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1,118 results for “sentinel”
Woody Cover Mapping in the Kruger National Park using Sentinel-1 time series and LiDAR data
<p>This data repository presents a workflow to derive woody cover information for the Kruger National Park, South Africa, from freely available Sentinel-1 C-Band time series and LiDAR data (modified from Smit et al. 2016) using machine learning (MLR and Ranger in R). The methodology is described in following publication:</p> <p><em>Urban, M., K. Heckel, C. Berger, P. Schratz, I.P.J. Smit, T. Strydom, J. Baade & C. Schmullius (2020): Woody Cover Mapping in the Savanna Ecosystem of the Kruger National Park Using Sentinel-1 C-Band Time Series Data. Koedoe.</em></p> <p>In order to derive woody cover percentage information, download all files into one folder and run the R-Files consecutively from 01_ to 04_. Follow the instruction within each of the R-Files, which are written as comments in the programming code.</p> <p>The data repository consist of the following files:</p> <p><strong>R-Files:</strong></p> <p>1. Script 1: 01_MLR_tune_spatial_final</p> <p>2. Script 2: 02_MLR_cross_validation_spatial_final</p> <p>3. Script 3: 03_MLR_RANGER_train_final</p> <p>4. Script 4: 04_MLR_prediction_woody_cover_final</p> <p> </p> <p><strong>Training dataset - ENVI FILE (layerstack of Sentinel-1 VH and VV backscatter between 2016 and 2017 and the woody cover reference derived from the LiDAR data) :</strong></p> <p>1. S1_A_VH_VV_16_17_lidar</p> <p> </p> <p><strong>Data for prediction - ENVI FILES (3 example regions in the Kruger National Park):</strong></p> <p>1. S1_A_VH_VV_16_17_subset_example_Letaba_Rest_Camp</p> <p>2. S1_A_VH_VV_16_17_subset_example_Lower_Sabie</p> <p>3. S1_A_VH_VV_16_17_subset_example_Pafuri</p> <p> </p> <p><strong>Final woody cover maps of the Kruger National Park:</strong></p> <p>1. xx_woody_cover_map_final.rar (contains final maps in 10m, 30m, 50m and 100m spatial resolution as .tif and a QGIS project)</p> <p> </p> <p><em>References:</em></p> <p>Smit, I.P.J., Asner, G.P., Govender, N., Vaughn, N.R. & Wilgen, B.W. van, 2016, ‘An examination of the potential efficacy of high-intensity fires for reversing woody encroachment in savannas’, <em>Journal of Applied Ecology</em>, 53(5), 1623–1633.</p>
Ecuador quake observed by Sentinel-1
<p>We have created a new Sentinel-1 interferogram capturing the massive earthquake that hit Ecuador on 16 April with a magnitude of 7.8. Two Sentinel-1 images have been used in the GEP: one pre event acquired by the satellite on 12 April and the other post event, on 24 April.</p> <p>The quake occurred as the result of shallow thrust faulting on or near the plate boundary between the Nazca and South America plates, according to the USGS. Ecuador lies above the plate boundary where the Nazca Plate subducts beneath the South American Plate at a velocity of 61 mm/yr. At least 654 people were killed and more than 16,600 were injured, this quake was the worst natural disaster in Ecuador since the 1949 Ambato earthquake.</p> <p>The Sentinel mission marks a new era in remote sensing aid for disaster response. These satellites play a crucial role in helping manage natural disasters because they provide timely information that can be rapidly processed. The results obtained with interferometry allow the monitoring of ground motion, down to a few mm, on wide areas. This type of information is essential for monitoring shifts from earthquakes, landslides, and volcanic uplift, among others.</p> <p>In the last year we have seen some impressive InSAR results from Sentinel-1A, the first from this two-satellite constellation, that has been in orbit for two years. On 25 April its identical twin Sentinel-1B was launched. With the two satellites in orbit we will now receive double the amount of data and achieve global coverage in six days.</p>
