Contributors : Lin Gilbert (University of Leeds), Sebastian B. Simonsen (Technical University of Denmark), Jan Wuite (ENVEO IT GmbH)
Issued by: University of Leeds / Lin Gilbert
Issued Date: 27/04/2021
Ref: C3S_312b_Lot4.D2.IS.1_v3.0_IV_Product_Quality_Assurance_Document_i1.0
Official reference number service contract: 2018/C3S_312b_Lot4_EODC/SC2
History of modifications
List of datasets covered by this document
Related documents
Acronyms
Scope of the document
This document is the Product Quality Assurance Document for Ice Velocity (IV) as part of the Copernicus Ice Sheets and Ice Shelves service. It describes the validated datasets and the methods used for validation.
Executive summary
The service addresses three essential climate variables (ECVs) by providing four separate products.
- Ice velocity is given for Greenland in product D3.IS.4
- Gravimetric mass balance is given for Greenland and Antarctica in product D3.IS.5
- Surface elevation change is given for
- Antarctica in product D3.IS.6.1
- Greenland in product D3.IS.6.2
Here we provide the validation methods and results from the CDR v3 for Ice Velocity.
1. Validated products
1.1. Greenland ice sheet velocity – D3.IS.4
The ice velocity (IV) product assessment referred to in this document concerns the annually-averaged IV maps of Greenland derived from Sentinel-1 SAR data acquired from 2017-10-01 to 2018-09-30, 2018-10-01 to 2019-09-30 (reprocessed from CDR v2) and 2019-10-01 to 2020-09-30 (combined in CDR v3) (Figure 1). surface velocity is derived applying advanced iterative offset tracking techniques. The ice velocity maps are annually averaged and provided at 250m grid spacing (previously 500 m in CDR v2) in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity is provided in true meters per day, towards easting (vx) and northing (vy) direction of the grid, and the vertical displacement (vz), is derived from a digital elevation model (TanDEM-X 90m DEM; Rizzoli et al., 2017). The product is provided as a NetCDF file with the velocity components: vx, vy, vz and vv (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 [D2]. 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 on which the annual maps are based. The individual maps are not provided as a separate product in C3S but are included here for validation as an extra quality assurance.
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Figure 1: C3S ice velocity maps of the Greenland Ice Sheet based on Sentinel-1 data, 2017/18 (left), 2018/19 (middle), 2019/20 (right).
2. Description of validating datasets
The quality assessment for ice velocity includes detailed validation with contemporaneous in-situ GPS data at various sites across the ice sheet (Figure 2). The GPS instruments are attached to Automatic Weather Stations (AWS) operated by GEUS in collaboration with DTU Space and Asiaq 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 Portal (https://promice.org/
). The version 3 data set provides hourly, daily and monthly average positions and is here updated until July 2020.
Figure 2: Locations of the PROMICE AWS stations equipped with GPS and used for ground validation.
The 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, in particular 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 NASA 'Making Earth System Data Records for Use in Research Environments' (MEaSUREs) program.
The TerraSAR-X-based data set consists of a collection IV maps covering the Greenland margins (including most of the major outlet glaciers) over the time-period from June 2008 to Jan 2020 , Figure 3 left and middle). The ice velocity is retrieved from repeat pass TerraSAR-X images (1 to 3 cycles) applying a combination of conventional InSAR and speckle tracking techniques (Joughin, 2002). Here we use release V3 of the data set, available through the NSIDC data portal (data set ID: NSIDC-0481; Joughin et al., 2020). Data files are delivered in GeoTIFF format at 100m grid spacing in North Polar Stereographic projection (EPSG: 3413). Separate files are provided for the x and y velocity components along with corresponding (pseudo-)error estimates for both velocity components. The data set is occasionally updated, the latest available data is included for the assessment (here updated until January 2020).
Figure 3: 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 (middle). Right: shapefile showing ice-free terrain in Greenland and used for the stable terrain test.
The Greenland-wide ice velocity maps used for product intercomparison are based on Sentinel-1, TerraSAR-X/TanDEM-X and Landsat-8 images. The maps are annually averaged from 1 December to 30 November and are provided at 200 m horizontal resolution. We have used the 2017/18 and 2018/19 maps, which are largely overlapping with the 2017/18 and 2018/19 C3S product (CDR v3), spatially as well as temporally. We use version 2 of the Greenland-wide data set, available through the NSIDC data portal (data set ID: NSIDC-0725; Joughin, 2020). The data set is occasionally updated, once the 2019-2020 map becomes available it will be included in the assessment.
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/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 3 (right).
3. Description of product validation methodology
1) Using GPS data: 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 mostly 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 the scatter plots.
