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1,118 results for “Sentinel”
Spectral Vegetation Indices from Harmonized Landsat and Sentinel-2 Data for Harvard Forest 2015-2020
The goal of this work is to exploit time series of remotely sensed data sets with ground observations to improve our understanding of how seasonal variation in canopy and environmental conditions affect the relationship between vegetation indices and leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (fAPAR). Using three different common vegetation indices (EVI2, NDVI, NIRV), we can estimate LAI, fAPAR, and daily absorbed photosynthetically active radiation (APAR) using a semi-empirical model.
InSAR stack of Fernandina volcano in Galápagos, Ecuador from Sentinel-1 descending track 128 processed with ISCE2/topsStack
<p>A stack of unwrapped interferograms on Fernandina volcano, Galápagos, Ecuador</p> <p>Sensor: Sentinel-1descending track 128</p> <p>Processor: ISCE/topsStack</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p> <p><strong>Version 1.x (~750 MB)</strong><br> Time: 2014.12.13 - 2018.06.19 (98 acquisitions, 288 interferograms)</p> <p><strong>Version 0.1 (~280 MB; for fast testing of code development)</strong><br> Time: 2014.12.13 - 2016.05..24 (36 acquisitions, 102 interferograms)</p>
Ground Truth and Automated Classification from Copernicus Sentinel-2 Imagery
<p>Ground-Truth and Sentinel2 imagery classification of <em>Trees Outside Forest</em> in an agroforestry landscape in Umbria, Italy.</p> <p>Location: Alfina plains, Castelgiorgio area, Umbria, Italy. Reference system: EPSG:32632 (WGS84, UTM zone 32 North) Extent: West 740609 — East 750828, South 4726490 — North 4737250</p> <p>Dataset format: geopackage, a single file <strong>data.gpkg</strong> containing 9 vector layers (in alphabetical order):</p> <ol> <li>Areas — Areas of interest, 2 polygons</li> <li>Classification — Automated classification from Sentinel2 imagery, 11781 polygons</li> <li>Hedgerows1 — Ground truth, hedgerows of Area1, 148 lines</li> <li>Hedgerows2 — Ground truth, hedgerows of Area2, 135 lines</li> <li>Sentinel2 — Sentinel2 scenes footprint, one polygon</li> <li>Trees1 — Ground truth, isolated trees of Area1, 55 points</li> <li>Trees2 — Ground truth, isolated trees of Area2, 64 points</li> <li>Woods1 — Ground truth, small forest patches of Area1, 33 polygons</li> <li>Woods2 — Ground truth, small forest patches of Area2, 37 polygons</li> </ol> <p>Accompanying map: <strong>map.qgz</strong>, Qgis 3.6 format. The geopackage dataset is supposed to be stored in the same directory of the map (relative path = ./)</p> <p>Dataset description and metadata: <strong>meta.pdf</strong> </p> <p> </p>
InSAR stack of San Francisco Bay, California from Sentinel-1 descending track 42 processed with GMTSAR
<p>A stack of unwrapped interferograms in the San Francisco Bay area, California, USA</p> <p>Sensor: Sentinel-1 descending track 42</p> <p>Processor: <a href="https://github.com/gmtsar/gmtsar" target="_blank" rel="noopener">GMTSAR</a></p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p> <p>The tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p><strong>Version 1.x (~2.3 GB)</strong><br>Time: 2014.12.31 - 2024.06.05 (333 acquisitions, 1297 interferograms)</p> <p><strong>Version 0.x (~290 MB; for fast testing of code development)</strong><br>Time: 2020.01.04 - 2021.07.15 (70 acquisitions, 184 interferograms)</p>
GRWSE-global river water surface elevation from sentinel-3
<p>This dataset includes time series of Water Surface Elevation (WSE) of large rivers at over 3000 virtual stations. The WSE time series were created using Sentinel-3A and Sentinel-3B altimetry data. </p>
Sentinel-5P Methane Density at 2 km from 2021-12 to 2023-11 Monthly Aggregation Time-series Reconstructed
<p><strong>General Description</strong></p><p>The <i>monthly aggregated Methane Volume Mixing Ratio </i>dataset is derived from Sentinel-5P to generate a time-series reconstructed monthly aggregated map. The dataset time spans from December 2021 to November 2023 and provides data that covers the entire globe. The mission is still underway and expected to update periodically.</p><p>For more info about the s5p Methane product see: <a href="">https://maps.s5p-pal.com/ch4/</a>.</p><p>The dataset can be used in many applications like emission tracing, livestock monitor, and greenhouse gas monitor.</p><ul><li><strong>Monthly time-series:</strong></li></ul><p>Methane monthly average value December 2021 – November 2023. Derived using the <a href="https://eumap.readthedocs.io/en/latest/">eumap</a> and <a href="https://github.com/openlandmap/scikit-map">scikitmap</a> package in Python . We derived three standard statistics: (1) 10th percentile (p10), median (p50), and 90th percentile (p90).