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181 results for “SENTINEL-2”
Mediterranean Sentinel-2 Litter Windrows Catalogue (Jun. 2015 - Sept. 2021) v1.0
<p>This dataset contains the <strong>14,374 Litter Windrows detections </strong>found using multispectral Copernicus Sentinel-2/MSI L1c data in the Mediterranean Sea for the period June 2015–September 2021. The dataset includes exclusively the filaments deemed as valid by the research team and used in the main research of the associated research paper. The following information is provided:</p> <ul> <li>Geocentric lat/lon coordinates (WGS84) of the 'centroid' position for the filament</li> <li>(X, Y) coordinates of the filament's centroid related to the multispectral Sentinel-2 image array, when considering all spectral bands resampled at 10m spatial resolution.</li> <li>Number of 10x10m pixels composing the filaments where floating matter has been identified.</li> <li>(X, Y) coordinates for all the detected pixels within the filament and related to the associated Sentinel-2 L1c image at 10m resolution.</li> <li>Original full spectrum (13 bands) for each of the pixels within the filament.</li> <li>Limits of the bounding box (in image (x, y) coordinates at 10m resolution) contain the entire filament.</li> <li>Full name of the Sentinel-2 L1c product containing the filament.</li> <li>Central decimal time (UTC) of the associated Sentinel-2 acquisition.</li> </ul> <p>The file has <strong>netcdf-4 format (NETCDF-CLASSIC)</strong>, with a size of <strong>2 GB</strong>. Note that to maintain structure, the variables related to each pixel in the filament have a size equal to the largest filament in the database. As most filaments are shorter, these variables are padded with dummie values (-999) in all the relevant fields.</p> <p><strong>ncdump </strong>will yield the following information:</p> <pre>netcdf file:/C:/Users/mab_l/Downloads/Science/WASP/WASP_LW_SENT2_MED_L1C_B_201506_202109_10m_6y_NRT_v1.0.nc { dimensions: n_filaments = 14374; box_dims = 4; n_max_pixels_fil = 2563; n_bands = 13; nchar = 65; variables: char s2_product(n_filaments=14374, nchar=65); :long_name = "Copernicus Sentinel-2/MSI L1c product"; :description = "Full name of the Sentinel-2 product where the filament was found."; double dec_time(n_filaments=14374); :description = "Decimal time of the Sentinel-2 acquisition where the filament was found."; :long_name = "Decimal Time"; :units = "year"; :calendar = "gregorian"; :_FillValue = -999.0; // double short x_centroid(n_filaments=14374); :units = "X image coordinate"; :description = "X-axis position of the filament in the S-2/MSI data array at 10m resolution."; :_FillValue = -999S; // short :long_name = "X-axis centroid position"; short y_centroid(n_filaments=14374); :description = "Y-axis position of the filament in the S-2/MSI data array at 10m resolution."; :_FillValue = -999S; // short :long_name = "Y-axis centroid position"; :units = "Y image coordinate"; double lat_centroid(n_filaments=14374); :units = "degrees North"; :axis = "Y"; :description = "Geocentric longitudinal coordinates of the filament (WGS84)."; :long_name = "Latitude"; :_FillValue = -999.0; // double double lon_centroid(n_filaments=14374); :long_name = "Longitude"; :units = "degrees East"; :axis = "X"; :_FillValue = -999.0; // double int n_pixels_fil(n_filaments=14374); :units = "none"; :description = "Number of detected pixels composing the filament."; :long_name = "Number of pixels in filament"; :_FillValue = -999; // int short limits(n_filaments=14374, box_dims=4); :description = "Image coordinates of the bounding box containing the filament [x_lower, y_lower, x_upper, y_upper]"; :coordinates = "n_filaments box_dims"; :units = "(X, Y) image coordinates"; :_FillValue = -999S; // short short pixel_x(n_filaments=14374, n_max_pixels_fil=2563); :units = "X image coordinate"; :coordinates = "n_filaments n_max_pixels_fil"; :description = "X-axis position of the pixels composing the filament in the S-2/MSI data array at 10m resolution."; :long_name = "Pixel x-axis coordinate"; :_FillValue = -999S; // short short pixel_y(n_filaments=14374, n_max_pixels_fil=2563); :description = "Y-axis position of the pixels composing the filament in the S-2/MSI data array at 10m resolution."; :units = "Y image coordinate"; :coordinates = "n_filaments n_max_pixels_fil"; :long_name = "Pixel y-axis coordinate"; :_FillValue = -999S; // short float pixel_spec(n_filaments=14374, n_max_pixels_fil=2563, n_bands=13); :units = "reflectance"; :coordinates = "n_filaments n_max_pixels_fil n_bands"; :description = "Spectral L1c (TOA) reflectance values for pixel and band on the filament."; :long_name = "Pixel Spectra"; :_FillValue = -999.0f; // float // global attributes: :title = "Mediterranean Sentinel-2 Litter Windrows Catalogue (Jun. 2015 - Sept. 2021) v1.0"; :institution = "Barcelona Expert Center (BEC), ICM-CSIC, Barcelona, Spain"; :url = "http://bec.icm.csic.es"; :email = "m.arias@icm.csic.es m.arias@zenithalblue.com"; :copyright = "BEC research products are freely distributed. If these data are used for publication, please \ncite the original research work."; :reference = "https://doi.org/10.5281/zenodo.11045944"; :project = "Mapping Windrows as Proxies for Marine Litter Monitoring from Space (WASP)"; :funding = "ESA contract no. 4000130627, within the Discovery Element of the ESA\'s Basic Activities."; :sensor = "Multi Spectral Instrument (MSI)"; :platform = "Copernicus Sentinel-2A/B"; :license = "This product is distributed under Creative Commons Attribution license (CC BY 4.0).\nYou are free to share and adapt this product under the following terms:You must give appropriate credit (see copyright), \nprovide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in \nany way that suggests the licensor endorses you or your use."; :license_url = "https://creativecommons.org/licenses/by/4.0/"; :Conventions = "CF-1.6"; :time_coverage_start = "20150601T00:00:00"; :time_coverage_end = "20210917T23:59:59"; :geospatial_lat_min = 29.905254805021364; // double :geospatial_lat_max = 45.740728823657236; // double :geospatial_lat_units = "degrees north"; :geospatial_lon_min = -6.703167546213006; // double :geospatial_lon_max = 36.20086905862957; // double :geospatial_lon_units = "degrees east"; :spectral_bands = "B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B10, B11, B12"; }<br><br></pre> <p>Please, if you use this dataset, make reference to it in your work: <strong>https://doi.org/10.5281/zenodo.11045944</strong><br>Additionally, please, refer to the original research paper (<strong>Cózar & Arias et al. (2024) 'Proof of concept for a new sensor to monitor marine litter from space', in Nature Communications</strong>)</p> <p>To contact authors about this dataset, please write to<strong> M. Arias (m.arias@zenithalblue.com, m.arias@icm.csic.es) and A. Cózar (andres.cozar@uca.es)</strong>.</p>
"Monthly velocity and seasonal variations of the Mont Blanc glaciers derived from Sentinel-2 between 2016-2024" - supplementary materials
<p>The repository contains the supplementary materials to be downloaded relative to the research article:</p> <p>“Monthly velocity and seasonal variations of the Mont Blanc glaciers derived from Sentinel-2 between 2016-2024” </p> <p>https://doi.org/10.5194/egusphere-2023-2771</p> <p>The available files are:</p> <p>-92 raster maps of monthly velocity of the study area.</p> <p>-Shapefiles whith the glacier outlines of the 30 studied glaciers.</p> <p>-Shapefiles of the velocity time series extraction areas.</p> <p>-Velocity time series 2016-2024 of the 30 glaciers from the study. </p>
HLWATER V1.0 Optical (water bodies Sentinel-2 TOA reflectance retrievals for 23/08/2019) - Western Nunavik (Subarctic Canada)
<p>This dataset refers to the retrieval of TOA reflectance from the Sentinel-2 L1C 10-m bands for 23/08/2019, having as reference the <a href="https://doi.org/10.5281/zenodo.12196313">Very High Resolution water body delineation dataset</a> computed with the <a href="https://doi.org/10.5281/zenodo.10203553">HLWATER V1.0 model</a> (<a href="https://doi.org/10.1016/j.rse.2024.114047">Freitas et al., 2024</a>) for Western Nunavik (Eastern Hudson Bay), Subarctic Canada. It covers a total area of 41,832 km2 within the latitudes 54° to 58° N and the longitudes 74° to 78° W.</p> <p>The dataset is composed of 167,755 water body reflectance retrievals. Additionally, 1 km2 hexagonal grids are provided with the calculation of the limnodiversity (diversity of water optical groups/colors). The optical groups were automatically defined using K-Means to 11 clusters, according to the highest Pseudo-F Score. Outputs are provided in shapefile and geodatabase formats.</p> <p>The manuscript detailing these outputs has been submitted to GIScience and Remote Sensing.</p>
MAJA look-up tables for Sentinel-2 A&B sensors, for a continental aerosol model
<p>These are the Look-up tables used by MAJA atmospheric correction software, used to process Sentinel-2 A&B sensors.</p> <p>These look-up tables correspond to a continental model.</p>
Calculation of damage in forests using Sentinel-2
