Last modified on May 20, 2024 12:07

Contributors: Sebastian B. Simonsen (Technical University of Denmark)

Issued by: Technical University of Denmark / Sebastian B. Simonsen

Date:08 Feb 2024

Ref: C3S2_312a_Lot4.WP2-FDDP-IS-v2_202312_SEC_ATBD-v5_i1.1

Official reference number service contract: 2021/C3S2_312a_Lot4_EODC/SC1

History of modifications

List of datasets covered by this document

Related documents

Acronyms

General definitions

Backscatter: The portion of the outgoing radar signal that the target redirects directly back towards the radar antenna.

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

Climate Data Record (CDR): A time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change.

Crossover analysis: A method for deriving elevation change at locations where two satellite ground tracks (one ascending and one descending) cross. 

Cycle: A satellite's cycle is one full completion of its track over the ground, after which the ground track repeats.

Glacial Isostatic Adjustment (GIA): The response of land and oceans to changes in the load of overlying ice. For example, when an ice mass is removed, the land beneath it rebounds.

Processing chain: A sequence of software routines run to convert input data to an output product.

Processing level: European Space Agency (ESA) datasets are labelled by the level of processing applied to them; level 0 corresponds to raw data, level 1 to acquired data in physical units, and level 2 to values of the parameter constituting the scientific objective of the experiment. Higher levels involve extrapolation or assimilation. Occasionally intermediate levels are produced, e.g., CryoSat-2 has an L2i product, which is intermediate between L2 and L3.

Stability: An estimate of the consistency of the measurements over time.

Retracking: Finding the range from the instrument to the point of closest approach on the ground by examining the shape of the radar echo.

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 global equipotential surface corresponding to mean sea level). An 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.

Tracking: Retrieving the radar echo from a given radar pulse.

Validation: Comparison between two independent datasets to test their agreement. 

Scope of the document

This document is the Algorithm Theoretical Basis Document (ATBD) for version 5.0 of the Surface Elevation Change (SEC) products made as part of the Copernicus Ice Sheets and Ice Shelves service. The products contain geographically gridded time series of the rate of change of ice sheet surface elevation in Greenland, from 1992 to the present. The latest version, Version 5.0, includes updates solely for the Greenland data product, while the production of Antarctic data has been temporarily halted. We refer the reader to Algorithm Theoretical Basis Document (ATBD) version 4.0 [D5] for a description of the Antarctic SEC, also hosted at the Copernicus Climate Data Store. This document describes the satellite-mounted instruments, auxiliary datasets, auxiliary models, and basic algorithms used to create the Greenland Surface Elevation Change product.

The products are hosted on the Copernicus Climate Data Store¹.

Executive summary

In this ATBD, we document the inputs, processing stages, and outputs of the Greenland SEC product version 5.0. We refer the reader to version 4.0 ATBD [D5] for a description of the Antarctic SEC. This document is structured as follows:

Section 1 describes the instruments used. Radar altimeters mounted on six different satellites provide the primary input data and are discussed here, along with issues relating to the datasets they provide. As the datasets are continually being improved and new versions released, changes newly incorporated into the SEC v5 products are discussed. Links to further information and a tutorial on radar altimetry are also provided.

Section 2 describes the input data in more detail including the auxiliary data used during SEC processing. Tables of mission and instrument characteristics for each input dataset are given. The uses and sources of auxiliary data are listed. The types of validation data used, and their sources are discussed. 

Section 3 describes the algorithms used in the processing. It gives an overview of the methodology, highlighting changes between this version of the product and previous versions.

Section 4 describes the output of the processing chains, and the product itself. The description includes the output file type, the difference between a Climate Data Record (CDR) and an interim CDR (ICDR), the file contents and format, and a table summarizing the variables contained.