</p><p><strong>Data Details</strong></p><ul><li><strong>Time period:</strong> December 2021 – November 2023</li><li><strong>Type of data:</strong> Methane Volume Mixing Ratio (Unit: ppbv)</li><li><strong>How the data was collected or derived:</strong> Derived from 2km Sentinel-5P Menthane using Python running in a local HPC. The time-series analysis were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> and <a href="https://eumap.readthedocs.io/en/latest/">eumap </a>Python package.</li><li><strong>Statistical methods used:</strong> percentiles 10, 50, and 90.</li><li><strong>Limitations or exclusions in the data:</strong> The dataset is not completed gap-filled. Certain areas have no data in the whole time series</li><li><strong>Coordinate reference system:</strong> EPSG:4326</li><li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -61.9966697, 180.0000072, 87.37000)</li><li><strong>Spatial resolution:</strong> 1/60 d.d. = 0.016666667 (2km)</li><li><strong>Image size:</strong> 21,600 x 8,962</li><li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li></ul><p><strong>Support</strong></p><p>If you discover a bug, artifact or inconsistency, or if you have a question please use some of the following channels:</p><ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">https://gitlab.com/openlandmap/global-layers/-/issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li></ul><p><strong>Name convention</strong></p><p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p><ol><li><strong>generic variable name:</strong> ch4.vmr = methane density methane volume mixing ratio</li><li><strong>variable procedure combination:</strong> m.seacov = monthly aggregated and gap filled by seasonal convolution</li><li><strong>Position in the probability distribution / variable type:</strong> p10/p50/p90 = 10th/50th/90th percentile</li><li><strong>Spatial support:</strong> 2km</li><li><strong>Depth reference:</strong> a = above surface</li><li><strong>Time reference begin time:</strong> 20211201 = 2021-12-01</li><li><strong>Time reference end time:</strong> 20231131 = 2023-11-31</li><li><strong>Bounding box:</strong> go = global (without Antarctica)</li><li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li><li><strong>Version code:</strong> v20230628 = 2023-12-08 (creation date)</li></ol>
Subsample of the maximum Water Area Extent of Telangana Rainwater Harvesting System from Sentinel-2
<p>Small Reservoirs Maximum Water Area Extent polygones (MWAE) composing the Rainwater Harvesting System (RHS) derieved from Sentinel-2 Multispectral data in the Telangana state, South-India. MWAE is extracted from Sentinel-2 cloud free images time serie collected from 2016 to 2021 (last access in 2021) over the area covered by stereoscopic images acquired from Pléiades satellites (DEM available 10.5281/zenodo.10403040). A random forest classification is used with a set of training and validation samples. These samples are Sentinel pixel locations (10 x 10 meters) corresponding to permanent water pixels extracted from Global Surface Water datasets (doi:10.1038/nature20584) and never flooded pixels derived from Height Above Nearest Drainage data-set (10.1016/j.jhydrol.2011.03.051).</p>
Supporting Data - Sentinel-1 Detection of Ice Slabs on the Greenland Ice Sheet
<p>This dataset contains supporting data accompanying Culberg, R., Michaelides, R. J., and Miller, J. Z.: Sentinel-1 Detection of Ice Slabs on the Greenland Ice Sheet, EGUsphere [preprint], <a href="https://doi.org/10.5194/egusphere-2023-2652">https://doi.org/10.5194/egusphere-2023-2652</a>, 2023. The final accepted manuscript will be linked via the same preprint server at the time of publication. The dataset contains the following files:</p> <ul> <li>Sentinel-1 HV and HV/HH backscatter mosaics of the Greenland Ice Sheet formed using data from 1 Oct 2016 - 30 April 2017.</li> <li>Estimated average annual summer melt extent between 1 Nov 2014 and 31 Aug 2020, detected using seasonal variations in Sentinel-1 HH backscatter.</li> <li>The firn aquifer extent over Greenland derived from Sentinel-1 in Brangers et al. (2020), reprojected to EPSG:3413.</li> <li>The ice mask used in the study, derived from the BedMachine Greenland ice mask.</li> <li>The training and validation datasets derived from the Jullien et al. (2023) ice slabs detections from ice penetrating radar data that were used to optimize ice slab detection thresholds for the Sentinel-1 backscatter mosaics. </li> </ul>
