Northwest Passage sea ice contrasts with the coast of Baffin Island, Canada, in this ALOS-PALSAR image taken 8 March 2011. © JAXA, METI 2011

Sea Ice MEaSUREs

The sea-ice imagery and data products available through the ASF DAAC are supported under NASA’s Making Earth System data records for Use in Research Environments (MEaSUREs) program.

Sea Ice MEaSUREs — Documents and Tools

Documents MEaSUREs Lagrangian and Eulerian Sea Ice Products: Product format specifications MEaSUREs Gridded Sea Ice Products: Product format specifications RADARSAT Geophysical Processor System Data User’s Handbook: Describes the background, procedures, and content of…

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SMAP – Tools


Name Description
ATBD Documents Algorithm theoretical basis documents are listed with the products available for download.
Product Specification Documents Product specification documents are listed with the products available for download.
ASF SMAP User Guide For Level 1 products, a streamlined guide to accessing data, using SMAP products, understanding acronyms and abbreviations, and more.
Radar Backscatter Calibration, L1B_S0_LoRes and L1C_S0_HiRes Beta Level Data Products Provides analysis and assessment of calibration quality of SMAP radar normalized backscatter cross-section for the L1B_S0_LoRes and L1C_S0_HiRes beta level data products. Dated 8/5/2015.
SMAP Handbook Written in 2013 as a compendium of information on the project near its time of launch. Contains essential information on programmatic, technological, and scientific aspects of the mission.
Ancillary Data Reports (table includes links to cited data) Application-related descriptions of datasets used with science-algorithm software in generating SMAP science-data products, as well as links to EASE grid information relevant to SMAP products. Also included are links to the ancillary data cited in the reports.
Publications Lists publications on remote sensing of soil moisture since 2001. Please submit additional publications to, with "SMAP Publications" on the subject line.


Name Description
ASF Software Tools ASF offers several tools that can be used on many datasets.
SMAP Analysis Client Interactive application developed by the Jet Propulsion Lab (JPL) / NASA for Soil Moisture Active Passive (SMAP) data. Users may want to start with the calendar icon on the far right of the page.
GDAL The Geospatial Data Abstraction Library helps explore the general contents of SMAP Hierarchical Data Format (HDF) 5 data. The gdalinfo tool summarizes the data structure in the file. The gdal_translate tool can extract the SAR data out of the structure and store it into a more versatile GeoTIFF.
ArcGIS (commercial GIS system) Recognizes the HDF5 structure and is able to extract the SAR data on the fly. Does not calculate standard statistics for this data layer and is slow in rendering the data this way. Can be significantly improved by converting the HDF5 data layer HH into a GeoTIFF file using the gdal_translate tool.
HDFView Provided by the HDF group; looks at data in two ways. For quantitative analysis, the selected data layer must be opened as spreadsheet. For a visual analysis, HDFView provides the image view. The program has no options for stretching data in a statistical fashion. However, the user can manually change the brightness and contrast.
Panoply Developed by NASA's Goddard Institute for Space Studies, primarily used for global datasets of lower resolution.
Interactive Data Language The Interactive Data Language (IDL) provides a more programmatic means to visualize the SMAP HDF5 data. The IDL H5 browser has very limited functionality in terms of changing visual value ranges and stretching the imagery for visualization purposes, but it does provide users the ability to view the HDF5 products.
Worldview quick-look tool This tool from NASA's EOSDIS provides the capability to interactively browse global, full-resolution satellite imagery and then download the underlying data. Most of the 100+ available products are updated within three hours of observation.
HDF Group The HDF Group offers a list of software using HDF5.

ASF list of potentially useful software tools.

Citation Policy for Data and Imagery Accessed Through ASF

Instructions for specific datasets

Whenever you publish data processed or archived at ASF or ASF DAAC, cite ASF or ASF DAAC as the data source, following the guidelines below. Examples include but are not limited to papers, presentations, websites, videos, and books. When you discuss with news organizations your use of data processed or archived at ASF or ASF DAAC, please cite ASF or ASF DAAC as well as the data source. In general, cite the following elements:

  1. Name of dataset
  2. Source of original data (requirements may vary depending on source; see links below)
  3. Year of data acquisition 
  4. “Processed by ASF” if applicable or “Processed by ASF DAAC” if applicable
  5. “Retrieved from ASF [day month year of data access]” or “Retrieved from ASF DAAC” if the data are ASF DAAC data
  6. DOI when available (some datasets have their own DOIs)

Crediting Imagery

Include credit with each image shown in publications such as journal papers, articles, posters, and websites. See links below for specific formats and examples for datasets with their own requirements.

