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 0.9.0
Dataset results
136 results for “sentinel-1”
Monsoon triggered landslides in Nepal timed with Sentinel-1 for 2015, 2017, 2018 and 2019
<p>This is a dataset of landslides during the 2015, 2017, 2018 and 2019 monsoons, intended as a supplement to a paper currently in review. The dataset includes .shp files of landslide polygons and .csv files of landslide characteristics including timings constrained by Sentinel-1 time series. Landslide polygons were mapped by comparing pre- and post-monsoon Landsat-8 images. Landslide timings were constrained by Sentinel-1 time series using the methods described in Burrows et al. (2022) <em>NHESS. </em>Other landslide characteristics (e.g. scar area) were derived based on formulae presented in Marc et al. (2018) <em>ESURF</em> or derived from the SRTM-30m DEM in QGIS.</p> <p>Landslide polygons corresponding to the timed landslide .csv files for 2019 are included here and were mapped as part of this study. In 2015, 2017 and 2018, the .csv files labelled "west-extension" correspond to shapefiles of polygons included here and mapped as part of this study. For the main 2015, 2017 and 2018 datasets, we only present the Sentinel-1 timing results. Landslide polygons were mapped as part of the study by Jones et al. (2021) <em>Nature Communications. </em>The authors of that study made their landslide polygons available as shapefile format on the National Geoscience Data Centre (ID: 166966) and we direct people towards that data at the following link: <em>https://webapps.bgs.ac.uk/services/ngdc/accession/index.html?simpleText=landslide%20nepal </em></p>
Sentinel-1 montlhy water masks for Central Amazonia
<p>This dataset contains water mask data created using Sentinel-1 data. </p>
Geometric Distortion Distribution of Sentinel-1 in Southeast Tibet (Including Active and Passive Distortions)
<p>SETPAscending_Distor.tif and SETPDescending_Distor.tif are the geometric distortion images, where the pixel values correspond to the types of geometric distortion as detailed in the table below. Here, L stands for Near layover, R for Far layover, S for Shadow, and F for Forshortening.</p> <table> <tbody> <tr> <td>L</td> <td>R</td> <td>S</td> <td>F</td> <td>LR</td> <td>LS</td> <td>LF</td> <td>RS</td> <td>RF</td> <td>SF</td> <td>LRS</td> <td>RSF</td> <td>LSF</td> <td>LRF</td> <td>LRSF</td> </tr> <tr> <td>1</td> <td>5</td> <td>7</td> <td>9</td> <td>6</td> <td>8</td> <td>10</td> <td>12</td> <td>14</td> <td>16</td> <td>13</td> <td>21</td> <td>17</td> <td>15</td> <td>22</td> </tr> </tbody> </table> <p>SETPAscending_Forshortening_Grading.tif and SETPDescending_Forshortening_Grading.tif are used for the grading of Forshortening, with the following recommendations for the grading scale: Grade 1: 0-0.4, Grade 2: 0.4-0.6, Grade 3: 0.6-0.8, Grade 4: 0.8-1.0, and Grade 5: 1.0 to Infinity (Inf).</p>
Sentinel-1 New Anomalies AI4QC
<p>This dataset was used in the AI4QC project (Artificial Intelligence for Quality Control), in the context of the detection of new anomalies through unsupervised learning (unlabeled data). It consists of 6344 Sentinel-1 images (quick-looks in png format). The dataset was divided into training and testing folders (80% training and 20% testing). Two criterias were considered for the train/test split: seasonality and geographic location. </p> <p>An additional folder, "S1_additional_data" contains 14 more products which were<span> collected from the region around Hawaii, which tends to give products containing RFIs.</span> This data is not included in the train/test folders but can be used if one wishes to increase the amount of S1 products.</p>
Sentinel-1 Radar satellite imagery for Land Subsidence Monitoring
<p>Sentinel-1 Radar satellite imagery for Land Subsidence Monitoring</p>
Dataset and figures for "Nationwide urban ground deformation in Japan for 15 years detected by ALOS, Sentinel-1, and LiCSBAS"
<p>This dataset contains the LiCSBAS results and figures (LOS velocities, decomposed vertical and EW velocities, optical images, SB network, mask) mentioned in the paper “Nationwide urban ground deformation in Japan for 15 years detected by ALOS, Sentinel-1, and LiCSBAS”.</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.
