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136 results for “Sentinel-1”
InSAR stack of Fernandina volcano in Galápagos, Ecuador from Sentinel-1 descending track 128 processed with ISCE2/topsStack
<p>A stack of unwrapped interferograms on Fernandina volcano, Galápagos, Ecuador</p> <p>Sensor: Sentinel-1descending track 128</p> <p>Processor: ISCE/topsStack</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> <p><strong>Version 1.x (~750 MB)</strong><br> Time: 2014.12.13 - 2018.06.19 (98 acquisitions, 288 interferograms)</p> <p><strong>Version 0.1 (~280 MB; for fast testing of code development)</strong><br> Time: 2014.12.13 - 2016.05..24 (36 acquisitions, 102 interferograms)</p>
InSAR stack of San Francisco Bay, California from Sentinel-1 descending track 42 processed with GMTSAR
<p>A stack of unwrapped interferograms in the San Francisco Bay area, California, USA</p> <p>Sensor: Sentinel-1 descending track 42</p> <p>Processor: <a href="https://github.com/gmtsar/gmtsar" target="_blank" rel="noopener">GMTSAR</a></p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p> <p>The tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p><strong>Version 1.x (~2.3 GB)</strong><br>Time: 2014.12.31 - 2024.06.05 (333 acquisitions, 1297 interferograms)</p> <p><strong>Version 0.x (~290 MB; for fast testing of code development)</strong><br>Time: 2020.01.04 - 2021.07.15 (70 acquisitions, 184 interferograms)</p>
Supporting Data - Sentinel-1 Detection of Ice Slabs on the Greenland Ice Sheet
<p>This dataset contains supporting data accompanying Culberg, R., Michaelides, R. J., and Miller, J. Z.: Sentinel-1 Detection of Ice Slabs on the Greenland Ice Sheet, EGUsphere [preprint], <a href="https://doi.org/10.5194/egusphere-2023-2652">https://doi.org/10.5194/egusphere-2023-2652</a>, 2023. The final accepted manuscript will be linked via the same preprint server at the time of publication. The dataset contains the following files:</p> <ul> <li>Sentinel-1 HV and HV/HH backscatter mosaics of the Greenland Ice Sheet formed using data from 1 Oct 2016 - 30 April 2017.</li> <li>Estimated average annual summer melt extent between 1 Nov 2014 and 31 Aug 2020, detected using seasonal variations in Sentinel-1 HH backscatter.</li> <li>The firn aquifer extent over Greenland derived from Sentinel-1 in Brangers et al. (2020), reprojected to EPSG:3413.</li> <li>The ice mask used in the study, derived from the BedMachine Greenland ice mask.</li> <li>The training and validation datasets derived from the Jullien et al. (2023) ice slabs detections from ice penetrating radar data that were used to optimize ice slab detection thresholds for the Sentinel-1 backscatter mosaics. </li> </ul>
Land Subsidence in Iran Estimated from a Nationwide InSAR Analysis of Sentinel-1 Observations 2014-2020
<p><strong>Overview</strong></p> <p>This dataset is a supplementary material to the paper "Haghighi and Motagh, 2024. Uncovering the Impacts of Depleting Aquifers: A Remote Sensing Analysis of Land Subsidence in Iran, Science Advances". It provides detailed insights into land subsidence across Iran, derived from Sentinel-1 InSAR observations. This dataset is intended for use by researchers, policymakers, and practitioners interested in land subsidence, groundwater depletion, and related fields.</p> <p><strong>Dataset Contents</strong></p> <ol> <li><em>Iran_subsidence_rate_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Annual rate of land subsidence in Iran over the six-year period, projected from satellite Line of Sight to vertical.</li> <li><em>Iran_subsidence_rate_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.jpg</em><br>Subsidence map of Iran visualized as jpg</li> <li><em>Iran_subsidence_seasonal_amplitude_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Amplitude of seasonal ground deformation, projected from satellite Line of Sight to vertical.</li> <li><em>Iran_subsidence_mask_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Land subsidence mask, based on the annual rate of land subsidence.</li> </ol> <p><strong>Methodology</strong></p> <p>The data were derived using Interferometric Synthetic Aperture Radar (InSAR) analysis of Sentinel-1 satellite imagery. The original SAR data includes more than 6000 scenes of Sentinel-1 images collected across 10 descending tracks between 2014 and 2020. The details can be found in the original paper.