The general guidelines are to credit the source organization with a copyright symbol and cite the year of data acquisition. For derivative images, cite the creator, date of image creation, source organization, and date of data acquisition. 

About ASF DAAC datasets: ASF DAAC datasets are provided through the NASA Earth Science Data and Information System (ESDIS) project. ASF DAAC is one of the Earth Observing System Data and Information System (EOSDIS) Distributed Active Archive Centers (DAACs), part of the ESDIS project. NASA data are not copyrighted; however, when you publish NASA data or results derived therefrom, NASA requests that you include an acknowledgment within the text of the publication and reference list. See the examples below. 

Please note that some datasets, particularly those from foreign space agencies, have special citation requirements.

Links to Specific Citation Formats and Examples

Using ASF Web Information

Some images at this website are explicitly restricted from reuse without the author’s permission. Where images are not marked, you may reuse them but must include any copyright associated with the image. Whenever you use any materials from this website, please also acknowledge ASF as follows: “Information [and images] on [SUBJECT] obtained from, Alaska Satellite Facility, UAF, [Month, Year].” 

Please send reprints and two copies of any work citing ASF data to, with “New ASF Citation” in the subject line or to

   Alaska Satellite Facility User Services
   2156 Koyukuk Dr
   Fairbanks, AK 99775


  If you have any questions about using ASF data or information, please contact us.

Alaska Satellite Facility DAAC Research Agreement

I understand that the data received from the Alaska Satellite Facility can be used only under the following terms and conditions:

  1. The data are for my use for bona fide research purposes only. No commercial use is allowed of the data or any products derived there from.
  2. The data will not be reproduced or distributed to any other parties, except that they may be shared among named members of my research team (co-investigators) and with other researchers who have signed a similar research agreement. I will be responsible for compliance with this condition for the data I obtain from ASF. Furthermore, I am responsible for compliance to these agreement terms by members of my research team with whom I share these data.
  3. I will submit for publication in the open scientific literature results of research accomplished with the requested data, including derived data sets, and the algorithms and models used. Application demonstrations are not required to supply algorithms or models.
  4. I agree to provide, if requested, a copy of such results including derived data sets, algorithms, models, and documentation, to the ASF for archive and distribution. Application demonstrations are not required to supply algorithms or models.
  5. I agree to pay the marginal cost to ASF of filling my specific requests including reproducing and delivering the data.
  6. I also understand that a product which involved ASF data in its production can only be freely distributed by me if it is in such a form that the original backscatter values cannot be derived from it.

I understand that if these conditions are violated NASA may take appropriate action, including the following:

NASA Investigators : Termination of the research agreement, and potential loss of funding support by NASA. Subsequent access would be under terms for commercial use, as outlined by the respective flight agency.

Other Users : Termination of the research agreement, and notification by NASA to the investigator’s sponsoring agency and to the relevant space agency of the violation. Subsequent access would be under terms for commercial use, as outlined by the respective flight agency.

Special Conditions for ERS Data :

I acknowledge and agree to respect the full title and ownership by the European Space Agency of all ERS-1 and -2 data. I agree to clearly mark all ERS-1 and -2 data, irrespective of the form in which it is reproduced, in such a way that the European Space Agency’s copyright is plain to all, as follows:

“© ESA (year of reception).”

Special Conditions for RADARSAT-1 Data :

I understand that the intellectual property rights of RADARSAT-1 data are reserved solely for the Canadian Space Agency (CSA), and I am entitled only to the right to utilize the data. I agree to clearly mark all RADARSAT-1 data, irrespective of the form in which it is reproduced, in such a way that the CSA copyright is plain to all, as follows:

“© Canadian Space Agency (year of reception).”

