Last modified on Sept 23, 2024 14:20

Contributors:  J. Wuite (ENVEO), S. B. Simonsen (Technical University of Denmark)

Issued by: EODC GmbH/Richard A Kidd

Date: 04/10/2023

Ref: C3S2_312a_Lot4.WP3-TRGAD-IS-v2_202304_IS_TR_GA_i1.1

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 licence, 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 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.

Grounding Line Location (GLL): The grounding line is the transition between the grounded and the floating part of an ice sheet. Due to ocean tides and air pressure changes floating parts experience short term vertical changes unlike the grounded parts. The deformation due to the unequal vertical behaviour can be detected using synthetic aperture radar (SAR) interferometry. The upper limit of flexure, a very good approximation of the actual grounding line can be mapped.

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 inputs for glacier models that try to quantify ice dynamical processes. Changes in velocity and velocity gradients can point to 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 a 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)¹.

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 geometry¹.

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 Ice Sheets and Ice Shelves Service. The gaps in this context refer to data availability to enable the Essential Climate Variable (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.

As of 2023, the Ice Sheets and Ice Shelves Service provides the following four products: (1) an Ice Velocity (IV) product for Greenland (Greenland IV), (2) an IV product for Antarctica (Antarctic IV), (3) a Surface Elevation Change product for Greenland (Greenland SEC) and (4) a Gravimetric Mass Balance (GMB) product, brokered from the European Space Agency (ESA) Climate Change Initiative (CCI) project, covering both Greenland and Antarctica. Note that from April 2023 the Antarctic SEC product, previously part of the service, is put on hold in the service and will no longer be updated.

Initially an overview of each product is provided, including the required input data and auxiliary products, a definition of the retrieval and processing algorithms and 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 and Antarctic Ice Sheets in two separate products; the Gravimetric Mass Balance (GMB) product covers both the Greenland and Antarctic Ice Sheets in one dataset; and the Surface Elevation Change (SEC) product covers the Greenland Ice Sheet.

The current IV products (Greenland IV and Antarctic IV) are gridded products that represent the mean annual ice surface velocity (IV) of the Greenland and Antarctic Ice Sheets in true metres per day. The products contain the horizontal and vertical velocity of the ice surface in NetCDF4 format according to the C3S Common Data Model (CDM) convention². 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. In August 2022 ESA and the European Commission announced the end of the mission for Sentinel-1B, which experienced an anomaly related to the instrument electronics power supply, leaving it unable to deliver radar data since December 2021. Sentinel-1A continues to operate normally and Sentinel-1C is planned to be launched in Q2 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., ESA Radar Observing System for Europe in L-band (ROSE-L), 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 product provides estimates of surface elevation change 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 Sentinel-3A and Sentinel-3B. The three first listed missions have been completed but may still issue reprocessed datasets in the future, the last three listed are still in operation. The gridded product is provided in NetCDF4 format at 25km spacing, with monthly estimates from 1992 to present day provided as Interim CDR (ICDR). From April 2023 the Antarctic SEC product, previously part of the service, is no longer updated.

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 of the CDR, v3.

The user requirements provided by GCOS [RD.1] 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 are already met in the products provided by the service.

All products will benefit from further development of the retrieval or processing methodology. Several possible developments have already been identified and/or implemented. For the IV product, this includes dynamic ice/ocean masking for outlet glaciers based on updated calving fronts, the development of monthly velocity mosaics (not yet included in the service) and increased spatial resolution to meet GCOS requirements.

For SEC, there are two major anticipated data stream changes. The current CryoSat-2 data stream 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 2023.  As it was the case for the v3 SEC product, the Sentinel-3 product available for v4 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 and reprocessing is ongoing with a currently scheduled full release at the end of 2023. This dedicated land ice data product is expected to provide additional data at the margins of the ice sheets, where the changes are the largest.

