigma0), speckle filtering (refined Lee), terrain correction (Range Doppler) and dB conversion using SNAP tool. Terrain correction has been performed using RAMP v2 DEM (200 m) and all the images have been projected to WGS 84/Antarctic Polar Stereographic projection and converted into dB. Through image interpretation, it is revealed that as of Mar 2020, iceberg D-28 has rotated almost 90 degrees anti-clockwise and drifted slightly northward away from Cape Darnley. In case of iceberg B-49, it is observed that the western portion of the calved ice, including the largest iceberg, has rapidly rotated out into Pine Island Bay, whereas the eastern half, including many smaller shards of ice, is following in similar fashion.

How to cite: Mitkari, K., Pallipad, J., Putrevu, D., and Misra, A.: Detecting Calving Events of Icebergs D-28 and B-49 using High Resolution Sentinel-1A SAR Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16264, https://doi.org/10.5194/egusphere-egu21-16264, 2021.

The frequent presence of cloud cover in polar regions limits the use of the Moderate-Resolution Imageing Spectroradiometer (MODIS) and similar instruments for the investigation and monitoring of sea-ice polynyas compared to passive-microwave-based sensors. The very low thermal contrast between present clouds and the sea-ice surface in combination with the lack of available visible and near-infrared channels during polar nighttime results in deficiencies in the MODIS cloud mask and dependent MODIS data products. This leads to frequent misclassifications of i) present clouds as sea ice/open water (false-negative) and ii) open-water/thin-ice areas as clouds (false-positive), which results in an underestimation of actual polynya area and subsequent derived information. Here, we present a novel machine-learning based approach using a deep neural network that is able to reliably discriminate between clouds, sea-ice, and open-water/thin-ice areas in a given swath solely from thermal-infrared MODIS channels and derived additional information. Compared to the reference MODIS sea-ice product for the year 2017, our data results in an overall increase of 20% in annual swath-based coverage for the Brunt Ice Shelf polynya, attributed to an improved cloud-cover discrimination and the reduction of false-positive classifications. At the same time, the mean annual polynya area decreases by 44% through the reduction of false-negative classifications of warm clouds as thin ice. Additionally, higher spatial coverage results in an overall better sub-daily representation of thin-ice conditions that cannot be reconstructed with current state-of-the-art cloud-cover compensation methods.

How to cite: Paul, S. and Huntemann, M.: Novel machine-learning based cloud mask and its application for Antarctic polynya monitoring using MODIS thermal-infrared imagery, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9603, https://doi.org/10.5194/egusphere-egu21-9603, 2021.

As the Arctic’s sea ice extent continues to decline, remote sensing observations are becoming even more vital for the monitoring and understanding of this process.  Recently, the sea ice community has entered a new era of synthetic aperture radar (SAR) satellites operating at C-band with the launch of Sentinel-1A in 2014, Sentinel-1B in 2016 and the RADARSAT Constellation Mission (RCM) in 2019. These missions represent a collection of 5 spaceborne SAR sensors that together can routinely cover Arctic sea ice with a high spatial resolution (20-90 m) but also with a high temporal resolution (1-7 days) typically associated with passive microwave sensors. Here, we used ~28,000 SAR image pairs from Sentinel-1AB together with ~15,000 SAR images pairs from RCM to generate high spatiotemporal large-scale sea ice motion products across the pan-Arctic domain for 2020. The combined Sentinel-1AB and RCM sea ice motion product provides almost complete 7-day coverage over the entire pan-Arctic domain that also includes the pole-hole. Compared to the National Snow and Ice Data Center (NSIDC) Polar Pathfinder and Ocean and Sea Ice-Satellite Application Facility (OSI-SAF) sea ice motion products, ice speed was found to be faster with the Senintel-1AB and RCM product which is attributed to the higher spatial resolution of SAR imagery. More sea ice motion vectors were detected from the Sentinel-1AB and RCM product in during the summer months and within the narrow channels and inlets compared to the NSIDC Polar Pathfinder and OSI-SAF sea ice motion products. Overall, our results demonstrate that sea ice geophysical variables across the pan-Arctic domain can now be retrieved from multi-sensor SAR images at both high spatial and temporal resolution.

