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136 results for “Sentinel-1”
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>
Codes and dataset of the publication "Effectiveness of Sentinel-1 and Sentinel-2 for Flood Detection Assessment in Europe"
<p>The folder contains the codes, input and output of the analysis carried out for supporting the publication of the paper:</p> <p>Tarpanelli A., Mondini A., Camici S.:Effectiveness of Sentinel-1 and Sentinel-2 for Flood Detection Assessment in Europe, Natural Hazards and Earth System Sciences, https://doi.org/10.5194/nhess-2022-63, 2022.</p> <p> </p> <p>The codes should be run in order A1-A7 to generate all the figures of the paper.</p> <p>For details please send an email to:</p> <p>angelica.tarpanelli@irpi.cnr.it</p> <p> </p>
EOOffshore: Sentinel-1 Wind Data for the Irish Continental Shelf Region
<p><a href="https://eooffshore.github.io">EOOffshore</a> is a <a href="https://www.seai.ie/">Sustainable Energy Authority of Ireland (SEAI)</a> funded <a href="https://www.seai.ie/data-and-insights/seai-research/research-projects/details/building-upon-copernicus-earth-observation-services-to-augment-wind-measurement-coverage-of-the-oredp-offshore-renewable-energy-assessment-areas">project</a>, which commenced in June 2020 in the <a href="https://www.ucd.ie/physics/">School of Physics</a> in <a href="https://www.ucd.ie/">University College Dublin (UCD)</a>. It presents a case study that demonstrates the utility of the <a href="https://pangeo.io/">Pangeo</a> software ecosystem in the development of offshore wind speed and power density estimates, increasing wind measurement coverage of offshore renewable energy assessment areas in the <a href="https://www.marine.ie/Home/site-area/irelands-marine-resource/real-map-ireland">Irish Continental Shelf (ICS)</a> region. It has involved the creation of a new <a href="https://eooffshore.github.io/datasets.html">wind data catalog</a> for this region, consisting of a collection of analysis-ready, cloud-optimized (ARCO) datasets featuring up to 21 years of available in situ, reanalysis, and satellite observation wind data products.</p> <p>The <a href="https://www.copernicus.eu/en/about-copernicus">European Union Copernicus Earth Observation (EO) programme</a> and services are based on data collected from EO satellites, in particular, the <a href="https://sentinels.copernicus.eu/web/sentinel/home">Sentinel satellite missions</a>. This includes the <a href="https://sentinel.esa.int/web/sentinel/missions/sentinel-1">Sentinel-1 mission</a>, which consists of C-band Synthetic Aperture Radar (SAR) imaging satellites in polar orbit. One of its main objectives is the provision of ocean monitoring services, where its <a href="https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-types-processing-levels/level-2">Level-2 Ocean (OCN)</a> products include an Ocean WInd field (OWI) component. This provides gridded estimates of wind speed and direction at 10 m above the surface, with a typical spatial resolution of 1 km. This particular catalog data set (<em>eooffshore_ics_level3_sentinel1_ocn.zarr.tar.gz</em>) contains 2015-2021 OCN wind products for the ICS region, which were retrieved from the <a href="https://scihub.copernicus.eu/">Copernicus Open Access Hub (COAH)</a> and the <a href="https://search.asf.alaska.edu/#/">Alaska Satellite Facility (ASF)</a>. The data set was used in the EOOffshore project outputs presented (<em><a href="https://meetingorganizer.copernicus.org/EGU22/EGU22-2746.html">Scalable Offshore Wind Analysis With Pangeo</a></em>) at the <em><a href="https://meetingorganizer.copernicus.org/EGU22/session/42046">Meeting Exascale Computing Challenges with Compression and Pangeo</a></em> <a href="https://www.egu22.eu/">2022 EGU General Assembly</a> session.</p> <p>Description and example usage of the Sentinel-1 data set in EOOffshore:</p> <ul> <li><a href="https://eooffshore.github.io/Sentinel-1_ICS_Wind_Data.html">Sentinel-1 Wind Data for Irish Continental Shelf region</a></li> <li><a href="https://eooffshore.github.io/Offshore_Wind_AOI.html">Offshore Wind in Irish Areas Of Interest</a></li> <li><a href="https://eooffshore.github.io/Comparison_Wind_Power.html">Comparison of Offshore Wind Speed Extrapolation and Power Density Estimation</a></li> </ul> <p>As requested by the <a href="https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice">Legal Notice on the use of Copernicus Sentinel Data and Service Information</a>, this data set:</p> <ul> <li>Contains modified Copernicus Sentinel data [2015 - 2021]</li> </ul>
