Last modified on Nov 08, 2023 12:36

Contributors: R. Kidd (EODC GmbH), C. Briese, A. Dostalova, L. Gilbert (University Leeds), S. B. Simonsen (Technical University of Denmark), J. Wuite (ENVEO)

Issued by: EODC GmbH/Richard A Kidd

Date: 17/11/2022

Ref: C3S2_312a_Lot4.WP3-TRGAD-IS-v1_202204_IS_TR_GA_i1.0

Official reference number service contract: 2021/C3S2_312a_Lot4_EODC/SC1

History of modifications

Related documents

Acronyms

General definitions

Baseline: A combination of processor versions, auxiliary data and other needed enablers that allows the generation of a coherent set of Earth observation products.

Brokered Product: A brokered product is a pre-existing dataset to which the Copernicus Climate Change Service (C3S) acquires a license, for the purpose of including it in the Climate Data Store (CDS).

Burst: Sentinel-1 interferometric wide swath (IW) single look complex (SLC) products contain one image per sub-swath for a total of three (single polarisation) or six (dual polarisation) images in an IW SLC product. Each sub-swath image consists of a series of bursts, where each burst has been processed as separate SLC image.

Crossover analysis: A method for deriving elevation change at locations where the orbits of a single or multiple satellites cross.

Cross-calibration: A method that merges datasets from multiple satellites into one consistent dataset.

Cross-calibration uncertainty: The uncertainty due to the merging of datasets.

Epoch uncertainty: Within the Surface Elevation Change (SEC) product, the epoch uncertainty is the sum of all uncertainties applicable to the input altimetry data in a given period (the 'epoch'), which in this case is the period to which the SEC measurement applies.

Generated Product: A generated product is a dataset made specifically for C3S, for the purpose of including it in the CDS.

Gravimetric Mass Balance (GMB): The mass balance of an ice sheet is the net difference between mass gained from snow deposition and mass lost by melting or iceberg calving. This is essentially the same as the mass change of the ice sheet. When mass balance is derived from measured changes in the Earth's gravitational field, this is referred to as a gravimetric mass balance.

Ice Velocity (IV): Ice flow velocity describes the rate and direction of ice movement. It is a fundamental parameter to characterize the behaviour of a glacier or an ice sheet. Ice velocity and its spatial derivative, strain rate (which is a measure of the ice deformation rate), are required for estimating ice discharge and mass balance and are essential input for glacier models that try to quantify ice dynamical processes. Changes in velocity and velocity gradients can point at changing boundary conditions. Remote sensing techniques that utilise synthetic aperture radar (SAR) and optical satellite data are the only feasible manner to derive accurate surface velocities of the remote Greenland glaciers on a regular basis. The ice velocity (IV) products generated in C3S are derived from SAR data. Details of the method are provided in the Algorithm Theoretical Basis Document (ATBD). Ice velocity is provided as gridded velocity fields (maps) in NetCDF format with separate files for the different velocity components and measurement uncertainty. A velocity grid represents the average ice surface velocity over the given period.

Interferometric Wide (IW): The Interferometric Wide (IW) swath mode is the main acquisition mode of Sentinel-1 over land, including ice sheets. It acquires data with a 250 km swath at 5 m by 20 m spatial resolution. IW mode captures three sub-swaths using Terrain Observation with Progressive Scans SAR (TOPS). (Adapted from: https://sentinels.copernicus.eu/)

Level 2 pre-processed (L2P): this is a designation of satellite data processing level. "Level 2" means geophysical variables derived from Level 1 source data on the same grid (typically the satellite swath projection). "Pre-processed" means ancillary data and metadata added.

Level 3 /uncollated/collated/super-collated (L3U/L3C/L3S): this is a designation of satellite data processing level. "Level 3" indicates that the satellite data is a geophysical quantity (retrieval) that has been averaged where data are available to a regular grid in time and space. "Uncollated" means L2 data granules have been remapped to a regular latitude/longitude grid without combining observations from multiple source files. L3U files will typically be "sparse" corresponding to a single satellite orbit. "Collated" means observations from multiple images/orbits from a single instrument combined into a space-time grid. A typical L3C file may contain all the observations from a single instrument in a 24-hour period. "Super-collated" indicates that (for those periods where more than one satellite data stream delivering the geophysical quantity has been available) the data from more than one satellite have been gridded together into a single grid-cell estimate, where relevant.

Modelling uncertainty: The uncertainty in the fitting of a model to a dataset. In the SEC dataset, the surface elevation change rate is derived from a model where elevation vs time is a straight line, and the modelling uncertainty is the standard deviation of the input data from the line that best fits the data.

Phase discontinuities: Phase discontinuities or phase jumps are discontinuities in an interferogram, caused by (small) co-registration errors that frequently manifest themselves in burst overlap regions. In TOPS mode the antenna beam is electronically steered in azimuth direction during each burst, resulting in a rapidly changing Doppler centroid within a single burst. Therefore, even a very small coregistration error may cause significant phase errors in an interferogram.

Single Look Complex (SLC): Level-1 SLC products are SAR images in the slant range by azimuth imaging plane, in the image plane of satellite data acquisition. Each image pixel is represented by a complex magnitude value and therefore contains both amplitude and phase information. The imagery is geo-referenced using orbit and attitude data from the satellite. SLC images are produced in a zero Doppler geometry.
(Adapted from: https://sentinels.copernicus.eu/)

Surface Elevation Change (SEC): The surface elevation of a point on an ice sheet is the height of the ice sheet surface above a reference geoid (a hypothetical solid figure whose surface corresponds to mean sea level and its imagined extension under land areas). Increase in surface elevation over time at a given location indicates a gain of ice or snow at that location, and conversely decrease indicates a loss. The surface elevation change product provides the rate of change given at monthly intervals at each location on a grid covering the ice sheet. The definition of the grid projection includes the geoid used. Given the rates of change, absolute change can be calculated for any time period.

Target requirement: ideal requirement which would result in a significant improvement for the target application.

Terrain Observation with Progressive Scans (TOPS): With the TOPS(AR) technique, in addition to steering the radar beam in range, the beam is also electronically steered from backward to forward in the azimuth direction for each burst, avoiding scalloping and resulting in homogeneous image quality throughout the swath (De Zan and Guarnieri, 2006).

Threshold requirement: minimum requirement to be met to ensure data are useful.

Scope of the document

This document aims to provide users with the relevant information on requirements and gaps for each of the given products within the Land Hydrology and Cryosphere service. The gaps in this context refer to data availability to enable the ECV products to be produced, or in terms of scientific research required to enable the current ECV products to be evolved to respond to the specified user requirements.

The Ice Sheets and Ice Shelves Service provide four products. These are (i) a Surface Elevation Change product for Greenland (Greenland SEC) and (ii) a SEC product for Antarctic (Antarctic SEC), (iii) an Ice Velocity (IV) product and (iv) a Gravimetric Mass Balance (GMB) product.

Initially an overview of each product is provided, including the required input data and auxiliary products, a definition of the retrieval algorithms and processing algorithms versions; including, where relevant, a comment on the current methodology applied for uncertainty estimation. The target requirements for each product are then specified which generally reflect the Global Cryosphere Observing System (GCOS) ECV requirements [RD.1]. The result of a gap analysis is provided that identifies the envisaged data availability for the next 10-15 years, the requirement for the further development of the processing algorithms, and the opportunities to take full advantage of current, external, research activities. Finally, where possible, areas of required missing fundamental research are highlighted, and a comment on the impact of future instrument missions is provided.

Executive Summary

The Ice Sheets and Ice Shelves Service represents three essential climate variables (ECVs) by providing four products. The Ice Velocity (IV) product covers the Greenland Ice Sheet. The Gravimetric Mass Balance (GMB) product covers both the Greenland and Antarctic Ice Sheets in one dataset. Finally, there are two Surface Elevation Change (SEC) products, one for each ice sheet. Although the two products have the same format, they necessarily use different map projections and processing methods, and so have been split to avoid possible confusion.

The current Ice Velocity product is a gridded product that represents the mean annual ice surface velocity (IV) of the Greenland Ice Sheet in true metres per day. It contains horizontal and vertical surface velocities of the ice surface in NetCDF 4 format according to the C3S Common Data Model (CDM) convention (Rozum, 2022). The IV product currently relies on data from the Copernicus Sentinel-1 satellite mission, until recently consisting of the twin satellites Sentinel-1A and Sentinel-1B. Since 23^rd December 2021, there has been a technical problem with the Sentinel-1B satellite and no data has been acquired after that time. Recovery attempts have been unsuccessful and In August 2022 European Space Agency (ESA) announced the end of the Sentinel-1B mission. The Sentinel-1A satellite continues to operate normally and Sentinel-1C is now planned to be launched in 2023 as a replacement for Sentinel-1B. The Sentinel-1 constellation will continue to operate well into the next decade with another satellite (Sentinel-1D) already in development. This, in combination with other new and planned synthetic aperture radar (SAR) missions (e.g., Satélite Argentino de Observación Con Microondas (SAOCOM), National Aeronautics and Space Administration (NASA) – Indian Space Research Organisation (ISRO) SAR Mission (NISAR)), ensures the long-term sustainability of the ice velocity Climate Data Record (CDR).