How to cite: Howell, S., Brady, M., and Komarov, A.: Large-scale Arctic sea ice motion from Sentinel-1 and the RADARSAT Constellation Mission, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-514, https://doi.org/10.5194/egusphere-egu21-514, 2021.

Across spatial and temporal scales, sea-ice motion has implications on ship navigation, the sea-ice thickness distribution, sea ice export to lower latitudes and re-circulation in the polar seas, among others. Satellite remote sensing is an effective way to monitor sea-ice drift globally and daily, especially using the wide swaths of passive microwave missions. Since the late 1990s, many algorithms and products have been developed for this task. Here, we investigate how processing sea-ice drift vectors from the intersection of individual swaths of the Advanced Microwave Scanning Radiometer 2 (AMSR2) mission compares to today’s status-quo (processing from daily averaged maps of brightness temperature).

We document that the “swath-to-swath” (S2S) approach results in many more (two orders of magnitude) sea-ice drift vectors than the “daily-maps” (DM) approach. These S2S vectors also validate better when compared to trajectories of on-ice drifters. For example, the RMSE of the 24 hour Arctic sea-ice drift is 0.9 km for S2S vectors, and 1.3 km for DM vectors from the 36.5 GHz imagery of AMSR2.

Through a series of experiments with actual AMSR2 data and simulated Copernicus Imaging Microwave Radiometer (CIMR) data, we study the impact that geo-location uncertainty and imaging resolution have on the accuracy of the sea-ice drift vectors. We conclude by recommending that a “swath-to-swath” approach is adopted for the future operational Level-2 sea-ice drift product of the CIMR mission. We outline some potential next steps towards further improving the algorithms, and making the user community ready to fully take advantage of such a product.

This work is currently under revision at EGU The Cryosphere as https://tc.copernicus.org/preprints/tc-2020-332/

How to cite: Lavergne, T., Piñol Solé, M., Down, E., and Donlon, C.: Towards a swath-to-swath sea-ice drift product for the Copernicus Imaging Microwave Radiometer (CIMR) mission., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7243, https://doi.org/10.5194/egusphere-egu21-7243, 2021.

Sea–ice concentration, the surface fraction of ice in a given area, is a key component of the Arctic climate system, governing for example the ocean–atmosphere heat exchange. Satellite–based remote sensing offers the possibility for large–scale monitoring of the sea–ice concentration. Using passive microwave measurements, it is possible to observe the sea–ice concentration all year long, almost independently of cloud coverage. The spatial resolution of these measurements is limited to 5 km and coarser. Data from the visible and thermal infrared spectrum offer finer resolutions of 250 m–1 km, but need clear–sky scenes and, in case of visible data, sunlight. In previous work, we developed and analysed a merged dataset of passive microwave and thermal infrared data, combining AMSR2 and MODIS satellite data at 1 km spatial resolution. It has benefits over passive microwave data in terms of the finer spatial resolution and an enhanced potential for lead detection. At the same time, it outperforms thermal infrared data due to its spatially continuous coverage and the statistical consistency with the extensively evaluated passive microwave data. Due to higher surface temperatures in summer, the thermal–infrared based retrieval is limited to winter and spring months. In this contribution, we present first results of extending the existing dataset to summer by using visible data instead of thermal infrared data. The reflectance contrast between ice and water is used for the sea–ice concentration retrieval and results of merging visible and microwave data at 1 km spatial resolution are presented. Difficulties for both, the microwave and visual, data are surface melt processes during summer, which make sea–ice concentration retrieval more challenging. The merged microwave, infrared and visual dataset opens the possibility for a year–long, spatially continuous sea ice concentration dataset at a spatial resolution of 1 km.

How to cite: Ludwig, V. and Spreen, G.: Sea–ice concentration at 1 km resolution in summer from merged visible and microwave radiometer observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12334, https://doi.org/10.5194/egusphere-egu21-12334, 2021.

