CryoSat-2 Satellite Research Articles

A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations

Abstract. Observations of sea ice freeboard from satellite radar altimeters are crucial in the derivation of sea ice thickness estimates, which in turn provide information on sea ice forecasts, volume budgets, and productivity rates. Current spatio-temporal resolution of radar freeboard is limited as 30 d are required in order to generate pan-Arctic coverage from CryoSat-2 and 27 d are required from Sentinel-3 satellites. This therefore hinders our ability to understand physical processes that drive sea ice thickness variability on sub-monthly timescales. In this study we exploit the consistency between CryoSat-2, Sentinel-3A, and Sentinel-3B radar freeboards in order to produce daily gridded pan-Arctic freeboard estimates between December 2018 and April 2019. We use the Bayesian inference approach of Gaussian process regression to learn functional mappings between radar freeboard observations in space and time and to subsequently retrieve pan-Arctic freeboard as well as uncertainty estimates. We also employ an empirical Bayesian approach towards learning the free (hyper)parameters of the model, which allows us to derive daily estimates related to radar freeboard spatial and temporal correlation length scales. The estimated daily radar freeboard predictions are, on average across the 2018–2019 season, equivalent to CryoSat-2 and Sentinel-3 freeboards to within 1 mm (standard deviations <6 cm), and cross-validation experiments show that errors in predictions are, on average, ≤ 4 mm across the same period. We also demonstrate the improved temporal variability of a pan-Arctic daily product by comparing time series of the predicted freeboards, with 31 d running means from CryoSat-2 and Sentinel-3 freeboards, across nine sectors of the Arctic, as well as making comparisons with daily ERA5 snowfall data. Pearson correlations between daily radar freeboard anomalies and snowfall are as high as +0.52 over first-year ice and +0.41 over multi-year ice, suggesting that the estimated daily fields are able to capture real physical radar freeboard variability at sub-weekly timescales.

Arctic sea ice thickness variations from CryoSat-2 satellite altimetry data

Arctic sea ice thickness variations from CryoSat-2 satellite altimetry data

Sea Level Variability around the Java Sea (study Area: northern of Gresik and Surabaya) using Cryosat-2 Altimeter

Sea level rise is one of the issues that are on the highlight today. Seawater that is in direct contact with the coastal area makes the region vulnerable to the environment. The Java Sea in Indonesia with a wide enough coverage will have many impacts on the coastal areas if there is an increase in sea level. For these observations, a sea-level anomaly (SLA) is calculated to understand sea level and its changes over time. SLA calculations use data from Cryosat-2 Satellite. Cryosat-2 is an altimetry satellite that was designed for the ice sheet observation mission and oceanography observation missions. This altimetry satellite has an excellent level of accuracy even though it does not have a microwave radiometer. This research is expected to know sea level variability of the Java Sea surface from 2011 to 2019. Obtained based on linear regression of the Cryosat-2 SLA trend, the Java Sea has decreased by 27.9746 mm at a rate of -3.55 mm/year. It is caused by the phenomenon of El-Niño Southern Oscillation (correlation coefficient of the SLA and MEI detrended indicating indicates a strong but not unidirectional correlation. The higher the MEI index, the lower the SLA value will be in the Java Sea, a vice versa, and MEI detrended, indicating a strong but not unidirectional correlation. The higher the MEI index, the lower the SLA value will be in the Java Sea, and vice versa.

Applications for CryoSat-2 satellite magnetic data in studies of Earth\u2019s core field variations

