Last modified on May 20, 2024 12:00

Contributors:  J. Wuite (ENVEO IT GmbH), T. Nagler (ENVEO IT GmbH)

Issued by: ENVEO / Jan Wuite

Date: 11/10/2023

Ref: C3S2_312a_Lot4.WP1-PDDP-IS-v2_202306_IV_PQAD-v5_i1.2

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 

Ice Velocity: Ice flow velocity describes the rate and direction of ice movement. It is a fundamental parameter to characterize the behaviour of a glacier or an ice sheet. Ice velocity and its spatial derivative, strain rate (which is a measure of the ice deformation rate), are required for estimating ice discharge and mass balance and are essential input for glacier models that try to quantify ice dynamical processes.

Scope of the document

This document is the Product Quality Assurance Document (PQAD) for Ice Velocity (IV) as part of the Copernicus Ice Sheets and Ice Shelves service. We document here the validated products, validation data, methods and results from the Climate Data Record (CDR) for Ice Velocity (v1.4). The same methods will be used to validate the upcoming CDR (v1.5).

Executive summary

In this document we describe the validation and quality assessment of the Copernicus Climate Change Service (C3S) Greenland and Antarctic Ice sheets ice velocity (IV) Climate Data Records (CDRs).

Chapter 1 details the IV products that are validated. These include the annual Greenland Ice Sheet ice velocity maps of 2017/18, 2018/19, 2019/20 and 2020/21. The products are derived from Sentinel-1 synthetic aperture radar (SAR) data using offset tracking techniques. The Antarctic Ice Sheet Velocity is provided as a new product within the service from v1.5 onwards. The CDR closely follows the Greenland IV product as the retrieval algorithm is largely identical. 

In Chapter 2 we describe the validation and intercomparison data sets available for the quality assessment. The quality assessment includes detailed validation and intercomparison with in-situ Global Positioning System (GPS) data and publicly available ice velocity maps, as well as internal consistency check, i.e. testing of the algorithm performance in stable (ice-free) terrain.

The product validation methodology is described in Chapter 3. Detailed and component-wise intercomparisons with validation data sets are performed, and statistics (bias and standard deviation, Root Mean Square Error (RMSE)) of the residuals are provided and visualized in plots (e.g. scatterplots, histograms). 

Chapter 4 provides a summary of the most recent (Ice Velocity CDR v1.4) validation results. The intercomparison of ice velocity on selected outlet glaciers show a high level of agreement with a negligible overall mean bias and an RMSE of 0.18 m/d for the easting and 0.21 m/d for the northing component based for the 2017/18, 2018/19, 2019/20 and 2020/21 maps respectively. The ice sheet wide product intercomparison between Making Earth System Data Records for Use in Research Environments (MEaSUREs) and C3S indicate a negligible bias with an RMSE of 0.02-0-03 m/d. The outcome of the stable ground test indicates for all annual maps on average a negligible mean bias with an RMSE of 0.01-0-02 m/d for both easting and northing components.  

1. Validated products

The ice velocity (IV) product assessment referred to in this document concerns the annually averaged IV maps of Greenland derived from Sentinel-1 Synthetic Aperture Radar (SAR) data acquired from 2017-10-01 to 2018-09-30, 2018-10-01 to 2019-09-30, 2019-10-01 to 2020-09-30 and 2020-10-01 to 2021-09-30  (Figure 1.1, Table 1.1). For CDR v1.5 the service will extend the timeseries for Greenland and also include a new product on Antarctic Ice Sheet. The validation of CDR v1.5 will be described and discussed in the Product Quality Assessment Report  (PQAR) [RD.1]. Surface velocity is derived by applying advanced iterative offset tracking techniques.For details see the Algorithm Theoretical Basis Document (ATBD) [RD.3]. The ice velocity maps are annually averaged and provided at 250 m grid spacing in North Polar Stereographic projection (EPSG: 3413) for Greenland and at 200 m grid spacing in Antarctic Polar Stereographic Projection (EPSG: 3031) for Antarctica. The horizontal velocity is provided in true meters per day, towards easting (v_x) and northing (v_y) direction of the grid, and the vertical displacement (v_z), is derived from a digital elevation model (DEM) covering the Greenland (TanDEM-X 90 m DEM; Rizzoli et al., 2017) and Antarctic (200 m Reference Elevation Model of Antarctica (REMA) DEM; Howat et al., 2019) ice sheets. The products are provided as a NetCDF4 file with the velocity components: v_x, v_y, v_z and v_v (magnitude of the horizontal components), along with maps showing the valid pixel count and uncertainty (std). For further details, see the Product User Guide and Specification (PUGS) document [RD.2]. For the product validation and intercomparisons discussed in this document, we consider the annually averaged maps as well as the individual (6/12-day repeat) ice velocity maps used to compile the annual maps. The individual maps¹ are not provided as products in C3S but are included here in the validation as an extra quality assurance.

