Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
136
datasets available to search
ShareScore release 0.7.1
Dataset results
136 results for “Sentinel-1”
Building fraction map of Germany (Sentinel-1/-2 based, 10m and 100m resolution)
<p>This dataset features a map of building fractions (as opposed to built-up fractions including other impervious surfaces such as roads) for Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. The data were created by using machine learning regression and spectral unmixing, using synthetically mixed training data. The dataset is completely based on freely accessible satellite imagery, and was validated with freely available building footprint reference data for three federal states.</p> <p>We recommend to use data at an aggregated resolution of 20m, 50m, or 100m, and to clip data at about 20% building fraction when using 10m resolution maps (or roughly the corresponding RMSE at any other resolution).</p> <p><strong>Temporal extent</strong><br> Used Sentinel-2 data were acquired in 2018, and Sentinel-1 data were acquired in 2017 (see publication). The map is, thus, representative for 2017/2018. Validation results can be affected by building footprint reference data from different years.</p> <p><strong>Data format</strong><br> The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Building fraction values are in percent, from 0 to 100. In the original dataset with 10m spatial resolution, fraction values are equivalent to area in m². In the aggregated dataset with 100m spatial resolution, the values must be multiplied with 100 in order to see area in m².</p> <p><strong>Further information</strong><br> For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de). A web-visualization of this dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/building-area/">here</a>.</p> <p><strong>Publication</strong><br> Schug, F.; Frantz, D.; Okujeni, A.; Hostert, P. (2022). Sub-pixel building area mapping based on synthetic training data and regression-based unmixing using Sentinel-1 and -2 data. Remote Sensing Letters. DOI: 10.1080/2150704X.2022.2088253</p> <p><strong>Acknowledgements</strong><br> The dataset was generated by FORCE v. 3.6.1 (<a href="https://doi.org/10.3390/rs11091124">paper</a>, <a href="https://github.com/davidfrantz/force">code</a>), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the <a href="https://scihub.copernicus.eu/">European Space Agency and the European Commission</a>. Sentinel-1 data were provided by <a href="https://eodc.eu/">EODC</a>. We thank the providers of the building footprint reference data (see publication).</p> <p><strong>Funding</strong><br> This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (<a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">MAT_STOCKS</a>, grant agreement No 741950).</p> <p> </p>
Teller 47 solifluction: Sentinel-1 deformation estimates
<p>Sentinel-1 deformation estimates from orbit 15/377</p> <p>Specifications:</p> <ul> <li>Location: Seward, Peninsula, AK</li> <li>Spatial resolution: ~100 m</li> <li>Unit: m</li> <li>Sign convention: positive: increasing distance to satellite</li> </ul> <p>Files:</p> <ul> <li>avg_disp171819.tif: average thaw-season displacement for years 2017, 2018, 2019; normalized to a 90-day period</li> <li>subseasonal.txt: subseasonal displacement time series for four points J1-J4 in years 2017-2019. Coordinates and measurement times are included.</li> </ul>
Sentinel-1 Satellite Imagery Based Ice Road Detection and Monitoring
<p>Canada’s northern ice roads in winter which is more than 3300 miles are freezing later and melting earlier, drastically reducing the forecasting capabilities for the safe use. An attractive Area of Interest (AoI) could be around the region “Yellowknife” in Canada, which is also mentioned in the recent TV show (<a href="https://en.wikipedia.org/wiki/Ice_Road_Truckers">https://en.wikipedia.org/wiki/Ice_Road_Truckers</a>). This area could be used to train the algorithm to detect the current situation and to forecast the time window to close and open the ice tracks to the citizens. Mockup could be achieved by building a screenshot mockup on mobile device (Smartphone/Tablet).</p>