We use 20 years of continuous magnetic field measurements from the Ørsted, CHAMP and Swarm satellite missions, supplemented by calibrated platform magnetometer data from the CryoSat-2 satellite, to study time variations of the Earth’s core field at satellite altitude and at the core–mantle boundary (CMB). From the satellite data we derive composite time series of the core field secular variation (SV) with 4-month cadence, at 300 globally distributed Geomagnetic Virtual Observatories (GVO). A previous gap in the GVO series between 2010 and 2014 is successfully filled using CryoSat-2, and sub-decadal variations are identified during this period. Tests showed that similar sub-decadal SV patterns were obtained from the CryoSat-2 data regardless of whether IGRF-13 or CHAOS-6x9 was used in their calibration. Cryosat-2 radial field SV series at non-polar latitudes have a mean standard deviation level compared to smoothing spline fits of 3.5 nT/yr compared to 1.8 nT/yr for CHAMP and 0.9 nT/yr for Swarm. GVO radial SV series display regional fluctuations with 5–10 years duration and amplitudes reaching 20 nT/yr, most notably at low latitudes over Indonesia (2014), over South America and the South Atlantic (2007, 2011 and 2014), and over the central Pacific (2017). Applying the Subtractive Optimally Localized Averages (SOLA) method, we also map the radial SV at the CMB as a collection of locally averaged SV estimates. We demonstrate that using 2-year windows of CryoSat-2 data, it is possible to reliably estimate the SV and its time derivative, the secular acceleration (SA), at the CMB, with a spatial resolution, corresponding to spherical harmonic degree 10. Along the CMB geographic equator, we find strong SA features with amplitude pm 2.5mu mathrm{T}/mathrm{yr}^2 under Indonesia from 2011–2014, under central America from 2015 to 2019, and sequences of SA with alternating sign under the Atlantic during 2004–2019. We find that platform magnetometer data from CryoSat-2 make a valuable contribution to the emerging picture of sub-decadal core field variations. Using 1-year windows of data from the Swarm satellites, we show that it is possible to study SA changes at low latitudes on timescales down to 1 year, with spatial resolution corresponding to spherical harmonic degree 10. We find strong positive and negative SA features appearing side-by-side in the Pacific in 2017, and thereafter drift westward.

Probing 3-D electrical conductivity of the mantle using 6 years of Swarm, CryoSat-2 and observatory magnetic data and exploiting matrix Q-responses approach

This study presents results of mapping three-dimensional (3-D) variations of the electrical conductivity in depths ranging from 400 to 1200 km using 6 years of magnetic data from the Swarm and CryoSat-2 satellites as well as from ground observatories. The approach involves the 3-D inversion of matrix Q-responses (transfer functions) that relate spherical harmonic coefficients of external (inducing) and internal (induced) origin of the magnetic potential. Transfer functions were estimated from geomagnetic field variations at periods ranging from 2 to 40 days. We study the effect of different combinations of input data sets on the transfer functions. We also present a new global 1-D conductivity profile based on a joint analysis of satellite tidal signals and global magnetospheric Q-responses.

Assessment of near-shore currents from CryoSat-2 satellite in the Gulf of Cádiz using HF radar-derived current observations

This study evaluated the possibility of studying mesoscale surface circulation in coastal areas, as is the Gulf of Cádiz, Spain, using high-resolution altimetry data (20-Hz of posting rate) along with the use of wind and bottom friction ageostrophic corrections. Absolute cross-track surface zonal current velocities, derived from filtered along-track CryoSat-2 SIRAL-SARM Absolute Dynamic Topography (ADT) measurements, are compared with high-frequency radar (HFR) data in the coastal area of the Gulf of Cádiz. The filter strategy followed in this study for the altimetry data is dependent on the HFR measurements for each track. Absolute surface geostrophic velocities obtained from 20-Hz altimetry data agree well HFR further than 25 km from the coast. Close to land ([3–25] km) the ageostrophic component of the surface current (due to the wind drag and the bottom friction) needs to be considered in the altimeter data. On average, the correlation between altimetry and HFR improved from 0.61 (no correction) to 0.72 (correcting these effects). The root mean square error (RMSE) lowered from 12.54 cm·s−1 to 7.35 cm·s−1. Furthermore, it has also been demonstrated that corrected altimeter measurements are useful for the study of dynamics and patterns of coastal areas.

Lateral variations of electrical conductivity in the lower mantle constrained by Swarm and CryoSat-2 missions

The electrical conductivity is an important geophysical parameter connected to the thermal, chemical, and mineralogical state of the Earth’s mantle. In this paper, we apply the previously developed methodology of forward and inverse EM induction modeling to the latest version of satellite-derived spherical harmonic coefficients of external and internal magnetic field, and obtain the first 3-D mantle conductivity models with contributions from Swarm and CryoSat-2 satellite data. We recover degree 3 conductivity structures which partially overlap with the shape of the large low-shear velocity provinces in the lower mantle.