Figure 1.1: C3S ice velocity maps of the Greenland Ice Sheet based on Sentinel-1 data, 2017/18 (top left), 2018/19 (top right), 2019/20 (bottom left) and 2020/21 (bottom right).

Table 1.1: Main characteristics of the C3S Greenland Ice Sheet ice velocity data sets available in the Copernicus Climate Data Store (CDS).

2. Description of validating datasets

This chapter describes the datasets available for validating the Greenland and Antarctic IV products for the upcoming release CDR v1.5. 

2.1. Greenland Ice Sheet 

The quality assessment for Greenland Ice Sheet IV includes 1) detailed validation and intercomparison with contemporaneous validating datasets, these comprise in-situ GPS data and publicly available ice velocity maps, and 2) testing of the algorithm performance in stable (ice-free) terrain. Table 2.1. shows the main characteristics of the validation data. 

Table 2.1: Main characteristics of validation data for Greenland.

In-situ GPS data is available at various sites across the ice sheet (Figure 2.1). The GPS instruments are attached to Automatic Weather Stations (AWS) operated by the Geological Survey of Denmark and Greenland (GEUS) in collaboration with the National Space Institute at the Technical University of Denmark (DTU Space) and ASIAQ Greenland Survey as part of the Danish Programme for Monitoring of the Greenland Ice Sheet (PROMICE; Fausto and Van As, 2019). The GPS data is available through the PROMICE Data Portal². The latest version of the data set provides hourly, daily and monthly average positions and is currently updated until June 2023.

Figure 2.1: Locations of the PROMICE AWS stations equipped with GPS and used for ground validation.

The IV product is also evaluated against publicly available products covering the same area and time span. Although not a ground truth validation, such an inter-comparison provides a good level of quality assurance, particularly in areas where little change is to be expected. For product inter-comparison we utilize an independent collection of ice velocity maps derived from higher resolution TerraSAR-X/TanDEM-X (TSX/TDX) data, and covering the Greenland margins, as well as annual Greenland wide ice velocity maps, based on SAR and optical data. These IV maps were produced as part of the National Aeronautics and Space Administration (NASA) 'Making Earth System Data Records for Use in Research Environments' (MEaSUREs) program³.

The TerraSAR-X-based data set consists of a collection of IV maps covering the Greenland margins including most of the major outlet glaciers (Figure 2.2). The ice velocity is retrieved from repeat pass TerraSAR-X images (1 to 3 cycles) applying a combination of conventional Interferometric SAR (InSAR) and speckle tracking techniques (Joughin, 2002). For the validation assessment we use the latest version of the dataset (version 4) covering the time-period from June 2008 to Oct 2021. This dataset is available through the National Snow and Ice Data Center ( NSIDC) data portal (data set ID: NSIDC-0481; Joughin et al., 2021). Data files are delivered in GeoTIFF format at 100 m grid spacing in North Polar Stereographic projection (EPSG: 3413). Separate files are provided for the x and y velocity components along with corresponding error estimates for both velocity components.

Figure 2.2: Sentinel-1 IV mosaic showing locations of MEaSUREs TerraSAR-X derived IV maps used for product inter-comparison in black (left). The IV data sets cover the margins of Greenland and include most of the major outlet glaciers. The red circle indicates the location of C.H. Ostenfeld Glacier shown here as an example (right).

The Greenland-wide ice velocity maps used for product intercomparison are based on Sentinel-1, TerraSAR-X/TanDEM-X SAR images and Landsat-8 optical images. The maps are annually averaged from 1^st December to 30^th November and are provided at 200 m horizontal resolution in North Polar Stereographic projection (EPSG: 3413). For the validation assessment we use the latest version of the dataset, version 3, covering the time-period from 2015 to 2020. This dataset is available through the NSIDC data portal (data set ID: NSIDC-0725; Joughin, 2021).