Monitoring of urban areas on Sentinel-1
<p>Data from Sentinel-1 SLC product was used to determine the extent of the urbanised area. Such a solution is necessary in the case of rapidly developing cities, as in the case of the capital of India - New Dehli.</p> <p>Links to the presentation:</p> <p>http://fabspace.pl/wp-content/uploads/2017/11/Urban-area-on-S-1.pdf</p> <p> </p>
High resolution Sea Surface Wind retrieval over coastal Protected Areas by means of Sentinel-1 data
<p><br> The algorithm used, i.e. SARWIND LG-Mod ver. v4.01 (see reference below), is aimed at producing the Sea Surface Wind (SSW), i.e. Speed and Direction, from a single co-polarized (VV or HH) SAR image. We used EW (Extended Wide) and IW (Interferometric Wide) Swath Mode GRD (Ground Range, Multi-Look, Detected) HR (High Resolution) Sentinel-1 images, with pixel spacings of 40m x 40m and 10m x 10m (azimuth x range) respectively. Associated auxiliary products were obtained from ESA SNAP 5.0 release. SSW fields were provided for the two coastal Protected Areas (PAs) named Camargue and Wadden Sea.</p> <p>Each output folder of the SARWIND LG-Mod results contains useful plots and the estimated SSW field, provided in the file 'SAR_Sigma0_pp_decimationL2P2Tn_gradientOptSobel_LGMod_Results.txt' (pp = VV or HH; n = smoothing/decimation level), which is in the sub-folder 'LG-Mod_Theoretical_Results/Results_MEdegTHxx.xxx_Fisher (where xx.xxx is the final threshold applied). This txt file reports the following 19 columns:</p> <p><br> 1) LAT; 2) LON; 3) AZI; 4) RNG; [Location of the centre of the processed AOI]</p> <p>5) REF_U; 6) REF_V; 7) REF_W; 8) REF_D; [ECMWF reference wind, as U/V components and speed/direction]</p> <p>9) SAR_U; 10) SAR_V; 11) SAR_W; 12) SAR_D; [SARWIND LG-Mod wind estimates, as U/V components and speed/direction]</p> <p>Both REF_D and SAR_D are wind directions (expressed in degrees) with respect to the geographic North (0°=North, 90°=East, 180°=South, 270°=West), that the wind is blowing to.<br> Both REF_W and SAR_W are wind speeds (expressed in m/s).<br> Regarding REF_U/SAR_U and REF_V/SAR_V, note that a positive U component represents wind blowing to the East; a positive V component represents wind blowing to the North.</p> <p>13) SceneCentre_TrueHeading_FF; [Mean angle formed between the geographical South-North direction and the SAR azimuth direction (wrt the centre of the SAR Full-Frame image)]</p> <p>SceneCentre_TrueHeading_FF is a positive clockwise angle. In particular: SceneCentre_TrueHeading_FF is in ]180,360[ [deg].<br> Thus:<br> Descending Pass <-> SceneCentre_TrueHeading_FF is in ]180,270[ [deg]<br> Ascending Pass <-> SceneCentre_TrueHeading_FF is in ]270,360[ [deg]</p> <p>14) ROI_Npoints_UnUsablePointsMasked; [Number of samples used for each SARWIND LG-Mod wind estimation]</p> <p>15) MeanIncAng; 16) MeanNRCS; [Mean incident angle (expressed in degrees) and NRCS of the ROI]</p> <p>17) MeanResultantLength; 18) Alpha2_Est; [Fisher's formula parameters]</p> <p>19) MEdeg [Margin of Error, i.e. accuracy of each wind direction estimate, between 0° and 45°]</p> <p>The accuracy MEdeg is given by the semi-width of the confidence interval, with a confidence level (1-α) fixed, which is assigned to the wind direction estimate. Consequently, lower MEdeg values correspond to better estimates. And, if MEdeg == 45°, wind estimates must be discharged.</p> <p><br> Finally, note also that you can cut an entire row when [SAR_U SAR_V SAR_W SAR_D] == [NaN NaN NaN NaN] (typically, this happens for 'land pixels').