Contributors: Richard Kidd Christian Briese (EODC GmbH), Lin Gilbert (University Leeds). Sebastian Bjerregaard Simonsen (Technical University of Denmark), Jan Wuite (ENVEO)
Issued by: EODC GmbH/Richard A Kidd
Issued Date: 16/06/2021
Ref: C3S_312b_Lot4_D1.S.1-2020_TRGAD_IS_i1.0.docx
Official reference number service contract: 2018/C3S_312b_Lot4_EODC/SC2
History of modifications
Related documents
Acronyms
General definitions
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 following GHRSST Data Specification, adopted in the case of LSWT.
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 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.
Target requirement: ideal requirement which would result in a significant improvement for the target application.
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, being a Surface Elevation Change product for Greenland (Greenland SEC) and a SEC product for Antarctic (Antarctic SEC), an Ice Velocity (IV) and 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 is then specified which generally reflect the GCOS ECV requirements. 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
Ice Sheets
The Ice Sheets and Ice Shelves Service represents three 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 convention Common Data Model (CDM). Whilst the IV product has a current reliance on Copernicus Sentinel-1 SLC, the Sentinel-1 constellation will continue to operate well into the next decade with two more satellites (Sentinel-1C and -1D) already in development. This, in combination with other new and planned SAR missions (e.g. SAOCOM , NASA-ISRO NISAR), ensures the long-term sustainability of the 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: ERS-1, ERS-2, EnviSat, CryoSat-2 and Sentinels 3A and 3B. 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 of 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 will be incorporated in the current version, v3.
The IV product currently relies on data from Copernicus Sentinel-1 SLC, the SEC products are reliant on CryoSat-2 and Sentinel-3A and B, and future GMB products will be reliant on the GRACE-FO Mission.
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 target for resolution has been revised to 25km. But, in most cases, the primary user requirements (i.e. horizontal resolution for GMB) are already met.
All products will benefit from further development of the retrieval or processing methodology. A number of possible evolutions 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, three datastream changes have been made. For the first time, the Sentinel-3B data stream is incorporated. The EnviSat GDR v2.1 product has been replaced by GDR v3. The CryoSat-2 baseline C data stream, which ended in 2019, has been replaced by baseline D, which is ongoing. The full mission reprocessing of the CryoSat-2 data into baseline D was recently made available, so the product has changed to use entirely baseline D, and restarted monthly updates from this data stream. As stated in the previous version of this document, 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 land ice processor available, but ESA has de-prioritised its use, and the land ice product is not expected to be released until the second quarter of 2021. This is too late for use in the v3.0 product.
Some fundamental research activities are also required outside of the C3S service, specifically for the IV products, and these focus on the development of Sentinel-1 TOPS mode InSAR to derive ice sheet velocity, to investigate methods for the reduction of the effects of differential ionospheric path delay, and the removal of ionospheric stripes and to further develop Sentinel-2 optical IV retrieval. For the SEC products, scientific research is required to identify ice dynamic trends, and for GMB a research activity is required for the evaluation of the data and products from the GRACE-FO mission.
The recent inclusion and exploitation of Sentinel-3 data is expected to have a major impact for SEC products. SEC processing requires a minimum two years of data from any one source, which has now been reached by Sentinel-3B, substantially increasing monitoring capabilities.
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. The monthly ice velocity maps for both Greenland and Antarctic have already been produced and are fit for inclusion in the service.
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. CCI+, 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.
Ice Sheets and Ice Shelves ECV Service
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 1.3 provides the target requirements for ice sheet related ECVs. Section 1.4 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).
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 productsheetssheets
This section describes the existing products in more detail.
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 (Vx, Vy) and vertical (Vz) 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 DEM (see auxiliary data). 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 slant range and azimuth displacement estimates at the output pixel position that are used in compiling the 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 convention Common Data Model. 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 Vx, Vy, Vz, and Vv (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.
Instruments
The IV product is primarily derived by applying feature tracking on repeat pass Copernicus Sentinel-1 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.
Algorithm name and version
The ENVEO software package (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 as 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 1.4.
