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136 results for “sentinel-1”
Subsidence of Beijing (China) mapped by Copernicus Sentinel-1 time series interferometry
<p><strong>RESULTS DESCRIPTION</strong></p> <p>Recent reports from scientific and mainstream media have indicated that the city of Beijing, together with its surroundings, is subsiding at fast and alarming rate as result of the overexploitation of groundwater. The depletion of groundwater causes underlying soil to compact, creating a phenomenon called subsidence. The Beijing region has been experiencing this phenomenon since 1935, but in last years the rate of sinking has significantly increased.</p> <p>A team of researchers, within ESA sponsored, SEOM InSARap project performed an interferometric analysis of Copernicus Sentinel-1 data which confirms the reported findings also with current data. While the results speak for themselves, we can just once more reiterate on the usefulness of the Copernicus Programme, in this case for deformation monitoring applications.</p> <p><strong>ANALYSIS SUMMARY</strong></p> <ul> <li>Data overview: <ul> <li>Sentinel-1 IW</li> <li>Track 47 descending</li> <li>Observation window December 2014 - June 2016</li> <li>Data download via Scientific Data Hub</li> </ul> </li> <li>Processing overview: <ul> <li>Time series analysis performed with Small Baseline Subset (SBAS) methodology</li> <li>Interferometric combinations of up to 96 days used</li> </ul> </li> </ul> <p><em>More information and context available at insarap.org</em></p> <p><em>Terms and Conditions:</em> All Sentinel-1 results that are available for download are Derived Works of Copernicus data (2014-2016), subject to the "<em>TERMS AND CONDITIONS FOR THE USE AND DISTRIBUTION OF SENTINEL DATA AND SERVICE INFORMATION</em>".</p> <p><em>Acknowledgments: </em> ESA SEOM InSARap project - Sentinel-1 InSAR Performance Study with TOPS Data, contract number 4000110680/14/I-BG-InSARap</p>
Mapping and analysis of the Central Italy Earthquake (2016) with Sentinel-1 A/B interferometry
<p><strong>Results Description</strong></p> <p><em>Background</em></p> <p>A 6.2M earthquake hit Central Italy, the area of city of Amatrice, on 24 August 2016. The quake epicentre was southeast of Norcia, Italy, in an area near the borders of the Umbria, Lazio, Abruzzo and Marche regions. The quake hypocentre was at a depth of approximately 5 km.</p> <p><em>InSAR</em></p> <p>We computed a set of coseismic Sentinel-1A/B interferograms, over the affected area.</p> <ul> <li>Descending track 95: S1A (2016-08-26) / S1A (2016-08-14)</li> <li>Descending track 22: S1A (2016-08-21) / S1B (2016-08-27)</li> <li>Ascending track 117: S1B (2016-08-21) / S1A (2016-08-27)</li> </ul> <p><em>Earthquake Motion Decomposition</em></p> <p>We also performed the two-dimensional earthquake motion decomposition. As an input for the decomposition, data of tracks 22 descending and 117 ascending were used, since they provide the full coverage of the earthquake.</p> <p><strong>Results and Data Package</strong></p> <p><em>Descending Interferogram, Track 95:</em></p> <ul> <li>Goldstein filtered wrapped interferometric phase, 16x4 ML (KMZ format)</li> <li>Interferometric coherence (KMZ format)</li> <li><em>Descending Interferogram, Track 22:</em></li> <li>Goldstein filtered wrapped interferometric phase, 16x4 ML (KMZ format)</li> <li>Interferometric coherence (KMZ format)</li> <li>Full geo-coded Goldstein filtered wrapped interferogram, 16x4 ML (GeoTiff format)</li> <li>Intereferorgram subset (GeoTiff format) <ul> <li>Specifically for subset: 8x2 ML + goldstein + unwrapping + geocoding, incl oversampling to 10 m + smoothing (5x5 pixel boxcar) + subsampling to ~50m + calibrated at the point at used for 2D decomposition</li> </ul> </li> </ul> <p><em>Ascending Interferogram, Track 117:</em></p> <ul> <li>Goldstein