Land Subsidence in Iran Estimated from a Nationwide InSAR Analysis of Sentinel-1 Observations 2014-2020
<p><strong>Overview</strong></p> <p>This dataset is a supplementary material to the paper "Haghighi and Motagh, 2024. Uncovering the Impacts of Depleting Aquifers: A Remote Sensing Analysis of Land Subsidence in Iran, Science Advances". It provides detailed insights into land subsidence across Iran, derived from Sentinel-1 InSAR observations. This dataset is intended for use by researchers, policymakers, and practitioners interested in land subsidence, groundwater depletion, and related fields.</p> <p><strong>Dataset Contents</strong></p> <ol> <li><em>Iran_subsidence_rate_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Annual rate of land subsidence in Iran over the six-year period, projected from satellite Line of Sight to vertical.</li> <li><em>Iran_subsidence_rate_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.jpg</em><br>Subsidence map of Iran visualized as jpg</li> <li><em>Iran_subsidence_seasonal_amplitude_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Amplitude of seasonal ground deformation, projected from satellite Line of Sight to vertical.</li> <li><em>Iran_subsidence_mask_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Land subsidence mask, based on the annual rate of land subsidence.</li> </ol> <p><strong>Methodology</strong></p> <p>The data were derived using Interferometric Synthetic Aperture Radar (InSAR) analysis of Sentinel-1 satellite imagery. The original SAR data includes more than 6000 scenes of Sentinel-1 images collected across 10 descending tracks between 2014 and 2020. The details can be found in the original paper.</p> <p><strong>Acknowledgements</strong></p> <p>We acknowledge the European Space Agency (ESA) for providing the Sentinel-1 satellite data used in this analysis.</p> <p><strong>License</strong></p> <p>This dataset is shared under CC BY 4.0 license, which allows for reuse and distribution, provided that the original authors and source are credited.</p> <p><strong>Citation</strong></p> <p>Please cite the following if you use this dataset:</p> <ol> <li>Haghighi and Motagh, 2024. Uncovering the Impacts of Depleting Aquifers: A Remote Sensing Analysis of Land Subsidence in Iran, Science Advances.</li> <li>Haghighi and Motagh, 2024. Land Subsidence in Iran Estimated from a Nationwide InSAR Analysis of Sentinel-1 Observations 2014-2020. Zenodo. doi:10.5281/zenodo.10815578</li> <li>The dataset contains modified Copernicus Sentinel data 2014-2020, processed by ESA.</li> </ol> <p><strong>Contact</strong></p> <p>Please contact Mahmud Haghighi for inquiries related to this dataset.<br>https://www.ipi.uni-hannover.de/en/haghighi</p>
Water Sentinels Motivation Survey
<p>The Water Sentinels motivation study was conducted within the ongoing H2020 project named <a href="https://actionproject.eu/">ACTION</a> (pArticipatory sCience Toolkit agaInst pollutiON) on citizen science. Volunteers participate to citizen science initiatives for multiple reasons: personal enjoyment, desire for improvement or achievement, establishment of personal relationships, care for the environment, etc.<br> Studying motivation and investigating the factors influencing people participation to citizen science projects is an essential aspect in the analysis of citizen science communities. Understanding the reasons that foster people to engage can support the successful design and implementation of effective participant involvement tasks, as well as pave the way for long-term engagement.<br> The goal of the study is to analyse the motivation to participate of a specific citizen science community focused on fighting water pollution in the Water Sentinels pilot supported by the ACTION project. More info on the pilot available at <a href="https://actionproject.eu/citizen-science-pilots/water-sentinels/">https://actionproject.eu/citizen-science-pilots/water-sentinels/</a>.</p> <p>The Water Sentinels motivation study is part of the study about motivation in citizen science projects conducted within the ACTION project (<a href="https://doi.org/10.5281/zenodo.5753092">https://doi.org/10.5281/zenodo.5753092</a>). The survey was designed using the <a href="https://coney.cefriel.com/">Coney</a> toolkit and administered using <a href="https://www.google.com/forms/about/">Google Forms</a>.