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>
High-resolution sea ice drift and deformation example data derived from Sentinel-1 in the Arctic Ocean during MOSAiC
<p>This data set contains two high-resolution sea ice drift and deformation fields from 30/31 December 2019 and 20/21 June 2021. They were acquired in the Transpolar Drift along the drift track of the research campaign "Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). Drift fields were calculated from Sentinel-1, HH polarization SAR images acquired in enhanced wide mode. These had a pixel resolution of 50 m in Polar Stereographic North projection (latitude of true scale: 70 N, center longitude: 45 W). We used an ice tracking algorithm introduced by Thomas et al. (2008, 2011) and modified by Hollands and Dierking (2011) to derive drift from sequential pairs. The time step between two sequential images is approximately one day. The resulting drift data set was defined on a regular grid with a spatial resolution of 700 m. Outliers in the velocity data were reduced by a 3x3 point running median filter covering an area of 2.1x2.1 km. For the deformation estimates, we calculated deformation using a linear approximation based on Green's Theorem that relates the double integral over a plane to the line integral along a simple curve surrounding the plane. We discretized the curve applying the trapezoid method that linearly interpolates velocity between the vertices of the grid cells. This work contains modified Copernicus Sentinel data (2020)</p> <p>Related publications:</p> <p><strong>von Albedyll, L., Haas, C., and Dierking, W.</strong>: Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations, The Cryosphere, 15, 2167–2186, <a href="https://doi.org/10.5194/tc-15-2167-2021">https://doi.org/10.5194/tc-15-2167-2021</a>, 2021.</p> <p><strong>Hollands, Thomas; Dierking, Wolfgang (2011):</strong> Performance of a multiscale correlation algorithm for the estimation of sea-ice drift from SAR images: initial results. <em>Annals of Glaciology</em>, <strong>52(57)</strong>, 311-317, <a href="https://doi.org/10.3189/172756411795931462">https://doi.org/10.3189/172756411795931462</a></p> <p><strong>Thomas, Mani; Geiger, Cathleen A; Kambhamettu, Chandra (2008):</strong> High resolution (400 m) motion characterization of sea ice using ERS-1 SAR imagery. <em>Cold Regions Science and Technology</em>, <strong>52(2)</strong>, 207-223, <a href="https://doi.org/10.1016/j.coldregions.2007.06.006">https://doi.org/10.1016/j.coldregions.2007.06.006</a></p> <p><strong>Thomas, Mani; Kambhamettu, Chandra; Geiger, Cathleen A (2011):</strong> Motion Tracking of Discontinuous Sea Ice. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <strong>49(12)</strong>, 5064-5079, <a href="https://doi.org/10.1109/TGRS.2011.2158005">https://doi.org/10.1109/TGRS.2011.2158005</a></p>
Horizontal and vertical velocities in Ho Chi Minh city by Sentinel-1 radar interferometry
<p>Ho Chi Minh City (HCMC), the most crowded city and economic hub of Viet Nam, has been experiencing land subsidence over the past decades. This effort aims to contribute the spatial distribution of subsidence in HCMC in its horizontal and vertical components using synthetic aperture radar interferometry (InSAR) time series. To this purpose, an advanced Persistent Scatterers and Distributed Scatterers (PSDS) InSAR technique was applied to two European Space Agency (ESA) Sentinel-1 datasets consisting of 96 ascending and 202 descending images, acquired from 2014 to 2020 over the HCMC area. The combination of ascending and descending satellite passes is used to decompose the light of sight velocities into horizontal east-west and vertical components. The obtained results revealed that subsidence is most pronounced in the areas along the Sai Gon River, in the northwest-southeast axis, and in the southwest of the city, with a maximum value of 80 mm/yr, which is in accordance with the findings of the literature. The amplitude of east-west horizontal velocities is relatively small and large-scale eastward movement can be observed in the west of the city at a rate of 3-5 mm/yr.