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15 results for “Sentinel-1 SAR”
Supraglacial lakes derived from Sentinel-1 SAR imagery over the Watson basin on the Greenland Ice Sheet.
<p>An experimental dataset produced for the 4D-Greenland project, one of the Polar+ projects funded by the European Space Agency. The dataset provides a classification of supraglacial lake extent, derived using Sentinel-1 SAR imagery, over the Watson case study site. The dataset is produced using a dynamic thresholding approach (Miles et al 2018). </p> <p>The dataset is produced for the period May 2017- Sept 2019. The temporal resolution of the dataset is approximately fortnightly (subject to methodological limitations) and is delivered as rasters in GeoTIFF format (epsg:3413). Raster pixels are denoted as: 0 where no surface water was detected; 1 where either HH or HV polarisation detected a backscatter signature representative of surface water; 2 where both HH and HV polarisations detected a backscatter signature representative of surface water; or 999 where the signal has been saturated and the output cannot distinguish if the signal is due to melt or other surface characteristics with the same backscattered signature. </p> <p>The naming convention indicates the original SAR tile used in the analysis and is identified by the sequence of fields described here:</p> <p><product_type>_<mission>_<mode>_<product>_<polarisation>_<starttime>_<endtime>_<orbitnumber>_<dataID>_<image>.fileextension</p> <p>For example:</p> <p>extent_S1B_EW_GRDH_1SDH_20180811T202931_20180811T203031_012220_016839_916F.tif</p>
SAR Stack of Pichincha volcano in Ecuador, from Sentinel-1
<p>A stack of Coregistered SLCs on Pichincha volcano, Ecuador</p> <p>Sensor: Sentinel-1 Descending track 142</p> <p>Time: 2016.04.19 - 2018.12.28, 46 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>
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>
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>
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>
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>
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>
Antarctica Supraglacial Lakes Sentinel-1 SAR Images
<p>This dataset contains synthetic aperture radar (SAR) images of supraglacial lakes on the Antarctic ice shelves, acquired by the Sentinel-1 satellite. Supraglacial lakes are surface meltwater features that can affect the stability and dynamics of ice shelves by hydrofracturing, basal sliding, or surface thinning. Monitoring the spatio-temporal distribution and evolution of these lakes is important for understanding the impact of surface meltwater on ice shelf collapse and sea level rise.</p> <p>The dataset covers four key regions: the Amery, Roi Baudouin, Nivlisen, and Riiser-Larsen ice shelves, which are characterized by extensive surface meltwater features. The images are single-polarized (VV or VH) and have a spatial resolution of 10 m. The images are processed using a modified U-Net algorithm for semantic segmentation of supraglacial lakes, based on deep learning and multiscale feature extraction. The algorithm is trained and validated on Landsat 8 and Sentinel-2 optical imagery, which provide high-resolution and multispectral information on supraglacial lake detection.</p> <p>The dataset provides binary masks of supraglacial lake extent for each Sentinel-1 image, as well as metadata on acquisition date, polarization, orbit number, and region name. The dataset also includes a decision-level fused product of Sentinel-1 and Sentinel-2 maximum lake extent for January 2020, which reveals a more complete supraglacial lake coverage than the individual single-sensor products. The dataset covers the austral summer seasons from 2015/2016 to 2019/2020 and contains over 1000 images.</p> <p>The dataset is intended to provide a first-ever continental record of supraglacial lake extent and volume in Antarctica using SAR imagery, which can complement optical datasets and overcome limitations of cloud cover and polar darkness. The dataset can be used for studying the intra-annual and interannual variability of supraglacial lake occurrence, depth, and drainage events, as well as their relationship with surface air temperature, ice shelf geometry, and fracture patterns. </p>
Sentinel-1 and TanDEM-X SAR data - Fehmarn Belt
<p>Sentinel1 A and B SAR data processed by NORCE and used for manuscript JGR Oceans 2024. TanDEM-X SAR data processed at Chalmers University of Technology and used for manuscript JGR oceans 2024.</p>
SD4EO - Physically Based Rendering images of crop fields (Sentinel-2 B2B3B4B8B11 & Sentinel-1 SAR)