Some fundamental research activities are also required or ongoing outside of the C3S service. For the IV products 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) methods for the reduction of the effects of differential ionospheric path delay and the removal of ionospheric stripes; and (iii) Sentinel-2 optical IV retrieval to fill in gaps in space and time of the existing products. For the SEC products, further 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 and other satellites, i.e. the grounding line location and products on surface melt processes (melt extent and start, duration and end of melt season). These products address current gaps 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. Time series of monthly ice velocity maps for both Greenland and Antarctic have already been produced and are fit for inclusion in the service pending funding.

Reliance on External Research

Since the C3S program 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 programs, 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 programs to support the R&D activities and the success of the C3S contractors in winning potentially 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 1.2describes the products currently provided by the service. Section 2 provides the target requirements for ice sheet related ECVs. Section 3 provides a 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 of C3S).

1.2. Ice Sheets Product description

The Ice Sheets and Ice Shelves Service covers three ECVs with four products:

• Ice sheet velocity – provided for the Greenland and Antarctic Ice Sheets, as two separate products
• Surface elevation change – provided for Greenland Ice Sheet only
• Gravimetric mass balance – provided for 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 CDR provides yearly updated gridded ice velocity maps of the Greenland Ice Sheet at 250 m resolution. The velocity grid represents the average annual ice surface velocity (IV) of Greenland in true metres per day. The geographic extent covers the entire 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 averaging the measured displacements between all repeat image pairs acquired within a year (running from Oct to Sept). The vertical velocity is derived from the difference between the interpolated height at start and end position of the displacement vector, 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 map projection, i.e. the polar stereographic grid. The velocities are true values and not subject to 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 observations at the output pixel position that is used in compiling the annually averaged map, as well as an uncertainty map (based on the standard deviation of all valid measurements).

The IV product is distributed in NetCDF4 format according to the C3S CDM convention. The files can be readily ingested and displayed by any geographic information system (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 provided in metres per day (Figure 1.1). The pixel count map and uncertainty map are provided as separate layers. The IV maps are gridded at 250 m in the NSIDC North Polar Stereographic projection with latitude of true scale at 70°N and central meridian at 45°W (EPSG: 3413).

Figure 1.1: 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 derived by applying feature tracking on repeat pass Copernicus Sentinel-1 C-band synthetic aperture radar (SAR) data acquired in the Interferometric Wide (IW) swath mode. The Interferometric Wide (IW) swath mode is the standard operational 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 by 22 m in slant range and azimuth, respectively, with a swath width of 250 km. The Sentinel­1 mission is the main source for regular and comprehensive monitoring of land ice motion. The mission previously consisted of two satellites (Sentinel-1A and Sentinel-1B) with a combined repeat cycle of 6-days. 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, the end of the mission for Sentinel-1B was announced. Sentinel-1A remains fully operational and Sentinel-1C is planned to be launched in the second quarter of 2023 as replacement for Sentinel-1B. This will re-establish the 6-day repeat cycle which was reduced to 12 days after the malfunction of Sentinel-1B, leading to some data loss.

1.2.1.2. Algorithm name and version

The CDR applies the ESP v2.1 (ENVEO software package; Nagler et al., 2015). ESP v2.1 is a state-of-the-art IV retrieval algorithm designed for various SAR sensors (e.g. Sentinel-1, TerraSAR-X, Advanced Land Observing Satellite Phased Array type L-Band SAR (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 and Antarctica. 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 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 Greenland, 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 that of 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 for marine terminating glaciers. The RGI 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). Since the calving front location (CFL) is a highly dynamical environment, dynamic ice-ocean masking for outlet glaciers based on periodically updated CFL's is added as a technical evolution for CDR's from version 1.4.

1.2.2.  Antarctic Ice Sheet velocity

The Antarctic Ice Sheet Velocity is provided as a new product within the service. The CDR closely follows the Greenland IV (see Section 1.2.1) as the main retrieval algorithm is identical. For consistency some of the information on product, instrument and algorithm is repeated in this section. The velocity grid, provided at 200 m grid spacing, represents the average annual ice surface velocity (IV) of Antarctica in true metres per day. The geographic extent covers the margins of Antarctica including peripheral glaciers. The extent is limited to where repeat pass observations of Sentinel-1 are available which depends on the acquisition planning as well as the polar gap which extends from the South Pole up to a latitude of _{78.5°S. The ice sheet boundaries are based on a regularly updated ice/ocean mask. 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 averaging the measured displacements between all repeat image pairs acquired within a year (running from April to March). 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 DEM (see Section 1.2.2.3.1). 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 Antarctic 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 is used in compiling the annually averaged map, as well as the uncertainty map (based on the standard deviation).