Another method used here, for quality assessment of the ice velocity product, is the analysis of stable terrain, i.e. where no velocity is expected. This gives a good overall indication of the bias introduced by the end-to-end velocity retrieval including co-registration of images, velocity retrieval, etc. For masking the moving ice, we use a polygon shapefile of the ice sheet and peripheral glacier outlines produced by Rastner et al (2012, updated 2018). This shapefile is derived semi-automatically from Landsat 5 and 7, using a band ratio approach (red/Short Wavelength Infra-Red (SWIR)) with scene specific thresholds and manual correction of debris cover, seasonal snow, shadow, water (outside of glaciers), sea ice and icebergs. By inverting the ice sheet/glacier shapefile, a land mask file is created as depicted in Figure 2.3. 

Figure 2.3: Shapefile showing ice-free terrain (black) in Greenland and used for the stable terrain test.

2.2. Antarctic Ice Sheet 

Due to sparsity of in-situ GPS data acquired in Antarctica the validation and quality assurance activities are restricted to intercomparisons with ice velocity maps for overlapping periods and the stable terrain test.

As part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program annual Antarctic IV maps were assembled derived from multi-sensor SAR data and optical imagery acquired between 2005 and 2020 (Mouginot et al., 2017). The maps combine data derived from the Japanese Space Agency's (JAXA) Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR), the European Space Agency's (ESA) Environmental Satellite (ENVISAT) Advanced SAR (ASAR) and Copernicus Sentinel-1, the Canadian Space Agency's (CSA) RADARSAT-1, RADARSAT-2 and the German Aerospace Agency's (DLR) TerraSAR-X (TSX) and TanDEM-X (TDX), and are integrated with optical imagery from the U.S. Geological Survey's (USGS) Landsat-8. Data are available in NetCDF format through NSIDC at 1 km spatial resolution (Data Set ID: NSIDC-0720; Mouginot et al., 2017). 

For the stable terrain test we use a rock outline shapefile derived from Landsat-8 Operational Land Imager (OLI) images (Figure 2.4; Burton-Johnson et al., 2016). The dataset is generated using a new and automated methodology for snow and rock differentiation that excludes areas of snow (both illuminated and shaded), clouds and liquid water whilst identifying both sunlit and shaded rock. The method achieves higher and more consistent accuracies than alternative data and methods such as the normalized difference snow index (NDSI). The images were acquired in austral summers between October 2013 and March 2015. The dataset is provided as a supplement with the paper (Burton-Johnson et al., 2016). 

Figure 2.4: Map of Antarctica showing in blue floating ice shelves and in red the rock outcrop shapefile used for the stable terrain test (Burton-Johnson et al., 2016).

3. Description of product validation methodology 

Validation is done through detailed intercomparison of the IV product with in-situ GPS data and publicly available IV maps. As a measure of quality, we provide statistics on the component-wise (e.g for v_x and v_y) mean, the standard deviation and the root mean square error (RMSE) of the residuals (defined here as C3S IV product minus validation dataset). Residuals larger than 1 m/d are excluded from the statistics as these contaminate the statistics and are in general obvious outliers that are easily filtered out in the IV products. Below follows specific information for each validation dataset.

1) Using GPS data (Greenland only): We use the monthly average positions provided in version 3 of the PROMISE AWS data set to calculate the local monthly averaged velocity magnitude, which is assigned to the station position. The most obvious outliers are removed manually. The monthly values are used to calculate yearly averages for Oct-Sept corresponding to the respective ice velocity map. For each monthly position, the corresponding pixel value in the Sentinel-1 IV map is selected and finally also averaged over a year. In this way there is one corresponding value for each station representing the annual mean IV. These are used to calculate the validation statistics and to create scatter plots for visualisation.

2) Inter-comparison with MEaSUREs TerraSAR-X-based data (Greenland only): As a pre-processing step, the IV maps are first converted from m/y (metres per year) to m/d (metres per day) and bilinear resampled to the same grid spacing. The separate v_x and v_y velocity files are then merged into a 2-band GeoTIFF to match the native Sentinel-1 IV maps geometry and format. Glacier velocity can fluctuate significantly over short time periods, particularly on the downstream sections of large outlet glaciers. Therefore, we only compare datasets derived from single Sentinel-1 repeat pass pairs (6/12 days only) and acquired with a maximum of two days between the respective start dates. We intercompare both v_x and v_y components separately on a pixel-by-pixel basis and provide validation statistics for these.
3) Inter-comparison with ice sheet wide velocity maps (Greenland and Antarctica): Pre-processing is the same as described above. For the intercomparison, the products are resampled to the same grid spacing and grid extent as the C3S product. The inter-comparison is performed on both the v_x and v_y components separately on a pixel-by-pixel basis. Validation statistics are provided for both.
4) Stable terrain test: The results for the ice covered (moving) area are separated from ice-free (stable) terrain. The masking is done using a polygon of the rock outlines derived from the icesheet/land/ocean boundaries. Validation statistics are provided for both velocity components.

