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 Package 
Data Set ID:
NSIDC-0720

MEaSUREs Annual Antarctic Ice Velocity Maps 2005-2017, Version 1

This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides 12 annual maps of Antarctic ice velocity from 2005-2017. The maps are assembled using SAR data from the Japanese Space Agency's (JAXA) ALOS PALSAR, the European Space Agency's (ESA) ENVISAT ASAR and Copernicus Sentinel-1, the Canadian Space Agency's (CSA) RADARSAT-1, RADARSAT-2, the German Aerospace Agency's (DLR) TerraSAR-X (TSX) and TanDEM –X (TDX), and the U.S. Geological Survey's (USGS) Landsat-8 optical imagery acquired between 2005 and 2017.

See Antarctic Ice Sheet Velocity and Mapping Data for related data.

Geographic Coverage

Parameter(s):
  • Glaciers/Ice Sheets > Ice Sheets > Ice Velocity
Spatial Coverage:

  • N: -60, S: -90, E: 180, W: -180
Spatial Resolution:
  • 1 km x 1 km
Temporal Coverage:
  • 1 July 2005 to 30 June 2017
(updated 2017)
Temporal Resolution: 1 year
Data Format(s):
  • NetCDF
Platform(s) ALOS, ENVISAT, LANDSAT-8, RADARSAT-1, RADARSAT-2, SENTINEL-1A, SENTINEL-1B, TDX, TSX
Sensor(s): ASAR, C-SAR, OLI, PALSAR, SAR
Version: V1
Data Contributor(s): Jeremie Mouginot, Bernd Scheuchl, Eric Rignot
Metadata XML: View Metadata Record

Data Citation

As a condition of using these data, you must cite the use of this data set using the following citation. For more information, see our Use and Copyright Web page.

Mouginot, J., B. Scheuchl, and E. Rignot. 2017, updated 2017. MEaSUREs Annual Antarctic Ice Velocity Maps 2005-2017, Version 1. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/9T4EPQXTJYW9. [Date Accessed].

Literature Citation

As a condition of using these data, we request that you acknowledge the author(s) of this data set by referencing the following peer-reviewed publication.

  • Mouginot, J., E. Rignot, B. Scheuchl, and R. Millan. 2017. Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data, Remote Sensing. 9. Art. #364. https://doi.org/10.3390/rs9040364

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Detailed Data Description

Format

This data set is provided in Network Common Data (NetCDF4) (.nc) format using CF-1.6 conventions. For more information about working with NetCDF formatted data, visit the UCAR Unidata Network Common Data Form Web site.

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File and Directory Structure

Data are available via HTTPS in the https://n5eil01u.ecs.nsidc.org/MEASURES/NSIDC-0720.001/ directory. This directory contains 12 folders, 1 for each year. Each folder contains 1 yearly data file.

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File Naming Convention

This section describes the naming convention for this product with an example. Refer to Table 1 for descriptions of the values in the file naming convention.

Example File Name:
Antarctica_ice_velocity_2015_2016_1km_v01.nc

Naming Convention:
Antarctica_ice_velocity_YYYY_YYYY_1km_v01.nc

Variable Definition
Table 1. File Naming Convention
Antarctica_ Geographical Location
ice_velocity_ Geophysical parameter
YYYY_YYYY_ Year(s) of data acquisitions (July 1 to June 30)
1km_ Spatial sampling
vxx Version 1
.nc File type: NetCDF4
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File Size

Each file is 1.38 GB.

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Volume

The total volume of the data set is 6.2 GB.

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Spatial Coverage

This data set spans the continent of Antarctica.

Southernmost Latitude: 90° S 
Northernmost Latitude: 60° S 
Westernmost Longitude: 180° W 
Easternmost Longitude: 180° E

Spatial Coverage Maps

The spatial coverage for the individual ice velocity maps is presented in Figure 1.  For each year, ice velocities were recorded where data was collected; therefore, coverage varies each year.

Figure 1. Annual ice velocity maps for Antarctica from 2005 to 2017

Spatial Resolution

The spatial resolution for the data set is 1 km.

Projection and Grid Description

The maps are provided in Polar Stereographic projection ESPG:3031.

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Temporal Coverage

Spaceborne SAR data were collected from multiple satellites between 2005 and 2017. Landsat-8 data were collected between 2013 and 2017. Data for each of the annual maps were acquired between July 1 to June 30 of the following year. See Table 4 for more information on the temporal coverage for the various satellites.

Temporal Resolution

Temporal resolution is 12 months (1 year).