Reports or publications describing RADARSAT-1 experiments which are copyrighted must provide royalty free rights for NASA, NOAA, CSA and RADARSAT International (RSI) to reproduce or use such work for their own purposes.

Eulerian data in the Sea Ice MEaSUREs South collection falls within the red bounding box visible in this image.

Sea Ice MEaSUREs – Data Products

Now GIS Compatible!

The sea-ice data available below draw on more than 11 years of nearly uninterrupted, three-day radar snapshots of sea ice from the RADARSAT-1 satellite (1995-2012) or four years of Envisat data (2008-2012) of the Arctic and Southern Oceans. Small-scale kinematics and deformation data are processed by tracking sea ice on a high-resolution grid. Some original RADARSAT-1 images are also available from Vertex at no cost to approved users.

Eulerian data in the Sea Ice MEaSUREs South collection falls within the red bounding box visible in this image.

Eulerian data in the Sea Ice MEaSUREs North collection falls within the red bounding box visible in this image.

Data Terms Explained


The Lagrangian sea-ice data products contain monthly measurements of dynamic and kinematic parameters over the Arctic Ocean sea-ice cover. “Lagrangian” refers to a mathematical way to study ice dynamics by noting changes in position and velocity of points over time. The sea-ice analysis in the dataset available here is initialized by laying a 10km-by-10km grid over a set of RADARSAT images of the sea ice during an initial 3-day period of a season. The grid forms cells for which a number of properties can be derived that describe the sea-ice dynamics. The grid points of the sea ice and related cell properties are then tracked throughout the season.

Four data products result from these measurements:

This image illustrates sea-ice-motion magnitude, one of the
parameters in the 
Lagrangian products, at three points in time in the Arctic Ocean basin. Dark blue indicates the most motion, more than 30 km a day. The spatial extent of the data illustrated here is the typical extent of the winter products.

1) Ice Motion – a record of the time and location of each point within the initial grid as tracked on RADARSAT images at approximate 3-day intervals. Note that a very small fraction of the points may be lost during the season through advection out of the Arctic basin, loss of ice, or untrackability of the ice cover.

2) Deformation – a record of the divergence, vorticity and shear occurring within each cell. As the verticies of each cell move within a time step, the kinematic properties can be calculated to characterize the response of the ice cover to stresses induced by wind and ocean currents.

3) Ice Age and Thickness – a record of the area, age and thickness of new ice and ridged ice that result from cell area changes. A spatial and temporal distribution of these ice areas is kept for each cell. If the area of a cell increases within a time step, an area of new ice is created. The ice thickness within all the new ice areas created during the season is increased using air temperature information. When the cell area decreases, the thinnest ice areas are rafted or ridged, depending on their thickness. The thickness of areas of ridged ice are also grown in time.

4) Backscatter Histogram – a record of the radiometric properties of the ice within each cell. A histogram of the radar brightness is kept within each cell at each time step, allowing the user to deduce multi-year ice fractions of the ice cover.

Note that in earlier winter products we use a 10km-resolution grid over the existing ice cover beginning sometime near the beginning of November. In later products we use a hybrid grid of 10km resolution over the multi-year ice pack and a 20km grid over the seasonal ice and began our analysis in early December so as to permit the tracking of the seasonal ice regions.

3-Day Gridded

The 3-day gridded datasets are produced from the Lagrangian products. Parameters from the Lagrangian dataset are processed to produce fields with constant grid spacing. The gridded parameters are ice age, ice thickness, backscatter histogram, divergence, vorticity, and shear. Data values cover a 3-day period on a 12.5km-x-12.5-km grid. Each of the downloadable files contains one month of these 3-day product files.

Melt Onset

The melt onset product consists of a gridded field containing the date of surface melt at each grid location. This date is derived from changes in the radar backscatter signature within the Lagrangian cells between April and June. The grid is at 10km resolution within the interior of the Arctic basin and 25km resolution near the coasts.


Between 2008 and 2012, an archive of Envisat SAR imagery of the Arctic and Southern Oceans was created. In a project, conducted by Senior Research Scientist Ron Kwok of the Jet Propulsion Laboratory, the SAR imagery from Envisat was used to produce a high-resolution dataset of small-scale sea ice kinematics and deformation. This work is supported under NASA’s Making Earth System data records for Use in Research Environments (MEaSUREs) program. The motion field is sampled in Eulerian mode. 