2) Inter-comparison with MEaSUREs TerraSAR-X-based data (data set ID: NSIDC-0481, Joughin et al., 2020): As a pre-processing step, the IV maps are first converted from m/y to m/d, resampled to a 250 m grid spacing and the separate vx and vy velocity files are merged into a 2-bands GeoTIFF to match the native Sentinel-1 IV maps geometry and format. As glacier velocity can fluctuate significantly over short time periods, in particular on the downstream sections of large outlet glaciers, we only compare datasets derived from single Sentinel-1 SAR repeat pass pairs (6/12 days only) and with a maximum time difference of 2 days between the respective start dates (master). We intercompare both vx and vy components separately on a pixel-by-pixel basis.
3) Inter-comparison with MEaSUREs Greenland Ice Sheet Velocity Map (data set ID: NSIDC-0725, Joughin, 2020): Pre-processing is similar as described above. For the intercomparison the 200m product is resampled to 250m with the same grid extent as the C3S product. The inter-comparison is performed on both the vx and vy components separately on a pixel-by-pixel basis.
4) Stable terrain test: The results for the ice covered (moving) area are separated from ice-free (stable) ground. The masking is done using a polygon of the rock outlines.
As a measure of quality, we provide, statistics on the component-wise (e.g for vx and vy) mean, the standard deviation and the root mean square error (RMSE) of the residuals (defined here as Sentinel-1 IV minus MEaSUREs IV for the IV intercomparisons). 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.
4. Summary of validation results
Four different tests were performed for product quality assurance:
1) Intercomparison of Sentinel-1 derived velocity with in-situ GPS measurements for 2017/18, 2018/19 and 2019/20.
2) Intercomparison of individual 6-12-day repeat Sentinel-1 derived ice velocity maps, used to produce the annually averaged C3S ice velocity map, with contemporaneous MEaSUREs TSX derived ice velocity maps for selected major Greenland outlet glaciers.
3) Intercomparison of annually averaged Greenland Ice Sheet ice velocity map for 2017/18 and 2018/19 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 1 provides a statistical overview of all inter-comparison results for CDR v3. For the annual maps the GPS intercomparison show excellent agreement with mean differences of only 1-2 cm/d and an RMSE of 2-3 cm/d. The intercomparison of ice velocity on selected outlet glaciers also show a high level of agreement for the CDR v3 products, with a negligible overall mean bias and an RMSE of 18 cm/d for the easting and 21 cm/d for the northing component based on sample sizes of 11.5 million, 10.4 million and 3.5 million for the 2017/18, 2018/19 and 2019/20 maps respectively. For the ice sheet wide product intercomparison between MEaSUREs and C3S (only available for the 2017/18 and 2018/19 maps) the results indicate a negligible bias with an RMSE of 2-3 cm/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 1-2 cm/d for both easting and northing components.
Table 1: Summary of the inter-comparison results for CDR v3. dmag, de and dn show the mean differences for the velocity magnitude, easting and northing, respectively. Similarly, RMSEmag, RMSEe and RMSEn show the root mean square error for velocity magnitude, easting and northing, respectively. Values are provided in m/d.
Product | Reference/Test | Pixels | dMag | RMSEMag | dE | RMSEE | dN | RMSEN |
2017/18 | GPS | 17 | 0.00 | 0.02 | - | - | - | - |
MEaSUREs TSX/TDX | 11.5 M | - | - | 0.00 | 0.18 | 0.00 | 0.21 | |
MEaSUREs (ice sheet) | 33.6 M | - | - | 0.00 | 0.02 | 0.00 | 0.03 | |
Stable Terrain | 5.1 M | - | - | 0.00 | 0.02 | 0.00 | 0.01 | |
2018/19 | GPS | 16 | 0.01 | 0.02 | - | - | - | - |
MEaSUREs TSX /TDX | 10.4 M | - | - | 0.00 | 0.18 | 0.00 | 0.22 | |
MEaSUREs (ice sheet) | 33.6 M | - | - | 0.00 | 0.03 | 0.00 | 0.03 | |
Stable Terrain | 5.1 M | - | - | 0.00 | 0.02 | 0.00 | 0.01 | |
2019/20 | GPS | 13 | 0.02 | 0.03 | - | - | - | - |
MEaSUREs TSX/TDX | 3.5 M | - | - | 0.00 | 0.17 | 0.00 | 0.20 | |
Stable Terrain | 5.1 M | - | - | 0.00 | 0.02 | 0.00 | 0.01 |
For further details please see related document, the Product Quality Assessment Report (PQAR) [D1].
References
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|https://doi.org/10.22008/promice/data/aws] \[Date Accessed: Jan 2021\].
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., I. Howat, B. Smith, and T. Scambos. 2020. MEaSUREs Greenland Ice Velocity: Selected Glacier Site Velocity Maps from InSAR, Version 3. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: [https://doi.org/10.5067/YXMJRME5OUNC|https://doi.org/10.5067/YXMJRME5OUNC]. \[Date Accessed: Jan 2021\]
Joughin, I. 2020. MEaSUREs Greenland Annual Ice Sheet Velocity Mosaics from SAR and Landsat, Version 2. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: [https://doi.org/10.5067/TZZDYD94IMJB|https://doi.org/10.5067/TZZDYD94IMJB]. [Date Accessed: Jan 2021\].
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.