The SEC products provide estimates of surface elevation change over Antarctic Ice Sheets and Ice Shelves (Antarctic SEC), and for the Greenland Ice Sheet (Greenland SEC), using radar altimeter data from six satellite missions: European Remote Sensing Satellite (ERS)-1, ERS-2, Envisat, CryoSat-2 and Sentinels 3A and 3B. The three first listed missions have been completed but may still issue reprocessed datasets in the future, and the last three listed are still in operation. The products are a 25km gridded product, with monthly estimates from 1992 to present day, and are presented as NetCDF 4 according to the C3S CDM.

The Gravimetric Mass Balance (GMB) product provides monthly estimates of mass balance changes in the major drainage basins of Greenland and Antarctica from 2002 to 2017. The first two versions of the product relied solely on data from the Gravity Recovery and Climate Experiment (GRACE) mission, which ceased in October 2017. A GRACE follow-on (GRACE-FO) mission was successfully launched in May 2018 and began to produce science data in summer 2019, and this is incorporated in the current version, v3.

The user requirements provided by GCOS are in some instances unrealistic for the Ice Sheet Service products considering the current available satellite data, i.e. the GCOS target for horizontal resolution is 100m but the SEC product uses an achievable 25km resolution. But, in most cases, the primary user requirements (e.g. horizontal resolution for GMB) are already met.

All products will benefit from further development of the retrieval or processing methodology. A number of possible developments have already been identified and/or implemented for the Ice Sheets products. For the IV product, this includes the development of monthly velocity mosaics (not yet included in the service) and increased spatial resolution from 500m to 250m (to meet GCOS requirements) as well as a revision and update of the outlier detection and gap filling scheme and improvements in the error estimation.

For SEC, there are two major anticipated datastream changes. The current CryoSat-2 datastream is baseline E, which replaced baseline D in August 2021. Currently, to provide full mission coverage, both baselines are in use. A full mission reprocessing to baseline E is expected by the end of 2022. As for the v3 SEC product, the Sentinel-3 product available is optimised for oceans and therefore it contains gaps in the land ice marginal regions where the satellite's orbit track transitions from ocean to land. There is a specialised thematic land ice processor available, but ESA has de-prioritised its use, and the land ice product is currently scheduled to be released at the end of 2022.

Some fundamental research activities are also required outside of the C3S service, specifically for the IV products, and these focus on the development of i) Sentinel-1 Terrain Observing by Progressive Scans (TOPS) mode interferometric SAR (InSAR) to derive ice sheet velocity; (ii) to investigate methods for the reduction of the effects of differential ionospheric path delay, and the removal of ionospheric stripes and (iii) to further develop Sentinel-2 optical IV retrieval. For the SEC products, scientific research is required to identify ice dynamic trends, and for GMB research activity is required for the evaluation of the data and products from the GRACE-FO mission.

In addition to the products currently provided by the Ice Sheets and Ice Shelves Service, we specify potential future products that provide additional opportunities to exploit the Sentinel satellites, i.e. Antarctic Ice Sheet velocity, the grounding line location and products on surface melt processes (melt extent and start, duration and end of melt season). These products address current gap areas for which there is a clear scientific research need. The processing lines for these products have already been developed, tested and implemented in external programs or are in an advanced stage of development. A time series of monthly ice velocity maps for both Greenland and Antarctic has already been produced and is fit for inclusion in the service pending funding.

Reliance on External Research
Since the C3S programme only supports the implementation, development and operation of the CDR processor, any scientific advances of the C3S products entirely rely on funding provided by external programmes, e.g. Climate Change Initiative plus (CCI+), Hydrological Satellite Application Facility (H-SAF), Horizon2020. Thus, the implementation of new scientific improvements can only be implemented if external funding allows for it. This depends both on the availability of suitable programmes to support the R&D activities and the success of the C3S contractors in winning potential suitable calls.

1. Product description: Ice Sheets and Ice Shelves ECV Service

1.1. Introduction

This section aims at providing users with the relevant information on requirements, and gaps, for the Ice Sheets and Ice Shelves Service. It is divided into three sections. Section 1.2 describes the products currently provided by the service. Section 2 provides the target requirements for ice sheet related ECVs. Section 3 provides a past, present, and future gap analysis for current and potential future products of the Ice Sheet and Ice Shelf Service covering both gaps in the data availability and scientific gaps that could be addressed by further research activities (outside C3S).

1.2. Ice Sheets Product description

The ice sheets and shelves service covers three ECVs with four products:

• Ice sheet velocity – Greenland Ice Sheet only
• Surface elevation change – Greenland and Antarctic ice sheets, as two separate products
• Gravimetric mass balance – Greenland and Antarctic ice sheets, in one combined product

This section describes the existing products in more detail.

1.2.1. Greenland Ice Sheet velocity

The velocity grid represents the average annual ice surface velocity (IV) of Greenland in true metres per day. The geographic extent is the Greenland Ice Sheet, including peripheral glaciers. The ice sheet boundaries are based on the latest version of the Randolph Glacier Inventory (RGI 6.0, RGI Consortium, 2017) with updated glacier fronts for marine terminating glaciers. The basic IV product contains the horizontal (V_x, V_y) and vertical (V_z) components of the velocity vector. The horizontal surface velocities are derived from measured displacements in radar geometry (range, azimuth). The vertical velocity is derived from the interpolated height at the end position of the displacement vector minus the elevation at the start position, taken from a digital elevation model (DEM) (see Section 1.2.1.3.1). The main data variables are defined on a three-dimensional grid (x, y, z), where x and y are defined by the used map projection, i.e. the polar stereographic grid. The velocities are true values and not subject to the distance distortions present in the polar stereographic grid. Along with the ice velocity maps, the products include a valid pixel count map, which provides the number of valid displacement estimates at the output pixel position that are used in compiling the annually averaged map, as well as an uncertainty map (based on the standard deviation).

The IV product is distributed in NetCDF4 format according to the C3S CDM convention (Rozum, 2022). The files can be readily ingested and displayed by any GIS package (e.g., the popular open-source GIS package QGIS) and are largely self-documenting. The NetCDF files contain the IV fields V_x, V_y, V_z, and V_v (magnitude of the horizontal components) as separate layers in metres per day (Figure 1). The pixel count map and uncertainty map are provided as separate layers. The IV maps are gridded at 250 m in NSIDC North Polar Stereographic projection with latitude of true scale at 70°N and central meridian at 45°W (EPSG: 3413).

Figure 1. Example IV product covering the Greenland Ice Sheet, depicted are from left to right the easting component, the northing component and the magnitude of velocity.

1.2.1.1. Instruments

The IV product is primarily derived by applying feature tracking on repeat pass Copernicus Sentinel-1 single look complex (SLC) data. Sentinel-1 is a C-band synthetic aperture radar (SAR) mission and the constellation currently comprises two identical satellites (Sentinel-1A and -1B) with a repeat cycle of 6/12-days. The Interferometric Wide (IW) swath mode is the standard operation mode over land surfaces including land ice. It applies the Terrain Observation by Progressive Scans (TOPS) acquisition technology, providing a spatial resolution of about 3 m and 22 m in slant range and azimuth, respectively, with a swath width of 250 km. Sentinel­1 is the main source for regular and comprehensive monitoring of land ice motion. On 23^rd December 2021, a technical problem occurred with Sentinel-1B and no data has been acquired since. Recovery attempts have been unsuccessful and in August 2022 ESA announced the end of the mission. Sentinel-1A remains fully operational and plans are in place to launch Sentinel-1C in early 2023.

1.2.1.2. Algorithm name and version

The ENVEO software package (ESP v2.1; Nagler et al., 2015) is a state-of-the-art IV retrieval algorithm designed for various SAR sensors (e.g., Sentinel-1, TerraSAR-X, ALOS PALSAR, Cosmo-SkyMed). The processor has been tested rigorously through intercomparisons with other packages and extensive validation efforts. The ESP-IV processing system runs on common Linux operating systems and has successfully been connected to cluster systems utilising several hundreds of cores. This is especially of interest for campaign processing of big data sets such as those for Greenland. The existing system for annual IV production for Greenland is fully operational. Further improvements of the software are planned and discussed in section 3.