</p> <p>File "Dinh_HCMUD_v1.tif" is the 50-m vertical velocity in mm/year. Negative velocities represent movement subsidence.</p> <p>File "Dinh_HCMEW_v1.tif" is the 50-m east-west horizontal velocity in mm/year. Positive velocities represent movement Eastward.</p> <p>For more details on the technique, the reader can be found in [1].</p> <p>[1] Ho Tong Minh, D.; NGO, Y.; Lê, T.T.; Le, T.C.; Bui, H.S.; Vuong, Q.V.; Le Toan, T. Quantifying Horizontal and Vertical Movements in Ho Chi Minh City by Sentinel-1 Radar Interferometry. <em>Preprints</em> <strong>2020</strong>, 2020120382. Available: https://www.preprints.org/manuscript/202012.0382/v2</p> <p> </p> <p> </p>
Sample version 2.0 ITS_LIVE Sentinel-1 image pair product of ice velocity in Greenland
<p>21 samples of version 2.0 ITS_LIVE Sentinel-1 image pair product of ice velocity in three test regions of Greenland Ice Sheet</p> <p>Sensor: Sentinel-1A/B</p> <p>Processor: <a href="https://github.com/isce-framework/isce2">ISCE</a> v2.4.1 (topsApp -> <a href="https://github.com/leiyangleon/Geogrid">Geogrid</a> v1.4.0 -> <a href="https://github.com/nasa-jpl/autoRIFT">autoRIFT</a> v1.4.0)</p> <p>Project: NASA MEaSUREs project <a href="https://its-live.jpl.nasa.gov">ITS_LIVE</a></p> <p>Region 1 (69.13N, 50.88W; Jakobshavn Isbræ Glacier): 7 ascending image pairs</p> <p>Region 2 (77.61N, 42.79W; central north of interior Greenland): 3 ascending image pairs</p> <p>Region 3 (72.48N, 35.87W; central south of interior Greenland): 10 descending image pairs and 1 ascending image pair</p> <p>This serves as a supplementary dataset for the companion journal article submitted to Earth System Science Data (to appear soon).</p> <p> </p> <p>Acknowledgement:</p> <p>This effort was funded by the NASA MEaSUREs program in contribution to the Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project (<a href="https://its-live.jpl.nasa.gov/">https://its-live.jpl.nasa.gov/</a>) and through Alex Gardner’s participation in the NASA NISAR Science Team</p>
SEN12TP - Sentinel-1 and -2 images, timely paired
<p>The SEN12TP dataset (<strong>Sen</strong>tinel-<strong>1</strong> and -<strong>2</strong> imagery, timely <strong>p</strong>aired) contains 2319 scenes of Sentinel-1 radar and Sentinel-2 optical imagery together with elevation and land cover information of 1236 distinct ROIs taken between 28 March 2017 and 31 December 2020. Each scene has a size of 20km x 20km at 10m pixel spacing. The time difference between optical and radar images is at most 12h, but for almost all scenes it is around 6h since the orbits of Sentinel-1 and -2 are shifted like that. Next to the <span class="math-tex">\(\sigma^\circ\)</span> radar backscatter also the radiometric terrain corrected <span class="math-tex">\(\gamma^\circ\)</span> radar backscatter is calculated and included. <span class="math-tex">\(\gamma^\circ\)</span> values are calculated using the volumetric model presented by Vollrath et. al 2020.</p> <p>The uncompressed dataset has a size of 222 GB and is split spatially into a train (~90%) and a test set (~10%). For easier download the train set is split into four separate zip archives.</p> <p>Please cite the following paper when using the dataset, in which the design and creation is detailed:<br> T. Roßberg and M. Schmitt. <strong>A globally applicable method for NDVI estimation from Sentinel-1 SAR backscatter using a deep neural network and the SEN12TP dataset</strong>. <em>PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science</em>, 2023. <a href="https://doi.org/10.1007/s41064-023-00238-y">https://doi.org/10.1007/s41064-023-00238-y</a>.