<p>Data from Senitnel-2 after atmospheric correction were used to determine forest damage caused by the storm. On the territory of Poland the destruction took place on 11-12 August 2017. In order to determine the area of destruction images before and after the storm were taken. Then the supervised classification was implemented for data from both dates. The area of damage was calculated on the basis of the classification result, the area of forests classified on the image before the storm and afterwards allowed to determine the area of losses.</p> <p>Links to the presentation:</p> <p>http://fabspace.pl/wp-content/uploads/2017/11/1_Wprowadzenie_a.pdf</p> <p>http://fabspace.pl/wp-content/uploads/2017/11/1_Wprowadzenie_b.pdf</p> <p>http://fabspace.pl/wp-content/uploads/2017/11/2_Klasyfikacja.pdf</p> <p>http://fabspace.pl/wp-content/uploads/2017/11/3_Platforma.pdf</p>
[FABSPACE2.0] - Sentinel-2 Rome True Color
<p>Sentinel-2 product over Rome Urban Area - RBG Natural Color Composite</p>
MAJA look-up tables for Sentinel-2 A&B sensors, for Copernicus Atmosphere Monitoring Service aerosol types
<p>The archive contains the Look-up tables used by MAJA atmospheric correction software, used to process Sentinel-2 A&B sensors. These look-up tables correspond to the aerosol types used by Copernicus Atmosphere Monitoring Service (CAMS). However, the default continental model is also provided.</p> <p>Version 1.1 has new LUT for water vapour estimates, which corrects for a bias observed for large water vapour contents (above 2.5 g/cm2)</p> <p>Version 1.2 just changed the Folder name for a better integration with Start_maja.</p> <p>Version 1.3 added the Header files</p>
RBC-SatImg: Sentinel-2 Imagery and WatData Labels for Water Mapping
<h2>Data Description</h2> <p>This dataset is linked to the publication "Recursive classification of satellite imaging time-series: An application to land cover mapping". In this paper, we introduce the recursive Bayesian classifier (RBC), which converts any instantaneous classifier into a robust online method through a probabilistic framework that is resilient to non-informative image variations. To reproduce the results presented in the paper, the RBC-SatImg folder and the code in the GitHub repository <a href="https://github.com/neu-spiral/RBC-SatImg">RBC-SatImg</a> are required.</p> <p>The <strong>RBC-SatImg</strong> folder contains:</p> <ul> <li>Sentinel-2 time-series imagery from three key regions: Oroville Dam (CA, USA) and Charles River (Boston, MA, USA) for water mapping, and the Amazon Rainforest (Brazil) for deforestation detection.</li> <li>The <strong>RBC-WatData</strong> dataset with manually generated water mapping labels for the Oroville Dam and Charles River regions. This dataset is well-suited for multitemporal land cover and water mapping research, as it accounts for the dynamic evolution of true class labels over time.</li> <li>Pickle files with output to reproduce the results in the paper, including: <ul> <li>Instantaneous classification results for GMM, LR, SIC, WN, DWM</li> <li>Posterior results obtained with the RBC framework</li> </ul> </li> </ul> <p><strong>The Sentinel-2 images and forest labels used in the deforestation detection experiment for the Amazon Rainforest have been obtained from the <a href="https://sites.google.com/view/rainforest-challenge/multiearth-2023">MultiEarth Challenge dataset</a>.</strong></p> <h2>Folder Structure</h2> <p>The following paths can be changed in the configuration file from the GitHub repository as desired. The <strong>RBC-SatImg</strong> is organized as follows:</p> <ul> <li><strong>`./log/`</strong> (EMPTY): Default path for storing log files generated during code execution.</li> <li><strong>`./evaluation_results/`</strong>: Contains the results to reproduce the findings in the paper, including two sub-folders: <ul> <li><strong>`./classification/`</strong>:<strong> </strong> For each test site, four sub-folders are included as: <ul> <li><strong>`./accuracy/`</strong>: Each sub-folder corresponding to an experimental configuration contains pickle files with balanced classification accuracy results and information about the models. The default configuration used in the paper is "conf_00."</li> <li><strong>`./figures/`</strong>: Includes result figures from the manuscript in SVG format.</li> <li><strong>`./likelihoods/`</strong>: Contains pickle files with instantaneous classification results.</li> <li><strong>`./posteriors/`</strong>: Contains pickle files with posterior results generated by the RBC framework.</li> </ul> </li> <li><strong>`./sensitivity_analysis/`</strong>: Contains sensitivity analysis results, organized by different test sites and epsilon values.