1.2.1.3. Auxiliary data

Auxiliary data needed for input in the IV processor are a digital elevation model (DEM) and polygon shapefiles of the ice sheet boundary.

1.2.1.3.1. DEM

A DEM is needed for geometric co-registration of repeat pass SAR data and geocoding of the final products. This requires an accurate DEM without artefacts, as spurious jumps in the derived velocity fields can occur otherwise. For C3S a DEM was compiled and implemented based on the 90 m TanDEM-X Global DEM (Rizzoli et al., 2017). Known issues relating to processing artefacts, outliers and gaps, are filled using an inverse distance weighted interpolation method. The extent and grid spacing of the DEM is equal to the IV product.

1.2.1.3.2. Ice sheet boundary

The ice sheet and glacier boundaries are based on the latest version of the Randolph Glacier Inventory (RGI 6.0, RGI Consortium, 2017) with updated glacier fronts. The inventory has been compiled from more than 70 Landsat scenes (mostly acquired between 1999 and 2002) using semi-automated glacier mapping techniques (Rastner et al., 2012).

1.2.2. Antarctic surface elevation change

The product provides estimates of surface elevation change over the Antarctic ice sheets and ice shelves, in long historical timeseries, using level 2 radar altimeter data from six satellite missions: ERS-1, ERS-2, Envisat, CryoSat-2, Sentinel-3A and Sentinel-3B. Its algorithms and processing scheme are based on previous work for the ESA Antarctic Ice Sheet Climate Change Initiative¹ and are guided by the GCOS (Global Climate Observing System) targets for the Ice Sheets Land ECV (Essential Climate Variable).

Data consist of estimates of surface elevation change rate in a 5-year moving window that advances in one-month steps. The first measurements used are taken from phase C of the ERS-1 mission, starting in April 1992, and extend to the present. Estimates are made, where possible, for each time period in each cell of a 25 km by 25 km polar stereographic grid, covering the ice sheets, ice shelves and associated ice rise and island areas. Data gaps are flagged, but not filled.

The product is distributed in NetCDF4 format according to the C3S CDM conventions. The main ECV and its uncertainties are accompanied by a map of surface type, i.e. ice sheet, ice shelf or island/ice rise, and a set of flags denoting regions of high surface slope.

Figure 2: Example Antarctic SEC product showing the rates of change derived for the period from 01-07-2007 to 01-07-2012. This merges data from Envisat and CryoSat-2. In this case the data extends only as far south as the Envisat southern orbit limit at 81.45º

1.2.2.1. Instruments

Six satellite-mounted radar altimeters are used. The instruments, data products and some comments on them are given below in Table 1.

Table 1: Instruments and data products used in the Antarctic SEC product.

1.2.2.2. Algorithm name and version

The software package has been assembled and tailored to the C3S requirements from previous work on IMBIE (the Ice sheet Mass Balance Intercomparison Exercise) and various ESA Climate Change Initiative projects. The current version is called C3S_Ant_Sec_ops_v3.0. The results have been tested against datasets from the IMBIE and Antarctic Ice Sheet CCI projects and validated against the multi-year IceBridge airborne laser altimetry campaigns². The underlying processing system runs on common Linux operating systems.

1.2.2.3. Auxiliary data

Four auxiliary datasets are needed.

1.2.2.3.1. DEM

Radar altimetry over regions of very high slope is generally of poor quality due to confusion over echo provenance. The digital elevation model is used to remove data from areas of extremely high slope, i.e. greater than 10°, from the input measurements. It is also used to provide a grid of flags ranking the slope angle in each cell. The model in use is the Slater et al. (2018) model based on CryoSat-2 data.

Ice extent
The processing area consists of all of the Antarctic ice sheets, ice shelves and associated ice rises and islands. Its boundaries are based on the IceSAT MODIS (Moderate Resolution Imaging Spectroradiometer) 1 km resolution mask, produced for the IMBIE2 project by Zwally et al. (2012).

1.2.2.3.2. Glacial isostatic adjustment

Movements of the surface related to glacial isostasy are corrected for by use of the Ivins et al. (2013) IJ05 model.

1.2.2.3.3. Tides

Due to the poor resolution of the satellites' land masks in processing Antarctic coastal regions, it is necessary to remove the tide elevations supplied in the L2 products and replace them with a consistent set. The replacements are generated using the Padman et al. (2002) CATS 2008a tide model.

1.2.3. Greenland surface elevation change

The Greenland surface elevation change closely follows the Antarctic SEC (see section 1.2.2). The main algorithms are based on previous work for the Greenland Ice Sheet CCI and are guided by the GCOS (Global Climate Observing System) targets for the Ice Sheets Land ECV. A full description of the processing approaches and algorithms are found in Sørensen et al. (2018) and Simonsen and Sørensen (2017, LSM5).

The product provides estimates of surface elevation change over the Greenland ice sheet, back to 1992, using level-2 radar altimeter data from the six ESA radar altimeter satellite missions: ERS-1, ERS-2, Envisat, CryoSat-2 and Sentinel-3A/B. Data consist of estimates of surface elevation change rate in a 5-year moving window that advances in one-month steps, for the older missions (ERS-1/2 and Envisat. The newer altimeters of CryoSat-2 and Sentinel-3 enabled the 5-year window to be shortened to a 3-year running mean.

The C3S-SEC product is distributed in NetCDF4 format according to the C3S CDM conventions, at 25 by 25 km grid resolution. The grid is an equal area grid as defined by the NSIDC North Polar Stereographic projection with latitude of true scale at 70°N and central meridian at 45°W (EPSG: 3413). This projection is the same as that used for the Ice velocity product. In addition to the gridded solution of SEC, the following fields are also available: i) cartesian x- and y-coordinate (x,y), (ii) geographical longitude and latitude (lon, lat), (iii) grid area (accounting for projection errors), (iv) relative elevation change since 1992 (dh), (v) start and end times for the altimeter data used (start_time, stop_time), (vi) distance from grid cell centre to observation location, and a number of different accuracy fields for the different parameters.

1.2.3.1. Instruments

The instruments used are the ERS-1 RA, ERS-2 RA, Envisat RA2, CryoSat-2 SIRAL and Sentinel-3A and B SRAL. The data products used for the ERS-1 and ERS-2 are the Reaper reprocessing L2 data. For Envisat it is the GDR_v3 L2 data, CryoSat-2 L2i LRM (Low Rate Mode) and SIN (Synthetic aperture radar Interferometer) and, finally for the Sentinel-3A/B L2 which is currently optimised for ocean studies. There has been a change in baselines for CryoSat-2, therefore baseline D is proving data prior to August 2021 and baseline E thereafter.

1.2.3.2. Algorithm name and version

The software package has been assembled and tailored to the C3S requirements from previous work in ESA Climate Change Initiative projects and undergoes an annual iteration, with the current version being provided as Version 3. The results have been validated against the multi-year NASA Operation IceBridge airborne laser altimetry campaigns (see section 3.3.3 and Simonsen and Sørensen. (2017)).

The underlying processing system runs on a common Linux operating system. For the older missions (ERS-1, ERS-2 and Envisat), the processing is implemented using a combination of repeat-track and plane-fitting algorithms as documented in Sørensen et al. (2018). This method has been independently validated and inter-compared with state-of-the-art methods in Levinsen et al. (2015). A 5-year running mean window is used to derive an annual SEC solution. The final monthly solution provided for the C3S-product is derived by a temporal-weighted mean of all solutions covering a given month. For CryoSat-2 and Sentinel-3 A/B, the plane-fitting algorithm (LSM5, Simonsen and Sørensen (2017)) has been tailored to the requirements of the C3S product and the inclusion of Sentinel-3. The monthly solution is derived in a similar fashion as for the older satellites, but the running-mean window has been shortened to 3 years. This is possible due to the more favourable orbit of CryoSat-2, which still ensures a stable plane-fit solution at the same time as it limits the imprint of interannual weather in the SEC product.

1.2.3.3. Auxiliary data

The processing approach for the Greenland SEC is not as dependent on auxiliary data as is the case for the Antarctic SEC, as Simonsen and Sørensen (2017) showed the best solution was derived using a limited auxiliary data. However, to provide consistent documentation, a full description of the same auxiliary data as in the Antarctic SEC is provided here. If not used, the reason for not considering them is provided.