</p> <p>% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> % %<br> % REFERENCES: %<br> % %<br> % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> % %<br> % The algorithm SARWIND LG-Mod is based on the Ph.D thesis below: %<br> % %<br> % [1] Rana, Fabio Michele (2016) "Exploitation of Satellite %<br> % Synthetic Aperture Radar Data for Geophysical Parameters Retrieval over %<br> % Land and Ocean". Unpublished Ph.D thesis. Politecnico di Bari. %<br> % %<br> % Some applications of the method are described in the following papers: %<br> % %<br> % [2] Fabio M. Rana, Maria Adamo, Guido Pasquariello, Giacomo De Carolis, %<br> % and Sandra Morelli, "LG-Mod: A Modified Local Gradient (LG) Method to %<br> % Retrieve SAR Sea Surface Wind Directions in Marine Coastal Areas," %<br> % Journal of Sensors, vol. 2016, Article ID 9565208, 7 pages, 2016. %<br> % doi:10.1155/2016/9565208. %<br> % %<br> % [3] Rana, F. M., Adamo, M., & Blanda, P. (2018, July). %<br> % LG-Mod Multi-Scale Approach for Sar Sea Surface Wind Directions %<br> % Retrieval. In IGARSS 2018-2018 IEEE International Geoscience and Remote %<br> % Sensing Symposium (pp. 3216-3219). IEEE. %<br> % %<br> % [4] Rana, F. M., Adamo, M., Lucas, R., & Blonda, P. (2019). Sea surface %<br> % wind retrieval in coastal areas by means of Sentinel-1 and numerical %<br> % weather prediction model data. Remote Sensing of Environment, 225, %<br> % 379-391. %<br> % %<br> % Suggestions and comments are always welcome. %<br> % Thanks in advance, %<br> % Fabio Michele Rana %<br> % %<br> % MOB: (+39) 3804114171 %<br> % E-MAILS: fabiomichele.rana@gmail.com; fabiomichele.rana@iia.cnr.it %<br> % %<br> % SKYPE: fabiomichelerana %<br> % %<br> % SARWIND_LG-Mod_v4.01, 2014-2019 %<br> % Author: Fabio M. Rana %<br> % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %<br> </p>
Global dataset of Sentinel-1 azimuth shift values and derived horizontal displacements from COMET LiCSAR system
<p>For this dataset, we were exploiting spatio-temporal behaviour of side-products of coregistration of Sentinel-1 radar images, that were generated in last couple of years, within the COMET LiCSAR system for computing interferograms. These side-products are sub-pixel (azimuth) offsets, allowing for a very precise match of the images (up to a 0.0005 pixels). We have shown that by a proper approach, these offsets can be used as measurements of large-scale horizontal motion - in our case, using 250x250 km resolution cells, we aimed to measure motion of tectonic plates. Our measurements are fitting well to the latest ITRF2014 plate motion model, although the E component contains an extra overall shift, not explained at the moment of sharing the outputs.</p> <p>The sub-pixel offsets (result of intensity cross correlation and spectral diversity estimation, w.r.t. precise orbit ephemerides) were corrected for solid Earth tides and ionospheric phase advance, using external models. The code shared within the dataset includes the correction functions. The data contains both original and corrected values. Also, the data contains ITRF2014 plate motion values.</p> <p>The dataset contains outputs from both COMET LiCSAR frame units and their decomposition into N, E motion vectors, as well as some metadata on the frames that are necessary for the reprocessing if needed.</p>
20 m Annual Paddy Rice Map for Mainland Southeast Asia Using Sentinel-1 SAR Data
<p>This dataset provides 20 m annual paddy rice map for mainland Southeast Asia since 2019 .</p> <p>*** The data file is in “.tif" format</p> <p>*** Pixel size: 20 m</p> <p>*** Projection information: EPSG: 4326 (WGS84)</p> <p>The map boundary employed in this database does not imply the expression of any opinion whatsoever on the part of us concerning the legal status of any country, territory, city or area or its authorities, or concerning the delimitation of its frontiers or boundaries.</p>
SNAPPING PSI surface motion measurements over selected sites presented in MDPI Remote Sensing paper "SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping"