Demo measurement using DIAPASON and Sentinel-1 after the 14 November 2016 earthquake in New Zealand
<p>Processing of Sentinel-1A acquisitions of 3rd and 15th Nov 2016 with CNES DIAPASON processing chain, integrated by TRE Altamira on ESA's Geohazard Exploitation Platform. Track 52, Ascending Orbit Direction.</p> <p>Contains modified Copernicus Sentinel data 2016</p>
February 2017 Western Turkey Earthquake Swarm Sentinel-1 TOPS Differential Interferogram (20170131-20170212)
<p>In february 2017 a series of earthquakes affected the Biga Peninsula in Western Turkey. Over 350 buildings sustained extensive damage. The seismic events occurred at the intersection of the Kestanbol Fault and the Edremit Fault Zone. The Sentinel-1 TOPS co-seismic interferogram was generated with the ESA SNAP toolbox (http://step.esa.int/).</p> <p>S1A data were downloaded from the Sentinel-1 Scientific Data Hub: S1A_20170131-S1A_20170212 from ASCENDING orbit 131.</p> <p> </p>
Castel Fusano preliminary burned area delineation through Sentinel 2A and Landsat 8
<p>Castel Fusano urban park has been affected by a series of wildfire that have destroyed a vast area of Mediterranean pine forest . The first huge fire started on 17 July 2017.Followed by a new one on 21 July 2017.</p> <p>Sentinel 2A and Landsat 8 data have been used to map burned areas at 20m and 30m respectively by testing NBR and dNBR with available pre and post images. Sentinel 2A - MSI data are dated 20 June 2017 (pre)and 20 July 2017(post). Landsat 8 - OLI data are dated 6 July 2017 (pre) and 22 July 2017 (post). By using both satellites it is possible to map the effects of the two fires in terms of burned areas. At this preliminary stage the perimeters (here available) have been derived manually as further analysis is in progress.</p> <p>Contains modified Copernicus Sentinel data [2017]; Landsat 8-OLI courtesy of the U.S. Geological Survey</p> <p>SNAP and HARRIS- ENVI </p>
EAST-WEST and VERTICAL deformation maps of Alto Tiberina Fault supersite: The Post-Proc service in Geohazard Exploitation Platform applied to Sentinel-1 dataset
<p>In the framework of the ESA funded project “MEMpHIS - Multi Scale and Multi Hazard Mapping Space based Solutions ”, the Istituto Nazionale di Geofisica e Vulcanologia (INGV) together with TRE-ALTAMIRA, generated the EAST-WEST and VERTICAL deformation maps of Alto Tiberina Fault supersite. The two maps of ground velocity were derived thanks to the adoption of a specific tool named "Post-Proc", implemented in MEMPHIS and with the support of Terradue, that is able to automatically re-project on the east-west and vertical directions the ascending and descending InSAR time series. In particular, the output refers to the deformation maps calculated by processing with SqueeSAR (TM) method, a large dataset acquired by the ESA Sentinel-1 mission.</p> <p>The Post-Proc tool also calculates the mean accelerations associated to each persistent scatterer in the scenes. Some additional features are also available from this tool:</p> <p>- Change the coherence threshold for selecting a subset of persistent scatterers</p> <p>- Activate a geometrical distortion filter to take into account the layover and foreshortening effects</p> <p>- Change the reference point (position and coherence)</p> <p>- Choose between two types of accelerations: 2<sup>nd</sup> order model or velocity derivative</p> <p>- Choose among three different projection: east-west, vertical, and downslope.</p> <p>The present dataset is composed of the EAST-WEST and VERTICAL acceleration maps. The data are in shapefile format: for each record (PS) the topography, velocity, acceleration, and InSAR coherence is reported.</p>
Dataset of processed Sentinel-2 images for chlorophyll-a estimation in high-altitude lakes in the Sierra Nevada, Spain
