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112 results for “InSAR”
InSAR stack of Fernandina volcano in Galápagos, Ecuador from Sentinel-1 descending track 128 processed with ISCE2/topsStack
<p>A stack of unwrapped interferograms on Fernandina volcano, Galápagos, Ecuador</p> <p>Sensor: Sentinel-1descending track 128</p> <p>Processor: ISCE/topsStack</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p> <p><strong>Version 1.x (~750 MB)</strong><br> Time: 2014.12.13 - 2018.06.19 (98 acquisitions, 288 interferograms)</p> <p><strong>Version 0.1 (~280 MB; for fast testing of code development)</strong><br> Time: 2014.12.13 - 2016.05..24 (36 acquisitions, 102 interferograms)</p>
InSAR stack of San Francisco Bay, California from Sentinel-1 descending track 42 processed with GMTSAR
<p>A stack of unwrapped interferograms in the San Francisco Bay area, California, USA</p> <p>Sensor: Sentinel-1 descending track 42</p> <p>Processor: <a href="https://github.com/gmtsar/gmtsar" target="_blank" rel="noopener">GMTSAR</a></p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p> <p>The tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p><strong>Version 1.x (~2.3 GB)</strong><br>Time: 2014.12.31 - 2024.06.05 (333 acquisitions, 1297 interferograms)</p> <p><strong>Version 0.x (~290 MB; for fast testing of code development)</strong><br>Time: 2020.01.04 - 2021.07.15 (70 acquisitions, 184 interferograms)</p>
Land Subsidence in Iran Estimated from a Nationwide InSAR Analysis of Sentinel-1 Observations 2014-2020
<p><strong>Overview</strong></p> <p>This dataset is a supplementary material to the paper "Haghighi and Motagh, 2024. Uncovering the Impacts of Depleting Aquifers: A Remote Sensing Analysis of Land Subsidence in Iran, Science Advances". It provides detailed insights into land subsidence across Iran, derived from Sentinel-1 InSAR observations. This dataset is intended for use by researchers, policymakers, and practitioners interested in land subsidence, groundwater depletion, and related fields.</p> <p><strong>Dataset Contents</strong></p> <ol> <li><em>Iran_subsidence_rate_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Annual rate of land subsidence in Iran over the six-year period, projected from satellite Line of Sight to vertical.</li> <li><em>Iran_subsidence_rate_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.jpg</em><br>Subsidence map of Iran visualized as jpg</li> <li><em>Iran_subsidence_seasonal_amplitude_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Amplitude of seasonal ground deformation, projected from satellite Line of Sight to vertical.</li> <li><em>Iran_subsidence_mask_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Land subsidence mask, based on the annual rate of land subsidence.</li> </ol> <p><strong>Methodology</strong></p> <p>The data were derived using Interferometric Synthetic Aperture Radar (InSAR) analysis of Sentinel-1 satellite imagery. The original SAR data includes more than 6000 scenes of Sentinel-1 images collected across 10 descending tracks between 2014 and 2020. The details can be found in the original paper.</p> <p><strong>Acknowledgements</strong></p> <p>We acknowledge the European Space Agency (ESA) for providing the Sentinel-1 satellite data used in this analysis.</p> <p><strong>License</strong></p> <p>This dataset is shared under CC BY 4.0 license, which allows for reuse and distribution, provided that the original authors and source are credited.</p> <p><strong>Citation</strong></p> <p>Please cite the following if you use this dataset:</p> <ol> <li>Haghighi and Motagh, 2024. Uncovering the Impacts of Depleting Aquifers: A Remote Sensing Analysis of Land Subsidence in Iran, Science Advances.</li> <li>Haghighi and Motagh, 2024. Land Subsidence in Iran Estimated from a Nationwide InSAR Analysis of Sentinel-1 Observations 2014-2020. Zenodo. doi:10.5281/zenodo.10815578</li> <li>The dataset contains modified Copernicus Sentinel data 2014-2020, processed by ESA.</li> </ol> <p><strong>Contact</strong></p> <p>Please contact Mahmud Haghighi for inquiries related to this dataset.<br>https://www.ipi.uni-hannover.de/en/haghighi</p>