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.
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 the IV maps produced in the Greenland Ice Sheet CCI, the Greenland Ice sheet Mapping Project (GIMP) DEM (Howat et al., 2014) was used. For C3S a new DEM was compiled and implemented based on the recently released 90 m TanDEM-X Global DEM (Rizzoli et al., 2017). Known issues relating to processing artefacts, outliers and gaps, are filled in using a gap interpolation method. The extent of the DEM is equal to the IV product.
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).
Antarctic surface elevation change
The product provides estimates of surface elevation change over the Antarctic ice sheets and ice shelves, over a long period, 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 Initiative 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 25km by 25km 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 Common Data Model 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
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 are the ERS-1 and ERS-2 Reaper L2, the EnviSat L2 GDR_v3, the CryoSat-2 L2i LRM (Low Rate Mode) and SIN (Synthetic aperture radar INterferometer), both baseline D, and the Sentinel-3A and B L2 which is currently optimised for ocean studies.
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 second-year version is called C3S_Ant_Sec_ops_v3.0. The results have been tested against datasets from the previous projects mentioned and validated against the multi-year IceBridge airborne laser altimetry campaigns. The underlying processing system runs on common Linux operating systems.
Auxiliary data
Four auxiliary datasets are needed.
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 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. 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 island. Its boundaries are based on the IceSAT MODIS (Moderate Resolution Imaging Spectroradiometer) 1km resolution mask, produced for the IMBIE2 project by Zwally et al.
Glacial isostatic adjustment
Movements of the surface related to glacial isostasy are corrected for using the Ivins et al. model IJ05.
Tides
Due to the poor resolution of the satellites' land masks in processing Antarctic coastal regions, it is necessary to remove the tides supplied in the L2 products and replace them with a consistent set. The replacements are generated using the Padman et al. CATS 2008a tide model.
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 novel 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 Common Data Model 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 used for the Ice velocity product. In addition to the gridded solution of SEC, the following fields are also available: cartesian x-coordinate ( x ) , cartesian y-coordinate ( y ), geographical longitude and latitude (lon, lat), grid area (accounting for projection errors), relative elevation change since 1992 (dh), start and end times for the altimeter data used (start_time, stop_time), distance from grid cell centre to observation location, and a number of different accuracy fields for the different parameters.
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 are the ERS-1 and ERS-2 Reaper L2, the EnviSat L2 GDR_v3, CryoSat-2 L2i LRM (Low Rate Mode) and SIN (Synthetic aperture radar INterferometer) both baseline D and the Sentinel-3A/B L2 which is currently optimised for ocean studies.
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 is evolved with 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 1.4.3.3 and Simonsen et al (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 Sentinel3 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 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 and predict climatic signals.
Auxiliary data
The processing approach for the Greenland SEC are in less degree in need of 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.
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. 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).
Ice extent
In the original version the processing was done for all Greenlandic grid-cells with an ice-cover of more than 95%, as given by the PROMICE ice-cover product (Citterio and Ahlstrøm 2013). From version 2 and onwards, the processing is done for all ice-covered grid-cells in accordance to 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).
GIA
No glacial isostatic adjustment is applied to the dataset, due to the large discrepancy in the model GIA signal in Greenland, and the limited bias in the resulting SEC.
Tides
As the extent of floating ice shelves is limited in Greenland, no tidal adjustment is added to the product.
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 mass-con 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 Groth and Horwath (2016) for the description of the derivation of GMB from the initial level-2, c20, 1-degree GRACE-data. A GIA model and land ice mask are used as auxiliary data, along with the drainage basin definitions.
Ice Sheets User requirements
The overall requirements for ice sheet related ECVs, as listed by the Global Climate Observing System (GCOS Implementation Plan, 2016) are given in Table 1 below.