HAQAST Sentinel-5P TROPOMI Nitrogen Dioxide (NO2) GLOBAL Annual Level 3 0.1 x 0.1 Degree Gridded Data Version 2.4 (HAQ_TROPOMI_NO2_GLOBAL_A_L3) at GES DISC
This product provides level 3 annual averages of tropospheric Nitrogen dioxide (NO2) vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) across the globe oversampled to a spatial resolution of 0.1˚ x 0.1˚ (~10 km2) using a consistent algorithm from the European Space Agency (ESA) version 2.4 that can be used for trend analysis of air pollution. The dataset record began in January 2019 and continues to the present. This L3 product was developed by the George Washington University Air, Climate and Health Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST) using Level 2 version 2.4 TROPOMI NO2 files from the ESA. The TROPOMI instrument on Sentinel-5 Precursor acquires tropospheric NO2 column contents from low Earth orbit (~824 km above ground level) once per day globally at approximately 13:30 local time.NO2 is an air pollutant that adversely affects the human respiratory system and leads to premature mortality. NO2 is also an important precursor for ozone and fine particulates, which also have severe health impacts. In urban areas, the majority of NO2 originates from anthropogenic NOx (=NO+NO2; most NOx is emitted as NO, which rapidly cycles to NO2) emissions during high-temperature fossil fuel combustion. Tropospheric NO2 vertical column contents are qualitatively representative of near-surface NO2 concentrations and NOx emissions in urban/polluted locations.
HAQAST Sentinel-5P TROPOMI Nitrogen Dioxide (NO2) GLOBAL Monthly Level 3 0.1 x 0.1 Degree Gridded Data Version 2.4 (HAQ_TROPOMI_NO2_GLOBAL_M_L3) at GES DISC
This product provides level 3 monthly averages of tropospheric Nitrogen dioxide (NO2) vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) across the globe oversampled to a spatial resolution of 0.1˚ x 0.1˚ (~10 km2) using a consistent algorithm from the European Space Agency (ESA) version 2.4 that can be used for trend analysis of air pollution. The dataset record began in January 2019 and continues to the present. This L3 product was developed by the George Washington University Air, Climate and Health Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST) using Level 2 version 2.4 TROPOMI NO2 files from the ESA. The TROPOMI instrument on Sentinel-5 Precursor acquires tropospheric NO2 column contents from low Earth orbit (~824 km above ground level) once per day globally at approximately 13:30 local time.NO2 is an air pollutant that adversely affects the human respiratory system and leads to premature mortality. NO2 is also an important precursor for ozone and fine particulates, which also have severe health impacts. In urban areas, the majority of NO2 originates from anthropogenic NOx (=NO+NO2; most NOx is emitted as NO, which rapidly cycles to NO2) emissions during high-temperature fossil fuel combustion. Tropospheric NO2 vertical column contents are qualitatively representative of near-surface NO2 concentrations and NOx emissions in urban/polluted locations.
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.
Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, Jason-3, and Sentinel-6 Version 5.2
This dataset contains the Global Mean Sea Level (GMSL) trend generated from the Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 5.2. The GMSL trend is a 1-dimensional time series of globally averaged Sea Surface Height Anomalies (SSHA) from TOPEX/Poseidon, Jason-1, OSTM/Jason-2, Jason-3, and Sentinel-6A that covers September 1992 to present with a lag of up to 4 months. The data are reported as variations relative to a 20-year TOPEX/Jason collinear mean. Bias adjustments and cross-calibrations were applied to ensure SSHA data are consistent across the missions; Glacial Isostatic Adjustment (GIA) was also applied. The data are available as a table in ASCII format. Changes between the version 5.1 and version 5.2 releases are described in detail in the user handbook.
