InSAR – Collaboration

InSAR Access — API

Among ASF’s many collaborations, University NAVSTAR Consortium (UNAVCO)/Western North American InSAR (WInSAR), the Alaska Satellite Facility (ASF), and the Jet Propulsion Laboratory (JPL) worked on an information technology and data-management development project to design and implement a seamless distributed access system for synthetic aperture radar (SAR) data and derived interferometric data products. The seamless SAR archive increases the accessibility and the utility of SAR science data to solid Earth and cryospheric science researchers.

An example of the Vertex baseline plot. Users are provided with the capability to observe the perpendicular and temporal baseline distribution of an InSAR stack based on the selection of a desired master, filter granules, and select granules to download.

Specifically, the project will provide simple web services tools to more seamlessly and effectively exchange and share SAR metadata, data and archived and on-demand derived products between the distributed archives, individual users, and key information technology development systems such as the NASA/JPL Advanced Rapid Imaging and Analysis (ARIA) projects that provide higher level resources for geodetic data processing, data assimilation and modeling, and integrative analysis for scientific research and hazards applications. The proposed seamless SAR archive will significantly enhance mature IT capabilities at ASF’s NASA-supported DAAC, the Group on Earth Observations (GEO) Supersites archive, supported operationally by UNAVCO, and UNAVCO’s WInSAR and EarthScope archives that are supported by NASA, the National Science Foundation (NSF), and the United States Geological Survey (USGS) in close collaboration with the European Space Agency (ESA)/European Space Research Institute (ESRIN).

ALOS-PALSAR amplitude image, coherence image, interferogram, and interferogram overlaid on the amplitude of the master image (left to right). Master image: acquired 2006-11-05. Paired image acquired 2008- 11-10 over Baja, California, Mexico. The copyright for the scenes used to create this image (and those below) is held by the Japan Aerospace Exploration Agency/Ministry of Economy, Trade and Industry.

As part of the proposed effort, data/product standard formats and new QC/QA definitions will be developed and implemented to streamline data usage and enable advanced query capability. The seamless SAR archive will provide users with simple browser and web service API access tools to view and retrieve SAR data from multiple archives, to place their tasking requests, to order data, and to report results back to data providers; to make a larger pool of data available to scientific data users; and to encourage broader national and international use of SAR data. The new Advancing Collaborative Connections for Earth System Science (ACCESS)-developed tools will help overcome current obstacles including heterogeneous archive access protocols and data/product formats, data provider access policy constraints, and an increasingly broad and diverse selection of SAR data that now includes ESA/European Remote Sensing Satellite (ERS)/Environmental Satellite (ENVISAT) (and upcoming Sentinel mission), the Canadian Space Agency (CSA)/Radarsat, the Japan Aerospace Exploration Agency (JAXA)/Advanced Land Observation Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR), German Aero-Space Research Establishment (DLR)/TerraSAR-X satellite data and NASA/Uninhabited Aerial Vehicle SAR (UAVSAR) data. The list will continue to expand with NASA/Deformation, Ecosystem Structure and Dynamics of Ice (DESDynI) further increasing the need to efficiently discover, access, retrieve, distribute, and process huge quantities of new and diverse data.

To facilitate terrain corrections, the proposed NASA SAR (NSAR) project will provide InSAR-ready topographic data through OpenTopography. Shown in (a) above is the terrain correction (EGM96 removed) via Generic Mapping Tools (GMT) SAR (GMTSAR) from NASA SRTM data. The terrain-corrected differential interferogram unwrapped phase in (b) from the same ALOS PALSAR pair was processed using ROI_PAC. Red star shows epicenter of April 2010 Mw 7.2 earthquake. The apparent range change variation is 30 cm. (c) shows the zenith path delay difference from Online Services for Correcting Atmosphere in Radar (OSCAR) Modeling, Data and Information Systems (MODIS) zenith path delay maps. The path delay difference map shows no large gradient due to the troposphere in this case. The NSAR project will standardize product and corrections/QC formats and facilitate this type of product quality evaluation and access to products critical to the interpretation of interferograms for earth surface motions and deformation.

Project Objectives:

  • Develop and implement a federated metadata query and product-download capability from distributed airborne (NASA UAVSAR) and spaceborne SAR archives at ASF and UNAVCO/WInSAR.
  • Define and make available new QC parameters and products that will enhance the usability of data and data products from these existing NASA-funded collections.
  • Implement a web services enabled terrain correction service for interferometry (InSAR) using NASA Shuttle Radar Topography Mission (SRTM) data at the San Diego Supercomputer Center (SDSC).
  • Enhance ASF InSAR processing service to access distributed data collections, utilize terrain correction service, and generate enhanced QC products.
  • Establish processed data products archive.