</li> </ul> </li> <li> <strong>`./Sentinel2_data/`</strong>: Contains Sentinel-2 images used for training and evaluation, organized by scenarios (Oroville Dam, Charles River, Amazon Rainforest). Selected images have been filtered and processed as explained in the manuscript. The Amazon Rainforest images and labels have been obtained from the MultiEarth dataset, and consequently, the labels are included in this folder instead of the RBC-WatData folder.</li> <li><strong>`./RBC-WatData/`</strong>: Contains the water labels that we manually generated with the <a href="https://labelstud.io/">LabelStudio tool</a>.</li> </ul>
The first 10-m China's national-scale sandy beach map in 2022 derived from Sentinel-2 imagery
<p>This is the first 10-meter national scale beach map dataset of China. Based on the cloudless Sentinel-2 images for the whole year of 2022, we use the image classification method to draw a 10-meter beach map of China. The projection coordinate system of this data is WGS_1984_UTM_Zone_51N and the geographic coordinate system is GCS_WGS_1984.<br>The "Shape_Leng" field in the data set represents the circumference of the beach, the "Shape_Area" field represents the area of the beach, and the "Province" field represents the province of each independent beach.</p>
PixBox Sentinel-2 pixel collection for CMIX
<p>The <strong>PixBox-S2-CMIX</strong> dataset was used as a validation reference within the first Cloud Masking Inter-comparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) in 2019. The <strong>PixBox-S2-CMIX </strong>pixel collection was existing prior to CMIX and conducted already in 2018.</p> <p>The overarching idea of PixBox is a quantitative assessment of the quality of a pixel classification which is the result of an automated algorithm/procedure. Pixel classification is defined as assigning a certain number of attributes to an image pixel, such as cloud, clear sky, water, land, inland water, flooded, snow etc. Such pixel classification attributes are typically used to further guide higher level processing.</p> <p>The <strong>PixBox dataset </strong>production: trained experienced expert(s) manually classify pixels of an image sensor into a pre-defined detailed set of classes. These are typically different cloud transparencies, cloud shadow, condition of underlying surface (“semi-transparent clouds over snow”, “clouds over bright scattering water”). An average collected dataset includes several 10-thousands of pixels because it has to be representative for all classes, and for various observation and environmental conditions, such as climate zones, sun illumination etc. Quality control of the collected pixels is important in order to detect misclassifications and systematic errors. An auto-associative neural network is trained for this purpose.</p> <p>The PixBox-S2-CMIX dataset is a pixel collection containing 17,351 pixels manually collected from 29 Sentinel-2 A & B Level 1C products. The dataset is spatially, temporally, and thematically well distributed. </p> <p> </p> <p><strong>PixBox-S2-CMIX dataset</strong></p> <p>The PixBox-S2-CMIX dataset consists of two two main ZIP files, one holding the pixel collection and description, and another one with all used Sentinel-2 L1C data. The dataset is structured as follows:</p> <ul> <li>PixBox-S2-CMIX.zip <ul> <li>The collected features (CSV file).</li> <li>A description to all categories and classes, incl. linkage to the used Sentinel-2 L1C products.</li> </ul> </li> <li>Sentinel-2_L1C.zip <ul> <li>29 zipped Sentinel-2 Level L1C products <sup>[1]</sup>, used to produce the dataset.</li> </ul> </li> </ul> <p><strong>Files </strong></p> <p><em>pixbox_sentinel2_cmix_20180425.csv - </em>This file contains all collected pixel information in CSV format. All collected classes are stored as integer values. A description of the categories and definition of the integers to class names is given in the additional description file.</p> <p> <em>pixbox_sentinel2_cmix_20180425_description.txt </em> - This file gives a clear description of the categories and classes. It can be used to convert the class ID numbers, stored in the CSV, to class strings. Additionally, it links the satellite product ID, given in the CSV, to the Sentinel-2 L1C product names.</p> <p><em>29 Sentinel-2 L1C products in ZIP format.</em></p> <p> </p> <p><strong>References</strong></p> <p>[1] Copernicus Sentinel data 2017/2018</p>
Sentinel-2 KappaZeta Cloud and Cloud Shadow Masks
