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Sentinel-1 Burst InSAR Product Guide

This document is a guide for users of Sentinel-1 Burst Interferometric Synthetic Aperture Radar (InSAR) products generated by the Alaska Satellite Facility (ASF).

Burst-based InSAR available for Sentinel-1

Users are now able to process burst-based Sentinel-1 InSAR products on demand using ISCE2 software!

This functionality is available on Vertex as well as through the HyP3 API and Python SDK.

Sentinel-1 Bursts

Sentinel-1 bursts are not available for all SLC granules

Burst products only are available for Sentinel-1 granules collected after June 9th, 2023.

ASF is currently only forward-populating burst products, which means that burst products are only available for Sentinel-1 granules collected after June 9th, 2023. If this affects your ability to utilize burst products to perform your analyses, please Email ASF User Services, and we will try to populate burst products for your region and time period of interest.

Single Look Complex (SLC) data from the Sentinel-1 mission that is suitable for use in interferometry has historically been packaged into Interferometric Wide (IW) SLC products. These IW SLC products include three sub-swaths, each containing many individual burst SLCs. The framing of the IW SLCs is not consistent through time, so when using IW SLCs as the basis for InSAR, scene pairs do not always fully overlap.

In contrast, working at the burst level of the Sentinel-1 SLC data provides a couple key benefits:

1. Bursts are consistently geolocated through time
The coverage of a burst is the same for every orbit of the satellite, so you can be confident that every burst with the same Full Burst ID in a stack of acquisitions will cover the same geographic location.

2. Bursts cover a smaller geographic area
IW SLC products are extremely large, and, in many cases, only a small portion of the image is of interest. You can process only the bursts that cover your specific area of interest, which significantly decreases the time and cost required to generate InSAR products.

Refer to the Sentinel-1 Bursts tutorial to learn more about how ASF extracts burst-level products from Sentinel-1 IW and EW SLCs.

Burst InSAR Processing

The Sentinel-1 Burst InSAR products are generated using the Jet Propulsion Laboratory's ISCE2 software. ASF is committed to transparency in product development, and we are pleased to be able to offer an InSAR product that leverages open-source software for processing.

For those who would prefer to work at the scale of a full IW SLC, our original On Demand InSAR products are still available. These products have a larger footprint, and are generated using GAMMA software.

Using Sentinel-1 Burst InSAR

Users can request Sentinel-1 Burst InSAR products On Demand in ASF's Vertex data portal, or make use of our HyP3 Python SDK or API. Input pair selection in Vertex uses either the Baseline Tool or the SBAS Tool search interfaces.

Users can also merge multiple Sentinel-1 Burst InSAR products together if their area of interest covers more than one burst. Please read the section on Merging Sentinel-1 Burst InSAR Products for more information.

Users are cautioned to read the sections on limitations and error sources in InSAR products before attempting to use InSAR data. For a more complete description of the properties of SAR, see our Introduction to SAR guide.

Introduction

Interferometric Synthetic Aperture Radar (InSAR) processing uses two SAR images collected over the same area to determine geometric properties of the surface. Missions such as Sentinel-1 are designed for monitoring surface deformation using InSAR, which is optimal when acquisitions are made from a consistent location in space (short perpendicular baseline) over regular time intervals.

The phase measurements of two SAR images acquired at different times from the same place in orbit are differenced to detect and quantify surface changes, such as deformation caused by earthquakes, volcanoes, or groundwater subsidence.

InSAR can also be used to generate digital elevation models, but the optimal mission for DEM generation has the opposite characteristics of the Sentinel-1 mission. Topography is best mapped when the two acquisitions are obtained as close together as possible in time (short temporal baseline), but from different vantage points in space (larger perpendicular baseline than would be optimal for deformation mapping).

Brief Overview of InSAR

SAR is an active sensor that transmits pulses and listens for echoes. These echoes are recorded in phase and amplitude, with the phase being used to determine the distance from the sensor to the target and the amplitude yielding information about the roughness and dielectric constant of that target.

Figure 1

Figure 1: Two passes of an imaging SAR taken at time T0 and T0 + ∆t, will give two distances to the ground, R1 and R2. A difference between R1 and R2 shows motion on the ground. In this case, a subsidence makes R2 greater than R1. Credit: TRE ALTAMIRA

InSAR exploits the phase difference between two SAR images to create an interferogram that shows where the phase and, therefore, the distance to the target has changed from one pass to the next, as illustrated in Figure 1. There are several factors that influence the interferogram, including earth curvature, topographic effects, atmospheric delays, surface motion, and noise. With proper processing, Sentinel-1 InSAR can be used to detect changes in the earth's surface down to the centimeter scale. Applications include volcanic deformation, subsidence, landslide detection, and earthquake assessment.