The objectives are to:

  • Process the Envisat data stream to construct ESDRs of small-scale ice motion of the Arctic and Southern Oceans;
  • Develop products that take advantage of the temporal (daily observations) and spatial sampling scheme of the Envisat mission;
  • Produce mosaics of the Envisat images of the Arctic Ocean and Southern Ocean.
This 1998 RADARSAT-1 image reveals grease ice along a pack-ice front in the Bering Sea. Grease ice forms after frazil (slush-like ice) crystals are pushed against each other. When the fragile "arms" of the crystals break and form a mixture of damaged crystals and crystal remnants, the result is grease ice—an oily-looking "ice soup" on the water surface. Its viscous nature smooths out small ocean waves. © CSA, 1998.

Sea Ice MEaSUREs – About

Sea Ice: Central Player in a Dynamic System

Sea ice is the central player in a dynamic system that affects the planet’s oceans and climate. Sea ice is also a force to be reckoned with as polar waters open to human activity, such as shipping that is already taking place through the Northern Route along the coast of Russia and is potentially slated for the fabled Northwest Passage along the coast of Canada. Sea-ice motion, revealed in the data available here, is a critical factor in the thinning and melting of Arctic sea ice as it forms, raftsridges, and opens into leads and polynyas — and as winds and currents move it through and out of the Arctic.

Sea Ice Moves: Radar helps reveal the global effects of sea-ice motion.

Sea Ice in the Bering Strait: See an animation of synthetic aperture radar (SAR) images of the Bering Strait from 2007 and 2008.

Dramatic changes: Though the extent of sea ice fluctuates, overall it is shrinking and substantially thinner than in past decades, and in spring and summer it is retreating earlier and faster. The melting, along with the absorption of solar energy by newly exposed, darker water, alters the circulations of oceans and the atmosphere, affecting climate and weather globally.

Observable through remote sensing: Remote sensing has been central to observing and researching changes in sea ice. Synthetic aperture radar (SAR), used to create the majority of the imagery available in the ASF archive, is among the power tools of remote sensing and has been used extensively in the science of sea ice. SAR bounces a microwave radar signal off the Earth’s surface, including water and ice, to detect physical properties. Unlike optical technology, SAR can “see” through darkness, clouds, and rain.

Critical for seafood: Sea ice also plays a substantial role in feeding the world. The ice serves as a farm for tiny organisms that drive the entire ecosystem. Seasonal sea ice in the Bering Sea is an integral part of an international fishery that provides more than half of the U.S. seafood catch. In addition, sea ice provides wildlife nurseries, molting sites, dens, hiding places, feeding grounds, resting platforms, and even transportation for Pacific walruses that migrate by riding on melting ice floes.

Sea-ice data, images, and data products available through the Alaska Satellite Facility (ASF) are supported under NASA’s Making Earth System data records for Use in Research Environments (MEaSUREs) program.


Sea-ice imagery and data products are supported under NASA’s Making Earth System data records for Use in Research Environments (MEaSUREs) program. These data have been used in a variety of applications.

Arctic and Southern Ocean imagery, data, and data products available at no cost to approved users from the Alaska Satellite Facility (ASF) DAAC data pool include:

  • More than 11 years of RADARSAT-1, nearly uninterrupted, three-day radar snapshots of Arctic and Southern Ocean sea ice.
  • Original synthetic aperture radar (SAR) images.

RADARSAT-1 data have been processed to:

  • Construct a near decadal record of small-scale ice motion of the Arctic and Southern Oceans.
  • Produce a record of ice motion of the northern Bering Sea.
  • Assemble monthly high-resolution image mosaics of the Arctic Ocean.

These datasets are available from the ASF DAAC and NASA’s Jet Propulsion Laboratory (JPL) through the project’s principal investigator, Ron Kwok. The original RADARSAT-1 images used to generate the products are available for download at no cost from the Alaska Satellite Facility datapool. Through ASF’s datapool, products such as SeasatRADARSAT-1ERS-1ERS-2JERS, and PALSAR sea ice images are offered at no cost to approved users. To become an approved user, please submit the required Research Agreement. Additional sea ice data is offered through Polar Year 07-08, a part of the Global Inter-agency International Polar-Snapshot Year (GIIPSY), which contains satellite snapshots of polar regions.