1.2.3.3.1. DEM

The Greenland SEC applies the official level-2 data solutions provided by ESA. When this level-2 product is generated by ESA, a DEM is applied in the geolocation of LRM data. For more information refer to the mission specific documentation for the specific DEM used in the geolocation of the echo (Gilbert et al. 2014; Brockley et al. 2017; Soussi et al. 2018; Batoula et al. 2011). No DEM are used for the combined cross-over and repeat-track solutions, however a DEM is used as an initial parameter for the LSM5-method applied for CryoSat-2 and Sentinel-3. The resulting solution from LSM5 is an update to the DEM. In the CryoSat-2 processing the Greenland Ice sheet Mapping Project (GIMP) DEM version 1 is used (Howat, Negrete, and Smith 2017).  

1.2.3.3.2. Ice extent

In the version 1 of the C3S SEC, the processing was done for all grid-cells over Greenland with an ice-cover of more than 95%, as provided in the PROMICE ice-cover product (Citterio and Ahlstrøm 2013). However, this was changed from version 2 and onwards, the processing is done for all ice-covered grid-cells in accordance with the ESA glaciers CCI ice-cover product for the Greenland ice sheet and strongly connected peripheral glaciers and ice caps (Rastner et al., 2012, file version: glaciers_cci_gi_rgi05_TMETM_19942009_v170525.zip).

1.2.3.3.3. GIA

No glacial isostatic adjustment is applied to the dataset, due to the large discrepancy in the model Glacial Isostatic Adjustment (GIA) signal in Greenland, and the limited bias in the resulting SEC.

1.2.3.3.4. Tides

As the extent of floating ice shelves is limited in Greenland, no tidal adjustment is added to the product.

1.2.4. Gravimetric mass balance

The Gravimetric mass balance (GMB) relies solely on data from the Gravity Recovery and Climate Experiment (GRACE) mission and its follow-on mission (GRACE-FO, until recently only available for the Greenland Ice Sheet). A GRACE-type mission consists of two twin satellites, which measure satellite-to-satellite distance. The gravity field of the Earth can then be derived from the change in the distance between the satellites. This precise evaluation of the gravity-field enables monthly solutions of Earth's gravity field anomalies from the first GRACE mission from March 2002 to October 2017 and GRACE-FO, only available for the Greenland ice sheet, from the end of 2018 to present. The GRACE (-FO) gridded mass solution from both the Greenland and the Antarctic ice sheet CCI+ projects are brokered for the C3S-product and provided for the major ice sheet basins. See Barletta, Sørensen and Forsberg (2013) and Groh and Horwath (2016) for the description of the derivation of GMB from the initial level-2, c20, 1-degree GRACE-data.

1.2.4.1. Auxiliary data

1.2.4.1.1. Ice extent

The processing is done for all ice-covered grid-cells in accordance with the ESA glaciers CCI ice-cover product for the Greenland ice sheet and strongly connected peripheral glaciers and ice caps (Rastner et al., 2012, file version: glaciers_cci_gi_rgi05_TMETM_19942009_v170525.zip).

1.2.4.1.2. GIA

The resulting mass change estimate is corrected for GIA, however, due to uncertainties in the GIA models, region-specific models are applied following Barletta, Sørensen, and Forsberg (2013).

2. User requirements

The overall requirements for ice sheet related ECVs, as listed by the Global Climate Observing System (GCOS Implementation Plan, 2016 [RD.1]) are given in Table 2 below.

Table 2: GCOS target requirements for ice sheet related ECVs (source: GCOS Implementation Plan, 2016)

*The GCOS resolution target cannot be met with current satellite data, so the C3S project has set a 25km resolution target.

**It should be noted that there is a difference between volume and mass change of the ice sheet, which seems to be undefined in the GCOS implementation plan.

***It should be noted that the grounding line location is currently not included as an ECV within C3S but is already developed and implemented within the Greenland and Antarctic Ice Sheet CCI projects

2.1. Ice velocity

The primary GCOS requirements for ice velocity are listed in Table 2. In addition, as part of Ice Sheets CCI Phase 1, user requirements were identified through an extensive user survey within the community. The User Requirements Document (URD) from the Ice Sheets CCI Phase 1 project contains a full description of the results from this survey (Hvidberg et al., 2012). The user requirements for ice velocity are summarised in Table 3.

Table 3: User requirements from Ice_Sheets_cci Phase 1 User Survey (Hvidberg et al., 2012).

2.2. Surface elevation change

The primary GCOS requirements for surface elevation change are listed in Table 2. These have not changed since 2016, and have not been altered by the latest updates to the Climate User Modelling Group (CMUG) baseline requirements in 2020. In addition, as part of the Ice Sheets Greenland/Antarctica CCI Phase 1, user requirements were identified through an extensive user survey within the community. The User Requirements Document (URD) generated contains a full description of the results from this survey (Hvidberg et al., 2012, Shepherd et al., 2018) and its first requirement The User Requirements reported by the ESA CCI Antarctic Ice Sheets Project provided the requirements to produce SEC product with a minimum spatial resolution of 1-5km or an optimum spatial resolution of <500m (Shepherd et al., 2018) matches the GCOS table (Table 2).

2.3. Gravimetric mass balance

The GCOS requirements regarding ice mass change do not adequately follow glaciological considerations, as there is a difference between ice sheet volume change (units: km³/year) and mass change (units: Gt/year). It has been assumed that the requirements should be given in water equivalent volumes, hence the conversion of 1-to-1 from volume to mass in Table 2.

3. Gap analysis

3.1. Ice Velocity

3.1.1. Description of past, current and future satellite coverage

The primary source dataset for the Greenland Ice Sheet (GrIS) ice velocity product comprises Sentinel-1 (S1) SLC SAR data acquired in Interferometric Wide (IW) swath mode. One of the unique aspects of the S1 mission is the systematic acquisition planning of polar regions, designed to cover the entire GrIS margin and large sections of the Antarctic coast continuously. The ongoing acquisition of ice sheet margins in Greenland is augmented by a dedicated annual ice sheet-wide campaign.

When the first S1 data became available, the GrIS CCI consortium generated and provided the first complete IV map of Greenland (Nagler et al., 2015) and demonstrated the capabilities of Sentinel-1A (S1A) for mapping ice dynamics at 12-day intervals. The launch of Sentinel-1B (S1B) in March 2016 reduced the repeat observation period from 12 to only 6 days, enabling an even denser time series, providing better coverage of fast outlet glaciers and high accumulation areas, as well as opening opportunities for advanced InSAR applications. Also, since June 2017, nearly the entire Antarctic perimeter is covered continuously at 6 to 12-day intervals.

For Greenland each year in winter (December-February) there is a dedicated mapping campaign during which, over about 2 months, the entire ice sheet is covered in IW mode with 4 to 6 acquisitions per track. The S1 mission is currently in its 8^th year and production of the 7^th consecutive ice sheet wide velocity map is in progress. The maps provide a detailed snapshot of contemporary ice flow in Greenland. The latest maps include data from both S1A and S1B and are nearly gapless and seamless.

The Sentinel-1 constellation is currently the primary source for year-round monitoring of ice velocity. In 2019, further expansion of the continuous coverage in Greenland commenced including also the interior ice sheet. This provides an opportunity to produce Greenland wide velocity maps at sub-annual, and even monthly, intervals. Since 23^rd December 2021, there has been a technical problem with the Sentinel-1B satellite and no data has been acquired after that time. Recovery attempts have been unsuccessful and In August 2022 ESA announced the end of the Sentinel-1B mission. The Sentinel-1A satellite continues to operate normally and Sentinel-1C is now planned to be launched in 2023 as a replacement for Sentinel-1B. The Sentinel-1 constellation will continue to operate well into the next decade with another satellite (Sentinel-1D) already in development. This, in combination with other new and planned synthetic aperture radar (SAR) missions (e.g., SAOCOM, NASA-ISRO NISAR), ensures the long-term sustainability of the ice velocity CDR.

3.1.2. Development of processing algorithms

The existing system (ESP v2.1) at ENVEO for annual IV production for Greenland is fully operational. ESP is a state-of-the-art IV retrieval algorithm suited to accommodate the ongoing development of the Copernicus Sentinel-1 mission data. The primary processor will continue to be developed and updated to accommodate new sensors and requirements. Further technical development activities, ongoing and planned, are described in sections 3.1.4, 3.1.5 and 3.1.6 .

3.1.3. Methods for estimating uncertainties

The error prediction framework described in Mohr and Merryman-Boncori (2008) is applied to derive estimates of the error standard deviation of slant-range and azimuth velocity measurements. The input to the framework consists of the location of the GCPs used for velocity calibration, and in models for the covariance function (or equivalently the structure function) of all error sources, including noise and atmospheric propagation. For a mathematical formulation, the reader is referred to Mohr and Merryman-Boncori (2008).