Surface roughness is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer melt pond extent, while also closely related to ice age. High resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness have remained elusive and do not extended over multi-decadal time-scales. The MISR (Multi-angle Imaging SpectroRadiometer) instrument acquires optical imagery at 275m (red channel) and 1.1 km (all channels) resolutions from nine near-simultaneous camera view zenith angles sampling specular anisotropy, since 1999. Extending on previous work to model sea ice surface roughness from MISR angular reflectance signatures, a training dataset of cloud-free pixels and coincident roughness is generated. Surface roughness, defined as the standard deviation of the within-pixel elevations to a best-fit plane, is modelled using several techniques and Support Vector Regression with a Radial Basis Function kernel selected. Hyperparameters are tuned using grid optimisation, model performance is assessed using nested cross-validation, and product performance is assessed with independent validation. We present a derived sea ice roughness product at 1.1km resolution over a two-decade timespan (1999 – 2020) and a corresponding time series analysis by region. These show considerable promise in detecting newly formed smooth ice from polynyas, and detailed surface features such as ridges and leads.

How to cite: Johnson, T., Tsamados, M., Muller, J.-P., and Stroeve, J.: Mapping Arctic Sea Ice Surface Roughness with Multi-angle Imaging SpectroRadiometry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15897, https://doi.org/10.5194/egusphere-egu21-15897, 2021.

Global Navigation Satellite Systems-Reflectometry (GNSS-R) offers novel observations over the cryosphere with the use of reflected navigation signals (eg. GPS or Galileo) as signals of opportunity. This technique has the potential for higher resolution measurements over sea ice than routinely acquired by passive microwave systems with a footprint of around 5 km2 and is much lower in power consumption, mass and therefore cost. Here we present sea ice classification and altimetry as observed at grazing angles by Spire’s Radio Occultation (RO) Satellite constellation, repurposed for GNSS-R.

The Spire RO constellation of 37 operational satellites (and growing) is relied upon to support critical numerical weather prediction and has been collecting GNSS signals as they refract through the atmosphere. The reprogramming of these satellites to receive signals reflected at grazing angle allows these signals to instead inform on Earth surface characteristics. From smooth surfaces, these signals are phase coherent at L-Band frequencies (~19 - 24 cm wavelength) and allow the detection of the roughness of the sea ice in addition to the height of the surface to several centimetres of precision. Three months of these operational sea ice detection and classification products are presented from Spring of 2020; with ice extent in agreement with external passive and active sources to around 98% in the Antarctic and 94% in the Arctic, and ice age classification (First Year/Multi-Year) agreeing in the Arctic to around 70%. First results are shown for the potential to detect other ice characteristics such as the Antarctic Marginal Ice Zone extent and floe size / type.

How to cite: Cartwright, J., Nguyen, V., Jales, P., Nogues-Correig, O., Yuasa, T., Irisov, V., and Masters, D.: Sea Ice Classification and Altimetry using Grazing Angle Reflected GNSS Signals Measured by Spire’s Nanosatellite Constellation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14851, https://doi.org/10.5194/egusphere-egu21-14851, 2021.

The sea level of the Polar Oceans is an important climate indicator. The CryoSat-2 satellite has been measuring the polar oceans with great success, and has improved the sea level uncertainties remarkably . We present the DTU/TUM sea level record based on more tahn 15 years of ESA radar satellite altimetry data in the Arctic Ocean from the ERS2 (1995) to CryoSat-2 (present) satellites. The Arctic sea level record is part of the ESA CCI Sea level initiative and has been updated with a new and better CryoSat processing from the ESA GPOD processing. Furthermore, we present a sea level record from the Southern Ocean as part of the ESA CryoSat+ Antarctica project based on ten years of CryoSat-2 measurements. The changes in the sea level are temporal and spatial analyzed.

How to cite: Rose, S. K., Andersen, O. B., Fleury, S., Ludwigsen, C., Tsamados, M., Dinardo, S., Bouffard, J., and Benveniste, J.: CryoSat-2’s contribution to the complete sea level records from the Polar Oceans, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15978, https://doi.org/10.5194/egusphere-egu21-15978, 2021.

Acquiring information about the thickness of thin Arctic sea-ice is an important aspect of assessing atmosphere – sea-ice – ocean interactions, as the ice thickness directly relates to the magnitude of energy fluxes at the sea-ice interfaces. In winter, these fluxes are linked to sea-ice formation and hence accompanying processes such as physically induced upper-ocean convection and turbulent mixing of the lower atmospheric boundary layer. It remains a big challenge to validate satellite-derived thin-ice thicknesses, first and foremost due to the lack of suitable in-situ data in these remote areas.