Table 1: GCOS target requirements for ice sheet related ECVs (source: GCOS Implementation Plan, 2016)
Product | Frequency | Resolution | Measurement uncertainty | Stability |
|---|---|---|---|---|
Ice Velocity | 30 days | Horizontal 100 m | 0.1 m/year | 0.1 m/year |
Surface Elevation Change | 30 days | Horizontal 100 m* | 0.1 m/year | 0.1 m/year |
Ice Mass change | 30 days | Horizontal 50 km | 10km3 /year | 10km3 /year** |
Grounding line location and thickness*** | Yearly | Horizontal 100 m | 1 m | 10 m |
*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.
Ice velocity
The primary GCOS requirements for ice velocity are listed in Table 1. 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 2.
Table 2: User requirements from Ice_Sheets_cci Phase 1 User Survey (Hvidberg et al., 2012).
Requirement | Minimum | Optimal |
Spatial Resolution | 100m-1km | 50m-100m |
Temporal Resolution | annual | monthly |
Accuracy | 30-100 m/year | 10-30 m/year |
Time of Observations | All year |
Surface elevation change
The primary GCOS requirements for surface elevation change are listed in Table 1. 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 requirement1 matches the GCOS table (Table 1).
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 1.
Ice Sheets Gap analysis
Ice velocity
Description of past, current and future satellite coverage
The primary source dataset for the Greenland Ice Sheet (GIS) ice velocity product comprises Sentinel-1 (S1) single look complex (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 GIS 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 GIS 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. Since June 2017, also 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, in the course of 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 7th year and production of the 6th 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 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. The Sentinel-1 constellation will continue to operate well into the next decade with two more satellites (Sentinel-1C and -1D) already in development. This, in combination with other new and planned SAR missions (e.g. SAOCOM, NASA-ISRO NISAR), ensures the long-term sustainability of the CDR.
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 evolution 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 1.4.1.4 and 1.4.1.5.
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 (DeZan, 2014):
\[ \sigma_C = \sqrt{\frac{3}{2N}}\frac{\sqrt{1-\gamma^2}}{\pi \gamma} \]where N is the number of pixels in the cross-correlation. For incoherent (intensity-based) offset-tracking applied to a coherent pair, the error becomes (DeZan, 2014):
\[ \sigma_I = \sqrt{\frac{3}{10N}}\frac{\sqrt{2+5\gamma^2-7 \gamma^4}}{\pi \gamma^2} \]which for γ→1 approach 1.8σC. For these noise error models to apply, it must be known that 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 σC 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 test/measures are developed providing various levels of validation. Table 3 gives an overview of the QA tests and the metrics that they provide. The tests are described in more detail below.
Table 3: Summary of QA tests and the metrics that it provides.
Test | Description | Metrics |
QA-IV-1 | Intercomparison with in situ data (e.g. in situ GPS). | Mean, RMSE [m/day] |
| QA-IV-2 | Sensor cross-comparisons: Inter-comparison of IV products from different sensors. | Mean, RMSE [m/day] |
QA-IV-3 | Intercomparison of IV products with available existing IV datasets (e.g. NASA MEaSUREs) | Mean, RMSE [m/day] |
QA-IV-4 | Local measure of IV quality estimate, attached to the product; Standard deviation, Number of available values for each pixel | STD [m/day], Count [px] |
QA-IV-5 | Stable terrain test: mean and RMSD of the velocity over stable terrain; mean values should ideally be 0. | Mean, RMSE [m/day] |
QA-IV-1 Comparison of satellite derived velocity products with in situ measured velocity data (GPS). The quality metrics of this test provides: Mean and 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 provides: Mean and 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 provides: 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 provides Mean and RMSD of the velocity over stable terrain; mean values should be close to 0.
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 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 annual and monthly ice velocity from Sentinel-1 offset tracking, 2016-2018.
Besides the product developments implemented in C3S, further technical developments of the IV retrieval algorithm are foreseen, building on the processing line developed in GIS CCI and AIS CCI projects and currently extended in the CCI+ phase of these projects. Below follows a brief description of on-going and planned research activities that 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 enables 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. The current Sentinel-1 acquisition plan for Greenland leaves large gaps in crossing-orbit coverage, but there are plans to close these gaps for the upcoming 2020/21 winter campaign. 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 is expected to significantly improve the accuracy as well as the resolution of the ice velocity maps and will greatly increase 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 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).
