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

Issued by: ENVEO/ Jan Wuite

Date: 06/03/2024

Ref: C3S2_312a_Lot4.WP2-FDDP-IS-v2_202312_IV_PQAR-v5_i1.0

Official reference number service contract: 2021/C3S_312a_Lot4_EODC/SC1

Table of Contents

History of modifications

Version

Date

Description of modification

Chapters / Sections

i0.1

 

Document update for new product release, including Antarctic Ice Velocity

All

i1.0

 

Internal review and document finalization

All

List of datasets covered by this document

Deliverable ID

Product title

Product type (CDR, ICDR)

C3S version number

Public version number

Delivery date

D3.IS.4-v3.0

Greenland Ice Sheet velocity

CDR

3.0

1.3

 

WP2-FDDP-IV-CDR-v4

Greenland Ice Sheet velocity

CDR

4.0

1.4

 

WP2-FDDP-IV-CDR-v5

Greenland Ice Sheet velocity

CDR

5.0

1.5

 

Related documents

Reference ID

Document

RD1

Wuite, J. and T. Nagler. (2024) C3S Ice Velocity Version 1.5: Algorithm Theoretical Basis Document. Document ref. C3S2_312a_Lot4.WP2-FDDP-IS-v2_202312_IV_ATBD-v5_i1.1

RD2

Wuite, J. and T. Nagler (2023) C3S Ice Velocity Version 1.5: Product Quality Assurance Document. Document ref. C3S2_312a_Lot4.WP1-PDDP-IS-v2_202306_IV_PQAD_i1.1

RD3

Wuite, J., and Simonsen, S.B. (2023) Target Requirements and Gap Analysis Document. Document ref. C3S2_312a_Lot4.WP3-TRGAD-IS-v2_202304_IS_i1.1

Acronyms

Acronym

Definition

AIS

Antarctic Ice Sheet

ALOS

Advanced Land Observation Satellite

ASAR

Advanced Synthetic Aperture Radar

ATBD

Algorithm Theoretical Basis Document

C3S

Copernicus Climate Change Service

CSA

Canadian Space Agency

CCI

Climate Change Initiative

CDR

Climate Data Record

DTU

Technical University of Denmark

DLR

Deutsches Zentrum für Luft- und Raumfahrt

ECV

Essential Climate Variable

ENVISAT

Environmental Satellite

ESA

European Space Agency

GCOS

Global Climate Observing System

GEUS

Geological Survey of Denmark and Greenland

GrIS

Greenland Ice Sheet

GPS

Global Positioning System

IV

Ice Velocity

JAXA

Japan Aerospace Exploration Agency

MEaSUREs

Making Earth System Data Records for Use in Research Environments

NASA

National Aeronautics and Space Administration

NSIDC

National Snow and Ice Data Center

OLI

Operational Land Imager

PALSAR

Phased Array type L-band Synthetic Aperture Radar

PQAD

Product Quality Assurance Document

PQAR

Product Quality Assessment Report

PROMICE

Danish Programme for Monitoring of the Greenland Ice Sheet

PVIR

Product Validation and Intercomparison Report

QA

Quality Assurance

RMSE

Root Mean Square Error

S1

Sentinel-1

SAR

Synthetic Aperture Radar

TRGAD

Target Requirements and Gap Analysis Document

TSX/TDX

TerraSAR-X/TanDEM-X

URD

User Requirements Document

USGS

U.S. Geological Survey

General definitions

Breakthrough (Global Climate Observing System (GCOS) requirement): An intermediate level between threshold and goal which, if achieved, would result in a significant improvement for the targeted application. The breakthrough value may also indicate the level at which specified uses within climate monitoring become possible. It may be appropriate to have different breakthrough values for different uses.

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

Goal (GCOS requirement): An ideal requirement above which further improvements are not necessary.

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.

Threshold (GCOS requirement): The minimum requirement to be met to ensure that data are useful.

Uncertainty: An estimate of the error in a measurement, due to limitations in the measuring instrument or statistical fluctuations in the quantity being measured.