</p> <p> </p> <p>The file <code>sen12tp-metadata.json</code> includes metadata of the selected scenes. It includes for each scene the geometry, an ID for the ROI and the scene, the climate and land cover information used when sampling the central point, the timestamps (in ms) when the Sentinel-1 and -2 image was taken, the month of the year, and the EPSG code of the local UTM Grid (e.g. EPSG:32643 - WGS 84 / UTM zone 43N).</p> <p>Naming scheme: The images are contained in directories called <em>{roi_id}_{scene_id}</em>, as for some unique regions image pairs of multiple dates are included. In each directory are six files for the different modalities with the naming <em>{scene_id}_{modality}.tif</em>. Multiple modalities are included: radar backscatter and multispectral optical images, the elevation as DSM (digital surface model) and different land cover maps.</p> <table summary="Included modalities in the dataset."> <caption>Data modalities</caption> <thead> <tr> <th scope="col">name</th> <th scope="col">Modality</th> <th scope="col">GEE collection</th> </tr> </thead> <tbody> <tr> <td>s1</td> <td>Sentinel-1 radar backscatter</td> <td><a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD"><code>COPERNICUS/S1_GRD</code></a></td> </tr> <tr> <td>s2</td> <td>Sentinel-2 Level-2A (Bottom of atmosphere, BOA) multispectral optical data with added cloud probability band</td> <td><a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR"><code>COPERNICUS/S2_SR</code></a><br> <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_CLOUD_PROBABILITY"><code>COPERNICUS/S2_CLOUD_PROBABILITY</code></a></td> </tr> <tr> <td>dsm</td> <td>30m digital surface model</td> <td><a href="https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_AW3D30_V3_2"><code>JAXA/ALOS/AW3D30/V3_2</code></a></td> </tr> <tr> <td>worldcover</td> <td>land cover, 10m resolution</td> <td><a href="https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100"><code>ESA/WorldCover/v100</code></a></td> </tr> </tbody> </table> <p> </p> <p>The following bands are included in the tif files, for an further explanation see the documentation on GEE. All bands are resampled to 10m resolution and reprojected to the coordinate reference system of the Sentinel-2 image.</p> <table> <caption>Modality Bands</caption> <tbody> <tr> <td><strong>Modality</strong></td> <td><strong>Band count</strong></td> <td><strong>Band names in tif file</strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td>s1</td> <td>5</td> <td>VV_sigma0, VH_sigma0, VV_gamma0flat, VH_gamma0flat, incAngle</td> <td>VV/VH_sigma0 are the <span class="math-tex">\(\sigma^\circ\)</span> values,<br> VV/VH_gamma0flat are the radiometric terrain corrected <span class="math-tex">\(\gamma^\circ\)</span> backscatter values<br> incAngle is the incident angle</td> </tr> <tr> <td>s2</td> <td>13</td> <td>B1, B2, B3, B4, B5, B7, B7, B8, B8A, B9, B11, B12, cloud_probability</td> <td>multispectral optical bands and the probability that a pixel is cloudy, calculated with the <a href="https://github.com/sentinel-hub/sentinel2-cloud-detector">sentinel2-cloud-detector</a> library<br> optical reflectances are bottom of atmosphere (BOA) reflectances calculated using <em>sen2cor</em></td> </tr> <tr> <td>dsm</td> <td>1</td> <td>DSM</td> <td>Height above sea level. Signed 16 bits. Elevation (in meter) converted from the ellipsoidal height based on ITRF97 and GRS80, using EGM96†1 geoid model.</td> </tr> <tr> <td>worldcover</td> <td>1</td> <td>Map</td> <td>Landcover class</td> </tr> </tbody> </table> <p> </p> <p><strong>Checking the file integrity</strong><br> After downloading and decompression the file integrity can be checked using the provided file of md5 checksum.<br> Under Linux: <code>md5sum --check --quiet md5sums.txt</code></p> <p> </p> <p><strong>References:</strong></p> <p>Vollrath, Andreas, Adugna Mullissa, Johannes Reiche (2020). "Angular-Based Radiometric Slope Correction for Sentinel-1 on Google Earth Engine". In: Remote Sensing 12.1, Art no. 1867. https://doi.org/10.3390/rs12111867.</p>
FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and GEDI data with a deep learning approach.