</p> <p><strong>Acknowledgements</strong></p> <p>We acknowledge the European Space Agency (ESA) for providing the Sentinel-1 satellite data used in this analysis.</p> <p><strong>License</strong></p> <p>This dataset is shared under CC BY 4.0 license, which allows for reuse and distribution, provided that the original authors and source are credited.</p> <p><strong>Citation</strong></p> <p>Please cite the following if you use this dataset:</p> <ol> <li>Haghighi and Motagh, 2024. Uncovering the Impacts of Depleting Aquifers: A Remote Sensing Analysis of Land Subsidence in Iran, Science Advances.</li> <li>Haghighi and Motagh, 2024. Land Subsidence in Iran Estimated from a Nationwide InSAR Analysis of Sentinel-1 Observations 2014-2020. Zenodo. doi:10.5281/zenodo.10815578</li> <li>The dataset contains modified Copernicus Sentinel data 2014-2020, processed by ESA.</li> </ol> <p><strong>Contact</strong></p> <p>Please contact Mahmud Haghighi for inquiries related to this dataset.<br>https://www.ipi.uni-hannover.de/en/haghighi</p>
Modelled and Sentinel-1 detected firn aquifers areas in the Antarctic Peninsula
<p>FDM results: This dataset contains firn aquifer extent output from IMAU-FDM (Firn Densification Model), version v1.2A, for the Antarctic Peninsula on a 5.5 km grid. The dataset consists of maps of the extent of simulated seasonal aquifers in at least one year (2017-2020), perennial aquifers in at least on year (2017-2020), and perennial aquifers in all years (2018-2020). Further details are described in Buth et al. (2022).<br> Model adjustments and run were performed by Sanne B. M. Velduijsen.</p> <p>S1 detection results: The GeoTIFF image is the result of the Sentinel-1 firn aquifer detection routine which makes use of the typical delayed increase of SAR backscatter after the peak melt season in case of an aquifer. The image has two bands per year (2017-2020), one containing the DOY80 parameter for the whole Antarctic Peninsula (excluding masked areas, see Buth et al, 2022), the other containing DOY80 only for detected aquifer areas, where it exceeds the threshold of DOY80=105. DOY80 here stands for the day of the year at which 80% of the September Sentinel-1 HH backscatter is reached. Further details are described in Buth et al. (2022).<br> S1 aquifer detection was performed by Lena G. Buth, using the Python API of the Google Earth Engine. The associated code is available as a GitLab project: https://gitlab.awi.de/lenbuth/tc-aquifers</p>
S1S2-Water: A global dataset for semantic segmentation of water bodies from Sentinel-1 and Sentinel-2 satellite images
<p>The S1S2-Water dataset is a global reference dataset for training, validation and testing of convolutional neural networks for semantic segmentation of surface water bodies in publicly available Sentinel-1 and Sentinel-2 satellite images. The dataset consists of 65 triplets of Sentinel-1 and Sentinel-2 images with quality checked binary water mask. Samples are drawn globally on the basis of the Sentinel-2 tile-grid (100 x 100 km) under consideration of pre-dominant landcover and availability of water bodies. Each sample is complemented with metadata and Digital Elevation Model (DEM) raster from the Copernicus DEM.</p><p>This work was supported by the German Federal Ministry of Education and Research (BMBF) through the project "Künstliche Intelligenz zur Analyse von Erdbeobachtungs- und Internetdaten zur Entscheidungsunterstützung im Katastrophenfall" (AIFER) under Grant 13N15525, and by the Helmholtz Artificial Intelligence Cooperation Unit through the project "AI for Near Real Time Satellite-based Flood Response" (AI4FLOOD) under Grant ZT-IPF-5-39. </p>
Sentinel-1 data stack for Masjed Soleyman Dam
<p>This is a Sentinel-1 sample dataset for the SARvey InSAR time series analysis software.</p> <p>This dataset consists of:</p> <ul> <li> A stack of coregistered SLCs for the Masjed Soleyman Dam and its corresponding geometry data in MiaplPy format. These files serve as the input data for SARvey.<br> SARvey_input_data_Masjed_Soleyman_dam_S1_dsc_2015_2018.zip</li> <li> The final products generated by SARvey for reference.<br> SARvey_final_results_Masjed_Soleyman_dam_S1_dsc_2015_2018.zip</li> </ul> <p><br>A cookbook is available to help you run the software using this dataset.</p> <p> </p>
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>
South Bay Salt Pond Restoration Project – Phase-1 (2010-2012) Sentinel Species Health Monitoring.