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

Scope of the document

This document is the Product Quality Assessment Report (PQAR) for Ice Velocity (IV) as part of the Copernicus Ice Sheets and Ice Shelves service. It presents results of the quality assessment for the provided datasets and a discussion of how well Global Climate Observing System (GCOS) and user requirements have been met.
The service addresses three Essential Climate Variables (ECVs) by providing the following products:

  • Ice velocity is given for Greenland and Antarctica in product WP2-FDDP-IV-CDR
  • Gravimetric mass balance is given for Greenland and Antarctica in product WP2-FDDP-GMB-CDR
  • Surface elevation change is given for Greenland in product WP2-FDDP-SEC-CDR-GrIS

The products are hosted on the Copernicus Climate Data Store1.

Executive summary

In this document, we provide the validation results for the Greenland Ice Sheet and Antarctic Ice Sheet velocity datasets derived from Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data. The assessment concerns the annually averaged ice velocity (IV) maps of Greenland, updated with the CDR v5 velocity map for Greenland, covering the period 2021-10-01 to 2022-09-30, and from CDR v5 onwards also including Antarctica, covering the period 2021-04-01 to 2022-03-31. For CDR v5 we use the latest up-to-date reference validation data sets available from external sources.

The quality assessment for IV includes various procedures (see Chapter 1) including detailed validation with contemporaneous in-situ Global Positioning System (GPS) data (Greenland), pixel-by-pixel intercomparisons against external velocity products and checking of the performance of the algorithm in stable (not-moving) terrain. The validation results for each of these quality assessment procedures are described in detail in Chapter 2. The IV product and quality assessment comply with the 2022 GCOS requirements for Ice Velocity for measurement uncertainty and spatial/temporal resolution (see Chapter 4).

1. Product validation methodology

The quality assessment for IV includes detailed validation with contemporaneous in-situ Global Positioning System (GPS) data at various sites across the Greenland Ice Sheet (Figure 1) that are acquired by the Danish Programme for Monitoring of the Greenland Ice Sheet (PROMICE; How et al., 2022, Fausto at al., 2021).

Figure 1: Locations of PROMICE GPS stations in Greenland used for validation.

The products are also evaluated, on a pixel-by-pixel basis, against publicly available products covering both Greenland and Antarctica that are produced as part of the National Aeronautics and Space Administration (NASA) ‘Making Earth System Data Records for Use in Research Environments’ (MEaSUREs) program and derived from multi-sensor SAR data and optical imagery (Joughin et al., 2021, Joughin, 2023, Mouginot et al., 2017). These intercomparisons provide a good level of quality assurance, in particular in areas where little change is to be expected. For the product intercomparison both annually averaged maps as well as individual (6/12-day repeat) ice velocity maps, on which the annual maps are based, are considered. The 6/12-day repeat maps are not provided as products in Copernicus Climate Change Service (C3S) but the intercomparison is included here as an extra quality assurance.

Additionally, the performance of the algorithm in stable terrain is assessed, i.e. where no velocity is expected. This provides a good overall indication for the bias introduced by the end-to-end velocity retrieval including co-registration of images, template matching, geocoding etc.

Further details on the validation data sets and methodology can be found in the Product Quality Assurance Document (PQAD [RD2]).

2. Validation results

2.1. Greenland

Figure 2 shows the results of the intercomparison between Sentinel-1 derived velocity and annually averaged in-situ GPS measurements for 2017-2018, 2018-2019, 2019-2020, 2020-2021 and 2021-2022 (How et al., 2022). The scatterplots show a very good agreement between the GPS and Sentinel-1 velocity. For 2017-2018, 17 stations could be used, the mean difference between the datasets is <0.01 m/d (Root Mean Square Error - RMSE 0.02 m/d); for 2018-2019, 16 stations could be used with a mean difference of ~0.01 m/d (RMSE 0.02 m/d); for 2019-2020, 13 stations could be used with a mean difference of ~0.02 m/d (RMSE 0.03 m/d); for 2020-2021, 22 stations could be used with a mean difference of ~0.01 m/d (RMSE 0.02 m/d), and for 2021-2022, 32 stations could be used with a mean difference of ~0.01 m/d (RMSE 0.02 m/d). Differences between the ice velocity maps and the GPS data can partly be attributed to uncertainties inherent to both methods including for example differences in spatial sampling: GPS provides a point measurement, while feature tracking averages an area of which the size is based on the window size used for image correlation.

a)

b)

c)

d) 

e)


Figure 2: Scatter plots showing annually averaged GPS versus Sentinel-1 ice velocity for a) 2017-2018, b) 2018-2019, c) 2019-2020, d) 2020-2021 and e) 2021-2022.