<p><strong>General information</strong></p> <p>The dataset consists of 4403 labelled subscenes from 155 Sentinel-2 (S2) Level-1C (L1C) products distributed over the Northern European terrestrial area. Each S2 product was oversampled at 10 m resolution for 512 x 512 pixels subscenes. 6 L1C S2 products were labelled fully. Among other 149 S2 products the most challenging ~10 subscenes per product were selected for labelling. In total the dataset represents 4403 labelled Sentinel-2 subscenes, where each sub-tile is 512 x 512 pixels at 10 m resolution. The dataset consists of around 30 S2 products per month from April to August and 3 S2 products per month for September and October. Each selected L1C S2 product represents different clouds, such as cumulus, stratus, or cirrus, which are spread over various geographical locations in Northern Europe.</p> <p>The classification pixel-wise map consists of the following categories:</p> <ul> <li>0 – MISSING: missing or invalid pixels;</li> <li>1 – CLEAR: pixels without clouds or cloud shadows;</li> <li>2 – CLOUD SHADOW: pixels with cloud shadows;</li> <li>3 – SEMI TRANSPARENT CLOUD: pixels with thin clouds through which the land is visible; include cirrus clouds that are on the high cloud level (5-15km).</li> <li>4 – CLOUD: pixels with cloud; include stratus and cumulus clouds that are on the low cloud level (from 0-0.2km to 2km).</li> <li>5 – UNDEFINED: pixels that the labeler is not sure which class they belong to.</li> </ul> <p>The dataset was labelled using Computer Vision Annotation Tool (<a href="https://github.com/openvinotoolkit/cvat">CVAT</a>) and <a href="https://segments.ai/">Segments.ai</a>. With the possibility of integrating active learning process in Segments.ai, the labelling was performed semi-automatically.</p> <p>The dataset limitations must be considered: the data is covering only terrestrial region and does not include water areas; the dataset is not presented in winter conditions; the dataset represent summer conditions, therefore September and October contain only test products used for validation. Current subscenes do not have georeferencing, however, we are working towards including them in next version.</p> <p>More details about the dataset structure can be found in README. </p> <p><strong>Contributions and Acknowledgements</strong></p> <p>The data were annotated by Fariha Harun and Olga Wold. The data verification and Software Development was performed by Indrek Sünter, Heido Trofimov, Anton Kostiukhin, Marharyta Domnich, Mihkel Järveoja, Olga Wold. Methodology was developed by Kaupo Voormansik, Indrek Sünter, Marharyta Domnich.<br> We would like to thank Segments.ai annotation tool for instant and an individual customer support. We are grateful to European Space Agency for reviews and suggestions. We would like to extend our thanks to Prof. Gholamreza Anbarjafari for the feedback and directions.<br> The project was funded by<strong><em> European Space Agency</em></strong>, Contract No. 4000132124/20/I-DT.</p>
Paired Waikato Region Aerial Photography and Sentinel-2 Imagery
<p>This dataset contains paired high resolution orthorectified aerial photography provided by the Waikato Region Aerial Photography initiative, paired with Sentinel-2 satellite images. These images were collected as part of a satellite imagery super-resolution research project. The aerial photograph was down-sampled to a spatial resolution of 2.5m per pixel, while the satellite images were taken at a spatial resolution of 10m per pixel. The satellite images were taken from 8 fly-bys between 16th January to 2nd March 2019 so that it is temporally consistent with the aerial photographs taken in February of 2019.</p>
Supraglacial lakes and channels in West Antarctica and Antarctic Peninsula during January 2017 - Sentinel-2 Group 2
<p>The maximum extent of supraglacial lakes and channels in West Antarctica and the Antarctic Peninsula in January 2017 was produced by a Dual-NDWI (Normalised Difference Water Index) approach with thresholds. >2000 individual scenes were captured by Sentinel-2 (S2) and Landsat-8 (L8) satellite sensors during the entire month of January 2017. To obtain maximum coverage on the cloudy Antarctic Peninsula, the time period is extended to February 10, 2017 over this region.