Wavelengths

The SAR sensors on the Sentinel-1 satellites transmit C-band signals, with a wavelength of 5.6 cm. The signal wavelength impacts the penetration capability of the signal, so it is important to be aware of the sensor wavelength when working with SAR datasets. C-band SAR will penetrate more deeply into canopy or surfaces than an X-band signal, but not nearly as deep as an L-band SAR signal, which, with a wavelength on the order of 25 cm, is better able to penetrate canopy and return signals from the forest floor.

Different wavelengths are also sensitive to different levels of deformation. To detect very small changes over relatively short periods of time, you may require a signal with a smaller wavelength (such as X-band). However, signals with shorter wavelengths are also more prone to decorrelation due to small changes in surface conditions such as vegetation growth.

For slower processes that require a longer time interval to detect movement, longer wavelengths (such as L-band) may be necessary. C-band sits in the middle. It can detect fairly small changes over fairly short periods of time, but is not as sensitive to small changes as X-band or as able to monitor surface dynamics under canopy as L-band.

Polarizations

Polarization refers to the direction of travel of an electromagnetic wave. A horizontal wave is transmitted so that it oscillates in a plane parallel to the surface imaged, while a vertical wave oscillates in a plane perpendicular to the surface imaged.

Most modern SAR systems can transmit chirps with either a horizontal or vertical polarization. In addition, some of these sensors can listen for either horizontal or vertical backscatter. This gives rise to 4 different types of returns: HH, HV, VV, and VH, with the first letter indicating the transmission method and the second the receive method. For example, VH is a vertically polarized transmit signal with horizontally polarized echoes recorded.

For InSAR applications, processing is generally performed on the co-pol (VV or HH) data and not on the cross-pol (VH or HV) data. Also, each image used in an InSAR pair is required to be the same polarization - two HH images of the same area could form a valid pair, while a single HH with a single VV of the same area would not.

Baselines

Perpendicular Baseline

The term baseline refers to the physical distance between the two vantage points from which images used as an InSAR pair are acquired. The baseline is decomposed into perpendicular (also called normal) and parallel components, as shown in Figure 2.

To monitor surface deformation, the perpendicular baseline for the two acquisitions should be very small in order to maximize the coherence of the phase measurements.

In order to determine topography, two slightly different vantage points are required. Sensitivity to topography depends on the perpendicular baseline, the sensor wavelength, the distance between the satellite and the ground, and the sensor look angle.

Figure 2

Figure 2: Geometry of InSAR baselines. Two satellite passes image the same area on the ground from positions S1 and S2 , resulting in a baseline of B, which can be decomposed into perpendicular (B) and parallel (B) components. Here Y is the direction of travel, referred to as the along-track or azimuth direction, and X is the direction perpendicular to motion, referred to as the cross-track or range direction. Credit: ASF

Temporal Baseline

In contrast to the (physical) baseline, the temporal baseline refers to the time separation between imaging passes. Along-track interferometry measures motion in the millisecond to second range. This technique can detect ocean currents and rapidly moving objects like boats. Differential interferometry is the standard method used to detect motion in the range of days to years. This is the type of interferometry that is performed by the Sentinel-1 HyP3 InSAR processing algorithm. Table 1 lists different temporal baselines, their common names, and what they can be used to measure.

Duration Known as Measurement of
ms to sec along-track ocean currents, moving object detection, MTI
days differential glacier/ice fields/lava flows, surface water extent, hydrology
days to years differential subsidence, seismic events, volcanic activity, crustal displacement

Table 1: Temporal baselines and what they measure. Different geophysical phenomena can be detected based upon the temporal baseline. In general, the longer the temporal baseline, the smaller the motion that can be detected.

Critical Baseline

Large baselines are better than small for topographic mapping. However, as the baseline increases, coherence decreases. At some point, it is impossible to create an interferogram because of baseline decorrelation. The maximum viable baseline per platform, referred to as the critical baseline, is a function of the distance to the ground, the wavelength, and the viewing geometry of the platform.

For Sentinel-1, this critical baseline is about 5 km. In practice, if the perpendicular baseline between images is more than 3/4 of the critical baseline, interferogram creation will be problematic due to the level of noise.