</p> <p>The research object adopts the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification. Files made available within the research object are:</p> <ul> <li><em>*-procedure.ttl</em> contains the RDF representation of the structure of the conversational survey (questions, answers, etc.) using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li><em>*-results.ttl </em>contains the RDF representation of the answers collected using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li><em>*-survey.tll </em>contains a comprehensive RDF representation of the survey data using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li><em>*-results.csv </em>contains the CSV of the collected answers</li> <li><em>*-results-google-forms</em><em>.csv </em>contains the CSV of the collected answers exported from Google Forms</li> <li>*-<em>script.R</em> is the R script developed to analyse the collected answers</li> <li>*-<em>mean-var-motivating-questions.csv </em>contains the computed mean and average for each question considered (observable variables)</li> <li>*-<em>mean-var-motivating-factor.csv </em>contains the computed mean and average for each motivation factor considered (latent variables)</li> <li>*-<em>correlation-factors-global-motivation.csv </em>contains the correlation analysis between each motivation factor and the global motivation </li> </ul>
DeepOrchidSeries: A Sentinel-2 Dataset to inform convolutional SDMs with twelve-month Sentinel-2 image time-series, Orchid family
<p><strong>Deep Species Distribution Modelling from Sentinel-2 Image Time-series: a Global Scale Analysis on the Orchid Family</strong> </p> <ul> <li><strong><em>DeepOrchidSeries</em></strong> dataset gathers Sentinel-2 image time-series around geolocated orchid occurrences. Seasonal evolutions of the habitats are captured in the twelve-month RGB/IR time-series with 640x640m spatial resolution. It allows novel Species Distribution Models (SDMs) coupled with convolutional networks to take advantage of both spatial and temporal information.</li> <li>Our <strong>associated article</strong> is describing the modeling choices made to shape this ambitious dataset. It is submitted to <a href="https://www.frontiersin.org/research-topics/18336/plant-biodiversity-science-in-the-era-of-artificial-intelligence">https://www.frontiersin.org/research-topics/18336/plant-biodiversity-science-in-the-era-of-artificial-intelligence</a>. We believe such global data, methods and scripts are valuable to the conservation ecology community and especially deep-SDMs users. To our knowledge, no similar ready-to-use dataset is available. In the article, the dataset's temporal dimension is proven to significantly improve SDMs performances.</li> <li><strong><em>sen2patch</em></strong> is the gitlab project gathering the code to create such dataset. It is available at <a href="https://gitlab.inria.fr/jestopin/sen2patch">https://gitlab.inria.fr/jestopin/sen2patch</a>.</li> <li><strong><em>DeepOrchidSeries.csv</em></strong> contains all occurrences-level information. <ul> <li>We advice to load it with: <pre><code class="language-python">import pandas as pd df = pd.read_csv("path/to/DeepOrchidSeries.csv", sep=';') df.columns ['gbifid', 'canonical_name', 'decimallatitude', 'decimallongitude', 'speciesKey', 'cell_index', 'bot_country', 'bot_code', 'lvl2_code', 'continent_code']</code></pre> <ul> <li>'gbifid' is the occurrences GBIF ID</li> <li>'canonical_name', is the species canonical name</li> <li>'decimallatitude', 'decimallongitude' are the species coordinates in decimal degrees</li> <li>'speciesKey' is the species GBIF unique identifier</li> <li>'cell_index' is a unique cell ID in a 0.0025° lon/lat grid partitioning the Earth (used to stratify train/val/test set by geographic blocks)</li> <li>'bot_country', 'bot_code', 'lvl2_code', 'continent_code' are geographic subdivisions defined in <a href="https://github.com/tdwg/wgsrpd">https://github.com/tdwg/wgsrpd</a> (code and string for WGSRPD level 1, the botanical countries)</li> </ul> </li> </ul> </li> <li> <p>Initial <a href="https://www.gbif.org/">GBIF</a> query DOI is <a href="http://https://doi.org/10.15468/dl.4bijtu">https://doi.org/10.15468/dl.4bijtu</a> (26 August 2019).</p> </li> <li><strong><em>DeepOrchidSeries.tar</em></strong> file contains the satellite image time-series and is available at <a href="https://lab.plantnet.org/deeporchidseries/">https://lab.plantnet.org/deeporchidseries/</a> <ul> <li><em>.tar</em> archive measure 286 GB and extends to 432 GB once decompressed.</li> <li>Image time-series relative tree paths are constructed from the occurrences unique GBIF IDs.</li> <li>For a given occurence <em>gbifid</em>, matching patches are located in: <em>final_dataset_by_gbifid/gbifid[-2:]/gbifid[-4:-2]</em>, <em>i.e.