Scientific research needs
A clear 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 hereby 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 since January 2015, derived from Sentinel-1.
Figure 5: Monthly ice velocity maps for the Antarctic Ice Sheet since January 2015, derived from Sentinel-1.
As already mentioned in the previous section 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.
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. Additionally, the increased temporal coverage in the interior could reduce the error in the annual maps and facilitate the removal of ionospheric stripes.
An opportunity that arises from the reduced revisit time and increased coverage provided by the dual satellite constellation is the application of InSAR for improving the accuracy and resolution of velocity retrievals. A further expansion in coverage of Sentinel-1 crossing-orbit pairs, as well as a further reduction in revisit time to potentially one day, when Sentinel-1C is launched, would be advantageous.
Antarctic surface elevation change
Description of past, current and future satellite coverage
The Antarctic SEC data initially came from four satellite missions. One more was added in the evolution to the v2 system, and another in the evolution to the v3 system.
Table 4: Mission summary
Mission | Used in product | Period covered | Orbit inclination | Repeat cycle |
ERS-1 | Yes | 1991 to 2000 | 98.5° | 3, 35 and 176 days |
ERS-2 | Yes | 1995 to 2011 | 98.5° | 35 days |
EnviSat | Yes | 2002 to 2012 | 98.6° | 35 days |
CryoSat-2 | Yes | 2010 to present | 92.0° | 369 days, with 30-day sub-cycle |
Sentinel-3A | Yes | 2016 to present | 98.6° | 27 days |
Sentinel-3B | Yes | 2018 to present | 98.6° | 27 days |
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 an altimeter can lose lock and 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.
Development of processing algorithms
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 update. The data stream from Sentinel-3B is newly incorporated.
Methods for estimating 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 is 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 and Figure 7 show histograms comparing the stability and accuracy of the v3 dataset to v2. To properly illustrate the differences, three datasets are used.
- The v2 dataset from the last iCDR at time of writing, November 2020
- The v3 test dataset
- A dataset derived from v3 at points where both v2 and v3 returned a result
Figure 6: Stability results from parts of the v3 dataset that show equivalence with v2.
Figure 7: Accuracy results from parts of the v3 dataset that show equivalence with v2.
Tabulated results for the percentage of each of the above datasets with stability or accuracy within its target range are given in Table 5 below.
Table 5: Stability and accuracy results within target, given as a percentage of all results.
Pixel-level stability | Pixel-level accuracy | Basin-level stability | Basin-level accuracy | Number of pixels | Number of basin results | |
v2 | 82 | 37 | 82 | 9 | 3884075 | 16835 |
v3 where coincident with v2 | 79 | 33 | 81 | 5 | 3698303 | 16001 |
v3 | 81 | 36 | 82 | 6 | 3972311 | 16015 |
The pixel and basin level stability distributions are mainly contained within the target value. All three datasets are comparable at both levels.
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 number of basin SEC values in v3 has dropped, mainly due to cross-calibration failure for land basin 27, a very small and rugged region covering only 109 pixels, where too little data from Sentinel-3B was available.
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. 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 8.
Figure 8: Annual aggregated coverage of the Antarctic Ice Sheet
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. Figure 9 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 9: Validation against Operation IceBridge of the v3 dataset.
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 D data will add coverage to the region around the south pole, and extra data density elsewhere. Incorporation of Sentinel-3B will also add data density. The EnviSat GDRv3 dataset will replace the current v2.1 in the product. When available, the Sentinel-3A and B land ice processor data products will fill the current gaps left where the orbital track transition from ocean to land is not handled properly.
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 interesting regions at the sub-drainage-basin scale.
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.
Greenland Surface elevation Change
Description of past, current and future satellite coverage
The original 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 called for 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 1.4.2.1 and Table 4. 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 insure 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 may be undertaken with care, considering the underlying geophysics of the Greenland ice sheet.
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 version2 system, C3SMontlyVers2, were done 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 original version.
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.
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 10 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 window, but is removed in the accuracy estimate by averaging data on sub-grid-cell level.