For deformation mapping, it is best to minimize the perpendicular baseline whenever possible, but there may be tradeoffs in terms of finding suitable temporal baselines. In most cases, however, pairs selected for deformation mapping will have perpendicular baselines much smaller than the critical baseline.

Ordering On Demand InSAR Products

All of ASF's On Demand InSAR products are generated using ASF's HyP3 platform. Jobs can be submitted for processing using the Vertex data portal, the HyP3 Python SDK or the HyP3 API.

Vertex

InSAR pairs are selected in Vertex using either the Baseline Search or the SBAS Search interface. The process of selecting pairs is the same for both IW SLC products and individual SLC bursts, but you will need to select the appropriate dataset when searching for content. As illustrated below, select the Sentinel-1 option in the Dataset menu to search for IW SLC products, and select the S1 Bursts option to search for individual SLC bursts.

Vertex Dataset Selection

The Baseline tool is the best option for selecting specific InSAR pairs. Use the Geographic Search to find an image that covers your time and area of interest, select that item in the results, and click the Baseline button in the center panel. The Baseline tool then displays all of the scenes that could be used to generate an interferogram using the selected image. Scroll through the results to find pairs to add to the On Demand queue, or click on items displayed in the plot to highlight that particular image pair.

The SBAS tool is designed for generating time series of InSAR pairs. As with the Baseline search, you can launch the SBAS search from the center panel of a Geographic Search result. It will display all of the valid InSAR pairs through time based on the acquisition location of the input scene. This functionality is designed for processing a series of interferograms to be used in SBAS (Small BAseline Subset) analysis. The results can be adjusted based on baseline criteria (both perpendicular and temporal), and restricted to specific periods of time. Once the list is refined, you have the option to add all of the InSAR pairs displayed in the results to the On Demand queue.

HyP3 SDK and API

The HyP3 SDK and API provide support for creating interferograms based on a pair of selected granules. To identify granules you'd like to process, we suggest using the Geographic, Baseline and SBAS search tools in Vertex. If you'd prefer to request interferogram processing programmatically, we suggest using Vertex's companion Python package: asf_search. This HyP3 SDK Jupyter Notebook provides you with an example of how you can use the asf_search and hyp3_sdk packages together to identify and create stacks of InSAR products.

Considerations for Selecting an InSAR Pair

When selecting an InSAR pair, observe the following required conditions:

  1. Images from an identical orbit direction (either ascending or descending)
  2. Images with identical incidence angles and beam mode
  3. Images with identical resolution and wavelength (usually from the same sensor)
  4. Images with the same viewing geometry (same path and frame)
  5. Images with identical polarizations (both HH or VV)

In addition, the following suggestions may be helpful:

  1. Use images from similar seasons/growth/weather conditions
  2. For deformation mapping: limited spatial separation of acquisition locations (small physical baseline)
  3. For topographic mapping: limited time separation between images (small temporal baseline)

To analyze deformation caused by a single discrete event, such as an earthquake, select images that bracket the event as closely in time as possible. Keeping the window narrowly focused on the time of the event will reduce the impacts of other processes that may mask the signal of the event of interest.

Processing Options

There are several options users can set when ordering Burst InSAR On Demand products:

  1. The number of looks drives the resolution and pixel spacing of the output products:

    Looks Resolution Pixel Spacing
    20x4 160 m 80 m
    10x2 80 m 40 m
    5x1 40 m 20 m

    Products generated with 10x2 looks have a file size roughly 4 times that of 20x4-look products. Similarly, 5x1-look products have a file size roughly 4 times that of 10x2-look products (or 16 times that of 20x4-look products).

    The default is 20x4 looks.

  2. There is an option to apply a water mask. This mask includes coastal waters and most inland waterbodies. Masking waterbodies can have a significant impact during phase unwrapping, as water can sometimes exhibit enough coherence between acquisitions to allow for unwrapping to occur over waterbodies, which is invalid. A GeoTIFF of the water mask is always included with the InSAR product package, but when this option is selected, the conditional water mask will be applied before the phase unwrapping process. Water masking is turned off by default. Visit our InSAR Water Masking Tutorial for more information.

Burst InSAR Workflow

The Burst InSAR workflow used in HyP3 was developed by ASF using ISCE2 software. The steps include pre-processing, interferogram preparation, and product creation. Once these steps are performed, an output product package is created. See product packaging for details on the individual files included in the package.

Pre-Processing

Pre-processing steps prepare the SAR images to be used in interferometry. The pre-processing steps include downloading the burst granules, downloading the DEM file, and downloading the orbit and auxiliary data files.