In order to address this issue, we here present the first insight into a comparison between high-resolution (2km) MODIS thermal infrared satellite data (available for 2002/2003 to 2017/2018) and comprehensive time series of ice-draft data obtained from moored Ice Profiling Sonar (IPS) data. The IPS data set comprises winter-seasons 2009/2010 to 2011/2012 in the Chukchi Sea and winter-seasons 2013/2014 to 2014/2015 in the Laptev Sea. For the MODIS data set, a 1D energy balance model serves as the base for deriving thin-ice thicknesses (0 to 50 cm) from ice-surface temperature swath-data and ERA-Interim atmospheric reanalysis data. In order to facilitate the comparison, the 1Hz IPS ice-draft data is first empirically converted to ice thickness and afterwards resampled to 5-minute modal-values to find matching MODIS swath data.

It shows that the agreement between the MODIS and IPS ice-thickness data largely depends on the thickness of the ice sampled by the IPS. We found the highest agreement for ice thickness values below 20 cm, which tend to appear more frequently at the Chukchi Sea mooring location. More generally, we notice that MODIS seems to overestimate ice thicknesses up to approximately 40 cm. For thicker ice, the limitations of the MODIS ice-thickness retrieval result in an underestimation.

How to cite: Preußer, A., Belter, H. J., Fukamachi, Y., and Heinemann, G.: Comparison of MODIS-based thin-ice thicknesses with ice draft measurements in the Laptev Sea & Chukchi Sea, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7909, https://doi.org/10.5194/egusphere-egu21-7909, 2021.

The ice cover of the Arctic Ocean is increasingly becoming dominated by seasonal sea ice. It is important to focus on the processing of altimetry ice thickness data in thinner seasonal ice regions to understand seasonal sea ice behaviour better. This study focusses on Baffin Bay as a region of interest to study seasonal ice behaviour.

We aim to reconcile the spring sea ice thickness derived from multiple satellite altimetry sensors and sea ice charts in Baffin Bay and produce a robust long-term record (2003-2020) for analysing trends in sea ice thickness. We investigate the impact of choosing different snow depth products (the Warren climatology, a passive microwave snow depth product and modelled snow depth from reanalysis data) and snow redistribution methods (a sigmoidal function and an empirical piecewise function) to retrieve sea ice thickness from satellite altimetry sea ice freeboard data.

The choice of snow depth product and redistribution method results in an uncertainty envelope around the March mean sea ice thickness in Baffin Bay of 10%. Moreover, the sea ice thickness trend ranges from -15 cm/dec to 20 cm/dec depending on the applied snow depth product and redistribution method. Previous studies have shown a possible long-term asymmetrical trend in sea ice thinning in Baffin Bay. The present study shows that whether a significant long-term asymmetrical trend was found depends on the choice of snow depth product and redistribution method. The satellite altimetry sea ice thickness results with different snow depth products and snow redistribution methods show that different processing techniques can lead to different results and can influence conclusions on total and spatial sea ice thickness trends. Further processing work on the historic radar altimetry record is needed to create reliable sea ice thickness products in the marginal ice zone.

How to cite: Glissenaar, I., Landy, J., Petty, A., Kurtz, N., and Stroeve, J.: The implications of selected processing methods on satellite altimetry derived sea ice thickness state and trends in the seasonal ice zone, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12240, https://doi.org/10.5194/egusphere-egu21-12240, 2021.

Snow depth on sea ice is a key parameter of climate change. For instance, it plays an isolating role which regulates sea ice growth and accelerates the melting. As it will be shown in this presentation, snow depth is mandatory to compute sea ice thickness (SIT). Nevertheless, there currently doesn't exist reliable snow depth products for sea ice. Nearly all SIT estimations in Arctic are computed using the Warren climatology (Warren et al, 1999) which has been constructed from in-situ data of the last century, prior to the first sensible impacts of the climate change. In addition, meteorological re-analyses have difficulties to faithfully reproduce snow falls in polar regions.
Recently, Guerreiro et al, 2016 has demonstrated the ability to retrieve the snow depth over sea ice from the difference of penetration between the CryoSat-2 Ku frequency radar, which reflects at the snow/ice interface and the Saral/AltiKa Ka frequency radar, which reflects on the top of the snow pack. Following this study, an Altimetric Snow Depth (ASD) product, covering the 2013-2019 winter periods in Arctic, is developed at the LEGOS as part of the ESA CryoSeaNice and Polar+ Snow on Sea Ice projects . The main objective of this presentation is to show and assess this dataset. In addition, in the context of the ESA Antarctica+ project, an equivalent snow depth product is also under process for the Austral sea ice. First results will be presented here.