InSAR – Find Data

Seamless Synthetic Aperture Radar Archive API

Search and Download Data from Multiple Archives

The Seamless Synthetic Aperture Radar (SAR) Archive (SSARA) Application Programming Interface (API) enables users to search and download:

  • Master and paired (slave) granules from the SAR archives at ASF and University NAVSTAR Consortium (UNAVCO)/Western North America InSAR (WInSAR);
  • Corresponding Digital Elevation Models (DEMs) from Open Topography and tropospheric data from Jet Propulsion Lab (JPL); and
  • Sample standardized InSAR data products from archives at ASF and WInSAR/UNAVCO.

SSARA API Services

SSARA API Keywords

absoluteOrbit asfResponseTimeout beamMode beamSwath
collectionName flightDirection frame intersectsWith
lookDirection masterStart/masterEnd maxBaselinePerp maxDoppler
maxFaradayRotation maxInsarStackSize maxResults minBaselinePerp
minDoppler minFaradayRotation minInsarStackSize minPercentCoherence
minPercentTroposphere minPercentUnwrapped output platform
polarization processingLevel relativeOrbit slaveStart/slaveEnd

To construct SSARA SAR API queries please visit the  API Tool or visit the SSARA Federated Querier GUI.

For information on the SSARA project please visit InSAR Collaborations.

If you have questions regarding the utilization of the ASF or SSARA API, please contact ASF User Support at

Vertex Search and Download

ASF provides users the ability to search, evaluate, and download InSAR pairs via the Baseline Tool in the Vertex interface.

Users are able to:

  • Identify stacks of SAR granules suitable for interferometric processing;
  • Assess the perpendicular and temporal baseline distribution of a stack by interacting with the online Baseline Tool and
  • Select pairs for download

Selecting this option will show those granules that belong to an InSAR stack over the geographic region of interest. 

Using the Path & Frame option when constructing a search allows a user to see results that closely match their requirements. The Path & Frame option requires that you already know which path corresponds to your area of interest.

Searching by Path allows the user to dramatically narrow their search, and speed up search results. Useful when you know the path(s) associated with your AOI.

Helpful info regarding repeat passes:

Platform  |  Number of orbits before the satellite revisits the same area  |  Number of days before the satellite revisits the same area

  • ALOS: 671 (46 days)
  • R1: 343 (24 days)
  • E1: 501 (35 days)
  • E2: 501 (35 days)
  • Sentinel-1A: 175 (12 days) (6 days as S-1 constellation)
  • Sentinel-1B: 175 (12 days) (6 days as S-1 constellation)

An InSAR pair consists of two granules, a Master image, and a Paired image, that can form an interferogram.

An InSAR stack is composed of all granules that cover the same geographic region, are from the same platform (see exceptions in the next question), and were acquired with the same beam mode. Theoretically, any two granules in an InSAR stack may be used to create an interferogram, as long as the baseline is not beyond a certain critical length.

In principle, data from all satellites in the ASF archive should be suitable for InSAR, as long as the image pair adheres to some very basic rules: the data needs to be acquired by the same satellite, in the same beam mode, and with the same look direction.

There are a few exceptions to this rule. Sentinel-1A and Sentinel-1B can be used interchangeably. The tandem mode of the ERS-1 and ERS-2 satellites provide data very suitable for InSAR applications because both satellites meet the criteria above and have a favorable temporal baseline (data acquired one day apart). All the data in the archive, except RAMP data, are right looking. This means that RAMP data cannot be combined with any other Radarsat-1 data, even if those had been acquired with the same beam mode.

ScanSAR data, available for R1 or ALOS PALSAR, are another special case, as they are formed by combining several beam modes in one data set. This requires special processing techniques and is at this stage considered research-level processing.

  1. Radarsat-1
    1. Fine Beam (FN1-5)
    2. Standard Beam (ST1-7)
    3. Wide Beam
    4. Extended High Beam
    5. Extended Low Beam
    6. RAMP
  2. JERS-1 
    1. Fine Beam Single Polarimetric (FBS)
    2. Fine Beam Dual Polarimetric (FBD)
    3. Fine Beam Polarimetric
  4. ERS-1/ERS-2
  5. Sentinel-1A/Sentinel-1B (IW)

Baseline length is the temporal and spatial distance between two satellite observations:

  1. Perpendicular Baseline (B┴) is the spatial distance between the first and second observations measured perpendicular to the look direction. It gives an indication of the sensitivity to topographic height, the amount of decorrelation due to phase gradients, and the effectiveness of the phase unwrapping. The longer B┴, the weaker the coherency and the lower the sensitivity to height changes (Hanssen, R. Radar Interferometry: Data Interpretation and Error Analysis. Kluwer Academic Publishers; 2001. 308 p.)
  2. Temporal Baseline is the time difference between the first and second acquisition. To minimize decorrelation, the temporal baseline should be as short as possible. However, this may not be the case for studies of continuous deformation such as earthquake or volcano monitoring.
  3. Critical baseline is the maximum horizontal separation between two satellite orbits at which the Interferometric correlation becomes zero. It is a function of wavelength (λ), incidence angle (θinc), topographic slope (ζ), range bandwidth (BR), and range (R):

Standard interferometric processing techniques can only be applied when the baseline of the interferometric pair is well below the critical baseline. Advanced interferometric processing techniques, such as persistent scatterer analysis, are able to overcome this limitation.

The Baseline Tool is an interactive tool that allows users to visualize the perpendicular and temporal baseline distribution of all granules within a given InSAR stack. To access this tool select “Baseline” button in the search results page of Vertex.

    • Critical Baseline: Shown as a gray box on the plot. Represents the maximum baseline viable for interferometry.
    • Master Granule: The default Master granule is the interferable granule corresponding to the granule you have selected to analyze. It is highlighted by a black dot on the baseline plot and by the blue radio button in the baseline table (RAW, GRD, L1, L1.5 products may display in the Analyze box, but will not display in the table or plot).
    • Paired Granules: Granules represented by gray dots.
    • Selected Granules: Granules that have been selected for download appear as blue dots in the plot, and have a check mark in the download column of the table.
    • Baseline Filters
      • Acquisition Date: Click on the blue box and use sliders to filter by date. 
      • Perpendicular Baseline: Click on the blue box and use sliders to filter by baseline.
      • Temporal Baseline: Click on the blue box and use sliders to filter by time. 
    • Granule Information 
      • Queue: Click to add to queue for download by Download script.
      • Set As Master: Click gray dot on plot, then click Set As Master to change master granule.
      • Download: Click to download granule in the Granule Information box.
    • Baseline Table
      • Download Select: Check the box to queue product for download via the download script.
      • Master Select: Click the radio button to set the master.
      • Export CSV: Exports the baseline table to CSV.
      • Download Script: Download a script which will download products checked in the download column.
      • One-Click Download: Click the blue down-arrow to immediately download the product.
      • Column Sorting: Click at top of column to sort.


Differential interferometric synthetic aperture radar (InSAR) is used to measure displacements on Earth’s surface to a precision of a few centimeters or less….

Deformation in the Carlsbad, New Mexico, region is revealed in this Sentinel-1 InSAR beta image. Created through the collaborative Getting Ready for NISAR project with JPL, this image is one of many available through either ASF DAAC’s Vertex or NASA’s Earthdata Search. See the full article for more about the project, available products, and how to offer feedback. Image credit: ASF DAAC & JPL 2017, contains modified Copernicus Sentinel data 2017.

Sentinel-1 Interferograms

Sentinel-1 Interferogram (BETA) products are prototype Level 2 interferometric products produced as part of the Getting Ready for NISAR (GRFN) project using the ARIA Science Data System under development for the NISAR mission….

Terrestrial Ecology – How To Cite

Citing Terrestrial Ecology Dataset

Cite datasets in publications such as journal papers, articles, presentations, posters, and websites. Please send copies of, or links to, published works citing data, imagery, or tools accessed through ASF to with “New Publication” on subject line.

Also see ALOS PALSAR Terms and Conditions and How to Cite ALOS-1 PALSAR data.

Format Example
Dataset: SAR Subsets for Selected Field Sites, ASF DAAC, ORNL DAAC, 2010. Retrieved from] ASF DAAC [day month year of data access]. Includes Material © JAXA/METI [year of data acquisition]. Dataset: SAR Subsets for Selected Field Sites, ASF DAAC, ORNL DAAC, 2010. Retrieved from ASF DAAC 11 June 2015. Includes Material © JAXA/METI 2007.

Crediting Terrestrial Ecology Imagery

Include credit with each image shown in publications such as journal papers, articles, presentations, posters, and websites.

Format Example
[creator credit, year created]; Includes Material © JAXA/METI [year of data acquisition]. ASF DAAC, ORNL DAAC 2010; Includes Material © JAXA/METI 2007.

Terrestrial Ecology – Data Acquisition

Imagery from synthetic aperture radar (SAR) satellites is not a familiar data set for most users of geographic information systems (GIS). There are several reasons why radar imagery is not commonly used, primarily because of the nature of the technology and its specialized applications. Another is that radar imagery is not optical, requiring more technical processing and specialized image interpretation skills.