filtered wrapped interferometric phase, 16x4 ML (KMZ format)</li> <li>Interferometric coherence (KMZ format)</li> <li>Full geo-coded Goldstein filtered wrapped interferogram, 16x4 ML (GeoTiff format)</li> <li>Intereferorgram subset (GeoTiff format, 7Mb zip file) <ul> <li>Specifically for subset: 8x2 ML + goldstein + unwrapping + geocoding, incl oversampling to 10 m + smoothing (5x5 pixel boxcar) + subsampling to ~50m + calibrated at the point at used for 2D decomposition</li> </ul> </li> </ul> <p><em>Decomposed Solution</em></p> <ul> <li>Vertical Component (KMZ format)</li> <li>East-West Component (KMZ format)</li> <li>Vertical Component (GeoTiff format)</li> <li>East-West Component (GeoTiff format)</li> </ul> <p><em>Additional Information</em></p> <ul> <li>Filenames are self descriptive and not require additional information</li> <li>KMZ and GeoTiff files that are available for download, are generated for high-resolution investigations in GoogleEarth and post-processing & interpretation. They are not prepared for overviews, and thus are not heavily smoothed nor filtered.</li> </ul> <p><em>Terms and Conditions:</em> All Sentinel-1 results that are available for download are Derived Works of Copernicus data (2014-2016), subject to the "TERMS AND CONDITIONS FOR THE USE AND DISTRIBUTION OF SENTINEL DATA AND SERVICE INFORMATION".</p> <p><em>Acknowledgments:</em> ESA SEOM InSARap project - Sentinel-1 InSAR Performance Study with TOPS Data, contract number 4000110680/14/I-BG-InSARap</p> <p><em>More information and context available at insarap.org .</em></p>
Amatrice Earthquake - Sentinel-1 TOPS Ascending - Coherence Map (S1A_20160815-S1A_20160827)
<p>Coherence levels of the ascending S1 TOPSAR interferogram (S1A_20160815-S1A_20160827).</p> <p>A Sentinel-1 TOPS co-seismic interferogram of the Amatrice earthquake in Italy on the 24th of August 2016. Processing was performed with the ESA SNAP toolbox (http://step.esa.int/).</p> <p>S1A data were downloaded from the Sentinel-1 Scientific Data Hub.</p> <p>Contains modified Copernicus data (2016)</p> <p> </p>
Amatrice Earthquake - Sentinel-1 TOPS - Vertical Motion
<p>Vertical motion component of the Amatrice earthquake from Sentinel-1.</p> <p>A Sentinel-1 TOPS co-seismic interferogram of the Amatrice earthquake in Italy on the 24th of August 2016. Processing was performed with the ESA SNAP toolbox (http://step.esa.int/).</p> <p>S1A data were downloaded from the Sentinel-1 Scientific Data Hub.</p> <p>Contains modified Copernicus data (2016)</p>
Amatrice Earthquake - Sentinel-1 TOPS - E-W Motion
<p>E-W motion component of the Amatrice earthquake from Sentinel-1.</p> <p>A Sentinel-1 TOPS co-seismic interferogram of the Amatrice earthquake in Italy on the 24th of August 2016. Processing was performed with the ESA SNAP toolbox (http://step.esa.int/).</p> <p>S1A data were downloaded from the Sentinel-1 Scientific Data Hub.</p> <p>Contains modified Copernicus data (2016)</p>
Amatrice Earthquake - Sentinel-1 TOPS Descending - Differential Interferogram (S1A_20160821-S1B_20160827)
<p>Differential S1 TOPS interferogram (S1A_20160821-S1B_20160827) from descending orbit 22.</p> <p>A Sentinel-1 TOPS co-seismic interferogram of the Amatrice earthquake in Italy on the 24th of August 2016. Processing was performed with the ESA SNAP toolbox (http://step.esa.int/) including TOPS InSAR processing, removal of topographic phase, phase filtering and orthorectification.</p> <p>S1A data were downloaded from the Sentinel-1 Scientific Data Hub.</p> <p>Contains modified Copernicus data (2016)</p>
Amatrice Earthquake - Sentinel-1 TOPS Descending - Coherence Map (S1A_20160821-S1B_20160827)