In this presentation, the ASD data will be compared with 2 Advanced Microwave Scanning Radiometer 2 (AMSR-2) snow depth products. The first version (Meier et al, 2018) available on the NSIDC website () has the inconvenience of being only available over First Year Ice. The Bremen AMSR-2 v1.0 product (Rostosky et al, 2018) is calculated over Multi Year Ice but only for March and April (during the Operation Ice Bridge campaigns). In the southern hemisphere, only the NSIDC product is available. This data set covers the entire southern region considering all sea ice as First Year Ice around Antarctica.

We will also assess the relevancy of the ASD data compared to these 2 AMSR-2 products, the Warren W99 climatology and the PIOMAS v2.1 model reanalyze. For this purpose we will present extensive comparisons with: 1) several Operation Ice Bridge (OIB) campaigns, 2) the 2017 ESA-CRYOsat Validation EXperiment (CryoVex) campaign which includes the Ka band KAREN altimeter and 3) the Beaufort Gyre Exploration Project (BGEP) data. Finally, impact of the various dataset (ASD, PIOMAS, AMSR-2) on SIT estimations will be presented.

The results presented here will also underline the interest and relevance of the data that should be obtained during the future CRISTAL mission

How to cite: Garnier, F., Fleury, S., Tsamados, M., Laforge, A., Bocquet, M., and Rémy, F.: Assessment of Ka-Ku Altimetric Snow Depth on Sea Ice during Arctic and Austral Winters, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15767, https://doi.org/10.5194/egusphere-egu21-15767, 2021.

Abstract: We propose new methods for multi-frequency snow thickness retrievals building on the legacy of the Arctic+ Snow project where we developed two products: the dual-altimetry Snow Thickness (DuST) and the Snow on Drifting Sea Ice (SnoDSI). The primary objective of this project is to investigate multi-frequency approaches to retrieve snow thickness over all types of sea ice surfaces in the Arctic and provide a state-of-the-art snow product. Our approach follows ESA ITT recommendations to prioritise satellite-based products and will benefit from the recent ‘golden era in polar altimetry’ with the successful launch of the laser altimeter ICESat-2 in 2018 complementing data provided by the rich fleet of radar altimeters, CryoSat-2, Sentinel-3 A/B, AltiKa. Our primary objective is to produce an optimal snow product over the recent ‘operational‘ period. This will be complemented by additional snow products covering a longer periods of climate relevance and making use of historical altimeters (Envisat, ICESat-1) and passive microwave radiometers for comparison purposes (SMOS, AMSRE, AMSR-2). In addition to snow thickness, and as a secondary objective, we will explore other snow characteristics (snow density, snow metamorphism, scattering horizon, roughness, etc) and compare these results with in-situ, airborne and other snow on sea ice products including from model studies and reanalysis on drifting sea ice products. In preparation to future multi-frequency mission we will put an emphasis on uncertainty analysis of our snow product, the impact of the snow on the sea ice thickness retrieval, and on climate physics via model runs with snow initialisation and data assimilation. Finally, learning from past and present campaings (i.e. CryoVex, MOSAiC) we will propose methodologies for effective future snow and sea ice thickness airborne validation campaigns via innovative inverse modelling approaches and airborne retrackers.

How to cite: Tsamados, M. and the POLAR+ Snow of Sea Ice team: Multi-Frequency Satellite Approaches for Snow on Sea Ice: first results from the POLAR+ Snow on Sea Ice ESA project, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12341, https://doi.org/10.5194/egusphere-egu21-12341, 2021.