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Parameter or Variable

This data set includes annual Antarctic ice velocity maps posted at 1 km grid spacing. The velocity components for the x and y direction, as defined by the polar stereographic grid, are stored in the NetCDF variables named VX and VY and are reported in meters per year. Error estimates for the velocity components are provided as variables ERRX and ERRY; however, these values should be used more as an indication of relative quality rather than absolute error. More information about the error estimates is provided in the Quality Assessment section as well as in Rignot, et al. 2011. The standard deviation (STDX, STDY) for the velocity estimates as well as a count (CNT) of scenes used to estimate the values for each pixel are also provided for assessing the quality of the data.

Parameter Description

The variables included in the NetCDF files are described in Table 2. All variables have grid dimensions of 5601 x 5601.

Table 2. Variable Description
Variable Description Data Type
VX Component of velocity in m/year in x direction float
VY Component of velocity in m/year in y direction float
ERRX Estimated error in m/year in x direction float
ERRY Estimated error in m/year in y direction float
STDX Standard deviation of VX float
STDY Standard deviation of VY float
CNT Count of scenes used per pixel integer

To view a sample of the velocity data, refer to the Spatial Coverage section, Figure 1. An example of the error estimates, the standard deviation of the velocity and the number of measurements per pixel is provided in Figure 2 below and Figure 3 in MEaSUREs InSAR-Based Antarctica Ice Velocity Map, Version 2 respectively.

To convert the VX and VY velocity components into magnitude (speed) and direction (angle), use the following equations:
(1)   speed = √(vx2 + vy2)
(2)   angle = arctan (vy / vx)
(3)   error = √(errx2 + erry2)
(4)   error of flow direction = error/(2*speed) (see Mouginot et al., 2012)

However, users should take care when computing the inverse tangent due to the function's inherent ambiguities. While the standard arctan function typically does not account for angles that differ by 180°, most modern computer languages and math software packages include the function ATAN2, which uses the signs of both vector components to place the angle in the proper quadrant.

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Software and Tools

Unidata at the University Corporation for Atmospheric Research maintains an extensive list of freely available Software for Manipulating or Displaying NetCDF Data.

Quality Assessment

A detailed description of these data and their quality is provided in Mouginot et al. 2017. Additional details on the methodology are provided in Rignot, et al., 2011 and Mouginot et al. 2012. The precision of ice flow mapping varies with the sensor, the geographic location, the technique of interferometric analysis (see Data Acquisition and Processing for details), the time period of analysis, the repeat cycle, and the amount of data stacking. The error estimates for each sensor are summarized in Table 3. The error maps in Figure 2 take into account the following error sources:

  • Error of speckle tracking and interferometric phase analysis respectively (SAR only)
  • Errors caused by ionospheric perturbations (strongest in the azimuth direction, stronger in L-band compared to C-band, stronger in the East Antarctic Ice Sheet (EAIS) compared to the West Antarctic Ice Sheet (WAIS) because ionospheric perturbations are more abundant near the magnetic pole)
  • Error of feature tracking analysis (Landsat-8 only)
  • Data stacking (reduces the error noise as the square root of the number of interferometric pairs averaged)
  • Respective weight of each instrument in the mosaicking

The total error is the square root of the sum of the independent errors squared. More details on the error estimates are provided in Mouginot, et al. 2017. Table 3 provides the error in ice velocity mapping for each sensor, without data stacking, in range (Rg) and azimuth (Az).

It should also be noted that tide correction was not included for the SAR data in question. The respective error is compensated by the use of multiple scenes to form the measurement for each annual map, but in some cases, the error on the ice shelves may exceed errors in nearby grounded areas. The annual maps are produced using a reduced number of measurements per pixel when compared to the Antarctic-wide velocity map (which utilizes the full data set to maximize geographic coverage and minimize errors).

One additional source of error is the fact that the DEM quality (the BEDMAP-2 DEM was used in the generation of these maps), together with the various sensor geometries and data resolutions, has an impact on the geolocation and the local incidence angle for each scene. This may locally result in larger errors in the final annual mosaics. Most affected are regions with complex topography, such as the Antarctic Peninsula or the Transantarctic Mountains.