<p>Coherence levels of the descending S1 TOPSAR interferogram (S1A_20160821-S1B_20160827).</p> <p>A Sentinel-1 TOPS co-seismic interferogram of the Amatrice earthquake in Italy on the 24th of August 2016. Processing was performed with the ESA SNAP toolbox (http://step.esa.int/).</p> <p>S1A data were downloaded from the Sentinel-1 Scientific Data Hub.</p> <p>Contains modified Copernicus data (2016)</p>
Amatrice Earthquake - Sentinel-1 TOPS Ascending - Differential Interferogram (S1A_20160815-S1A_20160827)
<p>Differential S1 TOPS interferogram (S1A_20160815-S1A_20160827) from ascending orbit 117.</p> <p>A Sentinel-1 TOPS co-seismic interferogram of the Amatrice earthquake in Italy on the 24th of August 2016. Processing was performed with the ESA SNAP toolbox (http://step.esa.int/) including TOPS InSAR processing, removal of topographic phase, phase filtering and orthorectification.</p> <p>S1A data were downloaded from the Sentinel-1 Scientific Data Hub.</p> <p>Contains modified Copernicus data (2016)</p>
Demo measurement using DIAPASON and Sentinel-1 after the 14 November 2016 earthquake in New Zealand
<p>This is a test of the automated chain on the GEP.</p>
Ground deformation maps of the Visso and Norcia 2016 earthquakes captured from Sentinel-1 SAR
<p>The dataset contains the Line Of Sight (LOS) deformation maps obtained by applying Differential SAR Interferometry (DinSAR) to Sentinel-1 Interferometric Wide-swath (or TOPSAR) imagery.</p> <p>Two maps are provided: the first maps is the LOS deformation estimated from ascending data, the second map is the LOS deformation from descending images.</p> <p>Maps are expressed in metres and are provided in raster geotiff format.</p> <p>Data have been processed with GAMMA Interferometric processor.</p> <p>Ascending data details:</p> <p>SAR pairs are dated 2016/10/27 and 2016/11/02, acquired on relative orbit number 44, by Sentinel-1B and Sentinel-1A, respectively.</p> <p>The DEM used for removing the topographic phase is the SRTM 1 arc second. The interferogram has been generated by applying a 1x5 multi-look factor in azimuth and range, respectively.</p> <p>Final product has been geocoded in UTM WGS84 33 Nord projection, with a posting of 20 m.</p> <p>Descending data details:</p> <p>SAR pairs are dated 2016/10/26 and 2016/11/01, acquired on relative orbit number 22, by Sentinel-1B and Sentinel-1A, respectively.</p> <p>The DEM used for removing the topographic phase is the SRTM 1 arc second. The interferogram has been generated by applying a 2x10 multi-look factor in azimuth and range, respectively.</p> <p>Final product has been geocoded in UTM WGS84 33 Nord projection, with a posting of 40 m.</p>
The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities
<p>Velocity field for the India-Eurasia collision zone from Sentinel-1 InSAR and GNSS data</p> <p>Citations:</p> <p>[1] Jin Fang, Gregory A Houseman, Tim J Wright, Lynn A Evans, Tim J Craig, John R Elliott and Andy Hooper (2023). The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10053499</p> <p>[2] Jin Fang, Gregory A Houseman, Tim J Wright, Lynn A Evans, Tim J Craig, John R Elliott and Andy Hooper (2024). The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities, Journal of Geophysical Research: Solid Earth, https://doi.org/10.1029/2023JB028571</p> <p>More details about the methodology to generate the velocity field can be found in Wright et al. (2023):</p> <p>[3] Tim J Wright, Greg Houseman, Jin Fang, Yasser Maghsoudi, Andy Hooper, John Elliott, Lynn Evans, Milan Lazecky, Qi Ou, Barry Parsons, Chris Rollins, Lin Shen, Hua Wang (2023). High-resolution geodetic strain rate field reveals dynamics of the India-Eurasia collision, submitted to Science, preprint available at https://doi.org/10.31223/X5G95R.</p>
A dataset for semantic segmentation of typical oceanic and atmospheric phenomena from Sentinel-1 images