Sentinel-1 InSAR Browse Service Image of the October 2016 Central Italian Earthquakes
<p>The surface deformation caused by the central Italian earthquakes which occured in October 2016 is captured in this terrain corrected interferogram produced by the Sentinel-1 InSAR Browse Service for the Geohazards Exploitation Platform.</p> <p>Two earthquakes occured on 26<sup>th</sup> October and one on 30<sup>th</sup> October. The Sentinel-1 datasets were acquired on 26-10-2016 for the master and 01-11-2016 for the slave from a descending pass so that the line of sight deformation is viewed from the east.</p> <p>Contains modified Copernicus Sentinel data (2016), processed by DLR/ESA/Terradue.</p>
Sentinel-1 InSAR Browse Service Image of the October 2016 Central Italian Earthquakes
<p>The surface deformation caused by the central Italian earthquakes which occured in October 2016 is captured in this terrain corrected interferogram produced by the Sentinel-1 InSAR Browse Service for the Geohazards Exploitation Platform.</p> <p>Two earthquakes occured on 26<sup>th</sup> October and one on 30<sup>th</sup> October. The Sentinel-1 datasets were acquired on 26-10-2016 for the master and 01-11-2016 for the slave from a descending pass so that the line of sight deformation is viewed from the east.</p> <p>Contains modified Copernicus Sentinel data (2016), processed by DLR/ESA/Terradue.</p>
InSAR Displacements in the Delaware Basin, TX
<p>These data are the vertical and east-west horizontal cumulative displacements in the Delaware Basin, between 2015-03-05 through 2020-03-31. They are presented in "Shallow Aseismic Slip in the Delaware Basin Determined by Sentinel-1 InSAR", submitted to <em>JGR: Solid Earth</em> on September 1st, 2021. </p> <p>The format of both files is [longitude, latitude, X, Y, displacement (cm)].</p> <p>For vertical displacements, negative values indicate subsidence and positive values indicate uplift. For horizontal displacement, negative values indicate westward displacement, and positive values indicate eastward displacement.</p> <p>Version 2 (_v2) were updated Dec. 28th, 2021.</p>
InSAR stack of Western Cape, South Africa from Sentinel-1 ascending track 29 processed with SNAP
<p>A stack of unwrapped interferograms on Western Cape, South Africa.</p> <p>Sensor: Sentinel-1ascending track 29</p> <p>Time: 2019.03.03 - 2019.05.14, 7 acquisitions, 15 interferograms</p> <p>Processor: SNAP (accessed on 14 July 2019)</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p>
InSAR stack of San Francisco Bay in California from Sentinel-1 descending track 42 processed with ARIA
<p>A stack of unwrapped interferograms in San Francisco Bay, California, USA from Sentinel-1descending track 42</p> <p>Processor: ARIA (processed using ISCE and prepared using <a href="https://github.com/aria-tools/ARIA-tools">ARIA-tools</a> as shown below)</p> <p>Tropospheric delay estimated from ERA5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p> <p><strong>Version 1.2 (~5 GB):</strong><br> Time: 2015.03.01 - 2022.06.04, 189 acquisitions, 961 interferograms<br> Used ARIA-tools and MintPy commands:</p> <pre><code>ariaDownload.py -b '37.25 38.1 -122.6 -121.75' --track 42 ariaTSsetup.py -f 'products/*.nc' -b '37.25 38.1 -122.6 -121.75' --mask Download prep_aria.py -s ../stack/ -d ../DEM/SRTM_3arcsec.dem -i ../incidenceAngle/*.vrt -a ../azimuthAngle/*.vrt -w ../mask/watermask.msk</code></pre> <p><strong>Version 0.2 (~280 MB; for fast testing of code development)</strong><br> Time: 2016.01.31 - 2017.05.10, 23 acquisitions, 91 interferograms<br> Used ARIA-tools commands (access date Jun 18th, 2022):</p> <pre><code>ariaDownload.py -b '37.35 38.00 -122.45 -121.80' --track 42 --start 20160101 --end 20170510 ariaTSsetup.py -f 'products/*.nc' -b '37.35 38.00 -122.45 -121.80' --mask Download</code></pre> <p> </p>