Figure 3 shows the intercomparison results of the S1 derived ice sheet wide velocity maps from C3S and MEaSUREs (Joughin, 2023). Based on a sample size greater than 33 million pixels, the overall mean bias between the data sets is generally ≤0.002 m/d with an RMSE varying between 0.019-0.028 m/d for both easting (vx) and northing (vy) components. Differences between the datasets are caused by, among others, different resolution of the SAR data (TerraSAR-X: TSX vs. Sentinel-1), slightly different temporal range, different algorithm settings used for IV retrieval (e.g. matching window, correlation threshold), differences in post processing (e.g. outlier removal, gap filling), different land/ocean and lay-over masks or short term velocity fluctuations. In general, higher resolution satellite data captures velocity better, in particular in shear zones, where the velocity gradient is high. The drawback is that often much smaller regions are covered.

a)

b)

c)

d)

e)

f)

g)

h)

i)

j)

Figure 3: Histogram of easting (left, a, c, e, g, i) and northing (right, b, d, f, h, j) velocity residuals of the intercomparison between the MEaSUREs and the Greenland Ice Sheet velocity maps for 2017-2018 (a, b) 2018-2019 (c, d), 2019-2020 (e, f), 2020-2021 (g, h) and 2021-2022 (i, j).

Figure 4 shows the results of the intercomparison of the ice velocity maps, based on 6/12-day repeat-pass Sentinel-1 data, with ice velocity maps derived from TSX/TanDEM-X: TDX and covering major Greenland outlet glaciers (Joughin et al., 2021). Depicted are histograms of the residuals for the easting and northing components. Excluding the TSX/TDX derived IV maps that do not fall within the desired temporal range (max time difference 2 days in comparison to S1 IV maps) leaves a total number of respectively 277 (2017-2018), 286 (2018-2019), 333 (2019-2020), 321 (2020-2021) and 281 (2021-2022), usable TSX/TDX IV maps for the inter-comparison. For each map of TerraSAR-X derived IV, multiple intercomparisons are possible as an area can be overlapped by several S1 tracks within the 2-day time range. In total 977 (2017-2018), 1084 (2018-2019), 1353 (2019-2020), 1429 (2020-2021) and 415 (2021-2022) S1 maps fulfil this 2-day criterium and have geographic overlap with the TSX/TDX data. For these maps the residuals and their statistics are calculated. Based on a sample size of, combined, more than 11.5, 10.4, 13.2, 12.8 and 3.8 million pixels (for 2017-2018, 2018-2019, 2019-2020, 2020-2021 and 2021-2022 respectively), the overall mean bias between the data sets is well below 0.01 m/d (RMSE <0.2 m/d) for both easting and northing components, demonstrating the good agreement between the datasets (Table 1).

a)

b)

c)

d)

e)

f)

g)

h)

i)

j)

Figure 4: Histogram of easting (left, a, c, e, g, i) and northing (right, b, d, f, h, j) residuals of the intercomparison with MEaSUREs TerraSAR-X derived IV maps (selected outlet glaciers) acquired within 2 days of Sentinel-1 derived IV maps for 2017-2018 (a, b), 2018-2019 (c, d), 2019-2020 (e, f), 2020-2021 (g, h) and 2021-2022 (i, j).

Finally, Figure 5 shows the results of the stable terrain test. Depicted are histograms of easting and northing velocity on stable terrain for all annual maps. Based on more than 5 million pixels (per map), the outcome of the stable terrain test indicates for all periods mean velocities of <0.001 m/d with an RMSE varying between 0.008 m/d and 0.016 m/d for both easting and northing velocity components.

a)

b)

c)

d)

e)

f)

g)

h)

i)

j)

Figure 5:  Histogram of easting (left, a, c, e, g, i) and northing (right, b, d, f, h, j) velocity in stable terrain for 2017-2018 (a, b), 2018-2019 (c, d), 2019-2020 (e, f), 2020-2021 (g, h) and 2021-2022 (i, j).