<p>We have constructed a SAR (Synthetic Aperture Radar) image semantic segmentation dataset that includes 12 oceanic and atmospheric phenomena: Atmospheric Front (AF), Oceanic Front (OF), Rainfall (RF), Iceberg (IC), Sea Ice (SI), Pure Ocean Wave (POW), Wind Streak (WS), Low Wind Area (LWA), Biological Slick (BS), Micro Convective Cells (MCC), Internal Wave (IW), and Eddy.</p> <p>This dataset is built using Sentinel-1 IW and WV mode images. For WV mode data, we referenced TenGeoP-SARwv and SAR_WV_SemanticSegmentation and selected 2,383 images for semantic segmentation and annotation. For IW mode images, we incorporated some images from Tao et al.'s internal wave detection dataset. We selected 484 Sentinel-1 IW mode images obtained from 2015 to 2022 and divided them into 2,628 sub-images.</p> <p>The dataset contains a total of 5,011 image slices, with approximately 400 images for each phenomenon. All images are 16-bit .tiff files with a resolution of 100m and a size of 256x256 pixels. The images were manually annotated using the Labelme software, generating corresponding JSON files, which were then used to create the related annotation .png files.</p> <p>The updated version(V2) provides geographic information for each image.</p> <p>Thank you for your interest in our dataset. Here are the meanings of each label:</p> <p>1. BG: The unlabelled parts in JSON files are "BG" (Background)<br>2. AF: Atmospheric Front<br>3. BS: Biological Slick<br>4. I: “I” is equivalent to “IB”, representing icebergs<br>5. LWA: Low Wind Area<br>6. MCC: Micro Convective Cells<br>7. OF: Oceanic Front<br>8. POW: Pure Ocean Wave<br>9. RC: “RC” (Rain Cells) is equivalent to “RF” (Rainfall), both representing the rainfall phenomenon in the SAR image. <br>10. SI: Sea Ice<br>11. WS: Wind Streak<br>12. Eddy<br>13. IW: Internal Wave<br><em>14. HM: Represents the artificial objects appearing in the image, such as ships, aquaculture floating rafts, wind power facilities, etc.</em><br><em>15. OS: Unlike “BS”,“OS” represents mineral oil spills appearing in the SAR image (currently, there is insufficient data available for training, which will be supplemented in the future).</em></p>
Present-day surface deformation of Sicily: Insights from Sentinel-1 data processed by a PS-InSAR approach
<p>The directory DATASET.zip provides PS-InSAR data used in Henriquet et al., (2022). The data set contains for each Sentinel-1 track (44, 117, 22, 124) the mean PS velocities along the LOS, before (ps_mean_v.xy.v-dos) and after (ps_mean_v-dos_adjusted2GPS.xy) their adjustment to the 3D-GNSS velocity field, as well as the disparities of the PS velocities (ps_mean_disp.xy). The data set also includes the East- and Up-component of the reconstructed mean PS velocity field (East.grd and Up.grd) used in the Figures 7 to 12 in the paper.</p>
Sentinel-1 InSAR time-series and velocity map over the Bay Area (Descending track 42, 2015-2020)
<p>Supplemental material for <em>"Spatiotemporal variations of surface deformation, shallow creep rate, and slip partitioning between the San Andreas and southern Calaveras Fault"</em> at JGR-Solid Earth</p> <p>Citation: <strong>Li, Y</strong>., Bürgmann, R., & Taira, T. (2023). Spatiotemporal variations of surface deformation, shallow creep rate, and slip partitioning between the San Andreas and southern Calaveras Fault. <em>Journal of Geophysical Research: Solid Earth</em>, 128, e2022JB025363. <a href="https://doi.org/10.1029/2022JB025363">https://doi.org/10.1029/2022JB025363</a></p>
Global offshore wind turbine analysis with Sentinel-1 - supplementary data