</em> in a first folder named with the <em>gbifid</em> last two numbers and a subfolder with the previous two ones. Example: the time-series files matching occurrence 2236837714 are located at <em>final_dataset_by_gbifid/14/77/</em>. </li> <li>Image time-series are composed of twelve 16 bits RGB <em>.png</em> and twelve 16 bits IR <em>.png</em> files containing data identical to the original L1C products, no lossy compression was made. There are one RGB and one IR .png file per month.</li> <li>Patches from month MM/YYYY of occurrence <em>gbifid</em> are named<em> </em><em>RGB_YYYY_MM_gbifid_.png</em> and <em>IR0_YYYY_MM_gbifid_.png</em>.</li> </ul> </li> <li><em><strong>models.zip</strong></em> is the archive containing the four PyTorch models weights described in our article and<strong><em> </em></strong><em><strong>inception_env.py</strong></em> the used Inception V3 architecture. <em><strong>index.json</strong></em> contains the dictionnary linking the models class indexes from 0 to 14128 with our labels <em>speciesKey</em>: {"class_index":speciesKey}.</li> </ul> <p> </p> <ul> <li><strong>ACKNOWLEDGMENTS</strong>: We warmly thank Alexander Zizka et al. for providing us the geographically and taxonomically curated set of Orchids occurrences. This dataset contains modified Copernicus Sentinel data and Copernicus Service information (2018). Sentinel-2 MSI data used were available at no cost from ESA Sentinels Scientific Data Hub.</li> </ul>
Data supporting 'Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers'
<p><strong>Note: An updated dataset covering the majority of Greenland's marine-terminating glaciers is available as part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) project through the National Snow and Ice Data Center (NSIDC) at <a href="https://doi.org/10.5067/B28FM2QVVYWY">https://doi.org/10.5067/B28FM2QVVYWY</a>. </strong></p> <p>Data supporting the paper:</p> <blockquote> <p>Chudley, T. R., Howat, I. M., Yadav, B. N., & Noh, M. J. (2022). Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers. <em>The Cryosphere. </em>16, 2629–2642, https://doi.org/10.5194/tc-16-2629-2022</p> </blockquote> <p>Dataset consists of four netCDF files containing stacked Sentinel-2 velocity data of four Greenlandic outlet glaciers (Helheim Glacier, Jakobshavn Isbræ, Store Glacier, and Kangerlussuaq) between 2017 and 2021. Velocity data are derived and corrected following the methods outlined in Chudley <em>et al.</em> (2022). </p> <p>NetCDF files are created by, and tested to be readable by, Python's xarray package.</p> <p>The dimensions of the netCDF file are as follows:</p> <ul> <li><strong>X</strong> - <em>x </em>coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li><strong>Y</strong> - <em>y</em> coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li><strong>time</strong> - temporal midpoint of velocity field.</li> </ul> <p>The variables of the netCDF file are as follows:</p> <ul> <li><strong>dmag</strong> - the absolute magnitude of the velocity, in metres per day.</li> <li><strong>dx</strong> - the velocity in the <em>x</em> direction, in metres per day.</li> <li><strong>dy</strong> - the velocity in the <em>y</em> direction, in metres per day.</li> <li><strong>date1</strong> - the date and time of the first scene acquisition.</li> <li><strong>date2</strong> - the date and time of the second scene acquisition.</li> <li><strong>baseline</strong> - the temporal baseline, in days, between scene acquisitions.</li> <li><strong>orbit_pair</strong> - the combination of orbital pathways in the string format 'RXXX_RYYY', where XXX is relative orbit number of the first scene and YYY the relative orbit number of the second scene.</li> <li><strong>mag_rmse</strong> - the root mean square error of the absolute velocity of the off-ice area. </li> <li><strong>dx_mean</strong> - the mean velocity of the off-ice area in the <em>x</em> direction.</li> <li><strong>dx_sd</strong> - the standard deviation of the velocity of the off-ice area in the <em>x</em> direction.</li> <li><strong>dy_mean</strong> - the mean velocity of the off-ice area in the <em>x</em> direction.</li> <li><strong>dy_sd</strong> - the standard deviation of the velocity of the off-ice area in the <em>y</em> direction.</li> </ul>
Sentinel2GlobalLULC: A dataset of Sentinel-2 georeferenced RGB imagery annotated for global land use/land cover mapping with deep learning (License CC BY 4.0)