InSAR stack of the 2019 Ridgecrest, California earthquake sequence from Sentinel-1 descending track 71 processed with ASF HyP3
<p>A stack of unwrapped interferograms on Owens Valley, California for the <a href="https://earthquake.usgs.gov/earthquakes/eventpage/ci38457511/executive">2019 Ridgecrest earthquake sequence</a>.</p> <p>Sensor: Sentinel-1 descending track 71</p> <p>Time: 2019.06.10 - 2019.08.15, 7 acquisitions, 11 interferograms</p> <p>Processor: <a href="https://hyp3-docs.asf.alaska.edu/guides/insar_product_guide/">ASF HyP3</a> (GAMMA)</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy">MintPy</a>.</p>
Ground Vertical and east Velocities for the Western Gulf of Corinth, Greece, combining InSAR and GPS
<p><strong>Data group</strong> :<br> Ground Vertical and East Velocities for the Western Gulf of Corinth combining InSAR and GPS, for the period 2002-2010</p> <p><strong>Data identifiers</strong> : </p> <ol> <li>Best constrained 951 vertical and east PS-SBAS velocities for pixels of 200m</li> <li>Best constrained 4391 vertical and east PS-SBAS velocities for pixels of 200m</li> </ol> <p><strong>Version</strong> : 1.0</p> <p><strong>Coordinate Reference System</strong>: Geographic WGS84, EPSG:4326</p> <p><strong>Citation</strong>: Elias & Briole, 2018, Ground deformations in the Corinth rift, Greece, investigated through the means of SAR multi-temporal interferometry<br> </p>
InSAR interferences and slip model related to the 2021 Maduo earthquake in Qinghai province, China
<p>The dataset includes the SAR unwrapped interferograms, and the slip model related to the 2021 Maduo earthquake on western Maduo County, Qinghai Province of China.</p> <p>SAR images:</p> <p>Sensor: Sentinel-1 A/B ascending and descending tracks interferograms, including the T099A, T026A, T172A, T106D, T004D and T033D.</p> <p>Time: 2021.05.13 - 2019.05.27, 6 radar phases images and 2 range offset images.</p> <p>Processing software: GAMMA</p> <p>Topographic data from the Shuttle Radar Topography Mission (SRTM) with a resolution of 1 arcsec were used to align the images and remove the topographic phase.</p> <p>First‐order tropospheric delays were mitigated by using the Generic Atmospheric Correction Online Service (GACOS)</p> <p>Silp Models:</p> <p>The model is generated through triangular dislocation inversion.</p> <p>The slip models is composed of two files:</p> <p> Main rupture: Slip_Maduo_Main.gmt</p> <p> Tip rupture: Slip_Maduo_Tip.gmt</p> <p>Format: GMT</p>
Data for: Iceland Kinematics from InSAR
<p>These datasets are associated with the paper "Iceland Kinematics from InSAR" submitted to <em>JGR-solid earth</em> by Cao et al., 2022. Totally 7 types of datasets (~ 40 GB) are included: 1) time-series of displacements from six tracks of Sentinel-1 InSAR with ICAMS correction during 2015 to 2021; 2) Nationwide InSAR-derived East and vertical velocity maps; 3) Nationwide InSAR-based GIA and plate-spreading models; 4) 2) InSAR LOS velocity maps that estimated using NVCE-based weighted least-squares; 5) InSAR temporal coherence maps used for evaluating quality of InSAR-derived time-series solutions (e.g., displacements and velocity); 6) InSAR incidence angles; 7) GPS-based velocity measurements (LOS, East, and Up). Spatial resolution of the InSAR results are about 100 m by 100 m.</p>