<p>This dataset contains Sentinel 2 satellite images clipped to 5 high-altitude lakes in the Sierra Nevada Mountain Range, Spain. The images were processed with the following atmospheric correction algorithms:</p><ul><li><a href="https://c2rcc.org/">C2RCC</a> (<a href="https://ui.adsabs.harvard.edu/abs/2016ESASP.740E..54B/abstract">Brockmann et al. 2016</a>)</li><li><a href="https://github.com/MarcYin/SIAC">SIAC</a> (<a href=" https://doi.org/10.5194/gmd-15-7933-2022">Yin et al. 2022)</a></li><li><a href="https://github.com/acolite/acolite/releases/tag/20221114.0">ACOLITE</a> (<a href="https://doi.org/10.1016/j.rse.2018.07.015">Vanhellemont & Ruddick, 2018</a>)</li><li><a href="https://grass.osgeo.org/grass83/manuals/i.atcorr.html">6SV</a> (<a href="https://doi.org/10.1109/36.581987">Vermote et al. 2006</a>)</li></ul><p><strong>Included Lakes and and their IDs:</strong></p><ul><li>Laguna de la Caldera (ID = P-2)</li><li>Laguna-embalse de las Yeguas (ID = D-6)</li><li>Laguna de Río Seco (ID = P-8)</li><li>Laguna Larga (ID = G-7)</li><li>Laguna de la Mosca (ID = G-11)</li></ul>
Harmonized Chlorophyll-a dataset from Landsat-8/9 OLI and Sentinel 2 MSI in lakes of the Yunnan-guizhou Plateau, China
<p>We generated a harmonized Chl-a dataset for the lakes in the Yunnan–Guizhou Plateau in China from 2013 to 2022 by the Landsat 8/9 and Sentinel-2A/B virtual constellation. Here, we shared the mean chlorophyll-a in nine major lakes in studied area. </p><p>These dataset were aggregated from MSI- and OLI-derived Chl-a images and were subseted using the boundary of each lake. Data format is Geo-Tiff (*.tif) and zero values are INVALID values. More details on Chl-a retrievals can be found in: </p><p>Z. Cao et al., "Harmonized Chlorophyll-a Retrievals in Inland Lakes From Landsat-8/9 and Sentinel 2A/B Virtual Constellation Through Machine Learning," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2022, Art no. 4209916, doi: 10.1109/TGRS.2022.3207345.</p>
Cartographie fine de la couverture de Macrocystis pyrifera sur l'archipel de Kerguelen à partir d'images Sentinel-2 acquises en 2020
<p>Cette donnée représente une cartographie fine de la couverture de Macrocystis pyrifera (MP) sur l’archipel de Kerguelen à partir d'images Sentinel-2 acquises en 2020. Etant donné la proximité de MP avec Durvilleae Antarctica (DA) (du point de vue de leur signature spectrale), les résultats intègrent aussi cette dernière.</p> <p>Les données d’Observation de la Terre utilisées sont les images satellitaires libres et gratuites Sentinel-2 (A et B). La chaîne de traitement mise en place est entièrement basée sur le langage R. Les téléchargements et prétraitements ont été effectués grâce à une librairie complète, sen2r, créée par Luigi Ranghetti (Ranghetti et al., 2020).<br>Les résultats de détection des algues brunes à partir de la chaîne sont proches de l’exhaustivité avec un Kappa global de 0.993. C’est l’indice du grNDVI de Wang et al. (2007) qui a été utilisé pour réaliser ces produits.</p> <p>Un produit final utilisable par un utilisateur n’ayant pas forcément été initié à la télédétection a été favorisé. Dans ce but une couche shapefile représentant la présence moyenne d’algue brune sur la zone (c.à.d. sur toute l’étendue temporelle disponible) est ici mise à disposition.</p> <p>Cette donnée a été réalisée dans le cadre du stage de master2 d'Alexis Pré co-encadré par l'UMR Espace-Dev à la station SEAS-OI et le service SIG des Terres Australes et Antarctiques Françaises.</p> <p>Wang, F. M., Huang, J. F., Tang, Y. L., & Wang, X. Z. (2007). New vegetation index and its application in estimating leaf area index of rice. <em>Rice Science</em>, <em>14</em>(3), 195-203.</p> <p>Ranghetti, L., Boschetti, M., Nutini, F., & Busetto, L. (2020). “sen2r”: An R toolbox for automatically downloading and preprocessing Sentinel-2 satellite data. <em>Computers & Geosciences</em>, <em>139</em>, 104473.</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>