<p>SNAPPING PSI surface motion measurements over selected sites as presented in the paper with the title "SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping" by Michael Foumelis, Jose Manuel Delgado Blasco, Fabrice Brito, Fabrizio Pacini, Elena Papageorgiou, Panteha Pishehvar and Philippe Bally on Remote Sensing Open Access Journal.</p> <p>Whenever using this dataset, please cite its original paper (<a href="https://doi.org/10.3390/rs14236075">https://doi.org/10.3390/rs14236075</a>) and include the reference to this dataset (<a href="https://doi.org/10.5281/zenodo.7369653">https://doi.org/10.5281/zenodo.7369653</a>).</p> <p>This dataset includes average Line-of-Sight velocities for the following sites and dates:</p> <table> <tbody> <tr> <td><strong>Site name</strong></td> <td><strong>Country</strong></td> <td><strong>Period</strong></td> <td><strong>Relative orbit</strong></td> <td><strong>Orbit direction</strong></td> </tr> <tr> <td>Cap-Haïtien</td> <td>Haiti</td> <td>Jan-2017 / Dec-2019</td> <td>106</td> <td>ascending</td> </tr> <tr> <td>Gran Renaissance Ethiopian Dam</td> <td>Ethiopia</td> <td>Jan-2019 / Jun-2021</td> <td>50</td> <td>descending</td> </tr> <tr> <td>La Palma Volcano</td> <td>Spain</td> <td>Jun-2019 / Dec-2021</td> <td>169</td> <td>descending</td> </tr> <tr> <td>Santorini Volcano</td> <td>Greece</td> <td>Apr-2015 / May-2021</td> <td>29</td> <td>ascending</td> </tr> <tr> <td>San Francisco</td> <td>USA</td> <td>Jan-2016 / Dec-2020</td> <td>115</td> <td>descending</td> </tr> <tr> <td>Thessaloniki International Airport (SKG)</td> <td>Greece</td> <td>Apr-2015 / Dec-2020</td> <td>102</td> <td>ascending</td> </tr> </tbody> </table>
Sicily Sentinel-1 dataset processed with MiaplPy
<p>Displacement on Sicily, Italy</p> <p>Sensor: Sentinel-1 Descending track 124</p> <p>Time: 2017.10 - 2019.09, 63 acquisitions</p> <p>Processor: ISCE/topsStack</p> <p>This is the outputs from time series analysis with <a href="https://github.com/insarlab/MiaplPy">MiaplPy</a>.</p>
Bristol Dry Lake Sentinel-1 dataset processed with MiaplPy and Mintpy
<p>Displacement data on Bristol Dry Lake, USA</p> <p>Sensor: Sentinel-1 Descending track 173</p> <p>Time: 2017.02 - 2021.01, 157 acquisitions</p> <p>Processor: ISCE/topsStack</p> <p>This is the outputs from time series analysis with <a href="https://github.com/insarlab/MiaplPy">MiaplPy</a> and <a href="https://github.com/insarlab/MintPy">MintPy</a>.</p>
Mud Creek landslide Sentinel-1 dataset processed with MiaplPy
<p>Displacement timeseries on Mud Creek landslide, USA</p> <p>Sensor: Sentinel-1 Descending track 42</p> <p>Time: 2015.03.01 - 2017.05.13, 63 acquisitions</p> <p>Processor: ISCE/topsStack</p> <p>This is the outputs from time series analysis with <a href="https://github.com/insarlab/MiaplPy">MiaplPy</a>.</p>
Miami Sentinel-1 dataset processed with MiaplPy
<p>A stack of Coregistered SLCs and displacement timeseries on Miami, USA</p> <p>Sensor: Sentinel-1 Ascending track 48</p> <p>Time: 2015.09.21 - 2021.11.12, 147 acquisitions</p> <p>Processor: ISCE/topsStack</p> <p>This is the outputs from time series analysis with <a href="https://github.com/insarlab/MiaplPy">MiaplPy</a>.</p>
SAR Stack of Miami in US, from Sentinel-1
<p>A stack of Coregistered SLCs on Miami, USA</p> <p>Sensor: Sentinel-1 Descending track 48</p> <p>Time: 2015.09.21 - 2021.11.12, 147 acquisitions</p> <p>Processor: ISCE/topsStack</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MiaplPy">MiaplPy</a>.</p>
Pre-seismic Sentinel-1 PSI surface motion measurements for the area affected by the February 2023 Türkiye–Syria earthquakes
<p>We have processed Copernicus Sentinel-1A data from 01/2019 to 01/2023 (descending track 21) over the broader area (approx. 48400 sq. km) affected by February 6, 2023, M7.8 and M7.5 earthquakes in Türkiye and Syria, utilizing the SNAPPING Persistent Scatterers Interferometry (PSI) medium resolution service of the Geohazards Exploitation Platform (GEP; <a href="https://geohazards-tep.eu">https://geohazards-tep.eu</a>).</p> <p>Measurements contain average Line-of-Sight (LoS) velocities, corresponding uncertainties, and the complete displacement time series. Please note that the original dataset of about 2M point measurements was split into parts, each containing 200k points, to facilitate easier manipulation and visualization.</p> <p>References</p> <p>[1] Foumelis, M.; Delgado Blasco, J.M.; Brito, F.; Pacini, F.; Papageorgiou, E.; Pishehvar, P.; Bally, P. SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping. Remote Sens. 2022, 14, 6075. <a href="https://doi.org/10.3390/rs14236075">https://doi.org/10.3390/rs14236075</a></p> <p>[2] SNAPPING – Surface motioN mAPPING Sentinel-1 on-demand processing service, Online tutorial, <a href="https://docs.terradue.com/geohazards-tep/tutorials/Snapping.html">https://docs.terradue.com/geohazards-tep/tutorials/Snapping.html</a>.</p>