OPERA Dynamic Surface Water Extent from Sentinel-1 (Version 1)
This dataset contains Level-3 Dynamic OPERA Surface Water Extent from Sentinel-1 (DSWx-S1) product version 1. DSWx-S1 provides near-global geographical mapping of surface water extent over land at a spatial resolution of 30 meters over the Military Grid reference System (MGRS) grid system, with a temporal revisit frequency between 6-12 days. Using Sentinel-1 radar observations, DSWx-S1 maps open inland water bodies greater than 3 hectares and 200 meters in width, irrespective of cloud conditions and daylight illumination that often pose challenges to optical sensors. Forward production of the DSWx-S1 data record began in Sept 2024. Each product is distributed as a set of 3 GeoTIFF (Geographic Tagged Image File Format) files including water classification and associated confidence layers.<br><br>The OPERA DSWx-S1 product contains modified Copernicus Sentinel data (2024-2025).<br><br>To access the calibration/validation database for OPERA Dynamic Surface Water Extent Products, please contact podaac@podaac.jpl.nasa.gov
OPERA Coregistered Single-Look Complex from Sentinel-1 Static Layers validated product (Version 1)
The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Coregistered Single-Look Complex (CSLC) from Sentinel-1 (S1) Static Layers (CSLC-S1-STATIC) validated product contains static radar geometry layers associated with the OPERA Coregistered Single-Look Complex (CSLC) from Sentinel-1 (S1) validated product. Due to the S1 mission’s narrow orbital tube, radar-geometry layers vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA CSLC-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. Each OPERA CSLC-S1-STATIC product is distributed as a Hierarchical Data Format version 5 (HDF5) file following the CF-1.8 convention containing both data raster layers and product metadata and corresponds to matching CSLC-S1 products with the same burst ID. OPERA CSLC-S1 products are available over North America which includes the USA and U.S. Territories, Canada within 200 km of the U.S. border, and all mainland countries from the southern U.S. border down to and including Panama. The CSLC-S1 products are available in the associated OPERA Coregistered Single-Look Complex from Sentinel-1 validated product (Version 1) dataset.
Data from: Synergetic use of Sentinel-1 and Sentinel-2 for assessments of heathland conservation status
Habitat quality assessments often demand wall-to-wall information about the state of vegetation. Remote sensing can provide this information by capturing optical and structural attributes of plant communities. Although active and passive remote sensing approaches are considered as complementary techniques, they have been rarely combined for conservation mapping. Here, we combined spaceborne multispectral Sentinel-2 and Sentinel-1 SAR data for a remote sensing-based habitat quality assessment of dwarf shrub heathland, which was inspired by nature conservation field guidelines. Therefore, three earlier proposed quality layers representing (1) the coverage of the key dwarf shrub species, (2) stand structural diversity and (3) an index reflecting co-occurring vegetation were mapped via linking in situ data and remote sensing imagery. These layers were combined in an RGB-representation depicting varying stand attributes, which afterwards allowed for a rule-based derivation of pixel-wise habitat quality classes. The links between field observations and remote sensing data reached correlations between 0.70 and 0.94 for modeling the single quality layers. The spatial patterns shown in the quality layers and the map of discrete quality classes were in line with the field observations. The remote sensing based mapping of heathland conservation status showed an overall agreement of 76% with field data. Transferring the approach in time (applying a second set of Sentinel 1 and 2 data) caused a decrease in accuracy to 73%. Our findings suggest that Sentinel-1 SAR contains information about vegetation structure that is complimentary to optical data and therefore relevant for nature conservation. While we think that rule-based approaches for quality assessments offer the possibility for gaining acceptance in both communities applied conservation and remote sensing, there is still need for developing more robust and transferable methods.
Analysis Ready Sentinel-1 and Sentinel-2 Data for an area of Cyprus (2018-2020)
<p>Data extracted from the Agriculture Data Cube</p>
Sentinel-1 interferograms and weather-based tropospheric delays maps (ERA5, GACOS) for two volcanoes: Piton de la Fournaise and Merapi
<div> <p>InSAR Sentinel-1 dataset and weather-based models used in the paper "Benefits of GNSS local observations compared to global weather-based models for InSAR tropospheric corrections over tropical volcanoes: case studies of Piton de la Fournaise and Merapi" by Albino et al. (2024).</p> <p>The two zip files are associated with the two targets: REUNION_ISLAND and MERAPI. The directories "ERA5" and "GACOS" contain the GACOS and ERA5 tropospheric delay maps per epoch in geotiff format, respectively. The directory "INTERFEROGRAM" contains the unwrapped interferograms in geotiff format. The "AUX" directory contains the DEM and incidence angle in geotiff format.</p> </div>
Data from: Synergetic use of Sentinel-1 and Sentinel-2 for assessments of heathland conservation status
Open the record for dataset details and reuse information.