The South Bay Salt Pond Restoration Program (SBSPRP) is the largest wetland restoration project in the western United States, restoring approximately 15,000 acres of former salt evaporation ponds (southbaysaltpond.org) to benefit wildlife and fish populations. Restoration on a large scale comes with many risks and uncertainties. Therefore, restoration was planned in several phases, with an adaptive management approach and applied scientific studies to address the uncertainty of different restoration strategies. These strategies included breaching ponds to create fully tidal habitats, installing tide gates to create muted tidal habitats and active management of existing ponds. This mosaic of restoration designs was intended to benefit many species of salt marsh dependent biota, including birds, fish and mammalian species. The Longjaw Mudusucker (Gillichthys mirabilis) is a resident estuarine fish, ranging from Mexico to Humboldt Bay, California, USA, and is one of the most abundant fishes in high intertidal salt-marsh habitat. The Longjaw Mudsucker depends on high intertidal creeks in marshes dominated by pickleweed (Sarcocornia sp). The fish reside within burrows in soft sediments and is the only fish species that can remain in intertidal creeks during low tide when the creeks completely de-water. Longjaw Mudsucker have a wide tolerance range for salinity, up to 80-ppt and can be the only fish species to occupy industrial salt ponds in the San Francisco Estuary. In this study, UC Davis conducted minnow trap sampling in remnant pickleweed marshes and adjacent salt pond restorations to document the distribution, relative abundance, and condition (length-weight) of fish occupying these extant and restored habitats. During the pilot effort in late summer-fall of 2010 we conducted minnow trap sampling across a number of sites in the Alviso Marsh, Eden Landing Marsh, Ravenswood Marsh and Bair Island Marsh, sampling muted restoration ponds, tidal restoration ponds and remn
Dataset of Sentinel-1 surface soil moisture time series at 1 km resolution over Southern Italy
<p>The dataset consists of a time series of the Sentinel-1 (S-1) surface soil moisture (SSM) product at 1 km spatial resolution validated in Balenzano et al. (2021 a) over the Southern Italy. The specifications of the S-1 SSM product are provided in Balenzano et al. (2021 b). The SSM time series was obtained in correspondence of the ascending (RON A146) S-1 Interferometric Wide swath (IW) acquisition dates from January 2015 to December 2018 with a temporal gap between consecutive of 6 days (when both S-1A and S-1B data are available) or 12 days. On each date (183 in total), two co-registered layers are provided: mean SSM [m3/m3] and its standard deviation [m3/m3], which provides the SSM uncertainty. The retrieval algorithm is a time series short term change detection (STCD) that is implemented in the “Soil MOisture retrieval from multi-temporal SAR data” (SMOSAR) code (Balenzano et al. 2013).</p>
Sentinel-1 InSAR Browse Service Image of the October 2016 Central Italian Earthquakes
<p>The surface deformation caused by the central Italian earthquakes which occured in October 2016 is captured in this terrain corrected interferogram produced by the Sentinel-1 InSAR Browse Service for the Geohazards Exploitation Platform.</p> <p>Two earthquakes occured on 26<sup>th</sup> October and one on 30<sup>th</sup> October. The Sentinel-1 datasets were acquired on 26-10-2016 for the master and 01-11-2016 for the slave from a descending pass so that the line of sight deformation is viewed from the east.</p> <p>Contains modified Copernicus Sentinel data (2016), processed by DLR/ESA/Terradue.</p>
Sentinel-1 InSAR Browse Service Image of the October 2016 Central Italian Earthquakes
<p>The surface deformation caused by the central Italian earthquakes which occured in October 2016 is captured in this terrain corrected interferogram produced by the Sentinel-1 InSAR Browse Service for the Geohazards Exploitation Platform.</p> <p>Two earthquakes occured on 26<sup>th</sup> October and one on 30<sup>th</sup> October. The Sentinel-1 datasets were acquired on 26-10-2016 for the master and 01-11-2016 for the slave from a descending pass so that the line of sight deformation is viewed from the east.</p> <p>Contains modified Copernicus Sentinel data (2016), processed by DLR/ESA/Terradue.</p>