InSAR Time Series Analysis (2018-2021) for Volcanic Monitoring in Northern Chile
<p>This dataset is for the paper "First onset of unrest captured at Socompa: A Recent Geodetic Survey at Central Andean volcanoes in Northern Chile" which is published in GRL: <a href="https://doi.org/10.1029/2022GL102480">https://doi.org/10.1029/2022GL102480</a>.</p> <p><strong>InSAR Data:</strong></p> <p>The folder of InSAR_149A.rar stores the InSAR time series analysis dataset on ascending track 149.</p> <ol> <li>The 'Imagedate' folder stores the empty *.rslc files to indicate the date of each SLCs.</li> <li>Data_Asc.mat stores the main InSAR time series data, which includes the UTC time of the acquisition (for accurate time calculation), the length of perpendicular baselines (unit is meter), the number of days counting from the first epoch, the unwrapped time series data (ifg), the unwrapped time series data with GACOS correction (ifg_aps), the look angles (la, unit is rad), and lat&lon.</li> <li>parms.mat stores the parameters used during the data processing by StaMPS.</li> <li>semi_fit.mat stores the results of the semi-variogram fitting of each interferogram on time series. It provides two versions for the original dataset (semi) and the GACOS-corrected dataset (semi_aps). This file is mainly used to weight the data during the time series fitting.</li> <li>runTSA.m, the main function to run the InSAR time series fitting. See more details in the Code part.</li> </ol> <p>The folder of InSAR_156D.rar stores the same content as the InSAR_149A.rar but for descending track 156.</p> <p><strong>Code:</strong></p> <p>This folder contains the codes of the InSAR time series fitting for this dataset, and the GBIS software.</p> <ol> <li>TSA_findref.m, this function is used to search the reference point of the InSAR data.</li> <li>TSA_EQ_fit.m, is the main function to perform InSAR time series fitting.</li> <li>rb_pixel_fit.m, is the robust way to fit the linear model.</li> <li>TSA_EQ_pixel.m, is the function used to plot the results.</li> </ol> <p>To perform the InSAR time series fitting, you need to put these four functions under your Matlab path, and then run the runTSA.m function in the data folder.</p> <p>The GBIS folder stores the updated version of the GBIS software, which allows you to perform the pCDM, CDM, and pECM. The core functions of these models are provided by Dr. Mehdi Nikkhoo, and you could find them here: https://www.volcanodeformation.com/software</p> <p><strong>GBIS_Modelling_Results:</strong></p> <p>This folder stores the data of InSAR and GPS joint inversion for Socompa Uplift.</p> <ol> <li>The folder Socompa stores the modelling results using the models of Okada(D), pECM(E), Mogi(M), pCDM(N), and Yang(Y), respectively. </li> <li>GPS_data.txt stores the cumulative displacements and the uncertainties of the SOCM station in three directions.</li> <li>Socompa.inp is the configuration file for GBIS running.</li> <li>Vol_asc.mat and Vol_asc_ds.mat stores the original and the downsampled ascending data, while Vol_dsc.mat and Vol_dsc_ds.mat store those of descending.</li> </ol> <p>Many thanks for using our dataset and please let me know if you have any further questions!</p>
Pre-eruption InSAR time-series at Kīlauea (Hawai`i, USA): COSMO-SkyMed Descending 2018