1.  Instruments

The Greenland surface elevation change product uses the altimetry record from a series of overlapping European Space Agency (ESA) radar altimetry missions. The record started in 1992 with two European Remote-Sensing Satellites (ERS-1 and ERS-2) and Envisat, whose missions are now finished, and will extend into the future with CryoSat-2 and Sentinel-3 A and B, which are still in operation. The initial Sentinel-3 data release was optimized for oceans and does not apply the most optimal processing scheme for land ice. This results in a loss of tracking by the radar altimeter as the satellite approaches the complex topography of the ice sheets from the oceans. ESA has developed a specialised land-ice processor for the Sentinel-3 mission, with the product scheduled for release at the end of 2023. Hence, this thematic land-ice product is intended to be used in v5. Second, the reprocessing of the CryoSat-2 baseline E is still ongoing. Consequently, both baseline D and E are included in the SEC product. These two baselines do not differ in major ways, see Geminale (2021). The most relevant changes are progressive improvements to the land ice retracking in both altimeter operating modes and the resolution of an issue with the computation of backscatter power in low-resolution mode (LRM) which caused a small drift in expected values. The backscatter coefficient is not applied as a parameter in the Greenland SEC production. Hence, we do not expect any effects of this possible drift in the backscatter. Table 1 summarises the missions/mission phases used as input to the Greenland SEC dataset. 

Table 1: Summary of mission-level parameters relating to datasets used as inputs.

For further information on these missions and their instruments see the ESA's Earth Observation portal, with a top-level directory at:

• https://www.eoportal.org/satellite-missions

Radar altimetry is described in detail in a PDF tutorial document at:

• https://www.altimetry.info/file/Radar_Altimetry_Tutorial.pdf ²

2. Input and auxiliary data

2.1. ESA Radar altimetry level 2 products

The following tables provide information on the primary input data sources for the SEC product, the satellite-mounted radar altimeters. Only operating modes used in Greenland SEC products are listed. 

Table 2: ERS1 altimeter parameters and data source information

Table 3: ERS2 altimeter parameters and data source information

Table 4: Envisat altimeter parameters and data source information

Table 5: CryoSat-2 altimeter parameters and data source information

Table 6: Sentinel-3A altimeter parameters and data source information

Table 7: Sentinel-3B altimeter parameters and data source information

2.2. Auxiliary data

2.2.1. Digital elevation model for Greenland

The Greenland surface elevation change uses the official level-2 data products provided by ESA for all missions, as seen above (Section 2.1). Within the generation of such level-2 products, ESAs processing facilities use a digital elevation model (DEM) in the geolocation of LRM data. As the DEMs used might be satellite-mission dependent, we here refer to the individual mission documentation for the specific DEM used in the geolocation of the radar echo.

2.2.2. Ice extent

The processing is done for all grid cells defined within the Greenland ice sheet or glaciers/ice caps with a strong connection to the ice sheet, as defined by the ESA Climate Change Initiative (CCI) glaciers project⁴ (Raster et al. 2012).

2.2.3. Glacial isostatic adjustment

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 (Barletta et al., 2013).

2.2.4.  Validation data

Independent estimates of the rate of surface elevation change at discrete locations and over specific time periods are provided by the Airborne Topographic Mapper (ATM), a scanning laser altimeter flown on board aircraft by Operation IceBridge (Studinger 2014), and the higher-order ATL-15 product from the National Aeronautics and Space Administration (NASA) ICESat-2 laser altimeter mission (Smith 2021).

3. Algorithms

The theoretical basis for the Greenland ice sheet SEC follows the research and development initiated in the ESA CCI project and is detailed in the published ATBD⁵. All applied algorithms are published in the relevant literature (Sørensen et al., 2015; Levinsen et al., 2015; Simonsen and Sørensen, 2017; Sørensen et al., 2018). The round-robin exercise completed during the ESA CCI project concluded that the Greenland ice sheet surface elevation change is best represented by a combination of repeat-track, along-track, and plane-fitting algorithms (Levinsen et al., 2015). The Greenland implementation of all three algorithms follows tightly Sørensen et al., (2018), in which the applied framework is presented. In the following, we present a summary of Sørensen et al., (2018). Here, we give an overview of the applied algorithms with special emphasis on the merging of data from different satellite missions.