Data archive for journal paper "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments"
<p>The datasets archived here include data assimilation results presented in the journal paper, "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments" (https://doi.org/10.1175/JHM-D-22-0198.1). The output was produced by combining land surface modeling (Noah-MP with HYMAP river routing) and Sentinel-1 backscatter data, applying a 1D Ensemble Kalman Filter using the NASA Land Information System. We provide Netcdf daily output files for 6 different experiments</p><p>- OLfd and OLgw: model-only (open-loop, OL) for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) <br>- DASMfd and DASMgw: data assimilation (DA) with soil moisture (SM) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) <br>- DASMLAIfd and DASMLAIgw: data assimilation (DA) with soil moisture (SM) and leaf area index (LAI) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) </p><p>Each experiment directory contains five subdirectories (DAOBS, EnKF, ROUTING, RTM, SURFACEMODEL) with corresponding outputs as described in https://nasa-lis.github.io/LISF/LIS_users_guide/LIS_users_guide.html</p>
Water surface occurrence and recurrence from the article "Amazon's 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction"
<p>This data package contains the 10 m spatial resolution occurrence and recurrence water surface masks from the article "Amazon's 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction" . These maps have been produced with Sentinel-1 images (10 m) and a Deep Learning method for image segmentation called U-net, methods and data are fully described in the article. Water surface occurrence is computed for the period 2022-2023 and indicates the percentage of time that a pixel is classified as water (100%: always water, 0%: never water, and values between 0 and 100 indicate seasonality). Water surface recurrence is computed for the period 2022-2023 and indicates the number of times that a pixel was classified as a water surface, i.e., 35 indicates that the pixel was classified 35 times as a water surface during the 2022-2023 period. The total size of the dataset is 158 Mo and is distributed in two Geotiffs, one for the water surface occurence and one for the water surface. When using this dataset, please cite the original article <a href="https://doi.org/10.3390/rs16061056">https://doi.org/10.3390/rs16061056</a></p>
Sentinel-1/2 derived Arctic Coastal Human Impact dataset (SACHI)
<p>The SACHI (Sentinel-1/2 derived Arctic Coastal Human Impact) V1 dataset was developed as part of the HORIZON2020 project Nunataryuk by b.geos (www.bgeos.com). V1 covered a 100km buffer from the Arctic Coast (land area), for areas with permafrost near the coast. V2 has been prepared as part of the ESA project EO4PAC. It covers additional selected areas extending the coverage to the south.<br>It is based on Sentinel-1 and Sentinel-2 data from 2016-2020 using the algorithms described in Bartsch et al. (2020). V1 is a supplement to Bartsch et al. (2023) and V2 to Tanguy et al. (2024).</p> <p>V2 consists of two shape files.</p> <p>1) SACHI_v2.shp - all identified objects with infrastructure/impact classes and auxiliary information<br>2) SACHI_v2_granules_acquisition_dates - processed Sentinel-2 granule extent polygons with dates of all used input data</p> <p>Dataset reference: see Zenodo 'Sentinel-1/2 derived Arctic Coastal Human Impact dataset (SACHI) v2' (zenodo.org)</p> <p>Description of fields in SACHI_v2.shp:</p> <p>class: SACHI class value. 11=linear transport infrastructure (asphalt), 12=linear transport infrastructure (gravel), 13=linear transport infrastructure (undefined),<br>20=buildings (and other constructions such as bridges), 30=other impacted area (includes gravel pads, mining sites), 40=airstrip, 50=reservoir or other water body impacted by human activities</p> <p>Description of fields in SACHI_v2_granules_acquisition_dates.shp:</p> <p>S2_date1 to S2_date3 - dates of individual Sentinel-2 images used for averaging<br>S1_winter - year(s) of Sentinel-1 images used for averaging (months December and/or January)</p>
MEaSUREs ITS_LIVE Sentinel-1 Image-Pair Glacier and Ice Sheet Surface Velocities: Version 2 (Greenland Sample Products)