Platform/Sensor Nominal Repeat Cycle
(day)		 Error (m/year)
Rg Az
Table 3. Error in Ice Velocity Mapping (m/year)
ALOS (WAIS)/PALSAR 46 6 17
ALOS (EAIS)/PALSAR 46 6 5
ENVISAT/ASAR 35 21 4
RADARSAT-1/SAR 24 26 8
RADARSAT-2/SAR 24 26 8
Copernicus Sentinel-1/SAR 12 12 43
Landsat-8/OLI 16 34* 34*
TanDEM-X (TDX)/TerraSAR-X (TSX)/SAR 11 8 8

*Landsat uses repeat image feature tracking in x and y

Figure 2. Standard deviation of vx and vy (top row) and standard error of the mean vx and vy on a linear scale color-coded from 1 to greater than 32 m/year
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Data Acquisition and Processing

Theory of Measurements

This data set provides annual ice velocities for the Antarctic ice sheet, derived from a variety of satellite radar interferometry (SAR) data as well as Landsat-8 optical imagery. Several techniques of interferometric analysis are used on SAR data to generate the maps:

  1. Speckle tracking in both along (azimuth) and across (range) track directions
  2. Calculation of two dimensional offsets in amplitude imagery
  3. Combinations of (range) interferometric phases along two independent tracks
  4. Combination of interferometric phases of two independent tracks to retrieve the surface flow vector

In all cases, surface parallel flow is assumed, a conventional approach for ice sheets. The Landsat-8 data are processed using repeat image feature tracking (see Mouginot, et al. 2017)

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Data Acquisition Methods

Data for each of the annual maps were acquired between July 1 to June 30 of the following year. These dates were chosen intentionally to avoid breaking up data acquisition campaigns, particularly between 2006 and 2010. Several campaigns ran through austral summer, and several SAR satellites do not acquire data in austral winter due to sensor eclipse conditions.

  • ALOS PALSAR (Japan Aerospace Exploration Agency (JAXA))
  • Envisat ASAR (European Space Agency (ESA))
  • RADARSAT-1 (Canadian Space Agency (CSA))
  • RADARSAT-2 (Canadian Space Agency (CSA) and MacDonald, Dettwiler and Associates Ltd. (MDA))
  • TerraSAR-X / TanDEM-X (German Aerospace Agency (DLR))
  • Copernicus Sentinel-1 (ESA)
  • Landsat-8 optical imagery (USGS)

The IPY Space Task Group and its successor, the Polar Space Task Group (PSTG), coordinated SAR acquisitions between 2005 and 2016.

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Data Sources

Table 4 describes the data sources used to produce this data set.

Table 4. Data Sources
Platform/Sensor Space Agency Look Dir. Mode Repeat Cycle
(day)		 Incidence Angle Resolution 
Rg x Az (m)		 Frequency (GHz) Year
01 Jul to 30 Jun
ENVISAT/ASAR ESA Right IS2 35 23 13x5 5.33
RADARSAT-1/SAR CSA Left/Right Varies 24 18-47 12x5-17x6 5.33 2005/06
2006/07
2007/08
RADARSAT-2/SAR CSA Left S5/EH4 24 41/57 12x5 5.33 2008/09
2010/11
2011/12
2012/13
2013/14
2014/15
2015/16
2016/17
ALOS/PALSAR JAXA Right FBS 46 39 7x4 1.27 2005/06
2006/07
2007/08
2008/09
2009/10
2010/11
Copernicus Sentinel-1A/SAR ESA Right IW-T OPS 12 12x43 5.33 2014/15
2015/16
2016/17
Copernicus Sentinel-1B/SAR ESA Right IW-T OPS 12 12x43 5.33 2016/17
Landsat-8/OLI USGS/NASA N/A Panchromatic 16 N/A 15x15 2013/14
2014/15
2015/16
2016/17
TanDEM-X/TerraSAR-X/SAR DLR Right N/A 11 46.3 1.4x1.8 9.65 2011/12
2012/13
2013/14

During the 2013/14 season, two pairs of data from the COSMO-SkyMed intrument were also used.

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Sensor or Instrument Description

For information about the SAR systems used to construct the mosaics from which this data set is derived, see ENVISAT - Earth Online - ESA, JAXA - About ALOS PALSAR, CSA's RADARSAT-1 and RADARSAT-2, DLR'sTerraSAR-X (TSX) and TanDEM-X (TDX), and ESA's Copernicus Sentinel-1. For information about Landsat-8, see the USGS description of the satellite.

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References and Related Publications

Li, X., E. Rignot, J. Mouginot, and B. Scheuchl. 2016. Ice flow dynamics and mass loss of Totten Glacier, East Antarctica, from 1989 to 2015. Geophysical Research Letters 43(12): 6366-6373. doi: 10.1002/2016GL069173.