</p> <p>This dataset consists of the maximum extent of supraglacial hydrological activity during January 2017 and detailed 10,478 supraglacial features (10,223 lakes and 255 channels), with cumulative area 119.4 square km in total on the West Antarctic ice sheet and Antarctic Peninsula. In addition to the final product, the supraglacial hydrological features from both sensors (23,389 polygons for S2 and 17,571 polygons for L8) overlapping the final map are included. The supraglacial lake and channel polygons are available as digital GIS, Geographic Information System, shapefiles (.shp) and GeoJSON files as well as Google Earth format (.kmz). The code used to produce the lake and channel dataset for each sensor (S2 and L8) is implemented using Python, and can be accessed on Zenodo (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&amp;reserved=0</a>) . Landsat-8 and Sentinel-2 imagery are freely available at (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&amp;reserved=0</a>) and (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&amp;reserved=0</a>), respectively.</p> <p>The products provide a scientific benchmark to monitor the development of these features in a warming climate, and thus enhancing our capability to predict the calving and collapse of any ice shelves in the future. The results provide a baseline for future monitoring of supraglacial hydrology and can be particularly useful to train supervised machine learning algorithms. The lake and channel dataset will be valuable as training data for pixel-based or object-based approaches to map large-scale features automatically using machine learning. This dataset can also provide an a-priori lake distribution for studies incorporating synthetic-aperture radar, SAR and other sensors and platforms.</p> <p>Alongside Sentinel-2 Group 1, this dataset provides the 23,389 polygons from S2 imagery.</p>
Supraglacial lakes and channels in West Antarctica and Antarctic Peninsula during January 2017 - Sentinel-2 Group 1
<p>The maximum extent of supraglacial lakes and channels in West Antarctica and the Antarctic Peninsula in January 2017 was produced by a Dual-NDWI (Normalised Difference Water Index) approach with thresholds. >2000 individual scenes were captured by Sentinel-2 (S2) and Landsat-8 (L8) satellite sensors during the entire month of January 2017. To obtain maximum coverage on the cloudy Antarctic Peninsula, the time period is extended to February 10, 2017 over this region.</p> <p>This dataset consists of the maximum extent of supraglacial hydrological activity during January 2017 and detailed 10,478 supraglacial features (10,223 lakes and 255 channels), with cumulative area 119.4 square km in total on the West Antarctic ice sheet and Antarctic Peninsula. In addition to the final product, the supraglacial hydrological features from both sensors (23,389 polygons for S2 and 17,571 polygons for L8) overlapping the final map are included. The supraglacial lake and channel polygons are available as digital GIS, Geographic Information System, shapefiles (.shp) and GeoJSON files as well as Google Earth format (.kmz). The code used to produce the lake and channel dataset for each sensor (S2 and L8) is implemented using Python, and can be accessed on Zenodo (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&amp;reserved=0</a>) . Landsat-8 and Sentinel-2 imagery are freely available at (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&amp;reserved=0</a>) and (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&amp;reserved=0</a>), respectively.</p> <p>The products provide a scientific benchmark to monitor the development of these features in a warming climate, and thus enhancing our capability to predict the calving and collapse of any ice shelves in the future. The results provide a baseline for future monitoring of supraglacial hydrology and can be particularly useful to train supervised machine learning algorithms. The lake and channel dataset will be valuable as training data for pixel-based or object-based approaches to map large-scale features automatically using machine learning. This dataset can also provide an a-priori lake distribution for studies incorporating synthetic-aperture radar, SAR and other sensors and platforms.</p> <p>Alongside Sentinel-2 Group 2, this dataset provides the 23,389 polygons from S2 imagery.</p>
Sentinel-2 Optical satellite imagery for Epidemic Disease Mapping
<p>Sentinel-2 Optical satellite imagery for Epidemic Disease Mapping</p>
Sentinel-2 Time Series for Pheno-VAE
<p>This repository contains a SQLite file with Sentinel-2 B4 (red) and B8 (near infra-red) bands time series, used to compute NDVI time series for pheno-VAE : https://gitlab.cesbio.omp.eu/zerahy/pheno-VAE</p> <p> </p>
Sentinel-2 derived Sphagnum and herbaceous CI, GCC, NDVI, MSI, SL2P10 LAI, and hourly temperature, water table depth, PAR on the Bernadouze peatland from 2017 to 2021 and 2D scans LAI over 2021.