The IV product is distributed in NetCDF4 format according to the C3S CDM convention. The files can be readily ingested and displayed by any GIS package (e.g., the popular open-source GIS package QGIS). The NetCDF files contain the IV fields V_x, V_y, V_z, and V_v (magnitude of the horizontal components) as separate layers with unit metres/day (Figure 1.2). The pixel count map and uncertainty map are provided as separate layers. The IV maps are gridded at 200 m in Antarctic Polar Stereographic projection with latitude of true scale at 71°S and central meridian at 0° (EPSG: 3031).

Figure 1.2: Example IV product covering the Antarctic Ice Sheet for 2021-2021, depicted are from left to right the easting component, the northing component and the magnitude of velocity.

1.2.2.1. Instruments

The IV product is derived by applying feature tracking on repeat pass Copernicus Sentinel-1 C-band synthetic aperture radar (SAR) data acquired in the Interferometric Wide (IW) swath mode. The 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. The Sentinel-1 mission is the main source for regular and comprehensive monitoring of land ice motion. The mission previously consisted of two satellites (Sentinel-1A and Sentinel-1B) with a repeat cycle of 6-days. 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, the end of the mission for Sentinel-1B was announced. Sentinel-1A remains fully operational and Sentinel-1C is planned to be launched in the second quarter of 2023 as replacement for Sentinel-1B. This will re-establish the 6-day repeat cycle which was reduced to 12 days after the malfunction of Sentinel-1B, leading to some data loss.

1.2.2.2. Algorithm name and version

The CDR applies the ESP v2.1 (ENVEO software package; Nagler et al., 2015). ESP v2.1 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 Antarctica. The existing system for annual IV production for Antarctica is fully operational. Further improvements of the software are planned and discussed in section 3.

1.2.2.3. Auxiliary data

Auxiliary data needed for input in the Antarctic Ice Sheet IV processor are a DEM, a polygon shapefile of the ice sheet/ocean boundary, a tide model and atmospheric pressure reanalysis data.

1.2.2.3.1. 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 Antarctica the 200m void-filled version of the Reference Elevation Model of Antarctica (REMA) DEM is used. The REMA DEM is a high-resolution elevation model covering the entire continent provided by the Polar Geospatial Center (PGC) at the University of Minnesota (Howat et al., 2019). The DEM is based on stereoscopic image pairs from WorldView-1, -2, -3 and GeoEye-1 acquired between 2009 and 2017, with most data from 2015-2016. Data are available both as individual strip files, with spatial resolution at 2 to 8 m, as well as mosaics covering the whole continent with a reduced resolution. The DEMs are vertically registered to satellite altimetry measurements from Cryosat-2 and Ice, Cloud and Elevation Satellite (ICESat), resulting in reported absolute uncertainties of less than 1 m over most of the ice sheet area, and relative uncertainties in the order of decimetres. The extent and grid spacing of the 200m void-filled version of the REMA DEM is equal to the IV product.

1.2.2.3.2. Ice sheet boundary

Because the ice extent is continuously changing as the ice advances or icebergs break off, the ice sheet boundaries are based on a regularly updated ice-ocean mask. For this purpose, a 200 m geocoded Sentinel-1 amplitude mosaic covering the ice sheet margin will be generated. The delineation of the calving front will be performed semi-automatically, using an edge detection technique, and is manually checked and - when needed - corrected.