Figure 10: 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 product generation and the real error estimate, which needs to meet the user-requirements, must be found by applying independent validation of the surface elevation 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 Center (https://nsidc.org/icebridge/portal/map). 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 11 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 11: 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.
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 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 at the coastal regions with the updated slope model being applied in the product.
Scientific research needs
The scientific research needs for the SEC product over Greenland are the same as for the Antarctic surface elevation change product, section 1.4.2.5.
Opportunities for exploiting the Sentinels and any other relevant satellites
These are the same as for the Antarctic surface elevation change product, section 1.4.2.6.
Gravimetric mass balance
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. Only the Greenland CCI project have released GRACE-FO solutions, therefore Greenland data is the only data brokered for the GRACE-FO period for now. When available the Antarctic counterpart will be brokered, which is expected in the first half of 2021.
Development of processing algorithms and methods for estimating uncertainties
The GRACE solution provided for the major drainage basins are 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 Groth and Horwath (2016).
The primary GCOS requirements for Gravimetric mass balance are met in terms of horizontal resolution (Table 1). 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.
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. In addition to the research and development in resolving the Antarctic issue, 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.
Grounding Line Location
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 (GCOS, 2016). 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.
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 are now regularly acquired every 6 to 12 days along the margins of the Greenland and Antarctic Ice Sheets, allowing for regular InSAR analysis for determining the grounding line location and its evolution.
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 12shows as example an interferogram and the grounding line of Ryder Glacier in northern Greenland derived from Sentinel-1 data acquired in 2017.
Figure 12: 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).
Surface Melt Processes
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 area 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, raising the internal water pressure and leading to enhanced lubrication at the base which has a strong impact on glacier motion. Surface melt also plays an important role for the stability of marine ice sheets and ice shelves, as the intensification of surface melting as precursor to the break-up of ice shelves in the Antarctic Peninsula has shown.
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, developed and operated by ESA, guaranteeing the availability of regular C-band SAR acquisitions free of charge. 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.
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.
References
Ice Sheets
Barletta, V.R.; Sørensen, L.S.; Forsberg, R. Scatter of mass changes estimates at basin scale for Greenland and Antarctica. The Cryosphere 2013, 7, 1411–1432. 757 42.
Citterio, M. and Ahlstrøm, A. P.: Brief communication "The aerophotogrammetric map of Greenland ice masses", The Cryosphere, 7, 445-449, https://doi.org/10.5194/tc-7-445-2013, 2013.
Cornford et al: Adaptive mesh, finite volume modelling of marine ice sheets, Journal of Computational Physics, 232(1):529-549 (2013)
De Zan, F: Accuracy of incoherent Speckle Tracking for Circular Gaussian Signals, IEEE Geoscience and Remote Sensing Letters, Vol 11, No 1, January 2014
Howat, I. M., Negrete, A., and Smith, B. E.: The Greenland Ice Mapping Project (GIMP) land classification and surface elevation data sets, The Cryosphere, 8, 1509-1518, https://doi.org/10.5194/tc-8-1509-2014, 2014.
Groh, A.; Horwath, M. The method of tailored sensitivity kernels for GRACE mass change estimates. Geophysical Research Abstracts 2016, 18, EGU2016–12065.
Howat, I., A. Negrete, and B. Smith. 2017. MEaSUREs Greenland Ice Mapping Project (GIMP) Digital Elevation Model from GeoEye and WorldView Imagery, Version 1. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/H0KUYVF53Q8M.
Hvidberg, C.S., et al., User Requirements Document for the Ice_Sheets_cci project of ESA's Climate
Change Initiative, version 1.5, 03 Aug 2012. Available from: http://www.esa-icesheets-cci.org/
Mohr, J.J. & J.P. Merryman Boncori, An Error Prediction Framework for Interferometric SAR Data, IEEE Trans. Geosc. Rem. Sens., Vol 46, pp. 1600-1613, Jun. 2008.
Ivins E. R., James T. S., Wahr J., Schrama O., Ernst J., Landerer F. W., and Simon K. M. (2013), Antarctic contribution to sea level rise observed by GRACE with improved GIA correction, J. Geophys. Res. Solid Earth, 118(6), 3126–3141.