In speckle tracking, where coherence is required, the noise component can be estimated from the correlation coefficient. For coherent offset tracking, the maximised coherence becomes equal to the interferometric coherence, and the following expression for the standard deviation, σC, of the shift estimate (in units of resolution elements) holds (De Zan, 2014):

where γ is the coherence and N is the number of pixels in the cross-correlation. For incoherent (intensity-based) offset-tracking applied to a coherent pair, the error becomes (De Zan, 2014):

which for γ→1 approach 1.8σ_C. For these noise error models to apply, it must be known if the signal is coherent, which is often not the case, especially at the outlet glaciers, where only intensity tracking of large features works. For coherent offset-tracking (rarely applied), the noise contribution is estimated by the equation for σ_C using the maximised correlation coefficient as γ.

For incoherent offset-tracking (the general case), the error is estimated for each pixel by calculating a local offset-map standard deviation in a 5x5 neighbourhood. A plane fit to the offset map in the 5x5 neighbourhood is subtracted prior to calculating the standard deviation, so that an actual velocity gradient is not interpreted as a noise signal. The standard deviation estimate is corrected for any averaging carried out, as well as correlation between neighbouring samples (i.e. if the radar data are oversampled). Each generated IV map is accompanied by its associated error standard deviation. The latter is also a map, in the same geometry as the associated measurement, providing a measure of uncertainty on a per-pixel basis.

Additionally, for estimating the quality of IV products a series of standard tests/measures are implemented providing various levels of validation. Table 4 gives an overview of the QA tests and the metrics that they provide. The tests are described in more detail below.

Table 4: Summary of QA tests and the metrics that it provides.

QA-IV-1 Comparison of satellite derived velocity products with in situ measured velocity data (Global Positioning System (GPS)). The quality metrics of this test are: Mean and Root Mean Square Deviation (RMSD) of the difference in velocity of IV products and in situ data.

QA-IV-2 Comparison of velocity fields generated from independent datasets from different sensors covering roughly the same period. The quality metrics of this test are: Mean and Root Mean Square Error (RMSE) of the difference of velocity components (Easting, Northing).

QA-IV-3 The product is evaluated against publicly available products covering the same area. These can be assembled from different sensors or cover a different time. Nevertheless, in the latter case they can still provide a level of quality assurance, in particular in areas where little change is to be expected (e.g. inland ice sheet). The quality metrics of this test are: Mean and RMSE of the difference of velocity components.

QA-IV-4 This is an internal QA method. Within the processing chain of the IV product generation, local quality measures of the IV retrieval are estimated, such as the number of valid matches and STD (described above) of available values for each pixel. These measures quantify the quality of the local IV estimates and are attached to each product.

QA-IV-5 Another internal QA method widely applied for quality assessment of velocity products is the analysis of stable ground where no velocity is expected. This gives a good overall indication for the bias introduced by the end-to-end velocity retrieval including co-registration of images, velocity retrieval, etc. After performing the matching for the entire region covered by the image pair, the results for the ice covered (moving) area are separated from ice-free (stable) ground. The masking is done using a polygon of the glacier/rock outline. The quality metrics of this test are: Mean and RMSD of the velocity over stable terrain; mean values should be close to 0.

3.1.4. Opportunities to improve quality and fitness-for-purpose of the CDRs

The current ice velocity (IV) CDR constitutes an annually averaged Greenland Ice Sheet velocity map, based on offset tracking, derived from all Sentinel-1 repeat acquisitions within a year (6- and 12-day repeats). These data have now been further exploited to assemble and merge IV maps at a higher spatial resolution (250m) and temporal frequency (monthly). This permits high-resolution comprehensive monitoring of the full Greenland Ice Sheet on a monthly basis which is useful for studying long term trends and short-term fluctuations. Monthly velocity maps are now routinely produced and are ready to be included as a product improvement (Figure 3).

Figure 3: Greenland monthly ice velocity from Sentinel-1 offset tracking, 2015-2021.

Besides the product developments implemented in C3S, further technical developments of the IV retrieval algorithm are foreseen, building on the processing line developed in GrIS CCI and Antarctic Ice Sheet (AIS) CCI projects and currently extended in the CCI+ phase of these projects. Below a brief description of on-going and planned research activities is provided. These provide opportunities to improve the current CDR.

The launch of Sentinel-1B in 2016 and subsequent reduction in satellite revisit time has opened new opportunities for InSAR applications. This enabled the extension of the IV processor for supporting Sentinel-1 TOPS mode InSAR. The InSAR method is capable of increasing the accuracy up to two orders of magnitude, in particular in slower moving areas. However, as the method only provides the component of ice velocity in the satellite line-of-sight (LOS) direction it requires the combination of both ascending and descending orbit pairs, contrary to the offset tracking method. Also, the InSAR method requires that coherence is maintained between repeat acquisitions. In fast flowing areas or areas with substantial melt or snow fall this is often not the case, leaving gaps in the InSAR coverage that might be (partly) filled in with other methods. A related research theme is therefore the development of methods and procedures for combining InSAR and offset tracking motion fields. The development significantly improves the accuracy as well as the spatial resolution of the ice velocity maps and greatly increases the versatility of the IV data sets, in particular for the slow-moving interior, smaller outlet glaciers and shear margins.

Another key development opportunity is the advancement of Sentinel-2 optical IV retrieval to exploit the operational synergies of Sentinel-1 and Sentinel-2 derived ice motion products. This provides a method to reduce temporal and spatial gaps in the surface velocity fields. As previous investigations have shown, this is particularly relevant during summer periods when surface melt leads to coherence loss thereby hampering the SAR IV retrieval. This leaves gaps in an otherwise complete and dense (Sentinel-1 derived) velocity time-series at time periods when ice flow is usually at its peak. From a science perspective, these gaps are undesirable as they can bias scientific analyses (e.g., modelling, ice discharge). When cloud-free scenes are available the optical trackers can be superior in such cases. The velocity fields can be merged to generate a consistent velocity product suitable for studying ice sheet dynamics. Procedures are developed and tested for integrating ice velocity products from Sentinel-1 and Sentinel-2 data. Figure 4 illustrates the improvement of the Sentinel-1 derived ice velocity field in summer achieved by combining ice velocity products from both sensors. The large gaps at the ice sheet margins and glacier terminus are effectively filled in by merging the Sentinel-1 and Sentinel-2 derived flow fields.

Figure 4: Ice velocity map of Nioghalvfjerdsbrae/79Fjord-Glacier and Zachariae Isbræ from Sentinel-1 only (left) and merged product based on Sentinel-1 and Sentinel-2 (right).

3.1.5. Scientific research needs

A first and foremost scientific research need is the expansion of the Ice Velocity service to include also the Antarctic Ice Sheet. The current C3S service foresees only the production of an ice velocity map for the Greenland Ice Sheet. While Greenland is a main factor for current sea level rise, the largest unknown for future sea level rise is caused by uncertainty in the predicted response of the Antarctic Ice Sheet to global warming. Refining these predictions requires accurate knowledge of the past and current ice mass imbalance of Antarctica and its main driving forces. Detailed homogenized ice velocity maps and velocity time series are therefore essential, as primary input for studies on dynamic processes, ice discharge, iceberg calving and possible spatial and interannual variations herein. This is of major importance in order to establish how short-term fluctuations relate to longer multiyear trends and to identify the principal driving mechanisms. The system for producing monthly and annual ice velocity mosaics, covering the Antarctic Ice Sheet margins, is already in place and implemented. Monthly and annual ice velocity maps are currently produced and can readily be included as an extension of the ice velocity CDR within the Ice Sheets and Ice Shelves service.

Figure 5 shows monthly ice velocity maps for Antarctica in 2021, derived from Sentinel-1.

Figure 5: Monthly ice velocity maps for the Antarctic Ice Sheet in 2021, derived from Sentinel-1.

As already mentioned in section 3.1.4 a key research need is the development of Sentinel-1 TOPS mode InSAR to derive ice sheet velocity. InSAR is capable of providing high precision and high-resolution velocity over large areal extents and can significantly improve the accuracy of the ice velocity in slower moving areas. The retrieval of ice velocity from TOPS InSAR is, however, challenging and labour intensive and requires additional investigation, particularly for the removal of phase discontinuities and burst boundaries. These are caused by azimuth motion and different line of sight direction at the transitions of adjacent bursts. The phase jumps get more significant with increasing azimuth motion. Additional developments are needed that include taking the variation of the line of sight within bursts into account and requiring separation of azimuth and slant range components of velocity. Additionally, a strategy for performing burst wise phase unwrapping needs to be implemented.