Download Bursts

The Burst InSAR workflow accepts as input two Interferometric Wide swath Single Look Complex (IW SLC) burst granules with the same burst ID. The bursts are downloaded using ASF's Sentinel-1 Burst Extractor.

Download the DEM File

In order to create differential InSAR products that show motion on the ground, one must subtract the topographic phase from the interferogram. The topographic phase, in this case, is replicated by using an existing DEM to calculate the actual topographic phase. This phase is then removed from the interferogram leaving just the motion or deformation signal (plus atmospheric delays and noise).

The DEM that is used for HyP3 InSAR processing is the 2022 Release of the Copernicus GLO-30 Public DEM dataset publicly available on AWS, which provides global coverage at 30-m pixel spacing (except for an area over Armenia and Azerbaijan, which only has 90-m coverage). For more information about the 2022 updates, see the 'Releases' section of this article.

The portion of the DEM that covers the input bursts is downloaded.

Download Orbit Files and Calibration Auxiliary Data Files

For Sentinel-1 InSAR processing, ISCE2 requires additional satellite orbit and calibration metadata files. The orbit files are downloaded from the Copernicus Data Space Ecosystem. The calibration auxiliary data files are downloaded from the Sentinel-1 Mission Performance Center.

InSAR Processing

The ISCE2 InSAR processing this product uses follows the workflow in topsApp.py from steps startup through geocode. These steps perform the following processing:

  1. Extract the orbits, Instrument Processing Facility (IPF) version, burst data, and antenna pattern if it is necessary.
  2. Calculate the perpendicular and parallel baselines.
  3. Map the DEM into the radar coordinates of the reference image. This generates the longitude, latitude, height and LOS angles on a pixel by pixel grid for each burst.
  4. Estimate the azimuth offsets between the input SLC bursts. The Enhanced Spectral Diversity (ESD) method is not used.
  5. Estimate the range offsets between the input SLC bursts.
  6. Co-register the secondary SLC burst by applying the estimated range and azimuth offsets.
  7. Produce the wrapped phase interferogram.
  8. Apply the Goldstein-Werner power spectral filter with a dampening factor of 0.5.
  9. Optionally apply a water mask to the data.
  10. Unwrap the wrapped phase interferogram using SNAPHU's minimum cost flow (MCF) unwrapping algorithm to produce the unwrapped phase interferogram.
  11. Geocode the output products.

Apply Water Mask

There is an option to apply a water mask. This mask includes coastal waters and most inland waterbodies. Masking waterbodies can have a significant impact during the phase unwrapping, as water can sometimes exhibit enough coherence between acquisitions to allow for unwrapping to occur over waterbodies, which is invalid.

A GeoTIFF of the water mask is always included with the InSAR product package, but when this option is selected, the conditional water mask will be applied along with coherence and intensity thresholds during the phase unwrapping process. Water masking is turned off by default.

The water mask is generated by the HyP3 team using data from OpenStreetMaps and/or ESA WorldCover depending on location. Areas within Canada, Alaska, and Russia are primarily covered by ESA WorldCover data, while the rest of the world is covered by OpenStreetMaps data. This water mask is available for all longitudes, but data is only available from -85 to 85 degrees latitude. All areas between 85 and 90 degrees north latitude are treated as water, and all areas between 85 and 90 degrees south latitude are treated as land for the purposes of the water mask.

Water masks were previously generated from the Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG) dataset, but we transitioned to using the OpenStreetMaps/ESA WorldCover datasets in February 2024 to improve performance. In addition to being a more recent and accurate dataset, this also allows us to mask most inland waterbodies. When using the GSHHG dataset, we only masked large inland waterbodies; with the new mask, all but the smallest inland waterbodies are masked.

We originally applied a 3 km buffer on coastlines and a 5 km buffer on the shorelines of inland waterbodies in the water mask dataset before using it to mask the interferograms, in an effort to reduce the chance that valid land pixels would be excluded from phase unwrapping. It appears, however, that the inclusion of more water pixels is more detrimental to phase unwrapping than the exclusion of some land pixels, so as of September 27, 2022, the water mask used for this option is no longer buffered.

Visit our InSAR Water Masking Tutorial for more information about how different water masking approaches can impact the quality of an interferogram.

