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136 results for “sentinel-1”
Complementarity Between Sentinel-1 and Landsat 8 Imagery for Built-Up Mapping in Sub-Saharan Africa
<p>The dataset contains input and output files for the following paper:</p> <p>Yann Forget, Michal Shimoni, Marius Gilbert and Catherine Linard. <em>"Complementarity Between Sentinel-1 and Landsat 8 Imagery for Built-Up Mapping in Sub-Saharan Africa"</em>. In Press. 2018.</p> <p>The source code used to produce the output files is available on <a href="https://github.com/yannforget/landsat-sentinel-fusion">Github.</a></p>
Code and Data for "A machine learning approach for estimating snow depth across the European Alps from Sentinel-1 imagery"
<p>Here we share the data and code for “A machine learning approach for estimating snow depth across the European Alps from Sentinel-1 imagery”</p> <p>Corresponding author: Devon Dunmire devon.dunmire@kuleuven.be</p> <p>‘model_training’ - contains script to train the ML model, and training data sets from (1) in-situ snow measurement sites (training_data.p) and (2) photogrammetry snow depth maps (map_training_data.p)</p> <p>‘Cross_val_predictions’ contains model predictions for our cross-validation of all the in-situ snow measurement sites</p> <p>‘run_model’ contains the trained model (final_model_xg.pkl) and scripts to retrieve snow depth with our ML model.</p> <p>‘SD_*’ zip folders contains daily ML snow depth output over the European Alps for each snow year from Sept. 1 2015 - Apr. 30 2023. Data from multiple orbits is averaged.</p> <p>Naming convention: ‘S1_ml_SD_{yyyymmdd}_.nc’</p>
Strain partitioning, interseismic coupling, and shallow creep along the Ganzi-Yushu fault from Sentinel-1 InSAR data
<p>The dataset includes the InSAR velocity data and fault coupling model in the article "Strain partitioning, interseismic coupling, and shallow creep along the Ganzi-Yushu fault from Sentinel-1 InSAR data" (<a href="https://doi.org/10.1029/2024GL111469">https://doi.org/10.1029/2024GL111469</a>). The "insardata.zip" file includes original data of 5 tracks export from MintPy software, and the detailed format of the data can be found in the instruction provided by the MintPy software (<a href="https://github.com/insarlab/MintPy">GitHub - insarlab/MintPy: Miami InSAR time-series software in Python</a>). The "couplingmodel.gmt" is the fault coupling distribution along the Ganzi-Yushu fault, formatted for utilization in GMT software (<a href="https://github.com/GenericMappingTools/gmt">GitHub - GenericMappingTools/gmt: The Generic Mapping Tools</a>).</p>
Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS
<p>InSAR Line-of-Sight (LOS) velocities and their associated uncertainties in the southeastern Tibetan Plateau, along with the strain rate fields.</p> <p><br>Citations:</p> <p><br>Fang, J., Wright, T. J., Johnson, K. M., Ou, Q., Styron, R., Craig, T. J., Elliott, J. R., Hooper, A., & Zheng, G. (2024). Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS. Geophysical Research Letters.</p> <p><br>Fang, J., Wright, T. J., Johnson, K. M., Ou, Q., Styron, R., Craig, T. J., Elliott, J. R., Hooper, A., & Zheng, G. (2024). Strain Partitioning in the Southeastern Tibetan Plateau from Kinematic Modeling of High-Resolution Sentinel-1 InSAR and GNSS [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13731812</p>
Mapping of glacial lakes using Sentinel-1 and Sentinel-2 data and a random forest classifier: Strengths and challenges
<p>The water body detection and mapping algorithm named 'glakemap' that I designed was aimed at specifically mapping glacial lakes across alpine regions where their detection and mapping are challenged by many factors such as shadows, cloud cover, turbidity, and ice surface. The algorithm uses Copernicus Sentinel-1 and -2 satellites data and machine learning model (random forest) in an integrated manner to automatically classify glacial lakes from other surface features. In specific, the algorithm takes Sentinel-1 and -2 satellites data as the main inputs. It calculates radar backscatter and Normalised Difference Water Indices (NDWIs) using these datasets, respectively. The radar backscatter and NDWIs products (images) are segmented using a set of rules producing many polygons including lake polygons. Lake polygons are then automatically separated/retained using the random forest model which is trained using features relevant to lakes.</p> <p>The dataset is also available at https://www.mountcryo.org/</p>
Sentinel-1 RTC imagery processed by ASF over central Himalaya in High Mountain Asia
<p>This is a dataset of Sentinel-1 radiometric terrain corrected (RTC) imagery processed by the Alaska Satellite Facility covering a region within the Central Himalaya. It accompanies a tutorial demonstrating accessing and working with Sentinel-1 RTC imagery using xarray and other open source python packages.</p>
Sentinel-1 InSAR LOS velocity map over the Bay Area (Descending track 42, 2015-2022)
<p>Sentinel-1 InSAR LOS velocity map over the Bay Area</p> <p>(Descending track 42, 2015-2022)</p> <p>Please cite the following paper If you find the data useful: </p> <p><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>
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>
New Zealand InSAR coherence based on nationwide ascending Sentinel-1 datasets acquired during 2015 to 2021
<p>New Zealand Sentinel-1 coherence using ascending datasets acquired during 2015 to 2021 with ~100m resolution.</p>