<p>This dataset contains part of the results of the SD4EO project, including images corresponding to the use case of crop fields identification. The dataset contains images that have been simulated corresponding to different sensors of the Sentinel 1 and Sentinel 2 satellites and regarding 9 different types of crops. Each image in the dataset includes pixel-level labels for each element present in the image, ensuring perfect accuracy due to the synthetic nature of the images. This eliminates common errors in manual or semi-automatic labeling processes.</p> <p><strong>Multispectral images included</strong></p> <p>Sentinel 1: SAR (Synthetic Aperture Radar) images.<br>Sentinel 2: Images from bands 2, 3, 4, 8, and 11.</p> <p><strong>Image labeling</strong></p> <div> <table> <tbody> <tr> <td> <p><strong>Crop</strong></p> </td> <td> <p><strong>R</strong></p> </td> <td> <p><strong>G</strong></p> </td> <td> <p><strong>B</strong></p> </td> </tr> <tr> <td> <p>Alfalfa or lucerne</p> </td> <td> <p>47</p> </td> <td> <p>255</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p>Barley</p> </td> <td> <p>35</p> </td> <td> <p>192</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p>Fallow and bare soil</p> </td> <td> <p>25</p> </td> <td> <p>135</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p>Oats</p> </td> <td> <p>118</p> </td> <td> <p>178</p> </td> <td> <p>105</p> </td> </tr> <tr> <td> <p>Other grain leguminous</p> </td> <td> <p>38</p> </td> <td> <p>79</p> </td> <td> <p>29</p> </td> </tr> <tr> <td> <p>Peas</p> </td> <td> <p>44</p> </td> <td> <p>217</p> </td> <td> <p>70</p> </td> </tr> <tr> <td> <p>Sunflower</p> </td> <td> <p>78</p> </td> <td> <p>123</p> </td> <td> <p>68</p> </td> </tr> <tr> <td> <p>Vetch</p> </td> <td> <p>33</p> </td> <td> <p>58</p> </td> <td> <p>28</p> </td> </tr> <tr> <td> <p>Wheat</p> </td> <td> <p>130</p> </td> <td> <p>255</p> </td> <td> <p>102</p> </td> </tr> </tbody> </table> </div> <p><br><strong>Image name convention</strong></p> <p>The name convention follows the next schema of fields, separated by the character “_”</p> <p>- ID number</p> <p>- Meters per pixel resolution</p> <p>- Month</p> <p>- Type of image (Sentinel 2 band, Sentinel 1 SAR or labels): B2B3B4, B8, B11, SAR, labels</p> <p><strong>Image formats</strong></p> <p>TIFF format is used to save each band with floating point precision in the original range of the satellite images.</p> <p>PNG format is used to display each band in normalized values [0..1].</p> <table> <tbody> <tr> <td> <p><strong>Type of data</strong></p> </td> <td> <p><strong>PNG range</strong></p> </td> <td> <p><strong>TIFF range</strong></p> </td> </tr> <tr> <td> <p>B2</p> </td> <td> <p>0 - 255</p> </td> <td> <p>0 - 2000</p> </td> </tr> <tr> <td> <p>B3</p> </td> <td> <p>0 - 255</p> </td> <td> <p>0 - 2500</p> </td> </tr> <tr> <td> <p>B4</p> </td> <td> <p>0 - 255</p> </td> <td> <p>0 - 3000</p> </td> </tr> <tr> <td> <p>B8</p> </td> <td> <p>0 - 255</p> </td> <td> <p>1180 - 5736</p> </td> </tr> <tr> <td> <p>B11</p> </td> <td> <p>0 - 255</p> </td> <td> <p>859 - 5648</p> </td> </tr> <tr> <td> <p>SAR</p> </td> <td> <p>0 - 255</p> </td> <td> <p>-31.45273 - -14.23931</p> </td> </tr> </tbody> </table> <p><strong>Creation and funding</strong></p> <p>All the images have been generated using a tool developed in Unity. This tool will be soon available to enable the generation of new datasets.</p> <p>This research work has been funded by the European Space Agency (ESA) under the FutureEO program and the SD4EO project (Contract No.: 4000142334/23/I-DT), supervised by the ESA Φ-lab.</p> <p><strong>License and attribution</strong></p> <p>This dataset is licensed under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a> (CC BY 4.0).</p> <p>When using the images from this dataset, please attribute them as follows: "Synthetic images created by the research group ARTEC - IRTIC - University of Valencia".</p>
SAR Stack of Pichincha volcano in Ecuador, from Sentinel-1 processed with GAMMA
<p>A stack of Coregistered SLCs on Pichincha volcano, Ecuador</p> <p>Sensor: Sentinel-1 Descending track 142</p> <p>Time: 2016.04.19 - 2018.12.28, 52 acquisitions</p> <p>Processor: GAMMA</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MiaplPy">MiaplPy</a>.</p> <p>This dataset is similar to <a href="https://doi.org/10.5281/zenodo.6539952">SAR Stack of Pichincha volcano in Ecuador, from Sentinel-1</a>, but has been pre-processed with GAMMA software. </p>
The dataset of Sentinel-1 SAR images for sea ice classification
<p>The dataset implementation of the paper "A Multi-scale Dual Attention Network for Automatic Polar Sea Ice Classification Based on Sentinel-1 SAR Images".</p> <p>There are 7381 images as the training set, 1210 images as the validation set, and 3630 images as the test set. </p> <p>The file contains original images and processed images.</p>
OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 Static Layers validated product (Version 1)
The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) Static Layers (RTC-S1-STATIC) validated product contains static radar geometry layers associated with the OPERA Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) (RTC-S1) validated product. Due to the S1 mission’s narrow orbital tube, radar-geometry layers such as incidence angle, local incidence angle, number of looks, and RTC Area Normalization Factor (ANF) vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA RTC-S1 product, as they are produced only once or a limited number of times, to account for changes in the DEM, in the S1 orbit, or in the static-layers generation algorithm. Static layers are provided as single-band cloud-optimized GeoTIFF (COG) files, with map grid matching RTC-S1 products with the same burst ID. The standard OPERA RTC-S1 product is derived from the original Copernicus Sentinel-1 (S1) interferometric wide (IW) single-look complex (SLC) data, provided by the European Space Agency, with a temporal sampling coincident with the availability of Sentinel-1A and Sentinel-1B SLC data. The OPERA RTC-S1-STATIC and RTC-S1 products are provided at a near global scope (land masses excluding Antarctica). The RTC-S1 products are available in the associated OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 validated product (Version 1) dataset.
OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 validated product (Version 1)
The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) validated product consists of radar backscatter normalized with respect to the topography. The product maps signals related to the physical properties of ground scattering objects, such as surface roughness and soil moisture and/or vegetation. The OPERA RTC-S1 product is derived from Copernicus Sentinel-1 Interferometric Wide (IW) Single Look Complex (SLC) data with a near global scope and temporal sampling coincident with the availability of S1 SLC data. Each OPERA RTC-S1 product corresponds to a single S1 burst projected onto a pre-defined UTM/Polar stereographic map projection system map grid with a 30-meter spacing. The Copernicus global 30 m (GLO-30) Digital Elevation Model (DEM) is the reference DEM used to correct for the impacts of topography and to geocode the product. The OPERA RTC-S1 product is normalized to the backscatter coefficient gamma-naught, ɣ0, obtained from the original radar brightness beta-naught, β0, through radiometric terrain correction. The RTC-S1 product is distributed as cloud optimized GeoTIFFs with one GeoTIFF file per processed polarization. The RTC-S1 product metadata is provided in the Hierarchical Data Format version 5 (HDF5) format. The OPERA RTC-S1 product contains modified Copernicus Sentinel data (2022-2025).Due to the S1 mission’s narrow orbital tube, radar-geometry layers such as incidence angle, local incidence angle, number of looks, and RTC Area Normalization Factor (ANF) vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA RTC-S1 product, as they are produced only once or a limited number of times, to account for changes in the DEM, in the S1 orbit, or in the static-layers generation algorithm. The static layers are available in the associated OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 Static Layers validated product (Version 1) dataset.
Sentinel-1 SAR Wet snow maps for Southern Norway, 2016-2020
<p>Sentinel-1 Synthetic Aperture Radar data have been processed to produce daily maps of wet snow for Southern Norway for the period 2016-2020. This study makes use of the Interferometric-wide (IW) swath mode which has a swath width of 250 km and nominal pixel spacing of 10 m. We use the S1 IW ground-range detected (GRD) product with co- (“VV”) and cross- (“VH”) polarizations, and pixels are aggregated to a spacing of 100 m to reduce noise. The SAR backscatter images have been processed to produce daily wet snow maps using the Nagler and Rott (2016) approach which utilises backscatter from both VV and VH polarizations and a weighting to the contributions is applied to represent an incident angle correction. SAR image pixels are classified by applying a threshold to the difference between the SAR backscatter and its reference value. These reference values are produced for each sensor and geometry by calculating the average radar backscatter per pixel, based on data acquired in the period November 1st - April 30th during which snow condition is assumed to be dry. </p>
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