Post-Processing

Product Creation

Image files are exported into the widely-used GeoTIFF format in a Universal Transverse Mercator (UTM) Zone projection. Images are resampled to a pixel size that reflects the resolution of output image based on the requested number of looks: 80 meters for 20x4 looks, 40 meters for 10x2 looks, and 20 meters for 5x1 looks.

Supporting metadata files are created, as well as a quick-look browse image.

Product Packaging

HyP3 Burst InSAR output is a zip file containing various files, including GeoTIFFs, a PNG browse image, a metadata file, and a README file.

Naming Convention

The Burst InSAR product names are packed with information pertaining to the processing of the data, presented in the following order, as illustrated in Figure 3.

  • The imaging platform name, always S1 for Sentinel-1.
  • Relative burst ID values assigned by ESA. Each value identifies a consistent burst footprint; relative burst ID values differ from one sub-swath to the next.
  • The imaging mode, currently only IW is supported.
  • The swath number, either 1, 2, or 3, indicating which sub-swath the burst is located in.
  • The acquisition dates of the reference (older) scene and the secondary (newer) scene
  • The polarizations for the pair, either HH or VV.
  • The product type (always INT for InSAR) and the pixel spacing, which will be either 80, 40, or 20, based upon the number of looks selected when the job was submitted for processing
  • The filename ends with the ASF product ID, a 4 digit hexadecimal number

Figure 3

Figure 3: Breakdown of ASF Burst InSAR naming scheme.

Image Files

All of the main InSAR product files are 32-bit floating-point single-band GeoTIFFs. The exceptions to this are the connected components and the water mask files, which are both 8-bit unsigned-integer single-band GeoTIFFs.

  • The coherence file pixel values range from 0.0 to 1.0, with 0.0 being completely non-coherent and 1.0 being perfectly coherent.
  • The unwrapped phase file shows the results of the phase unwrapping process. Negative values indicate movement towards the sensor, and positive values indicate movement away from the sensor. This is the main interferogram output.
  • The wrapped phase file indicates the interferogram phase after applying the adaptive filter immediately before unwrapping. Values range from negative pi to positive pi.
  • The connected components file delineates regions unwrapped as contiguous units by the SNAPHU unwrapping algorithm.
  • The look vectors theta (θ) and phi (φ) describe the elevation and orientation angles of the look vector in radians. The look vectors refer to the look direction back towards the sensor.
    • The lv_theta (θ) file indicates the SAR look vector elevation angle (in radians) at each pixel, ranging from -π/2 (down) to π/2 (up). The look vector elevation angle is defined as the angle between the horizontal surface and the look vector with positive angles indicating sensor positions above the surface.
    • The lv_phi (φ) file indicates the SAR look vector orientation angle (in radians) at each pixel. The look vector orientation angle is defined as the angle between the East direction and the projection of the look vector on the horizontal surface plane. The orientation angle increases towards north, with the North direction corresponding to π/2 (and south to -π/2). The orientation angle range is -π to π.
  • The DEM file gives the local terrain heights in meters, with a geoid correction applied.
  • The water mask file indicates coastal waters and large inland waterbodies. Pixel values of 1 indicate land and 0 indicate water. This file is in 8-bit unsigned integer format.

If the water mask option is selected, the water mask is applied prior to phase unwrapping to exclude water pixels from the process. The water mask is generated using the OpenStreetMaps and ESA WorldCover datasets. Refer to the Water Masking Processing Option section and our InSAR Water Masking Tutorial for more information about water masking.

There are also four non-geocoded images that remain in their native range-doppler coordinates. These four images compose the image data needed to merge Burst InSAR products together. These images include a range-doppler version of the wrapped interferogram, a two-band range-doppler look vector image in the native ISCE2 format, and latitude/longitude images that provide the information necessary to map range-doppler images into the geocoded domain.

A browse image is included for the unwrapped (unw_phase) phase file, which is in PNG format and is 2048 pixels wide.

The tags and extensions used and example file names for each raster are listed in Table 2 below.