Sentinel-1 InSAR unwrapped data of the 27 July 2022 Abra earthquake in Luzon, the Philippines
<p>This is a supporting dataset for Tang et al. (2023), "Oblique blind faulting underneath the Luzon volcanic arc during the 2022 M<sub>w</sub> 7.0 Abra earthquake, the Philippines". The original and downsampled line-of-sight displacements for modeling are presented in this repository. The coseismic interferogram using the synthetic aperture radar images from Copernicus Sentinel-1A descending track 32 on 21 July and 2 August, 2022 (6 days before and after the mainshock). The flight direction is ~N190° with a westward look angle ranging from 36° to 45°. Details of processing and downsampling schemes can be found in the paper. The Sentinel-1 images were processed by European Space Agency (ESA) and downloaded from Alaska Satellite Facility (ASF) Data Search Vertex (<a href="https://search.asf.alaska.edu/">https://search.asf.alaska.edu/</a>).</p>
Sample Sentinel-1 SAR data for sea ice type retrieval
<p>Sample Sentinel-1 SAR data for sea ice type retrieval processed with thermal noise removal (<a href="https://ieeexplore.ieee.org/document/8126233">https://ieeexplore.ieee.org/document/8126233</a>).</p> <p>Original data is available at ESA Scientific Hub <a href="https://scihub.copernicus.eu/">https://scihub.copernicus.eu/</a></p> <p> </p>
InSAR Time-series of Jakobshavn and Petermann from Sentinel-1 Data
<p>Dataset 1: Sentinel-1 ascending track 90, descending track 127</p> <p>Study areas: Jakobshavn glacier in Greenland. We separate Jakobshavn into three individual areas (N, NE, and S) based on different reference locations.</p> <p>Date: Ascending: April 2016 to March 2020; Descending: July 2017 to April 2020.</p> <p>Processor: ISCE/topsStack + MintPy</p> <p>Dataset 2: Sentinel-1 ascending track 90, descending track 26</p> <p>Study areas: Petermann glacier in Greenland. </p> <p>Date: Ascending: April 2017 to April 2020; Descending: January 2017 to April 2020.</p> <p>Processor: ISCE/topsStack + MintPy</p>
Dataset and figures for "Nationwide urban ground deformation monitoring in Japan using Sentinel-1 LiCSAR products and LiCSBAS"
<p>This dataset contains the deformation data for 191 data sets and figures (LOS velocities, amplitude and time offset of the annual deformation, decomposed vertical and EW velocities, rice paddy fields, NDVI, optical images, topography, and SB network) mentioned in the paper “Nationwide urban ground deformation monitoring in Japan using Sentinel-1 LiCSAR products and LiCSBAS”</p> <p>Morishita, Y. Nationwide urban ground deformation monitoring in Japan using Sentinel-1 LiCSAR products and LiCSBAS. <em>Prog Earth Planet Sci</em> <strong>8, </strong>6 (2021). https://doi.org/10.1186/s40645-020-00402-7</p> <p>View on a web map:</p> <p>https://yumorishita.github.io/gsimaps_S1_Japan_LiCSBAS/#9/35.766572/140.038605/&base=std&base_grayscale=1&ls=std%2C0.5%7Chillshademap%2C0.5%7CallUD%7Clanduse_veg&blend=100&disp=1110&vs=c1j0h0k0l0u0t0z0r0s0m0f2&d=m</p>
Sentinel-1 T156 co-seismic interferogram of Kumamoto EQ
<p>Sentinel-1ascending co-seismic interferogram (wrapped) of Kumamoto Earthquake.</p> <p>Master acquisition time: 2016-04-08</p> <p>Slave acquisition time: 2016-04-20 </p> <p>Track: 156</p> <p>Perpendicular Baseline: 68m</p> <p>Multilooking: 10 Azimuth, 2 Range</p>
Sentinel-1 T163 co-seismic interferogram of Kumamoto EQ
<p>Sentinel-1descending co-seismic interferogram (wrapped) of Kumamoto Earthquake.</p> <p>Master acquisition time: 2016-03-27</p> <p>Slave acquisition time: 2016-04-20 </p> <p>Track: 163</p> <p>Perpendicular Baseline: 4m</p> <p>Multilooking: 10 Azimuth, 2 Range</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.