<p>We provide 21 sample products of MEaSUREs ITS_LIVE Sentinel-1 Image-Pair Glacier and Ice Sheet Surface Velocities: Version 2 in three test regions of Greenland Ice Sheet. The full archive of version 2 ITS_LIVE products (including image pair maps, data cubes and mosaics) from Sentinel-1 as well as other optical sensors (Landsat-4/5/6/7/8 and Sentinel-2) can be found at the ITS_LIVE project website: <a href="https://its-live.jpl.nasa.gov/">https://its-live.jpl.nasa.gov</a>.</p> <p><strong>Sensor</strong>: Sentinel-1A/B</p> <p><strong>Processor</strong>: <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><strong>Project</strong>: NASA MEaSUREs project <a href="https://its-live.jpl.nasa.gov">ITS_LIVE</a></p> <p><strong>Region 1</strong> (69.13N, 50.88W; Jakobshavn Isbræ Glacier): 7 ascending image pairs</p> <p><strong>Region 2</strong> (77.61N, 42.79W; central north of interior Greenland): 3 ascending image pairs</p> <p><strong>Region 3</strong> (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).</p> <p> </p> <p><strong>Acknowledgement</strong>: 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>
Paddy Rice Mapping Learning Material(Sentinel-1 & labeling) in South Korea
<p>This dataset includes time series Sentinel-1 images and paddy rice labeling in South Korea for ML/DL model training. It consists of 7,762 training patches and 5,180 validation patches for each patch consists of 256 x 256 pixels. The dataset is saved in hdf5 format separated into training/valdation data, image/labeling, and part number which can be accessed by key: {tr/va}_{im/lb}_{0~4}.</p> <p>According to the phonological stage of paddy rice, the Sentinel-1 images were acquired through 8-time steps from May 10 to October 20 in 20 days’ interval. In order for the images to capture similar features of rice invariant to more or less difference of growth, minimum and maximum value composite were used at transplanting season and ripening season each. The acquisition year for each patch varies from 2017 to 2019 since it was matched to that of labeling source.</p> <p>The paddy rice labeling is a rasterized version of farm map produced by Korean Ministry of Agriculture, Food and Rural Affairs(MAFRA). The original source data was produced by visual interpreted by high-resolution satellite images and aerial photos referring the other national GIS data and it is accessible through the national open data platform (<a href="http://data.nsdi.go.kr/dataset/20210707ds00001">http://data.nsdi.go.kr/dataset/20210707ds00001</a>). As the data is distributed in a vector format, it was converted to 10 m x 10 m raster format which is compatible to the Sentinel-1, and used for labeling the images.</p>
Paired Sentinel-1 and Sentinel-2 Images for 2 Locations in Scotland and India for 2019 and 2020
<p>The dataset contains two years of coverage (2019 and 2020) for two distant geographical areas in India and in Scotland.</p> <p>If using this dataset, please cite the paper where it has been introduced:</p> <pre><code>@article{rs14061342, author = {Czerkawski, Mikolaj and Upadhyay, Priti and Davison, Christopher and Werkmeister, Astrid and Cardona, Javier and Atkinson, Robert and Michie, Craig and Andonovic, Ivan and Macdonald, Malcolm and Tachtatzis, Christos}, title = {Deep Internal Learning for Inpainting of Cloud-Affected Regions in Satellite Imagery}, journal = {Remote Sensing}, volume = {14}, year = {2022}, number = {6}, article-number = {1342}, url = {https://www.mdpi.com/2072-4292/14/6/1342}, ISSN = {2072-4292}, DOI = {10.3390/rs14061342} }</code></pre> <p> </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>
Utility of polarizations available from Sentinel-1 for tundra mapping
<p>This presentation reviews achievements with C-band SAR in general and specifically Sentinel-1 for Arctic land monitoring, the implications of the current acquisition strategy including the availability of certain polarizations and discusses differences to other wavelengths through the use of Kennaugh parameterization. Unfrozen as well as frozen season observations provide added value. C-VV has the best availability and can be used for a wide range of land cover and terrain change studies. C-HH has been proven applicable for soil characterization but can only be applied regionally. A combination with other wavelengths (demonstrated for X-band) is promising for specifically mapping of wetlands, which are of interest with respect to permafrost applications.</p>
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