As of CDR version 3.0, all the FORTRAN scripts described in the literature have been ported to Python, which allows for a true seamless processing chain and simplifies the generation of products. In version 4.0, the development focused on consolidating the processing baseline E for CryoSat-2. This means that improvements and enhancements were made to the data processing methodology specifically for CryoSat-2. In Version 5.0, the key development is the introduction of the thematic land ice product for Sentinel-3. This addition provides valuable information and analysis specifically for land ice areas.

3.1. Introduction

The product requirements are a stack of gridded surface elevation change rates from the Greenland ice sheet, at 25 km resolution, from the start of the ERS1 mission to present. The grids are given at monthly intervals, and flag grids for steep terrain and terrain type are also provided. The change rate unit is m/year. The Global Climate Observing System (GCOS) accuracy target is 0.1 m/year, and the stability target is also 0.1 m/year. All products are provided on the equidistant grid in polar stereographic projection defined by EPSG: 3413 (see Product User Guide and Specification document [D2] for full details), and a timeseries of surface elevation change is derived in each cell for each mission. The processing chain is described in the related System Quality Assurance Document [D3].

A review of the scientific background of surface elevation measurement for the Greenland ice sheet can be found in the ATBD⁴ provided by the ESA CCI project. Here, a combination of repeat-track, along-track, and plane-fitting algorithms was found to be the most optimal for the Greenland ice sheet. The mission-specific application of repeat-track, along-track, or plane-fitting algorithms is described in Sørensen et al. (2018). All algorithms for the Greenland ice sheet, are implemented in accordance with Sørensen et al (2018). In general terms, the input data are used to minimise the residual fit to the surface elevation change (dH/dt) model. The differences between the three algorithms are illustrated in Figure 3.1 and summarised in the following subsections.

Figure 3.1: Schematics of the algorithm differences, here the elevation change is estimated in the cyan hexagons. a)  Repeat track, when all observations are along the same exact track (blue), and along-track, when the tracks differ in position. In this case, the “green” observations are projected onto the blue reference track. b) Plane-fitting, here the solution is modelled within regular-shaped grid cells. The figure is modified from Moholdt et al., (2010)

3.1.1. Repeat-track

The repeat-track (RT) algorithm is used to minimise the residual fit to the surface elevation change (dH/dt) model along segments of the repeated ground track. Included in the model are different biases for different parameters, which have been shown to correlate to surface elevation change, e.g. backscatter (Bs) and seasonality in the altimetry data. According to Sørensen et al (2018), here we follow the formulation of Legresy et al., 2005; Sørensen et al., 2011, 2015; Flament and Rémy, 2012, who model the time evolving elevation of the ice sheet given by

3.1.2. Along-track

The along-track algorithm follows the RT solution, but the formulation defines a reference track instead of segments on a repeated track. Data is then grouped and referenced to the reference track by a spatial search and all data within 2 km of the reference track is used. This formulation of the RT allows for incorporating multiple satellite missions and satellite missions with changes in the orbit configuration.

3.1.3. Plane-fitting

The plane-fitting (PF) method proposed by Simonsen and Sørensen (2017), has its foundation in the RT-algorithm presented above and in Sørensen et al. (2015). However, instead of solving for elevation change along-track, the drifting orbit of CryoSat-2 is utilised to solve in the full plane spanned by the applied grid. To account for the drifting orbit, Simonsen and Sørensen, (2017) proposed to add parameters, to solve for the 2d-topography within regular grid-cells (adding the parameters cx, cy, cc), to the equation presented above for the RT-algorithm. This results in an elevation change model-fitting of,

the naming convention follows eq. 3.1, with the addition of biases for CryoSat-2 ascending/descending orbits (b_AD) and LRM/SARIN mode (b_m) were added (AD and m is boolians), alongside with replacing the backscatter bias with biases due to changing leading edge width of the recorded waveform (LeW).