Another research need required for improving the processing algorithm is reduction of the effects of differential ionospheric path delay and removal of ionospheric stripes. These stripes are clearly evident as streaks in the retrieved velocity (particularly over northern Greenland) that are aligned slightly oblique to the LOS direction. Ionospheric disturbances are one of the main sources of error in the IV maps and hinder applications. As the repeat cycle for S1 is short, the potential impact of ionosphere-induced noise on the velocity is high. A way to compensate the ionospheric effects is the implementation of the split-spectrum method in the processor, which permits separating the ionospheric and the non-dispersive phase terms.

3.1.6. Opportunities from exploiting the Sentinels and any other relevant satellite

As mentioned in section 1.1, further expansion of continuous acquisition coverage in Greenland of Sentinel-1 provides an opportunity to produce Greenland-wide velocity maps at high temporal resolution. A further expansion in coverage of Sentinel-1 crossing-orbit pairs, as well as a reduction in revisit time, when Sentinel-1C and Sentinel-1D are launched, would be advantageous.

Comprehensive 8-day repeat-pass L-Band SAR data over ice sheets is now also provided by the SAOCOM A/B mission. By using L-Band SAR data the use of the InSAR method can be extended to cover also faster moving areas than possible with C-band. Simulations show that these data have a reduced fringe frequency in shear zones and fast-moving areas, enabling reliable phase unwrapping so that the InSAR method can also be applied in zones where Sentinel-1 data decorrelate. Additionally, the L-band signal coherence is less affected by variable surface conditions than C-band. Consequently, further improvement for ice velocity monitoring can be expected from the synergy of C-band and L-band InSAR data, as rendered possible by combining Sentinel-1 and SAOCOM A/B data. This is in particular relevant considering the system failure of Sentinel-1B.

3.2. Antarctic Surface Elevation Change

3.2.1. Description of past, current and future satellite coverage

The SEC product merges data from radar altimeters on multiple satellite missions. The results are arranged in monthly geographic grids covering the Antarctic. Not every cell of every grid can contain data, and these gaps can be due to the altimeter performance, or the merging method, or both.

The Antarctic SEC data product initially used input from four satellite missions. One more mission was added to the v2 product, and another to v3. Previous products are maintained on the CDS for continuity, and their documentation is also available there. Future developments will rely on improved products from these missions rather than adding new missions, although it is proposed to use new data from ICESat-2 in validation.

Table 5: Mission summary

To retrieve surface elevation change data, a crossover method is used. This has to be applied where repeated orbits intersect, which creates a net of data sites that are closer together at more southerly latitudes. The spacing depends on the satellite repeat cycles. ERS-1 changed orbit several times, and only mission phases C (April 1992 to December 1993) and G (March 1995 to mission end) are suitable for crossover analysis. CryoSat-2's long cycle nearly repeats every 30 days, but in effect the net 'drifts' slowly, making long-term timeseries comparison more difficult. To mitigate this, a large 25km by 25km polar stereographic grid is used to accumulate data spatially while retaining a monthly temporal sampling rate. This basic data is then combined into timeseries for each grid cell and a surface elevation change rate found, where possible, for a 5-year window advancing in steps of one month.

Spatially, data gaps can occur if too little data is available, for example in coastal regions or rugged terrain (notably the Antarctic Peninsula) where radar echoes may not be received by the altimeter within their expected time period, causing it to fail to take measurements. When taken in combination with its long repeat cycle, this especially affects CryoSat-2. No data can be taken closer to the south pole than the orbital inclination of each satellite allows. Only CryoSat-2 approaches within 2° of the poles, the others are approximately 8.5° away. This affects the temporal gaps as well, as sufficient data to be representative of the 5-year surface elevation change rate in the region only CryoSat-2 can observe is limited to the central timespan of its mission. Launched after CryoSat-2, the Sentinel-3 missions provide coverage to an Envisat-like configuration.

In the data product there are no temporal data gaps. At each timestamp a varying pattern of grid cells contain no data. Estimation of the missing data values may be undertaken with care, considering the underlying geophysics of the Antarctic.

3.2.2. Development of processing algorithms

The processing method was developed for the Antarctic CCI project, and updated for C3S mainly by the incorporation of extra or better datasets, and an improved method for cross-calibration.

The original system, C3S_Ant_Sec_ops_v1.0, was used to make the initial data product. Its modular layout allowed it to be upgraded to the v2 system with minimal alteration. Sentinel-3A data was first included in v2, and the multi-mission cross-calibration algorithm was improved. Processing of CryoSat-2 data in v2 stopped when the baseline C data stream was halted by ESA. For v3, the system, now called C3S_Ant_Sec_ops_v3.0, was configured to process new releases of data. The Envisat GDRv2.1 dataset was replaced in its entirety by GDRv3. The CryoSat-2 baseline D data stream is incorporated from the start of the mission, replacing baseline C entirely, and continues to August 2021, where it merges into baseline E, which continues to update. The data stream from Sentinel-3B was newly incorporated in v3.

3.2.3. Methods for estimating uncertainties

Important considerations for the product include its uncertainty and its geographical and temporal coverage. There are three main sources of uncertainty in the product, which can be combined to provide a total uncertainty estimate. The coverage is defined by the input datasets. Both uncertainty and coverage can be compared to external targets from GCOS and internal targets from C3S, based on user requirements and practical limitations. In validation the product is compared to a similar dataset, which was derived from different inputs and methodology – their values should match to within their stated uncertainties.

The uncertainty in each surface elevation change rate is calculated from three components summed in quadrature. These components are independent of each other and independent in all grid cells. They are:

• the epoch uncertainty, derived from the supplied input data
• the cross-calibration uncertainty, derived from the cross-calibration method
• the model uncertainty, derived from the trend fitting

The epoch uncertainty is the standard deviation of the geophysically-corrected height measurements used in calculating the crossover height. The cross-calibration uncertainty is returned by the multiple linear regression algorithm used (IDL's REGRESS), giving the standard deviation of the biasing factor for each mission. The first mission has no bias, as the bias for all subsequent missions are calculated with respect to it. The model uncertainty is the standard deviation of the linear least-squares fit used to model the surface elevation change rate.

The GCOS user-requirement target metric for measurement uncertainty, 0.1 m/yr, applies to the total uncertainty. The target metric for stability, also 0.1 m/yr, applies to the model uncertainty.

The C3S project has mandated two key performance indicators, which are:

• the percentage coverage of the Antarctic Ice Sheet
• the uncertainty of the surface elevation change rate at drainage basin level

The coverage depends more directly on the performance parameters of the individual satellites, as discussed in section 1.2.1 above. The target is 65% coverage for all missions, except CryoSat-2, which has a 95% coverage target. As CryoSat-2's orbit drifts over a 369-day period, coverage should be aggregated yearly to give a true picture.

Amalgamating grid cells to basin level is a process that can be achieved with increasing levels of sophistication depending on how data gaps are handled. At a basic level, an elevation change timeseries can be derived from the given elevation change rates in each cell of the basin, and these can be averaged to create an effectively mean-filled basin timeseries, from which a change rate can be derived. This approach was used in v1. This may not be appropriate for all basins, depending on whether data gaps are randomly spread across the basin or not, and on how much coverage there is altogether, e.g., performance of all satellites over the Antarctic Peninsula is poor because of its rugged terrain. In v2, a velocity guided-approach, similar to that used for interpolating surface elevation change rate in Shepherd et al. (2019), was instituted. In each basin, a linear relationship was established between the mean ice velocity (from BISICLES, see Cornford et al., 2013) in each cell, and the surface elevation changes seen there. This was then used to fill cells with known velocity but unknown elevation change. In v3, new releases of data have been incorporated, and algorithm changes are restricted to the inclusion of these extra datasets – for example cross-calibration now handles one extra mission.

Figure 6 gives a summary of the key performance indicators for the v3 CDR. 

Figure 6: Key performance indicators, from v3 CDR. The top plot shows histograms of each component of the dataset's uncertainty and their total, demonstrating that all of the distributions peak within the GCOS target uncertainty. The middle plot shows histograms for the uncertainties when they are combined by Antarctic drainage basin. In this case the cross-calibration and modelling components peak within the target, but the epoch uncertainty, which dominates the total, does not. The bottom plot shows the percentage of the AIS area covered by the product, which is close to the C3S project's target for the historic missions, but does not reach the expected target where CryoSat-2 data is included.

Tabulated results for the percentage of the v3 dataset with stability or accuracy within its target range are given in Table 6 below.

Table 6: Stability and accuracy results within target, given as a percentage of all results.

The pixel and basin level stability distributions are mainly contained within the target value.