<p>Sentinel2GlobalLULC is a deep learning-ready dataset of RGB images from the Sentinel-2 satellites designed for global land use and land cover (LULC) mapping. Sentinel2GlobalLULC v2.1 contains 194,877 images in GeoTiff and JPEG format corresponding to 29 broad LULC classes. Each image has 224 x 224 pixels at 10 m spatial resolution and was produced by assigning the 25th percentile of all available observations in the Sentinel-2 collection between June 2015 and October 2020 in order to remove atmospheric effects (i.e., clouds, aerosols, shadows, snow, etc.). A spatial purity value was assigned to each image based on the consensus across 15 different global LULC products available in Google Earth Engine (GEE). </p> <p> </p> <p>Our dataset is structured into 3 main zip-compressed folders, an Excel file with a dictionary for class names and descriptive statistics per LULC class, and a python script to convert RGB GeoTiff images into JPEG format. The first folder called "Sentinel2LULC_GeoTiff.zip" contains 29 zip-compressed subfolders where each one corresponds to a specific LULC class with hundreds to thousands of GeoTiff Sentinel-2 RGB images. The second folder called "Sentinel2LULC_JPEG.zip" contains 29 zip-compressed subfolders with a JPEG formatted version of the same images provided in the first main folder. The third folder called "Sentinel2LULC_CSV.zip" includes 29 zip-compressed CSV files with as many rows as provided images and with 12 columns containing the following metadata (this same metadata is provided in the image filenames): </p> <ul> <li>Land Cover Class ID: is the identification number of each LULC class</li> <li>Land Cover Class Short Name: is the short name of each LULC class</li> <li>Image ID: is the identification number of each image within its corresponding LULC class </li> <li>Pixel purity Value: is the spatial purity of each pixel for its corresponding LULC class calculated as the spatial consensus across up to 15 land-cover products </li> <li>GHM Value: is the spatial average of the Global Human Modification index (gHM) for each image</li> <li>Latitude: is the latitude of the center point of each image</li> <li>Longitude: is the longitude of the center point of each image</li> <li>Country Code: is the Alpha-2 country code of each image as described in the ISO 3166 international standard. To understand the country codes, we recommend the user to visit the following website where they present the Alpha-2 code for each country as described in the ISO 3166 international standard:https: //www.iban.com/country-codes</li> <li>Administrative Department Level1: is the administrative level 1 name to which each image belongs</li> <li>Administrative Department Level2: is the administrative level 2 name to which each image belongs</li> <li>Locality: is the name of the locality to which each image belongs</li> <li>Number of S2 images : is the number of found instances in the corresponding Sentinel-2 image collection between June 2015 and October 2020, when compositing and exporting its corresponding image tile</li> </ul> <p>For seven LULC classes, we could not export from GEE all images that fulfilled a spatial purity of 100% since there were millions of them. In this case, we exported a stratified random sample of 14,000 images and provided an additional CSV file with the images actually contained in our dataset. That is, for these seven LULC classes, we provide these 2 CSV files:</p> <ul> <li>A CSV file that contains all exported images for this class </li> <li>A CSV file that contains all images available for this class at spatial purity of 100%, both the ones exported and the ones not exported, in case the user wants to export them. These CSV filenames end with "including_non_downloaded_images".</li> </ul> <p>To clearly state the geographical coverage of images available in this dataset, we included in the version v2.1, a compressed folder called "Geographic_Representativeness.zip". This zip-compressed folder contains a csv file for each LULC class that provides the complete list of countries represented in that class. Each csv file has two columns, the first one gives the country code and the second one gives the number of images provided in that country for that LULC class. In addition to these 29 csv files, we provided another csv file that maps each ISO Alpha-2 country code to its original full country name.</p> <p>© <a href="https://doi.org/10.5281/zenodo.5055632">Sentinel2GlobalLULC Dataset </a>by Yassir Benhammou, Domingo Alcaraz-Segura, Emilio Guirado, Rohaifa Khaldi, Boujemâa Achchab, Francisco Herrera & Siham Tabik is marked with Attribution 4.0 International (CC-BY 4.0)</p>
Modelled and Sentinel-1 detected firn aquifers areas in the Antarctic Peninsula