Li, X., E. Rignot, M. Morlighem, J. Mouginot, and B. Scheuchl. 2015. Grounding line retreat of Totten Glacier, East Antarctica, 1996 to 2013. Geophysical Research Letters 42(19): 8049-8056. doi: 10.1002/2015GL065701.

Michel, R., and E. Rignot. 1999. Flow of Glacier Moreno, Argentina, from Repeat-Pass Shuttle Imaging Radar Images: Comparison of the Phase Correlation Method with Radar Interferometry. Journal of Glaciology 45(9): 93-100.

Mouginot, J., et al. 2017. Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data. Remote Sensing 9(4): Art. #364. doi: 10.3390/rs9040364.

Mouginot, J., E. Rignot, and B. Scheuchl. 2015. Sustained increase in ice discharge from the Amundsen Sea Embayment, West Antarctica, from 1973 to 2013. Geophysical Research Letters 41: 1576–1584. doi: 10.1002/2013GL059069.

Mouginot, J., B. Scheuchl, and E. Rignot. 2012. Mapping of Ice Motion in Antarctica Using Synthetic-Aperture Radar Data. Remote Sensing 4(9): 2753-2767. doi: 10.3390/rs4092753.

Rignot, E., S. Jacobs, J. Mouginot, and B. Scheuchl. 2013. Ice Shelf Melting Around Antarctica. Science 341(6143): 266-270. doi: 10.1126/science.1235798.

Rignot, E., J. Mouginot, and B. Scheuchl. 2011. Ice Flow of the Antarctic Ice Sheet. Science 333(6048): 1427-1430. doi: 10.1126/science.1208336.

Rignot, E., J. Mouginot, and B. Scheuchl. 2011a. Antarctic grounding line mapping from differential satellite radar interferometry. Geophysical Research Letters 38(10): Art. #L10504. doi: 10.1029/2011GL047109.

Rignot, E., J. L. Bamber, M. R. Van Den Broeke, C. Davis, Y. H. Li, W. J. Van De Berg, and E. Van Meijgaard. 2008. Recent Antarctic ice mass loss from radar interferometry and regional climate modelling. Nature Geoscience 1(2): 106-110. doi: 10.1038/ngeo102.

Scheuchl, B., J. Mouginot, E. Rignot, M. Morlighem, and A. Khazendar. 2016. Grounding line retreat of Pope, Smith, and Kohler Glaciers, West Antarctica, measured with Sentinel-1a radar interferometry data. Geophysical Research Letters 43(16): 8572–8579, doi: 10.1002/2016GL069287.

Scheuchl, B., J. Mouginot, and E. Rignot. 2012. Ice velocity changes in the Ross and Ronne sectors observed using satellite radar data from 1997 and 2009. The Cryosphere 6: 1019-1030. doi: 10.5194/tc-6-1019-2012.

RELATED DATA COLLECTIONS

RELATED WEB SITES

Contacts and Acknowledgments

Dr. Jeremie Mouginot
University of California, Irvine
Department of Earth System Science
Croul Hall
Irvine, California 92697
USA

Dr. Eric Rignot
University of California, Irvine
Department of Earth System Science
Croul Hall
Irvine, California 92697
USA

Dr. Bernd Scheuchl
University of California, Irvine
Department of Earth System Science
Croul Hall
Irvine, California 92697
USA

Acknowledgments: 

These data were generated through a grant from the NASA MEaSUREs program.

Spaceborne Synthetic Aperture Radar (SAR) acquisitions were provided through the following agencies:

  • ALOS PALSAR: Japan Aerospace Exploration Agency
  • ENVISAT ASAR, ERS-1, ERS-2: European Space Agency
  • TerraSAR-X / TanDEM-X: German Aerospace Agency
  • Copernicus Sentinel-1: European Space Agency
  • RADARSAT-1, RADARSAT-2: Canadian Space Agency
  • Landsat-8 (optical) data were made available by United States Geological Survey

Contains modified Copernicus Sentinel-1 data (2014-2016), acquired by the ESA, distributed through the Alaska Satellite Facility, and processed by Mouginot, J., B. Scheuchl, and E. Rignot. Other agencies providing the data for these mosaics include TanDEM-X and TerraSAR-X missions processed by DLR, RADARSAT 1 and 2 data processed by CSA, and ALOS PALSAR by JAXA results derived from optical images collected by Landsat-8 and processed by USGS.

Data acquisitions between 2006 and 2016 are courtesy of the International Polar Year (IPY) Space Task Group and its successor, the Polar Space Task Group (PSTG).

Document Information

DOCUMENT CREATION DATE

April 2017

DOCUMENT REVISION DATE

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