Mapping sugarcane globally at 10 m resolution using GEDI and Sentinel-2
<p><strong>Dataset Abstract:</strong><br>Sugarcane is an important source of food, biofuel, and farmer income in many countries. At the same time, sugarcane is implicated in many social and environmental challenges, including water scarcity and nutrient pollution. Currently, few of the top sugar-producing countries generate reliable maps of where sugarcane is cultivated. To fill this gap, we introduce a dataset of detailed sugarcane maps for the top 13 producing countries in the world, comprising nearly 90% of global production. Maps were generated for the 2019-2022 period by combining data from the Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2 (S2). GEDI data were used to provide training data on where tall and short crops were growing each month, while S2 features were used to map tall crops for all cropland pixels each month. Sugarcane was then identified by leveraging the fact that sugar is typically the only tall crop growing for a substantial fraction of time during the study period. Comparisons with field data, pre-existing maps, and official government statistics all indicated high precision and recall of our maps. Agreement with field data at the pixel level exceeded 80% in most countries, and sub-national sugarcane areas from our maps were consistent with government statistics. Exceptions appeared mainly due to problems in underlying cropland masks, or to under-reporting of sugarcane area by governments. <br>The final maps should be useful in studying the various impacts of sugarcane cultivation and producing maps of related outcomes such as sugarcane yields.</p> <p><strong>USAGE: Users must mask the provided sugarcane map with the most appropriate crop mask from the ones provided. If none of the provided crop masks are suitable, users can use an external crop mask instead.</strong></p> <p>Validation results for the sugarcane maps are detailed in Section 4.3 of the paper. For Indonesia and Guatemala, no field-level data or raster datasets were available for validation of our sugarcane maps.</p> <p><br><strong>Dataset:</strong> <br>5 bands<br>b1: Number of tall months<br>b2: Sugarcane Map: 0 = non-sugarcane, 1 = sugarcane<br>b3: ESA crop mask: 0 = non-cropland, 1 = cropland<br>b4: ESRI crop mask: 0 = non-cropland, 1 = cropland<br>b5: GLAD crop mask: 0 = non-cropland, 1 = cropland</p> <p> </p> <p>The dataset can be accessed on Google Earth Engine (GEE) at <br><strong><a href="https://code.earthengine.google.com/?asset=projects/lobell-lab/gedi_sugarcane/maps/imgColl_10m_ESAESRIGLAD">https://code.earthengine.google.com/?asset=projects/lobell-lab/gedi_sugarcane/maps/imgColl_10m_ESAESRIGLAD</a><br></strong><br>Example GEE script for visualizing and masking the sugarcane maps by country available at:<br><strong><a href="https://code.earthengine.google.com/545a87ce9bc29f2b5ad180955d974f8c?asset=projects%2Flobell-lab%2Fgedi_sugarcane%2Fmaps%2FimgColl_10m_ESAESRIGLAD">https://code.earthengine.google.com/545a87ce9bc29f2b5ad180955d974f8c?asset=projects%2fl Bell-lab%2Fgedi_sugarcane%2 Maps%2FimgColl_10m_ESAESRIGLAD</a></strong></p>
Data for 'Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery'
<div>This dataset contains avalanche deposit outlines (as shapefiles) derived for the study 'Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery'</div> <div> </div> <div>They were outlined at three different sites (Mt Blanc, Everest and Hispar regions) for the periods 11/2016-10/2021 (Mt Blanc) and 11/2017-10/2022 (Everest and Hispar). The time period is indicated in the file name.</div> <div> </div> <div>For each dataset we give the raw outlines (Automated_outlines_dates), the manually updated (Automated_outlines_dates_ManualUpd) and the manually updated after accounting for surface elevation change (Automated_outlines_dates_ManualUpd_shifted). </div> <div> </div> <div>In order to know which scenes were used for the mapping (if no avalanche was detected, we did not provide a shapefile, but this doesn't been that there is a gap in the Sentinel-1 time series), we provide a Sentinel1_date file that shows all the Sentinel-1 RGB pairs that we used to detect the avalanches.