<p>FDM results: This dataset contains firn aquifer extent output from IMAU-FDM (Firn Densification Model), version v1.2A, for the Antarctic Peninsula on a 5.5 km grid. The dataset consists of maps of the extent of simulated seasonal aquifers in at least one year (2017-2020), perennial aquifers in at least on year (2017-2020), and perennial aquifers in all years (2018-2020). Further details are described in Buth et al. (2022).<br> Model adjustments and run were performed by Sanne B. M. Velduijsen.</p> <p>S1 detection results: The GeoTIFF image is the result of the Sentinel-1 firn aquifer detection routine which makes use of the typical delayed increase of SAR backscatter after the peak melt season in case of an aquifer. The image has two bands per year (2017-2020), one containing the DOY80 parameter for the whole Antarctic Peninsula (excluding masked areas, see Buth et al, 2022), the other containing DOY80 only for detected aquifer areas, where it exceeds the threshold of DOY80=105. DOY80 here stands for the day of the year at which 80% of the September Sentinel-1 HH backscatter is reached. Further details are described in Buth et al. (2022).<br> S1 aquifer detection was performed by Lena G. Buth, using the Python API of the Google Earth Engine. The associated code is available as a GitLab project: https://gitlab.awi.de/lenbuth/tc-aquifers</p>
A Map of Land Use and Land Cover in Southern Malawi Derived from Sentinel-2 Data (2023)
<h3><strong>Overview</strong></h3> <p>The land use and land cover map comprises the Mulanje and Phalombe districts, in Southern Malawi. It includes five classes: forest, natural vegetation, cropland, wetland, and other lands. The map is derived from Sentinel-2 mosaics, resulting in a spatial resolution of 10 meters, for 2023. </p> <p> </p> <h3><strong>Map Accuracy</strong></h3> <p>The land use and land cover map achieves an overall accuracy of 89%. Details of user and producer accuracies are provided in Table 1.</p> <p>Table 1: Land use and land cover classification validation,including overall, producer (PA) and user (UA) accuracies values for each class.</p> <div> <table> <tbody> <tr> <td> <p><strong>Class </strong></p> </td> <td> <p><strong>Producer Accuracy</strong></p> </td> <td> <p><strong>User Accuracy</strong></p> </td> </tr> <tr> <td> <p>Cropland</p> </td> <td> <p> 93%</p> </td> <td> <p> 85%</p> </td> </tr> <tr> <td> <p>Wetland</p> </td> <td> <p> 100%</p> </td> <td> <p> 100%</p> </td> </tr> <tr> <td> <p>Other Lands</p> </td> <td> <p> 90%</p> </td> <td> <p> 95%</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p> 79%</p> </td> <td> <p> 90%</p> </td> </tr> <tr> <td> <p>Natural Vegetation</p> </td> <td> <p> 90%</p> </td> <td> <p> 90%</p> </td> </tr> <tr> <td> <p><strong>Overall Accuracy</strong></p> </td> <td><br> <p><strong> 89%</strong></p> </td> </tr> </tbody> </table> </div> <h3> </h3> <h3><strong>Files descripion</strong></h3> <ul> <li>MLW_Sentinel_LULC_2023.tif / .qml: land use and land cover map and QGIS style file</li> <li>training_samples.gpkg: training samples with class labels</li> <li>validation_samples.gpkg: validation samples with class labels</li> </ul>
FNEWs Kalman-gefilterte Sentinel-2 Bilder 2017 bis 2022
<p>Im Projekt Fernerkundungsbasiertes Nationales Erfassungssystem für Waldschäden (FNEWS) werden Waldschäden auf Grundlage eines strukturelles Zeitreihenmodells in Kombination mit dem Kalman-Filter erfasst. Das auf Sentinel-2-Satellitendaten basierende Waldschadenerfassungssystem ermöglicht differenzierte Veränderungs- und Schadanalysen für die vier Untersuchungsgebiete des Projektes, die in Sachsen, Niedersachsen, Bayern und Baden-Württemberg liegen.</p> <p>Für die jährliche Kartierung von Waldschäden wird jeweils zum Stichtag 31.08. ein wolkenfreies Kalman-gefiltertes Sentinel-2 Bild (KFB) in einer CIR-Falschfarbendarstellung erzeugt. Die Kalman-Filterung bewirkt, dass atmosphärische Störeinflüsse unterdrückt werden und gleichzeitig der Zustand am Boden möglichst wirklichkeitsgetreu im KFB abgebildet wird. Die KFB sind die Datengrundlage für die Ableitung der FNEWs-Jahresprodukte (https://doi.org/10.3220/DATA20230907171359-0). Hier werden die Sentinel-2 KFB aus den Jahren 2017 bis einschließlich 2022 zur Verfügung gestellt. Sie können allgemein als Hintergrund-Layer verwendet werden oder speziell bei der Interpretation der FNEWS Jahresprodukte helfen. Die KFB werden als COG - cloud optimized GeoTIFF zum Download bereitgestellt.</p> <p>Bei Verwendung der KFB sind folgende Einschränkungen zu berücksichtigen: Waldänderungen sind möglicherweise noch nicht oder noch nicht in ihrer vollen Ausprägung im KFB abgebildet, wenn sie zeitlich erst kurz vor dem Stichdatum eingetreten sind. Das strukturelle Zeitreihenmodell ist auf Wald optimiert, d.h. außerhalb des Waldes ist mit größeren Abweichungen zu rechnen.</p>
MUDDAT: A SENTINEL-2 IMAGE-BASED MUDDY WATER BENCHMARK DATASET FOR ENVIRONMENTAL MONITORING.