Extension Description Example
_conncomp.tif Connected Components S1_136231_IW2_20200604_20200616_VV_INT80_12E3_conncomp.tif
_corr.tif Normalized coherence file S1_136231_IW2_20200604_20200616_VV_INT80_12E3_corr.tif
_unw_phase.tif Unwrapped geocoded interferogram S1_136231_IW2_20200604_20200616_VV_INT80_12E3_unw_phase.tif
_wrapped_phase.tif Wrapped geocoded interferogram S1_136231_IW2_20200604_20200616_VV_INT80_12E3_wrapped_phase.tif
_lv_phi.tif Look vector φ (orientation) S1_136231_IW2_20200604_20200616_VV_INT80_12E3_lv_phi.tif
_lv_theta.tif Look vector θ (elevation) S1_136231_IW2_20200604_20200616_VV_INT80_12E3_lv_theta.tif
_dem.tif Digital elevation model S1_136231_IW2_20200604_20200616_VV_INT80_12E3_dem.tif
_water_mask.tif Water mask S1_136231_IW2_20200604_20200616_VV_INT80_12E3_water_mask.tif
_lat_rdr.tif Range-Doppler latitude coordinates S1_136231_IW2_20200604_20200616_VV_INT80_12E3_lat_rdr.tif
_lon_rdr.tif Range-Doppler longitude coordinates S1_136231_IW2_20200604_20200616_VV_INT80_12E3_lon_rdr.tif
_los_rdr.tif Range-Doppler look vectors S1_136231_IW2_20200604_20200616_VV_INT80_12E3_los_rdr.tif
_wrapped_phase_rdr.tif Wrapped Range-Doppler interferogram S1_136231_IW2_20200604_20200616_VV_INT80_12E3_wrapped_phase_rdr.tif
_unw_phase.png Unwrapped phase browse image S1_136231_IW2_20200604_20200616_VV_INT80_12E3_unw_phase.png

Table 2: Image files in product package

Metadata Files

The product package also includes a number of metadata files.

Extension Description Example
.README.md.txt Main README file for Burst InSAR products S1_136231_IW2_20200604_20200616_VV_INT80_12E3.README.md.txt
.txt Parameters and metadata for the InSAR pair S1_136231_IW2_20200604_20200616_VV_INT80_12E3.txt

Table 3: Metadata files in product package

README File

The text file with extension .README.md.txt explains the files included in the folder, and is customized to reflect that particular product. Users unfamiliar with InSAR products should start by reading this README file, which will give some background on each of the files included in the product folder.

InSAR Parameter File

The text file with extension .txt includes processing parameters used to generate the InSAR product as well as metadata attributes for the InSAR pair. These are detailed in Table 4.

Name Description Possible Value
Reference Granule Granule name for reference burst (of the two scenes in the pair, the dataset with the oldest timestamp) S1_136231_IW2_20200604T022312_VV_7C85-BURST
Secondary Granule Granule name for secondary burst (of the two scenes in the pair, the dataset with the newest timestamp) S1_136231_IW2_20200616T022313_VV_5D11-BURST
Reference Pass Direction Orbit direction of the reference scene DESCENDING
Reference Orbit Number Absolute orbit number of the reference scene 30741
Secondary Pass Direction Orbit direction of the reference scene DESCENDING
Secondary Orbit Number Absolute orbit number of the secondary scene 31091
Baseline Perpendicular baseline in meters 58.3898
UTCTime Time in the UTC time zone in seconds 12360.691361
Heading Spacecraft heading measured in degrees clockwise from north 193.2939317
Spacecraft height Height in meters of the spacecraft above nadir point 700618.6318999995
Earth radius at nadir Ellipsoidal earth radius in meters at the point directly below the satellite 6370250.0667
Slant range near Distance in meters from satellite to nearest point imaged 799517.4338
Slant range center Distance in meters from satellite to the center point imaged 879794.1404
Slant range far Distance in meters from satellite to farthest point imaged 960070.8469
Range looks Number of looks taken in the range direction 20
Azimuth looks Number of looks taken in the azimuth direction 4
InSAR phase filter Was an InSAR phase filter used yes
Phase filter parameter Dampening factor 0.5
Range bandpass filter Range bandpass filter applied no
Azimuth bandpass filter Azimuth bandpass filter applied no
DEM source DEM used in processing GLO-30
DEM resolution Pixel spacing in meters for DEM used to process this scene 30
Unwrapping type Phase unwrapping algorithm used snaphu_mcf
Speckle filter Speckle filter applied yes
Water mask Was a water mask used yes
Radar n lines Number of lines (y coordinate) in range-doppler 377
Radar n samples Number of samples (x coordinate) in range-doppler 1272
Radar first valid line First line in range-doppler SLC containing valid data 8
Radar n valid lines Number of lines in range-doppler SLC containing valid data 363
Radar first valid sample First sample in range-doppler SLC containing valid data 9
Radar n valid samples Number of samples in range-doppler SLC containing valid data 1220
Multilook Azimuth Time Interval Time-based spacing of range-doppler SLC lines after multilooking in seconds 0.0082222252
Multilook Range Pixel Size Distance-based spacing of range-doppler SLC samples after multilooking in meters 46.59124229430646
Radar sensing stop Last date and time for data collection 2020-06-04T02:23:16.030988