Comparison of direct measurements of sea ice thickness and snow height, CryoSat-2 observations and PIOMAS numerical estimates

The paper presents the results of comparison of contact measurements of ice thicknesses and snow heights performed at the points of the hydrological stations of the “Transarktika-2019” expedition in April 2019 north of Franz-Josef Land archipelago, with altimetry observations of the Cryosat-2 satellite and numerical estimates of the PIOMAS (Pan-Arctic numerical Ice and Ocean Modeling system and data Assimilation). A significantly better correspondence is predictably shown between the variability of the ice thickness directly measured and observed using the CryoSat-2 satellite than that for the numerical PIOMAS system estimates. A trial correction of the algorithm for calculating the ice thickness by replacing the climatic values of the ice density, snow density and height with data from direct measurements also predictably improves the quality of calculating the ice thickness from satellite observations. The mean / route mean square differences obtained for ice thicknesses (+44/+96 cm for uncorrected and +30/+95 cm for corrected CryoSat-2 satellite, –14/+81 cm for PIOMAS system) and snow height (–4/+12 cm for CryoSat-2 satellite, –15/+12 cm for PIOMAS system) show the scale of uncertainty in estimating sea ice thickness and snow height for areas dominated by medium and thick first-year ice. An anomaly of the ice thicknesses observed during the expedition is given in comparison with the background characteristics based on historical ice charting data for 1970s — 1990s, earlier High-Arctic aircraft “Sever” expeditions during 1950s — 1970s and the stated remote observations and numerical estimates for 2000s — 2019. Comparison shows that the AARI expedition was actually carried out in one of the most favorable years for ice research in the last decade for this region — the average ice thickness in April 2019 was 15 — 28 cm higher than that for the interval 2011 — 2019 with a slightly lower (1 — 2 cm) height of the snow cover. In the earlier period of the 1970 — 1990s this area was characterized by significantly thicker old ice with characteristic thicknesses ~ 60 cm more than in April 2019.

Magnetic observations from CryoSat-2: calibration and processing of satellite platform magnetometer data

We describe and discuss the preprocessing and calibration steps applied to the magnetic data measured by the three “platform magnetometers” on-board the CryoSat-2 satellite. The calibration is performed by comparing the magnetometer sensor readings with magnetic field values for the time and position of the satellite as given by the CHAOS-6 geomagnetic field model. We allow for slow temporal variations of the calibration parameters by solving for scale values, offsets, and non-orthogonalities in monthly bins, and account for non-linearities as well as the magnetic disturbances caused by battery, solar panel and magnetorquer currents. Fully calibrated magnetic vector data, together with time and position, are provided as daily files in CDF data format at swarm-diss.eo.esa.int. The data show good agreement with Swarm satellite magnetic measurements during close encounters (rms difference between 1 and 5 nT for inter-satellite distances below 300 km).

Toward improved sea ice freeboard observation with SAR altimetry using the physical retracker SAMOSA+

Since 2010, the CryoSat-2 satellite mission has enabled to largely improve sea ice freeboard estimations. But due to the complexity of radar echoes over sea ice, freeboard retrieval from altimetry still presents some errors and biases that further limit the potential of these observations for climate studies or for assimilation into models. Various methods have been explored, producing a large range of freeboard estimations. In this study, we analyze the main steps of the radar freeboard computation developed as part of the Cryo-SeaNice Project. The objective is to quantify the impacts of each processing method and to identify optimal strategies to improve freeboard estimations from SAR altimetry measurements. We consider two SAR processing options: the Hamming Window (HW) and with the Zero-Padding (ZP), and 2 retrackers: the Threshold First Maximum Retracker Algorithm (TFMRA) based on heuristic measurements and SAMOSA+ a retracker declined from model based analysis of the surface back-scatter. Four freeboard solutions are generated from combinations of the 2 processing options (HW and ZP or ZP only) and the 2 types of retrackers. In addition, an alternative to the Hamming Window method to filter out side-lobes errors is presented. The impacts of the different approaches to estimate freeboard are quantified from comparisons with Operation Ice Bridge (OIB) and the Beaufort Gyre Exploration project (BGEP) in situ data. Our results show that SAMOSA+ provides more precise freeboard estimations. This new time-series is available on CTOH website. We also identified some impacts of the Hamming Window for both retrackers. Finally, we present the potential of using the simpler threshold retracker but with a correction to account for the surface roughness that is calibrated against SAMOSA+.