1.2.2.3.3. Tide model and atmospheric pressure reanalysis 

Ice velocity retrieval over ice shelves and floating extensions of outlet glaciers is strongly affected by vertical motion induced by ocean tides and, to a lesser degree, by atmospheric pressure changes. The vertical displacement between repeat acquisitions introduces an error in the reported horizontal velocity magnitude that hampers the identification of dynamical signals due to, for example, ungrounding through basal melt and/or iceberg calving. This issue is of particular relevance for Antarctica, as most of the Antarctic coastline is fringed by ice shelves and floating ice tongues, many of which underwent large changes in recent decades. The robustness of IV retrieval in these regions is improved by implementing a tidal correction module (TCM), which compensates for the vertical motion caused by tides and atmospheric pressure changes. For this a regional ocean tide model (CATS2008) and atmospheric pressure data (ERA5) is used. CATS2008 (Circum-Antarctic Tidal Simulation version 2008; Erofeeva et al., 2019) is a regional ocean tide model provided at 4 km resolution. ERA5 is the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate and provides hourly estimates of variables on pressure levels on a global grid with a spatial resolution of 0.25° × 0.25°^3.

1.2.3. Greenland Surface Elevation Change

The main algorithms for Greenland surface elevation change 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 is found in Sørensen et al. (2018) and Simonsen and Sørensen (2017).

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 over Greenland. 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 Radar Altimeter (RA), ERS-2 RA, Envisat RA2, CryoSat-2 SAR/Interferometric Radar ALtimeter (SIRAL) and Sentinel-3A and B SaR ALtimeter (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 providing 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 CCI projects and undergoes an annual iteration, with the current version being provided as Version4. The results have been validated against the multi-year NASA Operation IceBridge airborne laser altimetry campaigns (see section 3.2.3and Simonsen and Sørensen. (2017)) and newly acquired ICESat-2 data.

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

Simonsen and Sørensen (2017) showed that for the processing approach for Greenland SEC the best solution was derived using limited auxiliary data. Below follows a full description of the auxiliary data used.

1.2.3.3.1. Ice extent

In 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 Programme for Monitoring of the Greenland Ice Sheet (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 by 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).

1.2.3.3.2. 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.

1.2.3.3.3. 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

1.2.4.1. Instruments

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,from the end of 2018 to present.

1.2.4.2. Algorithm name and version

The GRACE (-FO) gridded mass solution from both Greenland and 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.

1.2.4.3. Auxiliary data

1.2.4.3.1. Ice extent

The processing is done for all ice-covered grid-cells by 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).

1.2.4.3.2. 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

2.1. Introduction

This chapter specifies the target requirements for each product. The 2022 Global Climate Observing System (GCOS) requirements for ice-sheet related ECVs are listed in Table 2.1 (GCOS 2022 [RD.1]). For the criteria in the GCOS Implementation Plan, a goal, breakthrough, and threshold value are defined as follows:

• Goal (G): an ideal requirement above which further improvements are not necessary.
• Breakthrough (B): an intermediate level between threshold and goal which, if achieved, would result in a significant improvement for the targeted application. The breakthrough value may also indicate the level at which specified uses within climate monitoring become possible. It may be appropriate to have different breakthrough values for different uses.
• Threshold (T): the minimum requirement to be met to ensure that data are useful.

Table 2.1. GCOS requirements for ice sheet related ECVs. G: Goal, B: Breakthrough, T: Threshold. (source: The 2022 ECVs Requirements; https://gcos.wmo.int/en/publications/gcos-implementation-plan2022)

¹ The GCOS resolution target cannot be met with current satellite data, so the C3S project has set a 25km resolution target.
² It should be noted that there is a difference between volume and mass change of the ice sheet.
³ It should be noted that the grounding line location is currently not included as an ECV within C3S but has already been developed and implemented within the Greenland and Antarctic Ice Sheet CCI projects.

2.2. Ice velocity

The primary GCOS requirements for ice velocity are listed in Table 2.1. These have largely been adapted from the User Requirements Document (URD) from the Ice Sheets CCI Phase 1 project and were identified through an extensive user survey within the ice sheet science community (Hvidberg et al., 2012, Shepherd et al., 2018).

2.3. Surface elevation change

The primary GCOS requirements for surface elevation change are listed in Table 2.1. 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 requirement⁴ matches the GCOS table (Table 2.1).