Thanks to the relatively high inclination (81.5°N/S) of the ERS2, Envisat, CryoSat-2, Saral and S3 space altimeters, the Polar Regions have been observed continuously by radar altimetry since the 1990s. We thus have time series over nearly 30 years of the topography of the polar ice caps and the thickness of the ice pack.  However, these measurements took a qualitative leap forward with the launch of CryoSat-2 in 2010, thanks to the advent of SAR/SARIN altimetry and a near-polar inclination of 88°N/S.

SAR/SARIN altimetry has led to considerable improvements in measurement accuracy thanks to better focusing (reducing the footprint by a factor of about 100) and better resolution (by a factor of about 2). The inclination of 88°N/S provides us with almost complete coverage of the Polar Regions, enabling us to carry out 10-year assessments of polar caps and sea-ice volume variations.

During this presentation, we will first show the many scientific advances made possible by polar altimetry and its various evolutions, including the high-precision lidar solution on board NASA's IceSat-2 satellite.

We will then present the HPCM CRISTAL mission, the only new polar altimetry mission planned to date.  We will see the technical advances proposed by this mission and its importance in monitoring the Polar Regions in the context of global warming.

How to cite: Fleury, S., Shepherd, A., Humbert, A., and Helm, V.: Observation of the cryosphere by altimetry: past, present and future contributions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15862, https://doi.org/10.5194/egusphere-egu21-15862, 2021.

Launched in 2010, the European Space Agency’s (ESA) polar-orbiting CryoSat satellite was specifically designed to measure changes in the thickness of polar sea ice and the elevation of the ice sheets and mountain glaciers. Beyond the primary mission objectives, CryoSat is also valuable source of data for the oceanographic community and CryoSat’s sophisticated SAR Interferometric Radar Altimeter (SIRAL) can measure high-resolution geophysical parameters from the open ocean to the coast.

CryoSat data is processed operationally using two independent processing chains: Ice and Ocean. To ensure that the CryoSat products meet the highest data quality and performance standards, the CryoSat Instrument Processing Facilities (IPFs) are periodically updated. Processing algorithms are improved based on feedback and recommendations from Quality Control (QC) activities, Calibration and Validation campaigns, the CryoSat Expert Support Laboratory (ESL), and the Scientific Community.

Since May 2019, the CryoSat ice products have been generated with Baseline-D, which represented a major processor upgrade and implemented several improvements, including the optimisation of freeboard computation in SARIn mode, improvements to sea ice and land ice retracking and the migration from Earth Explorer Format (EEF) to Network Common Data Form (NetCDF). The Baseline-D reprocessing campaign completed in May 2020, and the full mission Baseline-D dataset is now available to users (July 2010 – present). The next major processor upgrade, Baseline-E, is already under development and following testing and refinement is anticipated to be operational in Q3 2021.

The CryoSat ocean products are also generated in NetCDF, following a processor upgrade in November 2017 (Baseline-C). Improvements implemented in this baseline include the generation of ocean products for all data acquisition modes, therefore providing complete data coverage for ocean users. This upgrade also implemented innovative algorithms, refined existing ones and added new parameters and corrections to the products. Following the completion of a successful reprocessing campaign, Baseline-C ocean products are now available for the full mission dataset (July 2010 – present). Preparations are underway for the next major processor upgrade, Baseline-D.

Since launch, the CryoSat ice and ocean products have been routinely monitored as part of QC activities by the ESA/ESRIN Sensor Performance, Products and Algorithms (SPPA) office with the support of the Quality Assurance for Earth Observation (QA4EO) service (formerly IDEAS+) led by Telespazio UK. The latest processor updates have brought significant improvements to the quality of CryoSat ice and ocean products, which in turn are expected to have a positive impact on the scientific exploitation of CryoSat measurements over all surface types.

This poster provides an overview of the CryoSat data quality status and the QC activities performed by the IDEAS-QA4EO consortium, including both operational and reprocessing QC. Also presented are the main evolutions and improvements that have implemented to the processors, and anticipated evolutions for the future.

How to cite: Webb, E., Marsh, J., Benzan Valette, L., Bouffard, J., Parrinello, T., Baker, S., Brockley, D., Geminale, T., and Scagliola, M.: Quality Status of the CryoSat Data Products, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-212, https://doi.org/10.5194/egusphere-egu21-212, 2021.