River discharge estimation from radar altimetry: Assessment of satellite performance, river scales and methods

The ENVISAT, Jason-2 and -3, Sentinel-3A, CryoSat-2, and AltiKa satellite altimeters were used to estimate the discharge of two Arctic rivers: the Ob and Pur in western Siberia. The accuracy of the water height estimates from ENVISAT varied with river size from 0.63 m for the Ob (~2 km in width) to 1.1 m for the Pur (~0.5 km in width). A new method for water level estimation combining the CryoSat-2, AltiKa, and Sentinel-3A altimeters provided better height accuracy (0.49 m for the Ob). The rating curve method of discharge estimation outperforms the Manning formulation, on the Ob, with a root mean square error (RMSE) of 13% vs 20% for daily discharge and 1% vs 5% for annual discharge. Sensitivity analysis performed on the Manning formulation showed high sensitivity to the river depth parameterisation. A functional relationship was identified between the depth of rivers, in the north of western Siberia, and their widths and deposits types. The combination, for the Ob, of the rating curve method with the multi-satellite water level retrieval algorithm provided higher discharge accuracy than previous studies relying on satellite measurements, on numerical modelling or on their combination. A synthesis of worldwide river altimetry studies supported our finding that the accuracy of altimetric discharge estimations decreases for narrower rivers. The rating curve method applied to Jason 2/3 height measurements allows estimation of daily discharges with RMSE of 18% for the wide Ob River but 38% for the narrow Pur River.

Discrimination of different sea ice types from CryoSat-2 satellite data using an Object-based Random Forest (ORF)

Sea ice type is one of the most sensitive variables in Arctic sea ice monitoring, and it is important for the retrieval of ice thickness. In this study, we analyzed various waveform features that characterize the echo waveform shape and Sigma0 (i.e., backscatter coefficient) of CryoSat-2 synthetic aperture radar altimeter data over different sea ice types. Arctic and Antarctic Research Institute operational ice charts were input as reference. An object-based random forest (ORF) classification method is proposed with overall classification accuracy of 90.1%. Accuracy of 92.7% was achieved for first-year ice (FYI), which is the domain ice type in the Arctic. Accuracy of 76.7% was achieved at the border of FYI and multiyear ice (MYI), which is better than current state-of-the-art methods. Accuracy of 83.8% was achieved for MYI. Results showed the overall accuracy of the ORF method was increased by ∼8% in comparison with other methods, and the classification accuracy at the border of FYI and MYI was increased by ∼10.5%. Nevertheless, ORF classification performance might be influenced by the selected waveform features, snow loading, and the ability to distinguish sea ice from leads.

Extending the Arctic sea ice freeboard and sea level record with the Sentinel-3 radar altimeters

In February 2016 and April 2018 the European Space Agency launched the Sentinel-3A and 3B satellites respectively, as part of the European Commission’s multi-satellite Copernicus Programme. Here we process Sentinel-3A waveform data to estimate Arctic sea level anomaly and radar freeboard from November 2017 to April 2018. We compare our results to those from the CryoSat-2 satellite, and find an intermission bias on sea-level anomaly of 2 cm. We also find a mean radar freeboard difference of 1 cm, which we attribute to the use of empirical retrackers to retrieve lead and floe elevations. Ahead of Sentinel-3B waveform data being made available, we use orbit files to estimate the improvement in sampling resolution afforded by the addition of Sentinel-3A and 3B data to the CryoSat-2 dataset. By combining data from the three satellites, grid resolution or time-sampling can be almost tripled compared with using CryoSat-2 data alone.

RMetS National Meeting – Arctic prediction in a changing climate: understanding key processes and challenges

RMetS National Meeting – Arctic prediction in a changing climate: understanding key processes and challenges

An Attempt to Observe Vertical Land Motion along the Norwegian Coast by CryoSat-2 and Tide Gauges

Present-day climate-change-related ice-melting induces elastic glacial isostatic adjustment (GIA) effects, while paleo-GIA effects describe the ongoing viscous response to the melting of late-Pleistocene ice sheets. The unloading initiated an uplift of the crust close to the centers of former ice sheets. Today, vertical land motion (VLM) rates in Fennoscandia reach values up to around 10 mm/year and are dominated by GIA. Uplift signals from GIA can be computed by solving the sea-level equation (SLE), 
 
 
 
 S
 ˙
 
 
 