Table 1 provides a summary statistical overview of the intercomparison results for all Greenland IV maps. For further details, please see the related Product Quality Assurance Document (PQAD [RD2]).

Table 1: Summary of inter-comparison results for Greenland (values in m/day; d = mean bias, RMSE = root mean square error, Mag = velocity magnitude, E= easting velocity, N= northing velocity).

Product

Reference/Test

Pixels

dMag

RMSEMag

dE

RMSEE

dN

RMSEN

CDR v3 2017/18

In-situ GPS

17

0.00

0.02

-

-

-

-


MEaSUREs TSX (outlet glaciers)

11.5 M

-

-

0.00

0.18

0.00

0.21


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

CDR v3 2018/19

In-situ GPS

16

0.01

0.02

-

-

-

-


MEaSUREs TSX (outlet glaciers)

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

CDR v3 2019/20

In-situ GPS

13

0.02

0.03

-

-

-

-


MEaSUREs TSX (outlet glaciers)

13.2 M

-

-

0.01

0.16

0.00

0.19


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

CDR v4 2020/21

In-situ GPS

22

0.01

0.02

-

-

-

-


MEaSUREs TSX (outlet glaciers)

12.9 M

-

-

0.01

0.17

-0.01

0.20


MEaSUREs (ice sheet)

33.6 M

-

-

0.00

0.02

0.00

0.02


Stable Terrain

5.1 M

-

-

0.00

0.01

0.00

0.01

CDR v5 2021/22

In-situ GPS

32

0.01

0.02

-

-

-

-


MEaSUREs TSX (outlet glaciers)

3.8 M

-

-

0.01

0.17

0.00

0.18


MEaSUREs (ice sheet)

33.6 M

-

-

0.00

0.02

0.00

0.02


Stable Terrain

5.1 M

-

-

0.00

0.01

0.00

0.01

2.2. Antarctica

Due to sparsity of in-situ GPS data acquired in Antarctica the validation and Quality Assurance (QA) activities are restricted to intercomparisons with existing ice velocity maps for close or overlapping periods and the stable terrain test (assessment of ice velocity in stable terrain with no velocity) based on a published map of rock outcrops in Antarctica.

The external ice velocity maps are provided as part of the NASA MEaSUREs Program and include annual Antarctic wide maps that were derived from multi-sensor SAR data and optical imagery acquired between 2005 and 2020 (Mouginot et al., 2017a,b). 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). Figure 6 shows the intercomparison results of the S1 derived Antarctic ice velocity maps from C3S and MEaSUREs. Based on a sample size of ~133 million pixels, the overall mean bias between the data sets is generally ≤0.002 m/d with an RMSE 0.045 m/d for the easting and 0.041 m/d for the northing component. Differences between the datasets can in part be caused by the differences in the temporal coverage, algorithm settings used for IV retrieval (e.g. matching window, correlation threshold), post processing (e.g. outlier removal, gap filling) and land/ocean and lay-over masks.

a)

b)

Figure 6: Histogram of (a) easting and (b) northing velocity residuals of the pixel-wise intercomparison between the MEaSUREs and the Antarctic Ice Sheet velocity map for 2021-2022.


For the stable terrain test in Antarctica we use a rock outline shapefile derived from Landsat-8 Operational Land Imager (OLI) images. The dataset is generated using an automated methodology for snow and rock differentiation using the images 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 7 shows the results of the stable terrain test. Depicted are histograms of the easting and northing velocity on stable terrain as well as a scatter plot. Based on more than 400,000 pixels, the outcome of the stable terrain test indicates a mean velocity of 0.001 m/d with an RMSE of 0.015 m/d and 0.013 m/d for the easting and northing velocity components, respectively.

a)

b)

c)

Figure 7: Histogram of (a) easting and (b) northing velocity and (c) scatter plot of easting and northing velocity (color blue to red indicative of data density) in stable terrain for 2021-2022.

3. Application(s) specific assessments

Not applicable.