</div> <div> </div> <div>We also provide as geotiffs the temporally aggregated outlines (Automated_outlines_dates_ManualUpd_shifted_aggregated; over one specific year yn - from 01/11/yn-1 to 01/11/yn - or the full study period):</div> <div>- as heatmaps (where the value of each pixel corresponds to the number of avalanches that occured) </div> <div>- as binary maps of deposits (where 1 is when an avalanche occured over the time period and 0 is where none were detected).</div> <div> </div> <div> </div> <div>Finally we provide a csv file for each region with metrics per glacier:</div> <div> </div> <div>RGI ID</div> <div>Glacier size (in m^2)</div> <div>Catchment size (in m^2)</div> <div>Area of slopes steeper than 30° (in m^2)</div> <div>The area of total deposits detected (by summing all the pixels of the deposit binary maps) in the ascending obits (in m^2)</div> <div>The area of total deposits detected (by summing all the pixels of the deposit binary maps) in the descending obits (in m^2)</div> <div>The avalanche activity detected (by summing all pixels of the heat maps) in the ascending orbits (in m^2)</div> <div>The avalanche activity detected (by summing all pixels of the heat maps) in the descending orbits (in m^2)</div> <div>The area of the glacier visible in the ascending orbits (in m^2)</div> <div>The area of the glacier visible in the descending orbits (in m^2)</div> <div> </div> <div> </div> <div>The main Google Earth Engine and Matlab scripts used to pre-process the Sentinel-1 GRD images and to map the avalanches are available on GitHub: https://github.com/MarinKneib/S1_avalanches</div> <div> </div>
Sentinel-1 Derived Snow Depths and SnowEx Lidar Netcdfs
<p>These are netcdfs of S1 raw data, intermediate products, derived snow depths, ancillary data (IMS snow coverage, tree percentage) and lidar snow depths used in an analysis of the Lievens et al. (2021) algorithm.</p> <p> </p> <p>9 sites - Banner 2020, Banner 2021, Cameron 2021, Dry Creek 2020, Fraser 2020, Fraser 2021, Little Cottonwood Canyon 2021, Mores 2020, Mores 2021</p> <p> </p> <p>Data Variables:</p> <p>s1 - sentinel 1 backscatter data. contains 3 bands - VV, VH, and incidence angle</p> <p>ims - IMS snow coverage data (4 = snow covered, 2 = None) []</p> <p>fcf - Forest coverage fraction [%]</p> <p>deltaCR - change in the S1 cross ratio through time [dB]</p> <p>deltaVV - change in S1 VV backscatter through time [dB]</p> <p>deltaGamma - change in combined gamma variable [dB]</p> <p>snow_index - snow index in dB that is converted to derived snow depth by C parameter [dB]</p> <p>snow_depth - derived snow depth from S1 [m]</p> <p>wet_flag - flagged for snow with -2dB of change in CR. 1 = wet, 0 = dry</p> <p>alt_wet_flag - snow flagged by negative snow_index. 1 = wet 0 = dry</p> <p>freeze_flag - snow flagged as refreezing by increase of 1 dB in CR</p> <p>wet_snow - combined wet flag, alt wet flag, freeze flag, and previous time step's wet snow to get current wet snow flags</p> <p>perma_wet - snow that is flagged as wet more than 50% of last four acquisitions after Feb 1</p> <p>lidar-sd - Lidar derived snow depths [m]</p> <p>lidar-vh - lidar derived vegetation heights [m]</p> <p>lidar-dem - lidar derived snow free dems [m]</p> <p>aspect - aspect in degrees from lidar-dem [°]</p> <p>easting - degrees of easting from aspect[°]</p> <p>north - degrees of northing from aspect[°]</p> <p>confidence - unused metric of confidence</p>
Seasonal RGB composites from Sentinel-2 (2017-2024) for Catalonia, Spain; Sétif, Algeria; Behia and Kafr Elsheihk Governates, Egypt; Marseille, France; Sicily, Italy.