 = 
 
 
 
 N
 ˙
 
 
 
 − 
 
 
 
 U
 ˙
 
 
 
 . All three quantities can also be determined from geodetic observations: relative sea-level variations (
 
 
 
 S
 ˙
 
 
 
 ) are observed by means of tide gauges, while rates of absolute sea-level change (
 
 
 
 N
 ˙
 
 
 
 ) can be observed by satellite altimetry; rates of VLM (
 
 
 
 U
 ˙
 
 
 
 ) can be determined by GPS (Global Positioning System). Based on the SLE, 
 
 
 
 U
 ˙
 
 
 
 can be derived by combining sea-surface measurements from satellite altimetry and relative sea-level records from tide gauges. In the present study, we have combined 7.5 years of CryoSat-2 satellite altimetry and tide-gauge data to estimate linear VLM rates at 20 tide gauges along the Norwegian coast. Thereby, we made use of monthly averaged tide-gauge data from PSMSL (Permanent Service for Mean Sea Level) and a high-frequency tide-gauge data set with 10-min sampling rate from NMA (Norwegian Mapping Authority). To validate our VLM estimates, we have compared them with the independent semi-empirical land-uplift model NKG2016LU_abs for the Nordic-Baltic region, which is based on GPS, levelling, and geodynamical modeling. Estimated VLM rates from 1 Hz CryoSat-2 and high-frequency tide-gauge data reflect well the amplitude of coastal VLM as provided by NKG2016LU_abs. We find a coastal average of 2.4 mm/year (average over all tide gauges), while NKG2016LU_abs suggests 2.8 mm/year; the spatial correlation is 0.58.

Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system

Abstract. The accuracy of the initial state is very important for the quality of a forecast, and data assimilation is crucial for obtaining the best-possible initial state. For many years, sea-ice concentration was the only parameter used for assimilation into numerical sea-ice models. Sea-ice concentration can easily be observed by satellites, and satellite observations provide a full Arctic coverage. During the last decade, an increasing number of sea-ice related variables have become available, which include sea-ice thickness and snow depth, which are both important parameters in the numerical sea-ice models. In the present study, a coupled ocean–sea-ice model is used to assess the assimilation impact of sea-ice thickness and snow depth on the model. The model system with the assimilation of these parameters is verified by comparison with a system assimilating only ice concentration and a system having no assimilation. The observations assimilated are sea ice concentration from the Ocean and Sea Ice Satellite Application Facility, thin sea ice from the European Space Agency's (ESA) Soil Moisture and Ocean Salinity mission, thick sea ice from ESA's CryoSat-2 satellite, and a new snow-depth product derived from the National Space Agency's Advanced Microwave Scanning Radiometer (AMSR-E/AMSR-2) satellites. The model results are verified by comparing assimilated observations and independent observations of ice concentration from AMSR-E/AMSR-2, and ice thickness and snow depth from the IceBridge campaign. It is found that the assimilation of ice thickness strongly improves ice concentration, ice thickness and snow depth, while the snow observations have a smaller but still positive short-term effect on snow depth and sea-ice concentration. In our study, the seasonal forecast showed that assimilating snow depth led to a less accurate long-term estimation of sea-ice extent compared to the other assimilation systems. The other three gave similar results. The improvements due to assimilation were found to last for at least 3–4 months, but possibly even longer.

Cause of scale inconsistencies in DORIS time series

In this paper we analyze the scale of the DORIS (Doppler Orbitography and Radiopositioning Integrated by Satellite) solutions with respect to DORIS extension of the International Terrestrial Reference Frame (ITRF) for Precise Orbit Determination DPOD2014. The main goal is to explain the scale inconsistencies and to find the optimal solution reaching low-biased and consistent scale time series. Our analysis profits from 4 different strategies based only on the Geodetic Observatory Pecný analysis center solution, using DORIS exchange format data 2.2. A difference in the sequence of the solutions directly corresponds to one of the changes in the solution settings: data elevation dependent weighting, application of data validity indicators and application of phase center - reference point correction. We process multi-satellite and single-satellite solutions for the time period 2011.0–2017.0. Our analysis examines scale inconsistency issues in 2011/2012 and in 2015. The scale increment in 2011/2012 is explained as a result of the concurrence of changes in satellite constellation and change in the provider data validity standards for Cryosat-2 and Jason-2 satellites. The scale increment in 2015 is explained as the effect of change in the standards for phase center - reference center corrections for Saral, Jason-2 and Cryosat-2 satellites. Moreover, comparing the solutions with and without elevation dependent data downweighting using the same elevation cutoff (10°), we found a significant reduction of scale bias and scale variation applying the data downweighting. The data downweighting improved also the station positioning repeatability. We demonstrate that the solution, which is completely free from the additional data associated with observations in DORIS exchange format 2.2 and includes the data downweighting law, eventuates in a consistent scale time series with the lowest offset with respect to DPOD2014 (version 1.0) (12.7 ± 2.3 mm for 2011.0–2017.0). The only remaining scale issue is the part of 2011/2012 increment of around 5 mm, explained by a change in the DORIS satellite constellation.