<p>The products can be vizualized at <a href="https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer">https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer</a></p> <p>- FORMS-H: Canopy height map of France at 10 m resolution. The units are in centimeter (10^-2 m).</p> <p>- FORMS-B: Above-ground biomass density map of France at 30 m resolution. The units are in Mg ha-1</p> <p>- FORMS-V: Wood volume density map of France at 30 m resolution. The units are in m3 ha-1</p> <p>Please refer to the paper <a href="https://doi.org/10.5194/essd-15-4927-2023">https://doi.org/10.5194/essd-15-4927-2023</a> for further details.</p>
Woody Cover Mapping in the Kruger National Park using Sentinel-1 time series and LiDAR data
<p>This data repository presents a workflow to derive woody cover information for the Kruger National Park, South Africa, from freely available Sentinel-1 C-Band time series and LiDAR data (modified from Smit et al. 2016) using machine learning (MLR and Ranger in R). The methodology is described in following publication:</p> <p><em>Urban, M., K. Heckel, C. Berger, P. Schratz, I.P.J. Smit, T. Strydom, J. Baade & C. Schmullius (2020): Woody Cover Mapping in the Savanna Ecosystem of the Kruger National Park Using Sentinel-1 C-Band Time Series Data. Koedoe.</em></p> <p>In order to derive woody cover percentage information, download all files into one folder and run the R-Files consecutively from 01_ to 04_. Follow the instruction within each of the R-Files, which are written as comments in the programming code.</p> <p>The data repository consist of the following files:</p> <p><strong>R-Files:</strong></p> <p>1. Script 1: 01_MLR_tune_spatial_final</p> <p>2. Script 2: 02_MLR_cross_validation_spatial_final</p> <p>3. Script 3: 03_MLR_RANGER_train_final</p> <p>4. Script 4: 04_MLR_prediction_woody_cover_final</p> <p> </p> <p><strong>Training dataset - ENVI FILE (layerstack of Sentinel-1 VH and VV backscatter between 2016 and 2017 and the woody cover reference derived from the LiDAR data) :</strong></p> <p>1. S1_A_VH_VV_16_17_lidar</p> <p> </p> <p><strong>Data for prediction - ENVI FILES (3 example regions in the Kruger National Park):</strong></p> <p>1. S1_A_VH_VV_16_17_subset_example_Letaba_Rest_Camp</p> <p>2. S1_A_VH_VV_16_17_subset_example_Lower_Sabie</p> <p>3. S1_A_VH_VV_16_17_subset_example_Pafuri</p> <p> </p> <p><strong>Final woody cover maps of the Kruger National Park:</strong></p> <p>1. xx_woody_cover_map_final.rar (contains final maps in 10m, 30m, 50m and 100m spatial resolution as .tif and a QGIS project)</p> <p> </p> <p><em>References:</em></p> <p>Smit, I.P.J., Asner, G.P., Govender, N., Vaughn, N.R. & Wilgen, B.W. van, 2016, ‘An examination of the potential efficacy of high-intensity fires for reversing woody encroachment in savannas’, <em>Journal of Applied Ecology</em>, 53(5), 1623–1633.</p>
Ecuador quake observed by Sentinel-1
<p>We have created a new Sentinel-1 interferogram capturing the massive earthquake that hit Ecuador on 16 April with a magnitude of 7.8. Two Sentinel-1 images have been used in the GEP: one pre event acquired by the satellite on 12 April and the other post event, on 24 April.</p> <p>The quake occurred as the result of shallow thrust faulting on or near the plate boundary between the Nazca and South America plates, according to the USGS. Ecuador lies above the plate boundary where the Nazca Plate subducts beneath the South American Plate at a velocity of 61 mm/yr. At least 654 people were killed and more than 16,600 were injured, this quake was the worst natural disaster in Ecuador since the 1949 Ambato earthquake.</p> <p>The Sentinel mission marks a new era in remote sensing aid for disaster response. These satellites play a crucial role in helping manage natural disasters because they provide timely information that can be rapidly processed. The results obtained with interferometry allow the monitoring of ground motion, down to a few mm, on wide areas. This type of information is essential for monitoring shifts from earthquakes, landslides, and volcanic uplift, among others.</p> <p>In the last year we have seen some impressive InSAR results from Sentinel-1A, the first from this two-satellite constellation, that has been in orbit for two years. On 25 April its identical twin Sentinel-1B was launched. With the two satellites in orbit we will now receive double the amount of data and achieve global coverage in six days.</p>