<p>InSAR time-series data for Kīlauea (Hawai`i, USA), between Jan 2010 and Sep 2011 . Data were obtained by processing COSMO-SkyMed descending SAR data (track 165). Data were processed using the JPL-developed InSAR Scientific Computing Environment (<code>ISCE</code>) open-source software package, and further time-series analysis was performed using the <code>MintPy</code> software toolbox (<a href="https://github.com/insarlab/MintPy">Miami INsar Time-series software in PYthon</a>), developed at the University of Miami. </p> <p>The following file is available in Hierarchical Data Format:</p> <p><code>geo_timeseries_tropHgt_demErr_cskDT165.h5</code>: Descending Track timeseries file. Dates available:</p> <p><code>['timeseries-20101001', 'timeseries-20101009', 'timeseries-20101017', 'timeseries-20101025', 'timeseries-20101102', 'timeseries-20101110', 'timeseries-20101118', 'timeseries-20101126', 'timeseries-20101204', 'timeseries-20101212', 'timeseries-20101220', 'timeseries-20110129', 'timeseries-20110206', 'timeseries-20110214', 'timeseries-20110222', 'timeseries-20110302', 'timeseries-20110303', 'timeseries-20110310', 'timeseries-20110318', 'timeseries-20110319', 'timeseries-20110322', 'timeseries-20110326', 'timeseries-20110403', 'timeseries-20110404', 'timeseries-20110407', 'timeseries-20110411', 'timeseries-20110419', 'timeseries-20110420', 'timeseries-20110423', 'timeseries-20110505', 'timeseries-20110506', 'timeseries-20110509', 'timeseries-20110513', 'timeseries-20110521', 'timeseries-20110522', 'timeseries-20110525', 'timeseries-20110529', 'timeseries-20110606', 'timeseries-20110607', 'timeseries-20110614', 'timeseries-20110622', 'timeseries-20110630', 'timeseries-20110708', 'timeseries-20110709', 'timeseries-20110716', 'timeseries-20110724', 'timeseries-20110725', 'timeseries-20110801', 'timeseries-20110809', 'timeseries-20110810', 'timeseries-20110817', 'timeseries-20110825', 'timeseries-20110826', 'timeseries-20110902', 'timeseries-20110910', 'timeseries-20110918']</code></p> <p> </p> <p>These data are supplemental to: Farquharson, J. I. and Amelung, F. [2020], "<em>Extreme rainfall triggered the 2018 rift eruption at Kīlauea Volcano.</em>" <a href="https://doi.org/10.1038/s41586-020-2172-5">https://doi.org/10.1038/s41586-020-2172-5</a></p>
InSAR data from 2016 to 2018 for the Tulare Basin in California's Central Valley
<p>This file contains InSAR range change observations for the Tulare basin in California's Central Valley. The values are cumulative range change from January 1, 2016 to January 1, 2018. The range change estimates were provided by Tom Farr of CalTech's Jet Propulsion Laboratory.</p>
Bandung (Indonesia) area InSAR mean velocity maps
<p>Bandung is the capital of the West Java province of Indonesia. The larger metropolitan area of Bandung has a population of more than 8 million people, and a strong exposure to a variety of geohazards. Satellite SAR data provide information on ground deformation, needed to monitor and model the various sources of these hazards and to perform multi-hazard risk analysis.</p> <p>We show the results of an ALOS-1 and COSMO-SkyMed SAR data investigation over the Bandung metropolitan area retrieved by means of InSAR multi temporal technique aimed mainly at mapping urban subsidence.</p>
InSAR measured permafrost degradation of palsa peatlands in northern Sweden Datasets
<p>Datasets used in the writing of "InSAR measured permafrost degradation of palsa peatlands in northern Sweden" published in The Cryosphere. </p> <p>The processed interferometric data and deformation maps are commercially sensitive and<br>may be made available upon reasonable request (by email) from the corresponding author.</p>
Data for: "High-resolution Soil Moisture Evolution in Hyper-arid Regions: A Comparison of InSAR, SAR, Microwave, Optical, and Data Assimilation Systems in the southern Arabian Peninsula"