3.1.4. Adapting the plane-fitting algorithm for Sentinel-3

Sentinel-3 data were processed according to the plane-fitting algorithm applied to Cryosat-2. However, the experiments done in Simonsen and Sørensen, (2017) had to be redone in order to ensure the optimal fitting algorithm for the data from the new SAR sensor onboard Sentinel-3. Starting from the basic plane-fitting algorithm:

including a bias term for backscatter (Bs) correction, as the initial model guess, we performed 32 model perturbation experiments. Each of the 32-surface elevation change solutions were validated against Operation IceBridge to find the optimal choice of model-parameters in the plane-fitting algorithm. This model exercise showed that the most optimal surface elevation change solution was found by adding a LeW bias. This was also the case for the CryoSat-2 plane-fitting algorithm.

3.1.5. Merging of satellite missions.

The round robin performed as a part of the ESA CCI Greenland ice sheet project showed collocation as the optimal interpolation method for combining heterogeneous data of different kinds. Since CDRv2 surface elevation change processor the collocation-gridding procedure has been replaced by an ordinary kriging method to provide model estimates for all Greenlandic ice-covered grid-cells. As ordinary kriging is capable of extrapolation over unrealistic distances, a distance flag has also been added to the product. This is given to enable the end-user to perform filtering of undesired data if needed.

With the CDR version 3.0 surface elevation change processor upgrade, the cross-calibration of satellite missions went from being a part of the merging algorithm of surface elevation change grids to be included in the all-python processing scheme. This is illustrated by the updated RT-algorithm, given by

where dh_AB is the elevation bias between satellite mission A and B, given by the satellite mode parameter m_AB (0 for satellite A and 1 for satellite B). The LeW-bias is applied for each of the satellites A and B. For the Greenland ice sheet, this inter-mission implementation of the RT- and PF-algorithms derived at 1x1 km grid resolution, from 3- or 5-years of satellite altimetric observations. A new estimate of SEC (running average) is derived every 3^rd month, based on the optimal combination of cross-over, along-track, and plane-fitting method.

3.1.6. Deriving the monthly time series.

As the position of the data segments ( \( \overline{x}, \overline{y} \) ) varies from data window to data window, we need to combine multiple elevation change estimates to derive the Greenland-wide (gridded) estimates of surface elevation change, as illustrated in Figure 3.2. This is done for every month m_m by choosing the two elevation change estimates with the closest data window mid-point to m_m. Ordinary kriging is then applied to the resulting point cloud of dh/dt estimates to obtain the final 25x25-km gridded Copernicus Climate Change Service (C3S) product.

 
Figure 3.2: Schematic drawing of the combined product generation. The upper panel shows the solutions available at a selected data segment of 1x1 km and 3- or 5-years of satellite altimetric observations. Whereas the lower panel represents the merging of the raw 1x1 km dh estimates to the final 25x25km C3S product. 

3.2. Uncertainty processing

There are two main contributors to the uncertainty in the surface elevation change rate:

• Measurement errors in the input data and spatial distribution.
• Fitting errors in the surface elevation change modelling.

The measurement error introduced by the input data depends on the spatial distribution of elevation measurements within each grid cell/cycle. Additional measurement errors include radar penetration, volume scattering, radar speckle, satellite location uncertainty, and atmospheric attenuation uncertainty which decorrelate to the cycle period and geolocation of the echo (Wingham et al. 1998). For the Greenland ice sheet, the geolocation of the echo is the largest error source, as the nature of the radar-altimeter restricts the measurements to the "highest" point within the footprint of the satellite (Sørensen et al. 2018b). Hence, the surface elevation change estimate is a determination of the time evolution of the highest points within the radar footprint. This potentially biases the derived solution toward more positive values (less elevation loss). It also hampers the solutions in valleys especially evident around Jakobshavns Isbræ (see Figure 3.3), where the fixed grid solution (PF) struggles to provide accurate estimates of surface elevation in the main trough. In general, the radar altimeter performs better in the simple topography of the central, flat areas of the Greenland ice sheet compared to the coastal areas characterised by a more complex topography. Therefore, the error is generally larger in areas with steeper surface slopes.