Demo measurement using DIAPASON and Sentinel-1 after the 14 November 2016 earthquake in New Zealand
<p>Processing of Sentinel-1A acquisitions of 3rd and 15th Nov 2016 with CNES DIAPASON processing chain, integrated by TRE Altamira on ESA's Geohazard Exploitation Platform. Track 52, Ascending Orbit Direction.</p> <p>Contains modified Copernicus Sentinel data 2016</p>
February 2017 Western Turkey Earthquake Swarm Sentinel-1 TOPS Differential Interferogram (20170131-20170212)
<p>In february 2017 a series of earthquakes affected the Biga Peninsula in Western Turkey. Over 350 buildings sustained extensive damage. The seismic events occurred at the intersection of the Kestanbol Fault and the Edremit Fault Zone. The Sentinel-1 TOPS co-seismic interferogram was generated with the ESA SNAP toolbox (http://step.esa.int/).</p> <p>S1A data were downloaded from the Sentinel-1 Scientific Data Hub: S1A_20170131-S1A_20170212 from ASCENDING orbit 131.</p> <p> </p>
EAST-WEST and VERTICAL deformation maps of Alto Tiberina Fault supersite: The Post-Proc service in Geohazard Exploitation Platform applied to Sentinel-1 dataset
<p>In the framework of the ESA funded project “MEMpHIS - Multi Scale and Multi Hazard Mapping Space based Solutions ”, the Istituto Nazionale di Geofisica e Vulcanologia (INGV) together with TRE-ALTAMIRA, generated the EAST-WEST and VERTICAL deformation maps of Alto Tiberina Fault supersite. The two maps of ground velocity were derived thanks to the adoption of a specific tool named "Post-Proc", implemented in MEMPHIS and with the support of Terradue, that is able to automatically re-project on the east-west and vertical directions the ascending and descending InSAR time series. In particular, the output refers to the deformation maps calculated by processing with SqueeSAR (TM) method, a large dataset acquired by the ESA Sentinel-1 mission.</p> <p>The Post-Proc tool also calculates the mean accelerations associated to each persistent scatterer in the scenes. Some additional features are also available from this tool:</p> <p>- Change the coherence threshold for selecting a subset of persistent scatterers</p> <p>- Activate a geometrical distortion filter to take into account the layover and foreshortening effects</p> <p>- Change the reference point (position and coherence)</p> <p>- Choose between two types of accelerations: 2<sup>nd</sup> order model or velocity derivative</p> <p>- Choose among three different projection: east-west, vertical, and downslope.</p> <p>The present dataset is composed of the EAST-WEST and VERTICAL acceleration maps. The data are in shapefile format: for each record (PS) the topography, velocity, acceleration, and InSAR coherence is reported.</p>
Lake Ice Break-Up across Peripheral Greenland (2017-2021) from Sentinel-1 SAR