<p>This release contains data from field campaign over the Bernadouze Peatland and satellite sentinel-2 derived vegetation indices from 2017-01-01 to 2021-12-31.</p> <p>Sentinel-2 derived Sphagnum and herbaceous chlorophyll index, green chromatic coordinate, normalised difference index, moisture soil index retrived on google earth engine from 2017-01-01 to 2021-12-31 on the Bernadouze peatland.</p> <p>Sentinel-2 sphagnum and herbaceous leaf area index (m².m-²) computed with the SL2P10 algorithm from 2017-01-01 to 2021-12-31 thanks to google earth engine.</p> <p>Sphagnum leaf area index (m².m-²), measured with a 2D-scan (LI3100 Area Meter) over the 2021 season on the Bernadouze peatland.</p> <p>Reflectance over the 12 bands of Sentinel-2 on two areas of the Bernadouze peatland : one dominated by Sphagnum mosses and the other by herbaceous vegetation. Data related to an image acquired the 2021-07-21.</p> <p>Hourly air temperature (°C) and photosynthetically active radiations (umol.m-².s-1) derived from the S2M (SAFRAN–SURFEX, ISBA–Crocus–MEPRA) reanalysis chain on the Bernadouze peatland. Vertical resolution of 300m on the 'Couseran' massif.</p> <p>Hourly water table depth (m) from 10 piezometers (PZ1, ..., PZ10) over the Bernadouze peatland. Measured with 10 Orpheus Mini Water Level Logger, OTT HydroMet, Germany.</p> <p>Growth primary productivity of dominant peatland vegetation (umol.m-².s-1) calculated by the difference of measured net primary productivity and of measured ecosystem respiration flux under dark conditions. Measurements of GPP and ER are made with a soil chamber connected to a LI-COR LI-7810 analyser from 2017-01-01 to 2021-12-31 on the Bernadouze peatland.</p>
Quantifying wetness variability in aapa mires with Sentinel-2: towards improved monitoring of an EU priority habitat [Dataset]
<p>This repository contains data used in Jussila et al. 2023 paper "Quantifying wetness variability in aapa mires with Sentinel-2: towards improved monitoring of an EU priority habitat" (submitted). The files uploaded in the repository include (i) spatial polygon data of flark mires located in Finnish aapa mire occurrence zone, (ii) polygon subset of the flark mires observed in the study, focused on mires belonging to Natura 2000 network (ii) monthly information for April-September period in 2017-2020 of climatic water balance and Sentinel-2 -derived wetness metrics, as average values per mire, and (iii) training point data used to train the decision tree model which was used in the study to detect wet flark surfaces in the studied aapa mires. Retrieving the metrics from Sentinel-2 satellite imagery for the analysis of variability in wetness was executed with Sentinelhub Batch statistical API, and the process is documented in project GitHub repository: <a href="https://github.com/sykefi/feo-aapa">https://github.com/sykefi/feo-aapa</a>. </p> <p>Additional information of data is provided in the README file.</p>
CBRA: The first multi-annual (2016-2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with Super-resolution Segmentation from Sentinel-2 imagery
<p>Large-scale and up-to-date maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In addition, as a fine-grained indicator of human activities, BRA could contribute to urban planning and energy modeling to provide benefits to human well-being. However, existing large-scale BRA datasets, such as those from Microsoft and Google, do not include China, hence there are no full-coverage maps of BRA in China. To this end, we produce the multi-annual China building rooftop area dataset (CBRA) with 2.5 m resolution from 2016-2021 Sentinel-2 images. The CBRA is the first full-coverage and multi-annual BRA data in China. The CBRA achieves good performance with the F1 score of 62.55% (+10.61% compared with the previous BRA data in China) based on 250,000 testing samples in urban areas, and the recall of 78.94% based on 30,000 testing samples in rural areas. </p> <p>The CBRA is organized as GeoTIFF (.tif) raster file format with a single band and GCS_WGS_1984 coordinate system. The pixel values are 0 and 255, with 0 representing the background and 255 representing the building rooftop area. Furthermore, to facilitate the use of the data, the CBRA is split into 215 tiles of spatial grid, named “CBRA_year_E/W**N/S**.tif”, where “year” is the sampling year, the “E/W**N/S**” is the latitude and longitude coordinates found in the upper left corner of the tile data.</p> <p> </p> <p>Version 2.0: In version 1.0, there were empty raster images (because they didn't contain buildings). In version 2.0, these raster images were removed.</p>
Spatially Quantifying Forest Damage from Hurricane Michael using Sentinel-2 Imagery