<p>Gloabl offshore wind turbine analysis with Sentinel-1 - supplementary data</p> <p>The files are supplementary data of the publication:</p> <p>Global dynamics of the offshore wind energy sector monitored with Sentinel-1: Turbine count, installed capacity and site specifications</p> <p>which is currently under review in the International Journal of Applied Earth Observation and Geoinformation</p> <p>supplementary_data_B_OWT_height_capacity.csv holds 50 pairs of offshore wind turbine hub heights and the corresponding installed capacities along with the offshore wind farm project name, the number of turbines of this wind farm, and the source the information originates from.</p> <p>supplementary_data_B_DeepOWT_1_21_2_plus.geojson is the extended version of the DeepOWT data set (https://zenodo.org/record/5933967) with all of the derived attributes in the respective publication e.g. OWT hub height and installed capacity.</p>
Surface velocities due to the Southern San Andreas Fault from Sentinel-1 InSAR data
<p>Line of sight (LOS), fault-parallel, and vertical velocities in the area around the Southern San Andreas Fault in California, USA. Gzipped tar archive. All data are in Generic Mapping Tools (GMT) Netcdf format. </p>
Earthquake Cycle Deformation Associated with the 2021 Mw 7.4 Maduo (Eastern Tibet) Earthquake: An Intrablock Rupture Event on a Slow-Slipping Fault from Sentinel-1 InSAR and Teleseismic Data
<p>Coseismic slip models of the 2021 Mw 7.4 Maduo (eastern Tibet) earthquake derived from Sentinel-1 InSAR and teleseismic data.</p> <p>Interseismic eastward and vertical velocity and maximum shear strain rate fields.</p> <p>Citations:</p> <p>Fang, J., Ou, Q., Wright, T. J., Okuwaki, R., Amey, R. M. J., Craig, T. J., et al. (2022). Earthquake cycle deformation associated with the 2021 M<span>W </span>7.4 Maduo (eastern Tibet) earthquake: An intrablock rupture event on a slow-slipping fault from Sentinel-1 InSAR and teleseismic data. Journal of Geophysical Research: Solid Earth, 127, e2022JB024268. <span>https://</span>doi.org/10.1029/2022JB024268</p> <p>Fang, J., Ou, Q., Wright, T. J., Okuwaki, R., Amey, R. M. J., Craig, T. J., et al. (2022). Earthquake cycle deformation associated with the 2021 M<span>W </span>7.4 Maduo (eastern Tibet) earthquake: An intrablock rupture event on a slow-slipping fault from Sentinel-1 InSAR and teleseismic data [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7215161<span>.</span></p>
Sentinel-1 snow depth assimilation to improve river discharge estimates in the western European Alps
<p>This data set contains model output presented in the following paper: I. Brangers, H. Lievens, A. Getirana, and G. J. M. De Lannoy. (2024). Sentinel-1 snow depth assimilation to improve river discharge estimates in the western European Alps. Water Resources Research. Under review.</p> <div>The model simulations were carried out in NASA's Land Information System (LIS), using the NoahMP v3.6 land surface model, forced with ERA5. The land surface model was coupled to the HyMAP routing algorithm to produce streamflow estimates. The data contains model results for the western European Alps for the period of 2015-2021 for two seperate cases. 1) The OL run: model run without assimilation of external observations; and 2) DA run: model run with the assimilation of Sentinel-1 snow depth observations.</div> <div> </div> <div>The zip-folders contain netcdf files for each day of the simulation period, for 1) the river discharge (_ROUTING), </div> <div>2) land surface model variables such as snow depth and SWE (_SURFACEMODEL_yyyy) grouped per year, and 3) variables related to the data assimilation such as the spread and innovations (_EnKF).</div>
Data from: Investigating the Association of Seasonal Dynamics in GEDI Canopy Cover Profiles and Sentinel-1 Backscatter in Temperate Forests
<p>This dataset supports the analysis about <em>Investigating the Association of Seasonal Dynamics in GEDI Canopy Cover Profiles and Sentinel-1 Backscatter in Temperate Forests</em></p>
Set of Copernicus Sentinel-1 images around Iceland
<p>Set of Sentinel-1 images downloaded from the Copernicus API. They are set around Iceland and intended to validate and test ice block detection algorithms</p>
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