4. Compliance with user requirements

The 2022 GCOS requirements for measurement uncertainty and spatial/temporal resolution of ice velocity are listed in Table 2. For the criteria in the GCOS Implementation Plan a goal, breakthrough and threshold value are defined as follows:

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

The GCOS requirements are based on the User Requirements Document (URD) of the Ice Sheets CCI project (Hvidberg, et al, 2012), identified through an extensive user survey within the glaciology community. They list a minimum threshold measurement uncertainty of 100 m/y (0.27 m/d) with a breakthrough/goal accuracy of 10-30 m/y (0.03-0.08 m/d). The results of our quality assessments, all showing cell-averaged mean differences of less than 0.03 m/d, fall well within this optimum range. The C3S annual IV products also comply with the threshold requirements for temporal resolution (12 months) as well as for spatial resolution (1000 m). For further details related to the requirements, please see the Target Requirements and Gap Analysis Document (TRGAD [RD3]).

Table 2: GCOS target requirements for ice sheet velocity (source: GCOS, 2022: The 2022 ECVs Requirements; https://gcos.wmo.int/en/publications/gcos-implementation-plan2022). G: Goal, B: Breakthrough, T: Threshold.

Item needed

Unit

Metric

G/B/T

Value

Horizontal Resolution

m

 Grid cell size


G

50

B

100

T

1000

Temporal Resolution

month


Time

G

1

B


T

12

Required Measurement

 Uncertainty

m y-1


G

10

B

30

T

100

Standards and References

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.


References

Burton-Johnson, A., Black, M., Fretwell, P. T., and Kaluza-Gilbert, J. (2016): 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 (last viewed 12th January 2024).

How, P., Abermann, J., Ahlstrøm, A.P., Andersen, S.B., Box, J.E., Citterio, M., Colgan, W.T., Fausto, R., Karlsson, N.B., Jakobsen, J., Langley, K., Larsen, S.H., Mankoff, K.D., Pedersen, A.Ø., Rutishauser, A., Shields, C.L., Solgaard, A.M., van As, D., Vandecrux, B., Wright, P.J., 2022, "PROMICE and GC-Net automated weather station data in Greenland", https://doi.org/10.22008/FK2/IW73UU (last viewed 12th January 2024), GEUS Dataverse

Fausto, R. S., van As, D., Mankoff, K. D., Vandecrux, B., Citterio, M., Ahlstrøm, A. P., Andersen, S. B., Colgan, W., Karlsson,N. B., Kjeldsen, K. K., Korsgaard, N. J., Larsen, S. H., Nielsen, S., Pedersen, A. Ø., Shields, C. L., Solgaard, A. M., and Box, J. E.: Programme for Monitoring of the Greenland Ice Sheet (PROMICE) automatic weather station data, Earth Syst. Sci. Data, 13,  3819–3845, https://doi.org/10.5194/essd-13-3819-2021 (last viewed 12th January 2024), 2021.

GCOS, 2022. The 2022 GCOS Implementation Plan. Geneva: World Meteorological Organization, 85. https://gcos.wmo.int/en/publications/gcos-implementation-plan2022 (last viewed 12th January 2024)

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/ (last viewed 12th January 2024)

Joughin, I., I. Howat, B. Smith, and T. Scambos. (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. https://doi.org/10.5067/GQZQY2M5507Z. (last viewed 12th January 2024).

Joughin, I. (2023). MEaSUREs Greenland Annual Ice Sheet Velocity Mosaics from SAR and Landsat, Version 5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/USBL3Z8KF9C3. (last viewed 12th January 2024).

Mouginot, J., B. Scheuchl, and E. Rignot. (2017a). MEaSUREs Annual Antarctic Ice Velocity Maps, Version 1. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/9T4EPQXTJYW9. (last viewed 12th January 2024).

Mouginot, J., E. Rignot, B. Scheuchl, and R. Millan. (2017b). Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data. Remote Sensing. 9. DOI: 10.3390/rs9040364. (last viewed 12th January 2024)

Acknowledgement

Data from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) are provided by the Geological Survey of Denmark and Greenland (GEUS) at http://www.promice.dk


This document has been produced in the context of the Copernicus Climate Change Service (C3S).

The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose.

The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view.

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