<p><strong>ABSTRACT:</strong></p> <p>Hurricane Michael made landfall on Mexico Beach, Florida panhandle as a Category 5 storm on October 10<sup>th</sup>, 2018. The storm had a large impact on the forests in the Florida panhandle and into Georgia. In this study we use Sentinel-2 imagery and 248 forest plots collected prior to landfall in 2018 in the forests impacted by Hurricane Michael to build a general linear model of tree basal area across the landscape. The basal area model was constrained to areas where trees were present using a tree presence model as a hurdle. We informed the model with post hurricane Sentinel-2 imagery and compared the pre and post hurricane basal area maps to assess the loss of basal area following the hurricane. The basal area model had an r-squared value of 0.508. Our results provide a detailed map showing the extent of basal area loss across the Florida panhandle at 10m spatial scale. Plots were revisited to ground truth the modelled results and showed that the model performed well at categorizing forest hurricane damage. This study demonstrates the use of remotely sensed imagery and in-situ forest measurements to rapidly quantify, using common forestry metrics, forest damage from large natural disturbances at spatial resolution useful to inform disaster response management decisions.</p> <p><strong>METHODS:</strong></p> <p>The Restore .csv file is data from forestry plots established by the Florida Natural Areas Inventory (FNAI) as a baseline for a Restore Act project focused on the Florida panhandle. A total of 248 plots were visited between December 2017 and March 2018. These temporary plots were navigated to using handheld GPS units and laid out in 36m squares containing four 9m diameter non overlapping subplots. Measurements of vegetative cover, tree species count, tree condition, and diameter at breast height were taken for all trees in the subplots. Post hurricane Michael 70 plots were revisited, measurements at these plots were a subjective plot hurricane damage categorization, and count and diameter of downed or damaged trees as well as miscellaneous notes regarding site damage.</p> <p>The state parks .csv file is data from forestry plots established by the Florida Natural Areas Inventory (FNAI) at Florida State parks post hurricane Michael. These plots are 20 m circular radius that include subjective plot hurricane damage categorization, and count of downed or damaged trees, herbaceous cover, as well as miscellaneous notes regarding site damage. </p> <p>Several fields were added to these plot .csv files post field visit as a variables extracted from a principal component analysis that used Sentinel-2 imagery to inform a remote sensing analysis of basal area.</p> <p>A file geodatabase is attached that contains the project area boundary, Apalachicola National Forest boundary, the three shapefiles of all restore plots, revisited restore plots, and state parks plots. Raster outputs from our analysis are also available in this gdb, they contain metadata in their item descriptions. The general metadata for these rasters follows: </p> <p>A general linear regression model was built to estimate tree basal area across the study area of 11 counties in the Florida Panhandle. Basal area (BA) was calculated from Restore field plots where trees were present located in and around the Apalachicola National Forest. Plot measurements include all trees within four non-overlapping 9m radius circular subplots within a 36m square plot. Tree diameter at breast height (DBH), species, count, and condition measurements were recorded. Measurements were summarized to the plot and DBH (square inches) was converted to basal area per acre (square feet per acre) using the formula 0.005454 * DBH^2. Plot measurements of basal area per acre were related to the top ten principal components from a principal component analysis (PCA) at the spatial resolution of the Restore plots (40 m). The PCA used the normalized Sentinel-2 spectral values and two texture values from the 7 mosaicked images from two time periods, the winter of 2017-2018 and the spring of 2018, before Hurricane Michael. A softmax neural network model was built from the PCA and Restore plot datasets to identify areas where trees were present. The basal area model was applied to the pre and post hurricane PCA imagery to create modelled surfaces of estimated Basal Area in pixels that where over 50% likely to contain trees according to the softmax neural network model. The basal are linear regression model results and predictors for the model are in the tables below.</p> <p>Table Linear Regression Model. Model fit and predictors for BAA.</p> <table> <tbody> <tr> <td> <p><strong>N</strong></p> </td> <td> <p><strong>RMSE</strong></p> </td> <td> <p><strong>R2</strong></p> </td> <td> <p><strong>Adjusted R2</strong></p> </td> </tr> <tr> <td> <p>231</p> </td> <td> <p>2.811</p> </td> <td> <p>0.51</p> </td> <td> <p>0.50</p> </td> </tr> </tbody> </table> <p>Road, water and urban areas were masked out of this raster dataset using a 1 m landcover product created by the author and located here - <a href="https://doi.org/10.2737/RDS-2017-0014">https://doi.org/10.2737/RDS-2017-0014</a></p>
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.