<p>A dataset containing seasonal Sentinel-2 RGB images (2017 to 2024) for five case study areas of the TRANSITION project (https://www.transition-med.org/), funded by PRIMA (https://prima-med.org/). The areas are Catalonia, Spain; Sétif, Algeria; Behia and Kafr Elsheihk Governates, Egypt; Marseille, France; Sicily, Italy. The dataset can be useful for anyone looking to conduct agriculture-related research using Earth Observation data in these five areas.</p>
Evaluation of Spatiotemporal Fusion Methods Using Sentinel-2 And Sentinel-3: A New Benchmark Dataset And Comparison
<p>In Earth observation, data fusion is important to generate high temporal and spatial resolution images. Nevertheless, existing research on data fusion primarily concentrates on merging two sources of data (mostly MODIS and Landsat). Therefore, we offer the community a new benchmark dataset for evaluating data fusion using new European sensors (Sentinel-2 and Sentinel-3).</p> <p>The dataset is composed of three different sites located in different parts of the world to ensure the diversity of the ecosystem. The two components of the dataset are collected from operating missions ( Sentinel-2 and Sentinel-3). We also provide 10 bands for Sentinel-2 ranging from blue to SWIR, 4 bands at 10m resolution and 6 at 20m resolution. For Sentinel-3 16 bands are provided with a spatial resolution of 300m. The multiple bands allow for different applications for this dataset such as testing data fusion methods, etc.</p>
Mapping Tree Species Fractions in Temperate Mixed Forests Using Sentinel-2 Time Series and Synthetically Mixed Training Data
<p>This dataset contains the latest version of a selection of result data of the paper "Mapping Tree Species Fractions in Temperate Mixed Forests Using Sentinel-2 Time Series and Synthetically Mixed Training Data" (DOI: https://doi.org/10.1016/j.rse.2025.114740 )</p> <p>The dataset contains:</p> <ol> <li>A geopackage of training points of pure tree species</li> <li>The resulting 12-band tree species fraction map of Rhineland-Palatinate</li> <li>HSV-colored map of dominant tree species. For information which tree species are represented by the different colors, refer to the Supplemental in the original paper.</li> <li>CSV-table of predicted and reference propotion of the tree species in the validation polygon (the original polygon data can not be published due to data privacy regulations) </li> </ol> <p> </p>
Mapping canopy cover in African dry forests from combined use of Sentinel-1 and Sentinel-2 data: 2018 maps for Tanzania
<p>The monitoring of tropical forests has benefited from the increased availability of high-resolution earth observation data. However, the seasonality and openness of the canopy of dry tropical forests remains a challenge for optical sensors. The availability of time series of remote sensing images at 10-meters is changing this paradigm.</p> <p>In the context of REDD+ national reporting requirements, we investigated a methodology that is reproducible and adaptable in order to ensure user appropriation. The overall methodology consists of three main steps: (i) the generation of Sentinel-1 (S1) and Sentinel-2 (S2) layers, (ii) the collection of an ad-hoc training/validation dataset and (iii) the classification of the satellite data. Three different classification workflows are compared in terms of their capability to capture the canopy cover of forests in East Africa. Two types of maps are derived from these mapping approaches: i) binary tree cover/no tree cover (TC/NTC) maps, and ii) maps of canopy cover classes. The method is applied at scale, over Tanzania and one final map for each workflow is shared. Two big data computing platforms are combined to exploit the important volume of satellite data available over a yearly period.