MS3 Sentinel-1 Egadi
<p>This dataset is only a portion of an unprecedented dataset for vessel detection in Synthetic Aperture Radar (SAR) images, capitalizing on multi-frequency and multi-polarization data obtained from the Sentinel-1, COSMO-SkyMed, and SAOCOM missions</p>
Sentinel-1 T117 co-seismic interferogram of Amatrice earthquake (Italy) generated through the ESA G-POD platform
<p>Sentinel-1 T117 co-seismic interferogram (wrapped) of Amatrice earthquake (Italy).<br /> Data Type: wrapped Interferogram (radians)<br /> Sensor: Sentinel-1<br /> Observation interval: 15082016S1A_27082016S1A<br /> Applied Phase Filter: None<br /> Wavelength: 5.5465760 [cm]<br /> Look Angle: 39.060890 [deg]<br /> Projection: Geografic Lat-Long (WGS84)<br /> Author: IREA - CNR</p> <p><em>Acknowledgments</em>:<br /> Contains modified Copernicus data (2016), ESA GEP, G-POD, CNR-IREA, Italian DPC</p> <p> </p>
Bias Corrected and Gap Filled Sentinel-1 and University of Arizona Snow Depth Data
Open the record for dataset details and reuse information.
Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021)
<p>The dataset contains maps of the main classes of agricultural land use (dominant crop types and other land use types) in Germany, which are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in <a href="https://doi.org/10.1016/j.rse.2021.112831">Blickensdörfer et al. (2022)</a>.</p> <p>All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software <a href="https://force-eo.readthedocs.io/en/latest/">FORCE </a>- Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.</p> <p>The map extent covers all areas in Germany that are defined in the respective year as cropland, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020). </p> <p>Version v201:<br>Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015).</p> <p>Version v202:<br>Additional post-processing was performed to detect and mask additional non-plausible areas that were not adequately covered by the first post-processing (e.g., areas with sparse vegetation, montane forests) based on the „Ökosystematlas Deutschland“ (© Statistisches Bundesamt, Deutschland, 2024). As a consequence, the current version includes a new class “Small woody features on other land”. Furthermore, the class "permanent grassland" was refined. Each pixel that was classified as "cultivated grassland" in at least five years (between 2017 and 2022) was translated to "permanent grassland" in the annual maps.</p> <p>The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the provided URL to the datasets (right click on the respective data set --> “copy link address”). By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.</p> <p>Class-specific accuracies for each year are provided in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability. </p> <p> </p> <p><strong>References:<br></strong><br><em>Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831.</em></p> <p><em>BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).</em></p> <p><em>BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. </em><br><em>https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).</em></p> <p><em>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</em></p> <p><em>Statistisches Bundesamt, Deutschland (2024). Ökosystematlas Deutschland <br>https://oekosystematlas-ugr.destatis.de/ (last accessed: 08.02.2024).</em></p> <p>___________________________________________________________________________<br>National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0. </p> <p>Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (<a href="https://www.agrarmonitoring-monvia.de/en/">MonViA</a>, Monitoring of biodiversity in agricultural landscapes).</p> <p>The study was financially supported by the European Environment Agency and the European Union’s Horizon Europe Research and Innovation programme under Grant Agreement No 101060423 (LAMASUS).</p>
Land Subsidence PSI Measurements (2021-2023) in Oran, Algeria, Using Copernicus Sentinel-1 Data and SNAPPING Service
<p>We have processed data from the Copernicus Sentinel-1 satellite, covering the period from January 2021 to September 2023, over the borader area (around 48400 square kilometers) of Oran, Algeria. This analysis utilized the SNAPPING Persistent Scatterers Interferometry (PSI) service from the Geohazards Exploitation Platform (GEP; accessible at <a href="https://geohazards-tep.eu">https://geohazards-tep.eu</a>). Processing was performed by the EO.Lab of AUTh. </p> <p>Measurements contain average Line-of-Sight (LoS) velocities, corresponding uncertainties, and the complete displacement time series. The dataset contains ~3M points covering approximately 7800 sq.km (density of 385 points/km). </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 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>
Flood Detection with Sentinel-1 Satellite Based on Samarinda Januari 2020 Flood
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</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>
Sentinel-1 data over forest canopies ad different moisture content
<p>Data collected from three years of Sentinel-1 data over areas with full canopy cover and with consistent local incidence angle. Moisture was proxied from DC values taken from daily DC information estimated from weather stations.</p> <p>Process.R file will process the data and create the excel output that contains the output.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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