<p>Dataset from<br>Posch, C., Abermann, J., and Silva, T.: Lake ice break-up in Greenland: timing and spatiotemporal variability, The Cryosphere, 18, 2035–2059, https://doi.org/10.5194/tc-18-2035-2024, 2024.</p> <p>Timing of lake ice break-up for 563 lakes across peripheral South, Southwest and Northwest Greenland (< 71° N) from Sentinel-1 synthetic aperture radar (SAR) data for 2017-2021.<br>This is based on an automated detection from dynamic numerical thresholding which utilizes SAR backscatter differences between lake ice surface and open water.<br>The data proves to be conservative and exhibits a mean error of maximum 5 days (i.e., being 5 days later) in break-up timing which is based on a validation against lake ice observations.<br>Based on a observed variability of lake ice break-up timing in the studied period (+/- 8 days), we also produced estimates for a hypothetical 8-days-earlier break-up of all lakes and its manifestation in execess energy at the lake, which was calculated from site-specific RACMO2.3p incoming shortwave radiation data.<br>More information is to be found in the README file.</p>
Data for 'Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery'
<div>This dataset contains avalanche deposit outlines (as shapefiles) derived for the study 'Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery'</div> <div> </div> <div>They were outlined at three different sites (Mt Blanc, Everest and Hispar regions) for the periods 11/2016-10/2021 (Mt Blanc) and 11/2017-10/2022 (Everest and Hispar). The time period is indicated in the file name.</div> <div> </div> <div>For each dataset we give the raw outlines (Automated_outlines_dates), the manually updated (Automated_outlines_dates_ManualUpd) and the manually updated after accounting for surface elevation change (Automated_outlines_dates_ManualUpd_shifted). </div> <div> </div> <div>In order to know which scenes were used for the mapping (if no avalanche was detected, we did not provide a shapefile, but this doesn't been that there is a gap in the Sentinel-1 time series), we provide a Sentinel1_date file that shows all the Sentinel-1 RGB pairs that we used to detect the avalanches.</div> <div> </div> <div>We also provide as geotiffs the temporally aggregated outlines (Automated_outlines_dates_ManualUpd_shifted_aggregated; over one specific year yn - from 01/11/yn-1 to 01/11/yn - or the full study period):</div> <div>- as heatmaps (where the value of each pixel corresponds to the number of avalanches that occured) </div> <div>- as binary maps of deposits (where 1 is when an avalanche occured over the time period and 0 is where none were detected).</div> <div> </div> <div> </div> <div>Finally we provide a csv file for each region with metrics per glacier:</div> <div> </div> <div>RGI ID</div> <div>Glacier size (in m^2)</div> <div>Catchment size (in m^2)</div> <div>Area of slopes steeper than 30° (in m^2)</div> <div>The area of total deposits detected (by summing all the pixels of the deposit binary maps) in the ascending obits (in m^2)</div> <div>The area of total deposits detected (by summing all the pixels of the deposit binary maps) in the descending obits (in m^2)</div> <div>The avalanche activity detected (by summing all pixels of the heat maps) in the ascending orbits (in m^2)</div> <div>The avalanche activity detected (by summing all pixels of the heat maps) in the descending orbits (in m^2)</div> <div>The area of the glacier visible in the ascending orbits (in m^2)</div> <div>The area of the glacier visible in the descending orbits (in m^2)</div> <div> </div> <div> </div> <div>The main Google Earth Engine and Matlab scripts used to pre-process the Sentinel-1 GRD images and to map the avalanches are available on GitHub: https://github.com/MarinKneib/S1_avalanches</div> <div> </div>
Sentinel-1 Derived Snow Depths and SnowEx Lidar Netcdfs