Table 4: List of InSAR parameters included in the parameter text file

Merging Sentinel-1 Burst InSAR Products

Burst InSAR products created using the insar_tops_burst workflow can be merged together using the merge_tops_burst workflow. This can be useful when the area of interest you'd like to observe spans multiple bursts. Merging is done using underlying ISCE2 functionality, and steps 8-10 of InSAR processing (filtering, unwrapping, and geocoding) found in the InSAR Processing section are repeated for the merged products to ensure consistent results. Check out the Merge Processing section below for more details. Assuming that you have already installed the HyP3-ISCE2 plugin on your local machine, merging can be performed using the following syntax:

python -m hyp3_isce2 ++process merge_tops_bursts PATH_TO_UNZIPPED_PRODUCTS

Where PATH_TO_UNZIPPED_PRODUCTS is the path to a directory containing unzipped Burst InSAR products. For example:

PATH_TO_UNZIPPED_PRODUCTS
├─ S1_136232_IW2_20200604_20200616_VV_INT80_663F
├─ S1_136231_IW2_20200604_20200616_VV_INT80_529D

In order to be merging eligible, all burst products must:

  • Have the same reference and secondary dates
  • Have the same polarization
  • Have the same multilooking
  • Be from the same relative orbit
  • Be contiguous

The workflow should throw an error if any of these conditions are not met.

Merge Processing

During normal ISCE2 InSAR processing, initial interferograms are formed on a burst-by-burst basis. These range-doppler burst interferograms are combined during an ISCE2 step called mergebursts, then the remaining steps (filtering, unwrapping and geocoding) are conducted on the merged results.

By including select range-doppler data (wrapped interferogram, geolocation information, and line-of-sight information) as well as select metadata in our standard Burst InSAR products, we are able to restart ISCE2 processing from the mergebursts step, then proceed with the following steps as if it were a standard ISCE2 InSAR processing run.

The steps of the workflow are as follows:

  1. Recreate a pre-mergebursts ISCE2 InSAR processing state using the input Burst InSAR products.
  2. Run a modified version of ISCE2’s mergebursts step.
  3. Apply the Goldstein-Werner power spectral filter with a dampening factor of 0.5.
  4. Unwrap the wrapped phase interferogram using SNAPHU's minimum cost flow (MCF) unwrapping algorithm to produce the unwrapped phase interferogram.
  5. Geocode the output products.

As mentioned above, this workflow uses underlying ISCE2 functionality to perform these steps so the results of this workflow should be identical to the results obtained by performing a standard multi-burst ISCE2 InSAR run (assuming that the Enhanced Spectral Diversity technique is not used for co-registration).

Merge Processing Options

The processing options available for the merging are the same as those available for standard Burst InSAR products. Check out the Processing Options section for more details.

To learn about the command line argument syntax for this workflow, look at the help documentation using:

python -m hyp3_isce2 ++process merge_tops_bursts --help

Product Packaging

The product packaging of merged Burst InSAR products follows the same conventions outlined in the Product Packaging section above with two exceptions. First, the four range-doppler images are not included since the products have already been merged. Second, the product name is slightly modified; the burst ID is swapped for the zero-padded relative orbit number, and the swath number is removed. The resulting format is:

S1_rrr__yyymmdd_yyymmdd_pp_INTn_uuuu

Limitations

Baseline Calculation

The baseline is defined as the difference of the platform positions when a given area is imaged. HyP3 baselines are calculated using the best state vectors available. If precise orbits are not yet available for the input granules, restituted orbits will be used. The original predicted orbits are not used for InSAR processing in HyP3. If no restituted or precise state vectors are available, the process will not run.

Coherence

The phase measurements in the two images used in InSAR must be coherent in order to detect change. Random changes in phase from one acquisition to the next can mask actual surface deformation. Vegetation is a common driver of decorrelation, as changes can easily take place in the interval between two acquisitions due to growth, seasonal changes, or wind effects. It will be difficult to generate valid interferograms with C-band data in heavily vegetated regions due to lack of coherence even with fairly short time intervals.

Consider seasonality when selecting image pairs. Decorrelation can be particularly high when comparing phase from different seasons. Changes in the condition of vegetation (especially deciduous canopies), snow, moisture, or freeze/thaw state can impact phase measurements. In cases where a temporal baseline is required that spans seasons, it may be better to use an annual interferogram if possible so that the images are more comparable in terms of seasonality.