4. Summary of most recent validation results

In this section, we provide a short summary of the CDR v1.4 validation results (Greenland only). The full CDR v1.5 validation will be published in the Product Quality Assessment Report [RD.1].

Four different tests were performed for product quality assurance:

1. Intercomparison of Sentinel-1 derived velocity with in-situ GPS measurements.
2. Intercomparison of individual 6-12-day repeat Sentinel-1 derived ice velocity maps, used to compile the annually averaged C3S ice velocity map, with contemporaneously acquired MEaSUREs TSX derived ice velocity maps for selected major Greenland outlet glaciers (Figure 2.2).
3. Intercomparison of annually averaged Greenland Ice Sheet ice velocity with MEaSUREs Greenland-wide IV maps.
4. Stable terrain test, providing insight on the performance of the ice velocity retrieval algorithm, by analysing the results in stable terrain.

Table 4.1 provides a statistical overview of all inter-comparison results. For the annual maps the GPS intercomparison show excellent agreement with mean differences ranging between 0.00 m/d and 0.02 m/d with an RMSE ranging between 0.02 m/d and 0.03 m/d. The intercomparison of ice velocity on selected outlet glaciers also shows a high level of agreement for the CDR products, with a negligible overall mean bias ranging between 0.00 m/d and 0.01 m/d (RMSE 0.18-0.22 m/d). For the ice sheet wide product intercomparison the results indicate a negligible bias with an RMSE of 0.02-0-03 m/d. Based on 5.1 million pixels the outcome of the stable ground test indicates for all annual maps on average a negligible mean bias with an RMSE of 0.01-0-02 m/d for both easting and northing components. 

Table 4.1: Summary of inter-comparison results (values in m/day; d = mean bias, RMSE = root mean square error, Mag = velocity magnitude, E= easting velocity, N= northing velocity).

References

Burton-Johnson, A., Black, M., Fretwell, P. T., and Kaluza-Gilbert, J.: An automated methodology for differentiating rock from snow, clouds and sea in Antarctica from Landsat 8 imagery: a new rock outcrop map and area estimation for the entire Antarctic continent, The Cryosphere, 10, 1665-1677, https://doi.org/10.5194/tc-10-1665-2016, 2016

Fausto, R.S. and van As, D., (2019). Programme for monitoring of the Greenland ice sheet (PROMICE): Automatic weather station data. Version: v03, Dataset published via Geological Survey of Denmark and Greenland. DOI: https://doi.org/10.22008/promice/data/aws (Resource validated 5^th July 2023).

Howat, I. M., Porter, C., Smith, B. E., Noh, M.-J., and Morin, P.: The Reference Elevation Model of Antarctica, The Cryosphere, 13, 665-674, https://doi.org/10.5194/tc-13-665-2019, 2019.

Joughin, I. (2002). Ice-Sheet Velocity Mapping: A Combined Interferometric and Speckle-Tracking Approach. Annals of Glaciology 34: 195-201. doi: 10.3189/172756402781817978. 

Joughin, I., Howat, I., Smith, B. and Scambos, T. (2021): MEaSUREs Greenland Ice Velocity: Selected Glacier Site Velocity Maps from InSAR, Version 4. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/GQZQY2M5507Z.

Joughin, I. (2021): MEaSUREs Greenland Annual Ice Sheet Velocity Mosaics from SAR and Landsat, Version 3. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/C2GFA20CXUI4 (resource validated 5^th July 2023).

Mouginot, J., B. Scheuchl, and E. Rignot. 2017, updated 2017b. MEaSUREs Annual Antarctic Ice Velocity Maps, Version 1. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/9T4EPQXTJYW9.

Rastner, P., Bolch, T., Mölg, N., Machguth, H., Le Bris, R., Paul, F. (2012), updated 2018, The first complete inventory of the local glaciers and ice caps on Greenland. The Cryosphere, 6, 1483-1495. (doi:10.5194/tc-6-1483-2012) 

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. 

Acknowledgments of data contributors for IV validation

Data from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and the Greenland Analogue Project (GAP) were provided by the Geological Survey of Denmark and Greenland (GEUS) at http://www.promice.dk. Data from the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program were provided by the National Snow and Ice Data Center (NSIDC) at https://nsidc.org/data/measures Both references validated 5^th July 2023.

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