MEaSUREs supports the NASA Earth-science research community in providing Earth science data products and services driven by NASA’s Earth-science goals. MEaSUREs projects focus on the creation of Earth System Data Records (ESDRs), including Climate Data Records. An ESDR is a unified and coherent set of observations of a given parameter of the Earth system that is optimized to meet specific requirements in addressing science questions.

These records are critical to understanding Earth system processes; assessing variability, long-term trends, and change in the Earth system; and providing input and validation means for modeling efforts.

How do I read SAR images?

Interpretation of synthetic aperture radar (SAR) images is not always straightforward, in part because of the non-intuitive, side-looking geometry.

Here are some general rules of thumb:


Regions of calm water and other smooth surfaces appear black (the radar reflects away from the spacecraft). In the ESA image to the right of eddies around islands in the Bering Sea (© ESA 1992), the shades of grey indicate both rough water and ice in various stages of formation.

Wind-roughened water can backscatter brightly in the presence of capillary waves, which occur when the resulting waves are close in size to the incident radar’s wavelength.


Rough surfaces appear brighter, as they reflect the radar in all directions, and more of the energy is scattered back to the antenna. A rough surface backscatters even more brightly when it is wet.

  • Surface variations near the size of the radar’s wavelength cause strong backscattering.
  • If the wavelength is a few centimeters long, dirt clods and leaves might backscatter brightly.
  • A longer wavelength would be more likely to scatter off boulders than dirt clods, or tree trunks rather than leaves.


Any slope leads to geometric distortion. Radar signals that return to the spacecraft from a mountaintop arrive earlier or at the same time as the signal from the foot of the mountain, seeming to indicate that the mountaintop and the foot of the mountain are in nearly the same place — or the mountaintop may also appear before the foot. In a SAR image with layover, the mountains look as if they have fallen over toward the sensor. Steeper angles lead to more extreme layover, where mountain tops appear to lay over their base. Layover appears bright.

Geometric distortions are corrected by doing terrain correction. ASF has terrain correction tutorials for both Sentinel-1 and ERS-1 and -2, JERS-1, and RADARSAT-1. PALSAR RTC products are available already radiometrically terrain-corrected.

Hills and other large-scale surface variations tend to appear bright on one side and dim on the other. (The side that appears bright was facing the SAR.) Where slopes are very steep, the dim side may be completely dark because no radar signal is returned at all. This is called shadow. Slope-influenced brightness is corrected by doing radiometric correction. ASF has tutorials which combine radiometric and terrain correction instructions.

Various combinations of polarizations for transmitted and received signals have a large impact on the backscattering of the signal. The right choice of polarization can help emphasize particular topographic features.

Dipole eddies swirl in the vicinity of the Bering Sea's Sarichef Strait, between Hall and St. Matthew Islands, in this ERS-1 image acquired on 15 February 1992. The eddies are tidal generated and were observed only when frazil (slush-like ice) and grease ice acted as tracers. © ESA, 1992
Radar shadow occurs behind vertical features or steep slopes where the radar beam can't reach. Diagram courtesy of Natural Resources Canada.
Radar shadow behind steep slopes. Image courtesy of Natural Resources Canada.

Man-Made Structures
In urban areas it is at times challenging to determine the orbit direction. All buildings that are perfectly perpendicularly aligned to the flight direction show very bright returns.

Due to the reflectivity and angular structure of buildings, bridges, and other human-made objects, these targets tend to behave as corner reflectors and show up as bright spots in a synthetic aperture radar (SAR) image. A particularly strong response — for example, from a corner reflector or ASF’s receiving antenna — can look like a bright dot or a cross in a processed synthetic aperture radar (SAR) image.

Brooklyn neighborhoods such as Bedford -Stuyvesant with north-south running streets show strong radar return, as the buildings are oriented perpendicular to the imaging radar beam.
Closeup of Brooklyn street grid.
ASF's DJR 9 corner reflector shows a bright return amidst agricultural fields. Delta Junction, Alaska.