Line-of-Sight Measurements

When looking at a single interferogram, the deformation measurements in the line-of-sight orientation of the sensor indicate relative motion towards or away from the sensor. InSAR is not sensitive to motion in the azimuth direction of the satellite, so motion that occurs in the same direction as the satellite's direction of travel will not be detected.

A single interferogram cannot be used to determine the relative contributions of vertical and horizontal movement to the line-of-sight displacement measurement. To determine how much of the signal is driven by vertical vs. horizontal movement, you must either use a time series of interferograms, or use reference measurements with known vertical and horizontal components (such as GNSS measurements from the region of deformation) to deconstruct the line-of-sight displacement.

Phase Unwrapping Reference Point

The reference point for phase unwrapping is set automatically by the topsApp.py script. It may not be an ideal location to use as a reference point for phase unwrapping. If it is located in an area undergoing deformation, or in an area with low coherence, the unwrapping may be of lower quality than if the reference point was in a more suitable location.

Even when there are no phase unwrapping errors introduced by phase discontinuities, it is important to be aware that unwrapped phase differences are calculated relative to the reference point. The phase difference value of the reference point is set to 0 during phase unwrapping, so any displacement values will be relative to that benchmark.

If you are interested in the amount of displacement in a particular area, you may wish to choose your own reference point. The ideal reference point would be in an area of high coherence beyond where deformation has occurred. The unwrapped phase measurements can be adjusted to be relative to this new reference point. To adjust the values in the unwrapped phase GeoTIFF, simply select a reference point that is optimal for your use case and subtract the unwrapped phase value of that reference point from each pixel in the unwrapped phase raster:

ΔΨ* = ΔΨ - Δψref

where ΔΨ* is the adjusted unwrapped phase, ΔΨ is the original unwrapped phase, and Δψref is the unwrapped phase value at the new reference point.

Displacement Values from a Single Interferogram

In general, calculating displacement values from a single interferogram is not recommended. It will be more robust to use a time series approach to more accurately determine the pattern of movement. When using SAR time-series software such as MintPy, you have the option to select a specific reference point, and the values of the input rasters will be adjusted accordingly.

Error Sources

On Demand InSAR products do not currently correct for some common sources of error in interferometry, such as atmospheric effects. Further processing or time series analysis can be performed by the user to identify or reduce the impact of some of these errors when using On Demand InSAR products for analysis.

Atmospheric Delay

While SAR signals can penetrate clouds, atmospheric conditions can delay the transmission of the signal. This results in phase differences that can look like surface deformation signals but are actually driven by differences in the atmospheric conditions between the pair of acquisitions used to generate the interferogram.

In some cases, atmospheric errors can be corrected by using an atmospheric model to remove the impacts of the turbulent delay from the interferogram. Another approach is to use time series analysis to identify outliers.

Always doubt your interferogram first! View the interferogram critically, and consider if fringe patterns could potentially be driven by atmospheric effects. In general, it is best to avoid drawing conclusions from the outcome of a single interferogram.

Tropospheric phase may be less impactful when considering small-scale deformation. As such, if you are using ASF's Sentinel-1 Burst InSAR products to look at deformation signals that are smaller than 1 km², you should consider using methods other than the typical atmospheric model-based corrections to remove the effects of atmospheric delay. Potential methods in this case include applying band-pass or high-pass spatial filters, or spatial averaging filters such as the approach outlined in Bekaert et al., 2020.

Turbulent Delay

These delays are generally caused by differences in water vapor distribution from one image to the next. They often manifest as wobbly or sausage-shaped fringes, and can potentially mask the signal of a small earthquake.

Stratified Delay

This type of delay is driven mostly by pressure and temperature differences or gradients through the atmospheric column, and often correlates with topography. This atmospheric signature can be confused with movement caused by volcanic activity. If there are multiple volcanoes in an image, and they all exhibit similar patterns, the signal is likely being driven by this type of atmospheric delay.

DEM Errors

A DEM is used to remove topographic phase impacts, but if there are inaccuracies in the DEM, residual impacts of those errors can remain in the interferogram.

Orbit Uncertainties

This is generally not an issue for Sentinel-1 data, as the orbits are very precise and generally reliable. On Demand InSAR products are only processed once restituted or precise orbits are available. Orbit uncertainties are more problematic when working with datasets from older missions.