The SAR satellite used to create these images is the Advanced Land Observing Satellite (ALOS). ASF is an archive facility for ALOS data collected over the Americas. The SAR sensor is an L-Band phased array radar capable of imaging in several resolutions and polarizations. Because radar data is ranging data that measures the strength and scatter of the radar pulse, it is not like optical imagery which is visually intuitive. To make the SAR scenes more user friendly, the polarization data was classified as reds, greens, and blues in the image. Another aspect of radar remote sensing is that the ranging data must be terrain-corrected by a process that assigns the ranging values to geographic coordinates by utilizing a digital surface model (DSM). High resolution DSM data are not available for the entire planet and existing data at high latitudes is problematic, especially in areas of very little terrain relief, such as sheet glaciers. To use a consistent DSM for this project and all of the sites being investigated, the DSM data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imaging instrument was used for the terrain correction.


Launched on January 24th, 2006 aboard the Advanced Land Observing Satellite (ALOS), the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) instrument has promising applications for natural resource and land applications including parameters applicable to terrestrial nutrient cycle estimates. Research has shown that SAR data by itself, or combined with other optical or active systems, can enhance land characterization with information not otherwise available from passive remote systems. Because Radar emits its own signal, imaging can occur anytime of the day or night independent of sun angle. This is in contrast to passive imaging systems that require the Sun’s illumination. Due to its longer wavelength than visible light, the microwaves used in Radar also have the advantage of not being impeded by cloud cover or other atmospheric contamination. Some examples of PALSAR land applications include estimates and mapping of vegetation above ground biomass, deforestation mapping, wetland (including high latitude) characterization, and cropland monitoring.

Radar Imaging Basics

The PALSAR instrument is a type of Synthetic Aperture Radar (SAR) that emits energy in the long wavelength L-Band (1270 MHz) frequency. SAR radar systems are able to generate high-resolution imagery with a synthetic aperture (or virtual long antenna) by combining signals received by the physically short (real) antenna as it moves along its flight track.

As an imaging radar system moves along a flight path it emits and receives pulses in a single particular microwave wavelength and orientation (waves polarized in a single vertical (V) or horizontal (H) plane). The radar pulse interacts with the Earth’s surface and is scattered in all directions, with some energy reflected back toward the radar’s antenna. Known as backscatter, the returned signal is received by the antenna a fraction of a second later and in a specific polarization (H or V). The brightness, or amplitude, of the backscatter is measured and recorded and the data are used to derive an image. Radar waves interact differently with soil, vegetation, water, ice, and man-made objects such as buildings and roads because the backscatter is affected by the surface properties of objects. For a smooth surface such as water or a road, most of the incident energy is reflected away from the radar system resulting in a very low return signal. In contrast, rough surfaces will scatter the emitted energy in all directions and return a significant portion back to the antenna. In general, vegetation is usually moderately rough with most radar wavelengths.

Terrestrial Ecology – Data Application

PALSAR Terrestrial Biophysical Applications

The data can be used for a number of purposes: (1) to validate the synthetic aperture radar (SAR) measurements using flux tower site characterization data; (2) to examine the impacts of vegetation dynamics on climate; (3) to understand human impacts on vegetation at a local scale; (3) to detect deforestation and forest degradation; (4) to map and differentiate growth stages and change; (5) to retrieve woody biomass and structural attributes; and (6) to characterize, map and monitor ecoregions such as mangroves and wetlands.

Satellite radar can be important to Earth system monitoring because the properties of the signal return are better suited for certain vegetative biophysical estimates and are more accurate or not otherwise obtainable by passive remote sensing systems. A number of studies have shown a significant relationship between L-Band SAR backscatter coefficients and forest structure parameters including above ground biomass and vegetative structural attributes. Other examples of terrestrial applications include wetland characterization, mapping, and monitoring and forest change analysis.

The Phased Array L-band Synthetic Aperture Radar (PALSAR) subsets provided in this data set might be useful for visual interest and preliminary analysis of the field area. For in-depth analyses, such as biomass estimation, vegetation characterization, etc., users might have to download the lower level products from ASF.

Quantitatively Comparing Multi-Temporal Data

The data values in the image are Digital Numbers (DN) that can be used in the following equation to extract the Normalized Radar Cross Section (NRCS).

NRCS (dB) = 10*log10(<DNˆ2>) + CF

Where the Calibration Factor (CF) is a constant -83. 

The cross section parameter is useful to quantitatively compare multi-temporal data.