<p>These are netcdfs of S1 raw data, intermediate products, derived snow depths, ancillary data (IMS snow coverage, tree percentage) and lidar snow depths used in an analysis of the Lievens et al. (2021) algorithm.</p> <p> </p> <p>9 sites - Banner 2020, Banner 2021, Cameron 2021, Dry Creek 2020, Fraser 2020, Fraser 2021, Little Cottonwood Canyon 2021, Mores 2020, Mores 2021</p> <p> </p> <p>Data Variables:</p> <p>s1 - sentinel 1 backscatter data. contains 3 bands - VV, VH, and incidence angle</p> <p>ims - IMS snow coverage data (4 = snow covered, 2 = None) []</p> <p>fcf - Forest coverage fraction [%]</p> <p>deltaCR - change in the S1 cross ratio through time [dB]</p> <p>deltaVV - change in S1 VV backscatter through time [dB]</p> <p>deltaGamma - change in combined gamma variable [dB]</p> <p>snow_index - snow index in dB that is converted to derived snow depth by C parameter [dB]</p> <p>snow_depth - derived snow depth from S1 [m]</p> <p>wet_flag - flagged for snow with -2dB of change in CR. 1 = wet, 0 = dry</p> <p>alt_wet_flag - snow flagged by negative snow_index. 1 = wet 0 = dry</p> <p>freeze_flag - snow flagged as refreezing by increase of 1 dB in CR</p> <p>wet_snow - combined wet flag, alt wet flag, freeze flag, and previous time step's wet snow to get current wet snow flags</p> <p>perma_wet - snow that is flagged as wet more than 50% of last four acquisitions after Feb 1</p> <p>lidar-sd - Lidar derived snow depths [m]</p> <p>lidar-vh - lidar derived vegetation heights [m]</p> <p>lidar-dem - lidar derived snow free dems [m]</p> <p>aspect - aspect in degrees from lidar-dem [°]</p> <p>easting - degrees of easting from aspect[°]</p> <p>north - degrees of northing from aspect[°]</p> <p>confidence - unused metric of confidence</p>
Mapping canopy cover in African dry forests from combined use of Sentinel-1 and Sentinel-2 data: 2018 maps for Tanzania
<p>The monitoring of tropical forests has benefited from the increased availability of high-resolution earth observation data. However, the seasonality and openness of the canopy of dry tropical forests remains a challenge for optical sensors. The availability of time series of remote sensing images at 10-meters is changing this paradigm.</p> <p>In the context of REDD+ national reporting requirements, we investigated a methodology that is reproducible and adaptable in order to ensure user appropriation. The overall methodology consists of three main steps: (i) the generation of Sentinel-1 (S1) and Sentinel-2 (S2) layers, (ii) the collection of an ad-hoc training/validation dataset and (iii) the classification of the satellite data. Three different classification workflows are compared in terms of their capability to capture the canopy cover of forests in East Africa. Two types of maps are derived from these mapping approaches: i) binary tree cover/no tree cover (TC/NTC) maps, and ii) maps of canopy cover classes. The method is applied at scale, over Tanzania and one final map for each workflow is shared. Two big data computing platforms are combined to exploit the important volume of satellite data available over a yearly period.</p> <p>The reference dataset (training and validation), the three best maps and the codes to produce the S1 and S2 composites on Google Earth Engine are shared here.</p> <p>The folder “reference_dataset.zip” contains the expert based training and validation dataset. The point shapefile corresponding to the center of the plot as well as the 3x3 and 5x5 polygon shapefile are shared together with qml layer file for each type of shapefile.</p> <p>Three maps (binary TC-NTC “pixel” RF, forest type “pixel” RF and “window” ETC) are shared. A 40 km buffer from national boundaries is kept in order to let users refine their area of interest. The qml style file are also shared.</p> <p>In the “script.zip” folder, the javascript codes to generate the S1 and S2 mosaics are shared.</p>
A tempοral Deep Convolutional Neural Network model on Sentinel-1 Image Time Series for pixel-wise Flood Classification (dataset)
<p>This is a dataset which has been designed to be used for flood time series classification. Each time series is annotated as flood or no-flood and represents a pixel-wise time series derived from stack of Sentinel-1 IW GRD images that have been pre-processed according to <a href="http://doi.org/10.5281/zenodo.6510223">https://doi.org/10.5281/zenodo.6510223</a>.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.