The pixel-level accuracy distributions also all peak within the target value but have a longer tail outside the target than for stability. The dominant accuracy component is the epoch uncertainty, which relates to the input satellite measurements. The addition of Sentinel-3B data necessitates an extra cross-calibration term in the error budget for the later SEC periods, which has a small effect at pixel level, but a larger, cumulative effect at basin level. The basin-level accuracy distributions also show effects from incorporating datapoints recovered by the updated Envisat and CryoSat-2 datasets, in the more challenging and thus more uncertain regions of the ice sheets and shelves – these datapoints fall in the tail of the pixel-level accuracy histogram but are part of the body of the basin-level histogram.

The coverage target is a function of the satellite orbits and the observation area, which incorporates both the near-pole regions and hard-to-observe rugged terrain. The distribution of coverage varies depending on which and how many satellites' data were used in each time period. The 'pole hole' for ERS-1, ERS-2, Envisat and Sentinels 3A and B covers 20.0% of the total observation area. Thus, the maximum possible coverage is 80% most of the time. The pole hole for CryoSat-2 covers only 1.1% of the area, but its drifting orbit makes data retrieval more difficult at the Antarctic coast and ice shelves. To better represent the CryoSat-2 contribution, coverage results are aggregated yearly. In practice, even when CryoSat-2 polar data is included, marginal crossover performance is relatively poor, and the higher target is not achieved. The coverage dip in 2011 comes from declining amounts of Envisat data combined with the initiation of the CryoSat-2 coverage. Most surface elevation change rate values come from data from a mix of missions, but there is a coverage dip centred on 2014, when (briefly) only CryoSat-2 data was available within the 5-year SEC period spans. However, the complementary orbital configuration of the Sentinels, when used together, improves the coverage of the later periods. See Figure 6.

Validation is provided by comparison to NASA's Operation IceBridge airborne laser altimetry campaigns. These have been flying over the Antarctic, mainly in the west and on the peninsula, since 2002 but stopped at the end of winter 2019/2020. They provide a level 4 surface elevation change rate data product, available from +https://nsidc.org/icebridge/portal/map⁴. Figure 7 shows validation data for the v3 test dataset. The map (left) shows where the validation was made. The scattergram (centre) shows the comparison of surface elevation change per averaged cell. If IceBridge matched exactly to the v3 dataset, then all the datapoints would lie along the X=Y line shown. They actually cluster around the line, as expected. The differences between the corresponding datapoints are shown as a histogram (right), with the mean difference marked as a vertical dotted line. The mean difference is within 0.1m, which corresponds to the accuracy target.

Figure 7:  Validation against Operation IceBridge of the v3 dataset. The left-hand map shows where coincident measurements from both datasets were available. The central scattergram shows measurements from the two datasets, averaged per grid cell, plotted against each other. As expected, they lie along or close to the line where measurements for both datasets are the same. The right-hand histogram shows the distribution of differences between these grid-cell-averaged measurements, which is centred close to zero.

3.2.4. Opportunities to improve quality and fitness-for-purpose of the CDRs

The input data used in the product comes from data streams that are constantly being upgraded and refined. Incorporation of CryoSat-2 baseline E data fixes a recently-discovered long-term drifting problem in the backscatter power data (used to correct for radar performance degradation over time), and this will be extended to the full CryoSat-2 dataset when the mission is reprocessed to baseline E. When available, the Sentinel-3A and B thematic land ice products will fill the current gaps left where the orbital track transition from ocean to land is not handled properly.

3.2.5. Scientific research needs

In order to identify ice dynamic trends, the main emphasis for scientific research is in a long period of continuous acquisition. Progressive improvements in instrumentation allow for greater accuracy and areal coverage and thus a better focus on regions of rapid change at the sub-drainage-basin scale.

3.2.6. Opportunities from exploiting the Sentinels and any other relevant satellites

The CryoSat-2 and Sentinel-3 A and B missions continue to extend the product time series, providing greater simultaneous coverage, both geographically and temporally, than has previously been possible.

3.3. Greenland Surface Elevation Change

3.3.1. Description of past, current and future satellite coverage

The version 1 release of the Greenland ice sheet surface elevation change data utilised four radar-altimeter satellite missions. The evolution to version 2 included Sentinel-3A data, and the newest evolution, version 3, now includes new data from Sentinel-3B, and reprocessed data from Envisat and CryoSat-2. The amount of new data has required a full reprocessing of the elevation change time series for Greenland using an updated processing chain.

The satellite coverage for the GrIS is the same as for Antarctica and is listed in section 3.2.1 and Table 5. However, as the north-pole is covered by ocean and not ice sheet, the coverage of the GrIS is more complete than for the Antarctic ice sheet, as only the northernmost part of the ice sheet is not covered by the orbit inclination of ERS-1, ERS-2, Envisat, Sentinel-3A and Sentinel-3B. Satellite radar altimetry is more challenging for the GrIS than the Antarctic ice sheet, as a larger proportion of the ice sheet is located in areas with complex topography. The traditional radar altimeters, with the large footprint size, are especially challenged. Here, the principle of observations only at the point-of-closest-approach results in biasing the observations to points at higher elevation. Hence, to retrieve surface elevation change an optimal combination of along-track and plane-fitting methods are used for 5-year or 3-year data-windows advancing in steps of one month. To ensure good spatial coverage, the individual methods are averaged at a larger grid (25km by 25km polar stereographic) than their native grid resolution by ordinary kriging. At each timestamp, a varying pattern of grid cells contains no data. Estimation of the missing data values should be undertaken with care, considering the underlying geophysics of the Greenland ice sheet.

3.3.2. Development of processing algorithms

The original system, C3SMontly, had a modular layout in terms of missions. This allows for alterations throughout the processing chain. The version 2 system, C3SMontlyVers2, was created without any changes to the main structure of the operational code of the original version. However, a major update to the system was the addition of the ordinary-kriging module, which allows for surface elevation change predictions at all ice sheet grid-cells, and not only at low slope as in the version 1 of the data product.

The version 3 system upgrades, now called C3SV3, included a reprocessing of ERS-1, ERS-2, and Envisat, which allowed for the structural code to be revised to include the true-repeat track algorithm in the python environment of version 2 and not being processed separately. This will in the future also allow for a fast transition of the applied method if CryoSat-2 should be decommissioned.

3.3.3. Methods for estimating uncertainties

The uncertainty is given by the combination of the epoch uncertainty (derived from the supplied input data) and the model uncertainty. Figure 8 shows the distributions of the fitting stability and accuracy evaluated for all surface elevation estimates. We see more values closer to the GCOS requirements in version 3 fitting stability compared to version 2. The main improvement in the stability is ascribed to the reprocessing of Envisat. There are still a substantial number of values just above the GCOS requirements, which are introduced by the shortening of the data-record used for CryoSat-2 and Sentinel-3, alongside the increased number of observations at coastal locations, where the uncertainty is larger due to the complex topography. This is mainly due to more weather variability introduced by the shortening of the averaging time-window, but is removed in the accuracy estimate by averaging data on sub-grid-cell level.

Figure 8: The comparison of model fitting stability and accuracy for both version 2 and 3 of the GrIS surface elevation change. (Left) The distribution of grid-cells with a given fitting stability from the applied method of surface elevation change generation. (Right) The distribution of grid-cells with a given uncertainty, here the GCOS requirement of 0.1 m/yr is also highlighted.

This uncertainty estimate is purely from the SEC-fitting algorithm, and does not include measurement uncertainties hence, the true product uncertainty, which needs to meet the user-requirements, can only be found by independent validation of the SEC product. Here, we utilise the independent validation-data provided by NASA's Operation Ice Bridge (OIB) airborne laser altimetry campaigns. Operation Ice Bridge started in 2009, however similar instrumentation has been operated in Greenland since 1993 and these data are included in the OIB level-4 data-product (rate-of-surface elevation), which is available from the National Snow and Ice Data Center⁵. The OIB product derives the surface elevation change from repeated flightpaths of the OIB-campaigns. The OIB level-4 product is thereby the ideal dataset for judging how well the GCOS requirements are fulfilled. Figure 9 shows the result of the inter-comparison between the OIB and the C3S surface elevation changes. The monthly time-series of surface elevation change grids makes it possible to tailor the time-series to resolve the timespan of OIB repeat locations on the Greenland ice sheet. Based on more than 25,000 observations, distributed both in time and space, we see a slight improvement in the median bias between the two records. This shows the product's overall compliance to the GCOS requirements; however, it is also clear that the radar altimeter still is challenged in areas of complex topography. 
Figure 9: Difference in the rate of elevation change between OIB and the C3S product version 2 and 3. As the OIB level 4 data consist of data from all repeats of older flight paths, the years in the figure refer to the first year of observations, e.g., 1993 includes data for all repeats of the 1993 flightpath until 2017. The upper-left panel shows the point-to-point agreement, alongside the one-to-one line. The lower-left panel shows the complete distribution for all years, which is averaged in the right panel to show the spatial distribution.