2.4. Gravimetric mass balance

The GCOS requirements regarding ice mass change have been replaced with ice sheet volume change (Table 2.1) in the latest update. The reason for this is unclear as ice sheet volume change is in principle the same as surface elevation change and hence measured with an entirely different method.

3. Gap analysis

3.1.  Ice Velocity

3.1.1. Description of past, current and future satellite coverage

The Sentinel-1 constellation is currently the primary source for year-round monitoring of land ice velocity. One of the unique aspects of the S1 mission is the systematic acquisition planning of polar regions, designed to cover the entire Greenland Ice Sheet (GrIS) margin and the majority of the Antarctic coast continuously. The ongoing acquisition of ice sheet margins is augmented by a dedicated annual ice sheet-wide campaign in Greenland.

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, providing also the opportunity to generate IV products for the Antarctic Ice Sheet perimeter on a regular basis.

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 planned to be launched in 2023 (Q2) 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. ESA ROSE-L, 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 IV production for Greenland and Antarctica 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, 3.1.5and 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 Ground Control Points (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 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 3.1 gives an overview of the Quality Assurance (QA) tests and the metrics that they provide. The tests are described in more detail below.

Table 3.1. 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) CDRs constitute annually averaged ice sheet velocity maps for Greenland and Antarctica, based on offset tracking, derived from all Sentinel-1 repeat data acquired within a year (6- and 12-day repeats). These data have been further exploited to assemble and merge IV maps at a higher temporal frequency (monthly). This permits high-resolution comprehensive monitoring of the Greenland and Antarctic Ice Sheets 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 3.1 and Figure 3.2).

Figure 3.1: Greenland monthly ice velocity from Sentinel-1 offset tracking, 2015-2021.

Figure 3.2: Monthly ice velocity maps for the Antarctic Ice Sheet margins in 2021, derived from Sentinel-1.

Further technical developments of the IV retrieval algorithm are foreseen, building on the processing line developed in the Greenland CCI and Antarctic Ice Sheet CCI projects and 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 CDRs. 

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. When Sentinel-1C is launched there is, again, the opportunity to apply the InSAR technique for IV retrieval. 

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 evaluated for integrating ice velocity products from Sentinel-1 and Sentinel-2 data. Figure 3.3 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 3.3. Ice velocity map of Nioghalvfjerdsbrae/79Fjord-Glacier and Zachariae Isbræ (NE-Greenland) from Sentinel-1 only (left) and merged product based on Sentinel-1 and Sentinel-2 (right).

3.1.5. Scientific research needs

As already mentioned in section 3.1.4 a key research need is the further 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 for 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 satellites

8-day repeat-pass L-Band SAR data over Greenland are since 2021 also acquired by SAOCOM-A/B SAR mission as a background mission (i.e. without systematic acquisition plan). The primary payload of the dual satellite constellation is an L-band polarimetric SAR instrument, managed and operated by CONAE (Comisión Nacional de Actividades Espaciales - Argentina's Space Agency). 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 decommissioning of Sentinel-1B. Within the next phase of the CCI and 4DGreenland projects⁵ it is planned to develop tools for processing SAOCOM data and to investigate methods for combining L- and C-Band interferometric data for ice velocity monitoring. This work also contributes to the preparation for the upcoming NISAR L-Band Mission scheduled for launch in Jan 2024, and the Copernicus ROSE-L Mission.

3.2. Surface Elevation Change

3.2.1. 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 and 3 included Sentinel-3A and Sentinel-3B data, and the newest evolution, version 4, has an updated baseline of Cryosat-2 data, and improved outlier detection.

The satellite coverage for the GrIS is listed in Table 3.2. The orbit inclination of ERS-1, ERS-2, Envisat, Sentinel-3A and Sentinel-3B, hampers the sensing of the northernmost parts of the GrIS. Satellite radar altimetry is challenging for the GrIS, as a larger proportion of the ice sheet is located in areas with complex topography. The traditional radar altimeters, with their 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 elevations. 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. 