<p>Data accompanying the publication: High-resolution Soil Moisture Evolution in Hyper-arid Regions: A Comparison of InSAR, SAR, Microwave, Optical, and Data Assimilation Systems in the southern Arabian Peninsula. For filenames starting with T: Exponential fit parameters time0 and mag0 for InSAR coherence data. they are binary files, where fit = a*exp(-b*x); a = -log(mag0); b = 1/time0. timeerr contains the uncertainty of the time0 parameter, and maghigh/maglow contain the high and low uncertainty for the mag0 parameter, respectively. For for each frame or overlap region (T101, T28, T130, T28_T101, T130_T28), there is a vrt file (T..._20180524.time0.vrt), which is the metadata file applicable to all files of the same frame. Files starting with mags_times: Exponential fit parameters for ASCAT/SMAP/GLDAS data. the same parameters (time0, timeerr, mag0, maghigh, maglow) can be found in these matlab structure files. In addition, the .mat files contain the offset parameter and related uncertainty, as well as lat/lon information. </p>
Rupture Process of the 2017 Mw 6.3 Earthquake in Jinghe, Northwest China Constrained by GNSS, InSAR and teleseismic waveforms
<p>This dataset include:</p> <p>1. Slip model of 2017 Mw 6.3 Jinghe earthquake invert with GNSS, InSAR and teleseismic waveforms.</p> <p>2. InSAR LOS offsets caused by the mainshock (The file named by sar.static )</p> <p>3. Aftershocks locations relocated with hypoDD</p> <p> </p>
Cyclic unrest in a geothermal field as a harbinger of the Fagradalsfjall eruption, Iceland - INSAR data repository
<p>This repository is providing the InSAR data generated from the Copernicus Sentinel-1A and 1B satellites and as published in the paper</p> <p>Flóvenz et al. (2022) Cyclic unrest in a geothermal field as a harbinger of the Fagradalsfjall eruption, Iceland. Nature Geosciences, NGS-2021-06-01178</p> <p>We analyse deformation and seismicity for one year prior to the March 2021 Fagradalsfjall eruption in Iceland. We generate a high-resolution catalogue of 39,500 earthquakes using optical cable recordings and develop a poroelastic model to describe three pre-eruptional uplift and subsidence cycles at the Svartsengi geothermal field, 8 km west of the eruption site. We find the observed deformation is best explained by cyclic intrusions into a permeable aquifer by a fluid injected at 4 km depth below the geothermal field, with a total volume of 0.11±0.05 km3 and a density of 850±350 kg/m3.</p> <p>The geodetic data relevant for the publication and provided here include:</p> <p>1. Displacement from ascending geometry</p> <p>2. Displacement from descending geometry</p> <p>3. Vertical displacement component</p> <p>4. Horizontal displacement component</p> <p>These data are available for the following bands and dates:</p> <p>band date<br> 58 07-01-2020<br> 57 13-01-2020<br> 56 19-01-2020<br> 55 25-01-2020<br> 54 31-01-2020<br> 53 06-02-2020<br> 52 12-02-2020<br> 51 18-02-2020<br> 50 24-02-2020<br> 49 01-03-2020<br> 48 07-03-2020<br> 47 13-03-2020<br> 46 19-03-2020<br> 45 25-03-2020<br> 44 31-03-2020<br> 43 06-04-2020<br> 42 12-04-2020<br> 41 18-04-2020<br> 40 24-04-2020<br> 39 30-04-2020<br> 38 06-05-2020<br> 37 12-05-2020<br> 36 18-05-2020<br> 35 24-05-2020<br> 34 30-05-2020<br> 33 05-06-2020<br> 32 11-06-2020<br> 31 17-06-2020<br> 30 23-06-2020<br> 29 29-06-2020<br> 28 05-07-2020<br> 27 11-07-2020<br> 26 17-07-2020<br> 25 23-07-2020<br> 24 29-07-2020<br> 23 04-08-2020<br> 22 10-08-2020<br> 21 16-08-2020<br> 20 22-08-2020<br> 19 28-08-2020<br> 18 03-09-2020<br> 17 09-09-2020<br> 16 15-09-2020<br> 15 21-09-2020<br> 14 27-09-2020<br> 13 03-10-2020<br> 12 09-10-2020<br> 11 15-10-2020<br> 10 21-10-2020<br> 9 27-10-2020<br> 8 02-11-2020<br> 7 08-11-2020<br> 6 14-11-2020<br> 5 20-11-2020<br> 4 26-11-2020<br> 3 02-12-2020<br> 2 08-12-2020<br> 1 14-12-2020</p> <p> </p>
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