3.3.4. Opportunities to improve quality and fitness-for-purpose of the CDRs

The input data used in the SEC product comes from data-streams that are constantly being upgraded and refined, as we see with the update of Envisat GDRv3 and CryoSat-2 baseline-D/E datasets. In the future, the Sentinel-3 land ice processor will become available and will fill the current gaps left where the orbital track transition from ocean to land is not handled properly. When the land-ice processing is operational, the processing chain can be switched to the more optimal Sentinel-3 data product to improve the data quality in the coastal regions with the updated slope model being applied in the product.

3.3.5. Scientific research needs

The scientific research needs for the SEC product over Greenland are the same as for the Antarctic surface elevation change product as described in section 3.2.5.

3.3.6. Opportunities for exploiting the Sentinels and any other relevant satellites

These are the same as for the Antarctic surface elevation change product as described in section 3.2.6.

3.4. Gravimetric Mass Balance

3.4.1. Description of past, current, and future satellite coverage

As the first GRACE mission ended in October 2017, the gravimetric mass balance has a data gap between GRACE and GRACE-Follow-On missions. GRACE-FO was launched on May 22, 2018. Now both Greenland and Antarctic CCI projects have released GRACE-FO solutions, and these are now included in the current version 3 of the product, whereas version 2 only had the Greenland data. New follow-up missions are in the planning but for now, we need to hope for the GRACE-FO mission to be as long-lasting as its predecessor.

3.4.2. Development of processing algorithms and methods for estimating uncertainties

The GRACE datasets provided for the major drainage basins is brokered from the Greenland and the Antarctic ice sheet CCI projects. For both processing algorithms and uncertainty estimates we refer to Barletta, Sørensen and Forsberg (2013), and Groh and Horwath (2016). Here, the processing algorithm is a point-like mass inversion method that uses the reconstruction of the gravity field at the satellite altitude, and which in a pre-processing phase mitigates the contamination of the gravity field due to sources outside Greenland. For the uncertainty, they add several components to the total error, namely the propagation of the formal errors from the GRACE L2 data, the uncertainty from the degree one, the GIA correction, and the ocean and atmospheric model uncertainty.

The primary GCOS requirements for Gravimetric mass balance are met in terms of horizontal resolution (Table 2). If typical ice densities are assumed, the measurement uncertainties are at present about twice the requirement. This emphasises the outstanding scientific question of how to deal with the signal leakages between changing bodies of mass, such as individual drainage basins and peripheral glaciers and ice caps.

3.4.3. Opportunities to improve quality, fitness-for-purpose of the CDRs

In addition to understanding the signal leakage, a major opportunity lies with the R&D activity anticipated in the community in relation to the GRACE-FO mission to fully understand the Antarctic record. An unresolved issue for both hemispheres remain: How should GRACE and GRACE-FO missions be merged/bridged? This will not be solved by the GMB alone but will require inputs from the surface elevation change and the ice velocity records.

3.5. Grounding Line Location

3.5.1. Introduction

The grounding line separates the floating part of a glacier/ice shelf from the grounded part. Processes at the grounding lines of floating marine termini of glaciers and ice streams are important for understanding the response of the ice masses to changing boundary conditions and to establish realistic scenarios for the response to climate change and implications for sea level rise. The discharge of an ice sheet is measured at the grounding line and enhanced ice discharge directly affects sea level rise. Furthermore, the migration of the grounding line is a sensitive indicator of ice thickness change and the Grounding Line Location (GLL) is listed as an "important parameter for ice sheets" in the IGOS Cryosphere Theme Report (IGOS, 2007), and listed in the user requirements for ice sheet related ECVs in the GCOS Implementation Plan (RD.1). Remote sensing observations do not provide direct measurements of the grounding line position but can be used to detect the tidal flexure zone, which is a proxy for the GLL. InSAR provides an excellent tool for directly observing the tidal motion of a marine terminating outlet glacier or ice shelf, as it shows up as distinct fringe patterns in the interferograms.

3.5.2. Description of past, current and future satellite coverage

With the launch of Sentinel-1 in April 2014 a new SAR data set became available for mapping the location of the grounding line. The main acquisition mode of Sentinel-1 is Interferometric Wide Swath Mode, which applies TOPS mode for acquiring the data. Initially, due to the repeat interval of 12 days, coherence was low over fast moving outlet glaciers, complicating the formation of interferograms suitable for GLL delineation. The launch of Sentinel-1B, in April 2016, has reduced the repeat pass period to 6 days providing significant improvements. Sentinel-1 data has since been regularly acquired every 6 to 12 days along the margins of the Greenland and Antarctic Ice Sheets, allowing for InSAR analysis for determining the grounding line location and its evolution.

3.5.3. Development of processing algorithms

In the ESA Antarctic Ice Sheet CCI and Greenland Ice Sheet CCI projects, ENVEO was ECV lead and ECV collaborator in developing algorithms for mapping the Grounding Line Location using SAR data, focussed on Sentinel-1. The processing chains have already been developed and implemented and can be rolled out for deriving valuable new climate data records on grounding line positions. The method has the potential to deliver a systematic and continuous record of GLLs and GLL migration around Antarctica and main Greenland outlet glaciers. This will greatly benefit the investigation of environmental forcings on ice discharge and of the current and future sea level rise contribution of the ice sheets. Figure 10 shows an example of an interferogram and the grounding line of Ryder Glacier in northern Greenland derived from Sentinel-1 data acquired in 2017.

Figure 10: Geocoded double difference interferogram of the grounding zone of Ryder Glacier derived from repeat pass SAR data of Sentinel-1A and 1B acquired at 6, 12 and 18 January 2017 (background: Google Earth). Thick black lines indicate the lower and upper boundary of the tidal flexure zone. Inset shows location of Ryder Glacier in North Greenland (figure adapted from Mottram et al., 2019).

3.6. Surface Melt Processes

3.6.1. Introduction

The availability of Copernicus Sentinel-1 C-band SAR data since 2014 provides the opportunity for producing a consistent high-resolution climate data record on the presence of liquid water ("melt extent") and properties over Antarctica and Greenland. The areal extent and duration of surface melt on ice sheets are important parameters for climate and cryosphere research and key indicators for climate change. Surface melting has a significant impact on the surface energy budget of snow areas, as wet snow has a relatively low albedo in the visible and near-infrared spectral regions. Moreover, enhanced surface meltwater production, by raising the internal water pressure and leading to enhanced lubrication at the base has a strong impact on glacier motion. Surface melt also plays an important role in the stability of marine ice sheets and ice shelves, as the intensification of surface melting as a precursor to the break-up of ice shelves in the Antarctic Peninsula has shown.

3.6.2. Description of past, current and future satellite coverage

Passive and active microwave satellite sensors are the main data sources for products on melt extent over Greenland and Antarctica. In particular, low resolution passive microwave data has been widely used to map and monitor melt extent on ice sheets with earlier work focusing on melt zones of the Greenland Ice Sheet. The difficulty in accessing higher resolution SAR data, that existed in the past, has been overcome with the launch of the Copernicus Sentinel-1 (S1) mission. S1 SAR data are now regularly acquired every 6 to 12 days in many parts of the world, allowing for time series analysis at a high resolution for investigating the evolution of snow melting and refreezing processes during the season. The dedicated acquisition plan for the polar regions, covering Greenland and Antarctica with short revisit times of 6 to 12 days, enables the production of a dense year-round time-series of high-resolution radar backscatter maps, which form the basis for deriving melt products. Over Greenland S1 IW mode data is collected in co- and cross polarisation, that is, both horizontal-horizontal (HH) and horizontal-vertical (HV). This provides additional benefit for the identification of surface melt and surface refreezing due to different backscatter signatures in HV- and HH-polarized data.

3.6.3. Development of processing algorithms

In the ESA projects 4DAntarctica and 4DGreenland ENVEO is developing algorithms for mapping the Surface Melt Extent from Sentinel-1. For filling in gaps in time and space, that are not covered by Sentinel-1, METOP ASCAT data is used. The final goal is the generation of monthly time series of liquid water presence over Greenland and Antarctica at 200m spatial resolution and covering the duration of the Sentinel-1 mission (2014-onward). Derived products include maps of the start, duration and end of the annual surface melt periods.

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