</p> <p>The reference dataset (training and validation), the three best maps and the codes to produce the S1 and S2 composites on Google Earth Engine are shared here.</p> <p>The folder “reference_dataset.zip” contains the expert based training and validation dataset. The point shapefile corresponding to the center of the plot as well as the 3x3 and 5x5 polygon shapefile are shared together with qml layer file for each type of shapefile.</p> <p>Three maps (binary TC-NTC “pixel” RF, forest type “pixel” RF and “window” ETC) are shared. A 40 km buffer from national boundaries is kept in order to let users refine their area of interest. The qml style file are also shared.</p> <p>In the “script.zip” folder, the javascript codes to generate the S1 and S2 mosaics are shared.</p>
Central and Eastern Himalaya glacier velocities 2017-2019 (Sentinel 2)
<p> </p> <p>This dataset contains the median glacier surface velocity for the Central and Eastern Himalaya glacier velocities 2017-2019 (Sentinel 2). The velocities have been obtained by feature-tracking of November Sentinel 2 images spaced 1 year apart.</p> <p>The folder contains the following fields at 80 m resolution in GeoTiff format:</p> <ul> <li>the velocity magnitude 'vel' (meters per year)</li> <li>the x/y velocity components x_vel/y_vel (meters per year)</li> <li>the associated errors err, x_err, y_err (meters per year)</li> <li>the median absolute deviation of all the merged velocities 'MAD' (meters per year)</li> <li>the number of image pairs that have been merged in the median</li> </ul> <p>I recommend filtering data with error larger than 5 m/yr.</p> <p> </p> <table> <caption>Metadata Properties</caption> <tbody> <tr> <td>CRS</td> <td>EPSG:32645 - WGS 84 / UTM zone 45N - Projected</td> </tr> <tr> <td>Extent</td> <td>9425.6070999999992637,3045085.1858000000938773 : 883803.9936000000452623,3368628.0986000001430511</td> </tr> <tr> <td>Unit</td> <td>meters</td> </tr> <tr> <td>Width</td> <td>11192</td> </tr> <tr> <td>Height</td> <td>3981</td> </tr> <tr> <td>Data type</td> <td>Float32 - Thirty two bit floating point</td> </tr> </tbody> </table> <p> </p>
A tempοral Deep Convolutional Neural Network model on Sentinel-1 Image Time Series for pixel-wise Flood Classification (dataset)
<p>This is a dataset which has been designed to be used for flood time series classification. Each time series is annotated as flood or no-flood and represents a pixel-wise time series derived from stack of Sentinel-1 IW GRD images that have been pre-processed according to <a href="http://doi.org/10.5281/zenodo.6510223">https://doi.org/10.5281/zenodo.6510223</a>.</p>
Sentinel 2 meltpond volume
<p>Melt pond volumes based on the threshold-based method for lake detection/volume of Moussavi et al., 2020, to 19213 Sentinel-1 Level-1C top-of-atmosphere reflectance granules with less than 30% cloud cover and solar elevation angles above 25 degrees over all Antarctic ice shelves and the surrounding ice sheet. Each granule is processed in the Google Earth Engine at 100 m resampled resolution and converted into lake presence and lake depth. The depth observations were subsequently converted into total accumulated meltwater volumes per lake (in meter water equivalent) by integrating the derived lake depths per pixel over all granules. The resulting melt pond volume dataset (this repository) is then aggregated on the 27 km RACMO2.3p2 grid, accumulating the melt ponds that fall within one model grid cell.</p> <p>Original lake volumes per pixel are available through https://code.earthengine.google.com/?asset=projects/ee-earthmapps/assets/S2_LakesAntarctica_v3</p>
ScienceDex guides
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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.