Figure 3.3: The surface slope of the Greenland ice sheet (Simonsen and Sørensen 2017)

The fitting error has a well-determined contribution to the total error as this error is directly derived when the residuals in the surface elevation change modelling are minimised. As the internal elevation change grids are generated the resulting errors are independent and can be summed as independent errors by the root-mean-square error. This summed error estimate is given in the present product release. 

The number of unknowns in the measurement errors limits the end-to-end uncertainty characterisation. The errors induced by the time-varying penetration of the surface snow and the geo-location of the radar echo are especially difficult to quantify. Here, we rely on an independent dataset to estimate this contribution to the error. Applying a similar approach as to the C3S validation effort, Simonsen and Sørensen (2017) estimated a bias of 9 cm per year for the entire Greenland ice sheet with lower bias in the interior (6 cm per year), a direct implication of changing scattering horizon observed by the radar altimeter, not seen by an airborne altimeter measuring the true ice sheet surface.

Output data

The output CDR is in a single netCDF4 file, updated monthly as ICDRs (intermediate CDR). Each of the ICDRs contains the whole dataset from the previous release as well as the new additions, i.e., the product outputs are cumulative. See Figure 4.1 for an example of long-term elevation change rates derived by averaging the elevation change product.

Figure 4.1: The C3S surface elevation product accumulated from 1992-2023, at the native 25x25 km grid in EPSG 3413 projection.

The main variable, the surface elevation change rate, is stored in a stack of polar stereographic grids (25x25 km grid, EPSG:3413) at monthly intervals. Each grid cell contains the calculated surface elevation change rate for the five-year period centered on the time given for that grid. The time information is stored in a separate time array. A corresponding status array flags whether valid data exists in a grid cell or not. The grid projection coordinates of each cell and its longitude and latitude equivalents are given. Two masks are included, one of surface type (i.e. ice sheet, ice shelf, ice rise or island, and ocean) and one of slope levels (i.e., 0° to 2°, 2° to 5°, above 5°). More details are given in the Product User Guide and Specification document [D2] and summarised in Table 8 below.

Table 8. Greenland SEC primary output variables summary

References

ACRI-ST IPF Team (2020). Product Data Format Specification – SRAL/MWR Level 2 Land products. ESA document reference S3IPF.PDS.003.2. Available from https://sentinel.esa.int/documents/247904/2753172/Sentinel-3-Product-Data-Format-Specification-Level-2-Land (resource validated 19/12/23)

Barletta, V. R., Sørensen, L. S., and Forsberg, R. (2013). Scatter of mass changes estimates at basin scale for Greenland and Antarctica. The Cryosphere, 7(5), 1411–1432. 

Brockley, D. et al. (2017). REAPER: Reprocessing 12 Years of ERS-1 and ERS-2 Altimeters and Microwave Radiometer Data. IEEE TGRS, June 2017. DOI: 10.1109/TGRS.2017.2709343 

CryoSat-2 Team (2019). CryoSat-2 Product Handbook, baseline D 1.1. ESA document reference C2-LI-ACS-ESL-5319. Available from
https://earth.esa.int/eogateway/documents/20142/37627/CryoSat-Baseline-D-Product-Handbook.pdf/c76df710-2a5c-c8b8-00c1-13c8db0e9f51 (resource validated 19/12/23)

Femenias, P. (editor) (2018). Envisat Altimetry Level 2 Product Handbook. ESA document CLS - ESLF - 18 -0003. Available from https://earth.esa.int/handbooks/ra2-mwr/ (resource validated 19/12/23)