Table 3.2. Mission summary

3.2.2. Development of processing algorithms

The original system, C3SMonthly, 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 slopes as in the version 1 of the data product. The version 3 system upgrades included a full reprocessing of ERS-1, ERS-2, and Envisat, which allowed for the structural code to be revised to include the true-repeat track algorithm within the Python environment of version 2 and not being processed in C separately. This allowed for seamless incorporation of the improved outlier detection in version 4 and will, in the future, also allow for a fast transition of the applied method if CryoSat-2 should be decommissioned.

3.2.3. 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 3.4 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 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 the sub-grid-cell level.

Figure 3.4: The comparison of model fitting stability and accuracy for both versions 3 and 4 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 and ICESat-2. 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 Center⁶. 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 3.5 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 with the GCOS requirements; however, it is also clear that the radar altimeter still is challenged in areas of complex topography.

Figure 3.5: Difference in the rate of elevation change between OIB and the C3S product versions 3 and 4. As the OIB level 4 data consists 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 flight path 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.

As OIB is no longer operational, the validation efforts are with the commission of CDR version 4 targeted toward the new NASA ICESat-2 ATL15 data product. ATL15 provides surface elevation change estimates at different resolutions (1 km, 10 km, 20 km, and 40 km) at 3-month intervals. The 20 km resolution product is used here as this comes closest to the native resolution of the CDR. Figure 3.6 shows an example of this intercomparison of these two operational surface elevation products, which, again, highlights the product’s overall compliance with the GCOS requirements.

Figure 3.6: October 2018 to October 2020 surface elevation change as seen in the CDR product (to the left) and the ICESat-2 data product (middle). The right panel shows the difference.  

3.2.4. 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 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.

3.2.5. Scientific research needs

In order to identify ice dynamic trends, the main emphasis for scientific research is on a long period of continuous acquisitions. 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 for 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. Gravimetric Mass Balance

3.3.1. 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 of the product. 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.

3.3.2. 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, as listed in the previous GCOS report [RD.2], are met in terms of horizontal resolution. 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.

3.3.3. Opportunities to improve quality, and 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 remains: 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.

3.4. Grounding Line Location

3.4.1. 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.

3.4.2. 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, 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. Unfortunately, the decommissioning of Sentinel-1B has reduced the potential of the Sentinel-1 mission for InSAR applications on moving ice.

3.4.3. 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 the 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 3.7 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 3.7: 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).

3.5. Surface Melt Processes 

3.5.1. 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 surface properties over Antarctica and Greenland. The aerial 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.

3.5.2. 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 mission. S1 SAR data are now regularly acquired 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.

3.5.3. Development of processing algorithms

In the ESA projects 4DAntarctica⁷ and 4DGreenland⁸, ENVEO has developed two algorithms for mapping the Surface Melt Extent from active microwave sensors. Two types of products to map surface melt conditions in Greenland and Antarctica have been produced. The first dataset provides daily medium resolution icesheet-wide melt extent and melting stage maps and is derived from Advanced Scatterometer (ASCAT) data (Figure 3.8). The ASCAT product provides coverage of the Greenland Ice Sheet since 2007. The daily Greenland-wide melt maps form the source for deriving products on annual melt onset, end and duration. The second product provides high-resolution 6-day repeat melt extent maps of the ice sheet margins and is derived from Copernicus Sentinel-1 SAR data (Figure 3.9). The detection of melt relies on the strong absorption of the radar signal by liquid water. The dense backscatter time series yields a unique temporal signature that is used, in combination with backscatter forward modelling, to identify the extent and the different stages of the melt/freeze cycle and to estimate the melting intensity of the surface snowpack.

Figure 3.8: ASCAT derived melt-state time series for selected dates during the 2012 melt season in Greenland.

Figure 3.9: Sentinel-1 derived melt extent time series for selected dates during the 2020 melt season in Greenland. The melt extent is derived from the ratio of the backscatter signal and the mean of the previous winter months (provided here in Db). Acquisitions of one complete cycle have been combined to cover the complete ice sheet margin.

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