A new digital elevation model of Antarctica derived from CryoSat-2 altimetry

Abstract. We present a new digital elevation model (DEM) of the Antarctic ice sheet and ice shelves based on 2.5×108 observations recorded by the CryoSat-2 satellite radar altimeter between July 2010 and July 2016. The DEM is formed from spatio-temporal fits to elevation measurements accumulated within 1, 2, and 5 km grid cells, and is posted at the modal resolution of 1 km. Altogether, 94 % of the grounded ice sheet and 98 % of the floating ice shelves are observed, and the remaining grid cells north of 88∘ S are interpolated using ordinary kriging. The median and root mean square difference between the DEM and 2.3×107 airborne laser altimeter measurements acquired during NASA Operation IceBridge campaigns are −0.30 and 13.50 m, respectively. The DEM uncertainty rises in regions of high slope, especially where elevation measurements were acquired in low-resolution mode; taking this into account, we estimate the average accuracy to be 9.5 m – a value that is comparable to or better than that of other models derived from satellite radar and laser altimetry.

PARAMETERS COMPARSION OF LEADS DETECTION IN ARCTIC SEA ICE USING CRYOSAT-2 WAVEFORM DATA

Abstract. Leads are only a small part of the polar sea ice structure, but they play a dominant role on the turbulence exchange between the ocean and the atmosphere, they are also important factors about sea ice thickness inversion. Since the early 2000s, Satellite altimetry has been applied to monitor the Arctic sea ice thickness, Satellite altimetry data can be used to distinguish leads and sea ice. In this paper, four parameters including Pulse peakiness (PP), stack standard deviation (SSD), stack kurtosis (SKU) and stack skewness (SSK) are extracted from CryoSat-2 satellite altimetry waveform data. The four parameters are combined into five combinations (PP, PP&SSD, PP&SSD&SKU, PP&SSD&SSK, PP&SSD&SSK&SKU) with constrain conditions to detect the leads. The results of the five methods are compared with MODIS (moderate-resolution imagining spectroradiometer) images and show that, the combination of PP&SSD is better than the single PP, the rest of combinations are the same as the combination of PP&SSD. It turns out, there is no promotion when we add SSK and SKU, successively or simultaneously.

CryoSat: ESA’s ice mission – Eight years in space

CryoSat-2 is a satellite of the European Space Agency that was launched in April 2010. It is intended to monitor changes in the thickness of the marine ice, floating in the polar oceans, and to measure variations in the thickness of the vast ice sheets that overlie Greenland and Antarctica.The CryoSat-2 satellite has replaced the original CryoSat, which became lost due to a launch failure in October 2005.CryoSat-2 carries an innovative radar altimeter called SIRAL (Synthetic Aperture Interferometric Altimeter). It has two radar antennas, which met the measurement requirements for ice-sheet elevation and sea-ice freeboard with unprecedented accuracy. The satellite orbits the planet at an altitude of around 720 km with a retrograde drifting orbit inclination of 92° and a “quasi” repeat cycle of 369 days (30 days sub-cycle). CryoSat-2 is therefore able to reach latitudes up to 88° covering more than 4.6 million km2 of unexplored areas over the poles when compared with previous polar missions carrying an altimetry.The mission has achieved its prime objectives and is delivering high quality data, providing unique contributions to several Earth Science and applications domains, including ice, ocean, geodesy and hydrology, both at global and regional scales.The purpose of this paper is to provide a general overview of the mission and of its main scientific achievements after eight years in space.