Flament, T. and Remy, F. (2012). Antarctica volume change from 10 years of Envisat altimetry. Conference paper, Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International. DOI: 10.1109/IGARSS.2012.6351149 (resource validated 29/06/23)

Geminale, T. (2021). CRYOSAT Ground Segment Instrument Processing Facility Baseline E Evolutions, C2-RP-ACS-ESL-5330, Issue 2. Available from https://earth.esa.int/eogateway/documents/20142/37627/Cryosat-Baseline-E-Evolutions.pdf/bdd53679-1de5-ebd4-3d18-b72096f7b7c8 (resource validated 19/12/23)

Legresy, B. et al. (2005). Envisat radar altimeter measurements over continental surfaces and ice caps using the ICE-2 retracking algorithm. Remote Sensing of Environment, v95, p150-163. 

Levinsen, J.F. et al. (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, v36 (2), p551-573. DOI: 10.1080/01431161.2014.999385 

Moholdt, G., Hagen, J. O., Eiken, T. and Schuler, T. V. (2010) Geometric changes and mass balance of the Austfonna ice cap, Svalbard. The Cryosphere, 4, 21-34. DOI:10.5194/tc-4-21-2010.

REAPER Team. (2014). REAPER product handbook. Available from https://earth.esa.int/eogateway/documents/20142/37627/reaper-product-handbook-for-ers-altimetry-reprocessed-products.pdf (resource validated 19/12/23)

Simonsen, S.B., and Sørensen, L.S., (2017). Implications of changing scattering properties on Greenland Ice Sheet volume change from CryoSat-2 altimetry. Remote Sensing of Environment, v190, p 207-216. 

Smith, B. (2021). ICESat-2 Algorithm Theoretical Basis Document for Land Ice DEM and Land Ice Height Change Release 001 Algorithm Theoretical Basis Document (ATBD) for Land-ice DEM (ATL14) and Land-ice height change (ATL15). icesat2_atl14_atl15_atbd_r001_0.pdf (nsidc.org) (resource validated 16/05/24) 

Sørensen, L.S., et al. (2011). Mass balance of the Greenland ice sheet (2003-2008) from ICESat data – the impact of interpolation, sampling and firn density. The Cryosphere, v5, p173-186. DOI: 10.5194/tc-5-173-2011 

Sørensen, L.S., et al. (2015). Envisat-derived elevation changes of the Greenland Ice Sheet, and a comparison with ICESat results in the accumulation area. Remote Sensing of Environment, v160, p56-62. DOI: 10.1016/j.rse.2014.12.022 

Sørensen, L.S., et al. (2018). 25 years of elevation changes of the Greenland Ice Sheet from ERS, Envisat and CryoSat-2 radar altimetry. Earth and Planetary Science Letters, vol 495, p 234-241. DOI: 10.1016/j.epsl.2018.05.015 

Sørensen, L.S, Simonsen, S. B., Langley, K., Gray, L., Helm, V., Nilsson, J., Stensengm, L., Skourup, H., Forsberg, R. and Davidson, M. W. J. (2018b). Validation of CryoSat-2 SARIn Data over Austfonna Ice Cap Using Airborne Laser Scanner Measurements. Remote Sensing, 10(9), 1354. DOI:10.3390/rs10091354 

Studinger, M. (2014). IceBridge ATM L4 Surface Elevation Rate of Change, Version 299 1, Antarctica subset. N. S. a. I. D. C. D. A. A. Center. Boulder, Colorado, USA. DOI: 10.5067/BCW6CI3TXOCY 

Wingham, D.J., Ridout, A.J., Scharroo, R., Arthern, R.J., and Shum, C.K. (1998). Antarctic Elevation Change from 1992 to 1996. Science vol 282, issue 5388, pp 456-458, DOI:10.1126/science.282.5388.456 

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