<p>This is a dataset for mapping muddy waters based on Sentinel-2 (L2A products) satellite imagery. The image data are saved as GeoTIFF files and metadata files are provided in json format. There are 19 images in total, based on 16 distinct European Areas of Interest (AOIs), covering a total of 9 countries such as:</p> <ul> <li>Greece</li> <li>Italy</li> <li>France</li> <li>Spain</li> <li>Belgium</li> <li>UK</li> <li>Sweden</li> <li>Finland and</li> <li>Serbia</li> </ul> <p>From the Sentinel-2 L2A products were extracted 10 spectral bands and then resampled to a 10m spatial resolution. All spectral bands used can be found in the Metadata/Source files. The annotated images comprise 3 classes, "Non-muddy", "Muddy" and "Ambiguous". More details about the annotation methodology can be found on the accepted abstract (file: <a href="../api/records/11220437/draft/files/Accepted_Abstract_03_15_2024.pdf/content" target="_blank" rel="noopener noreferrer">Accepted_Abstract_03_15_2024.pdf</a>) or the published paper, that you can find here: <a href="https://doi.org/10.1109/IGARSS53475.2024.10642051" target="_blank" rel="noopener">10.1109/IGARSS53475.2024.10642051</a>.</p>
S1S2-Water: A global dataset for semantic segmentation of water bodies from Sentinel-1 and Sentinel-2 satellite images
<p>The S1S2-Water dataset is a global reference dataset for training, validation and testing of convolutional neural networks for semantic segmentation of surface water bodies in publicly available Sentinel-1 and Sentinel-2 satellite images. The dataset consists of 65 triplets of Sentinel-1 and Sentinel-2 images with quality checked binary water mask. Samples are drawn globally on the basis of the Sentinel-2 tile-grid (100 x 100 km) under consideration of pre-dominant landcover and availability of water bodies. Each sample is complemented with metadata and Digital Elevation Model (DEM) raster from the Copernicus DEM.</p><p>This work was supported by the German Federal Ministry of Education and Research (BMBF) through the project "Künstliche Intelligenz zur Analyse von Erdbeobachtungs- und Internetdaten zur Entscheidungsunterstützung im Katastrophenfall" (AIFER) under Grant 13N15525, and by the Helmholtz Artificial Intelligence Cooperation Unit through the project "AI for Near Real Time Satellite-based Flood Response" (AI4FLOOD) under Grant ZT-IPF-5-39. </p>
Sentinel-1 data stack for Masjed Soleyman Dam
<p>This is a Sentinel-1 sample dataset for the SARvey InSAR time series analysis software.</p> <p>This dataset consists of:</p> <ul> <li> A stack of coregistered SLCs for the Masjed Soleyman Dam and its corresponding geometry data in MiaplPy format. These files serve as the input data for SARvey.<br> SARvey_input_data_Masjed_Soleyman_dam_S1_dsc_2015_2018.zip</li> <li> The final products generated by SARvey for reference.<br> SARvey_final_results_Masjed_Soleyman_dam_S1_dsc_2015_2018.zip</li> </ul> <p><br>A cookbook is available to help you run the software using this dataset.</p> <p> </p>
Sentinel-2 Satellite Imagery Based Forest Fire Monitoring
<p><strong>Forest Fire in Villages near Berlin - Normalized Burn Ratio (NBR)</strong></p> <p>Villages in Treuenbrietzen (Frohnsdorf, Klausdorf and Tiefenbrunnen) around 50 km southwest of Berlin have been severely affected by recent unpredicted wildfire and the size of the burned area is about of 400 hectares, which started to spread on 23rd of August, 2018. More than 500 people had to leave their homes as a result of the fire in Treuenbrietzen and the burning fire with dense smoke continued for days. This year Europe has faced a long hot dry summer with almost no rain and as a consequence some European countries like Germany are on high alert regarding possible forest fires.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.