J.F. Levinsen, K. Khvorostovsky, F. Ticconi, A. Shepherd, R. Forsberg, L.S. Sørensen, A. Muir, N. Pie, D. Felikson, T. Flament, R. Hurkmans, G. Moholdt, B. Gunter, R.C. Lindenbergh & M. Kleinherenbrink (2015) ESA ice sheet CCI: derivation of the optimal method for surface elevation change detection of the Greenland ice sheet – round robin results, International Journal of Remote Sensing, 36:2, 551-573, DOI: 10.1080/01431161.2014.999385
Mottram, R.; Simonsen, S., Høyer Svendsen, S.;,Barletta, V.R., Sandberg Sørensen, L., Nagler, T., Wuite, J., Groh, A., Horwath, M., Rosier, J., Solgaard, A., Hvidberg, C.S., Forsberg, R.: An Integrated View of Greenland Ice Sheet Mass Changes Based on Models and Satellite Observations. Remote Sens. 2019, 11, 1407.
Nagler, T., Rott, H., Hetzenecker, M., Wuite, J., Potin, P.: The Sentinel-1 Mission: New Opportunities for Ice Sheet Observations. Remote Sens. 2015, 7, 9371-9389.
Padman, L., H. A. Fricker, R. Coleman, S. Howard, and S. Erofeeva, 2002: A new tidal model for the Antarctic ice shelves and seas, Ann. Glaciol., 34, 247-254.
Rastner, P., Bolch, T., Mölg, N., Machguth, H., Le Bris, R., and Paul, F.: The first complete inventory of the local glaciers and ice caps on Greenland, The Cryosphere, 6, 1483-1495, https://doi.org/10.5194/tc-6-1483-2012, 2012.
RGI Consortium (2017). Randolph Glacier Inventory – A Dataset of Global Glacier Outlines: Version 6.0: Technical Report, Global Land Ice Measurements from Space, Colorado, USA. Digital Media. DOI: https://doi.org/10.7265/N5-RGI-60
Rizzoli, P., Martone, M., Gonzalez, C., Wecklich, C., Borla Tridon, D., Bräutigam, B., Bachmann, M., Schulze, D., Fritz, T., Huber, M., Wessel, B., Krieger, G., Zink, M., and Moreira, A. (2017): Generation and performance assessment of the global TanDEM-X digital elevation model. ISPRS Journal of Photogrammetry and Remote Sensing, Vol 132, pp. 119-139.
Shepherd et al, Antarctic Ice Sheet (AIS) Essential Climate Variable (ECV) User Requirements Document (URD), ST-UL-ESA-AISCCI- URD-001, version 3.0, 28th June 2018 at http://esa-icesheets-antarctica-cci.org/
Shepherd et al: Trends in Antarctic ice sheet elevation and mass, Geophysical Research Letters, 16 May 2019, https://doi.org/10.1029/2019GL082182
Slater, T., Shepherd, A., McMillan, M., Muir, A., Gilbert, L., Hogg, A. E., Konrad, H., and Parrinello, T.: A new digital elevation model of Antarctica derived from CryoSat-2 altimetry, The Cryosphere, 12, 1551-1562, https://doi.org/10.5194/tc-12-1551-2018, 2018.
Simonsen, S.B.; Sørensen, L.S. Implications of changing scattering properties on Greenland ice sheet volume change from CryoSat-2 altimetry. Remote Sensing of Environment 2017, 190, 207–216.
Sørensen, LS.; Simonsen, SB.; Forsberg, R.; Khvorostovsky, K.; Meister, R.; Engdahl, M.E. 25-years of elevation changes of the Greenland Ice Sheet from ERS, EnviSat, and CryoSat-2 radar altimetry. Earth and Planetary Science Letters 2018, 495, 234–241.
Zwally, H. Jay, Mario B. Giovinetto, Matthew A. Beckley, and Jack L. Saba, 2012, Antarctic and Greenland Drainage Systems, GSFC Cryospheric Sciences Laboratory