OPERA Dynamic Surface Water Extent from Sentinel-1 CalVal Database (Version 1)
This dataset contains the calibration/validation (CalVal) database for the OPERA DSWx-S1 product. The CalVal database is a zip file of an Amazon Web Services S3 bucket containing classification items that enable algorithm calibration and validation of OPERA products. The CalVal database contains a Reference Document that further describes the structure and usage of the database, as well as a Validation Results document. Example notebooks demonstrating how to read the database tables, query for specific items, and download corresponding data files are available through the CalVal GitHub repository here: https://github.com/OPERA-Cal-Val/calval-database
OPERA Coregistered Single-Look Complex from Sentinel-1 validated product (Version 1)
The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Coregistered Single-Look Complex (CSLC) from Sentinel-1 validated product consists of Single Look Complex (SLC) images which contain both amplitude and phase information of the complex radar return. The amplitude is primarily determined by ground surface properties (e.g., terrain slope, surface roughness, and physical properties), and phase primarily represents the distance between the radar and ground targets corrected for the geometrical distance between the two based on the knowledge from Digital Elevation Model and platform’s position, i.e., the CSLC phase represents residual geometrical distance between the sensor and target, the atmospheric propagation delay and the target movements. The CSLC-S1 product is derived from Copernicus Sentinel-1A and Sentinel-1B Interferometric Wide (IW) SLC data. The CSLC images are precisely aligned or “coregistered” to a pre-defined UTM/Polar stereographic map projection systems and posted at 5x10 m spacing in east and north direction, respectively. Each CSLC-S1 product corresponds to a single S1 burst and is distributed as a Hierarchical Data Format version 5 (HDF5) file following the CF-1.8 convention containing both data raster layers (e.g., geocoded complex backscatter, low-resolution correction look-up tables) and product metadata. OPERA CSLC-S1 products are available over North America which includes the USA and U.S. Territories, Canada within 200 km of the U.S. border, and all mainland countries from the southern U.S. border down to and including Panama. The OPERA CSLC-S1 product contains modified Copernicus Sentinel data (2016-2025).Due to the S1 mission’s narrow orbital tube, radar-geometry layers vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA CLSLC-S1 product, as they are produced only once or a limited number of times. The static layers are available in the associated OPERA Coregistered Single-Look Complex from Sentinel-1 Static Layers validated product (Version 1).
OPERA Surface Displacement from Sentinel-1 validated product (Version 1)
The Level-3 OPERA Sentinel-1 Surface Displacement (DISP) product is generated through interferometric time-series analysis of Level-2 Coregistered Sentinel-1 Single Look Complex (CSLC) datasets. Using a hybrid Persistent Scatterer (PS) and Distributed Scatterer (DS) approach, this product quantifies Earth's surface displacement in the radar line-of-sight. The DISP products enable the detection of anthropogenic and natural surface changes, including subsidence, tectonic deformation, and landslides. The OPERA DISP suite comprises complementary datasets derived from Sentinel-1 and NISAR inputs, designated as DISP-S1 and DISP-NI, respectively. Each product, created per acquisition, adheres to a consistent structure, HDF5 file format, file-naming convention, and a 30 m spatial posting. This collection specifically includes DISP-S1 products, derived from Sentinel-1 data. DISP-S1 products provide spatial coverage across North America, encompassing the United States, U.S. territories within 200 km of the U.S. border, Canada, and mainland countries from the southern U.S. border to Panama. These products are generated from Sentinel-1 Interferometric Wide (IW) swath mode acquisitions starting in mid-2016.The OPERA DISP-S1 product contains modified Copernicus Sentinel data (2016-2025).
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.