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1,118 results for “sentinel”
Satellite-derived chlorophyll-a concentrations for Lake Harsha (USA) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery
<p>This dataset contains satellite-derived chlorophyll-a data of Lake Harsha (USA) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery.</p> <p>Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following:</p> <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. <em>Remote Sensing of Environment, 270</em>, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. <em>Frontiers in Remote Sensing, 1</em></li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. <em>Remote Sensing of Environment, 240</em>, 111604</li> </ul>
Codes and dataset of the publication "Effectiveness of Sentinel-1 and Sentinel-2 for Flood Detection Assessment in Europe"
<p>The folder contains the codes, input and output of the analysis carried out for supporting the publication of the paper:</p> <p>Tarpanelli A., Mondini A., Camici S.:Effectiveness of Sentinel-1 and Sentinel-2 for Flood Detection Assessment in Europe, Natural Hazards and Earth System Sciences, https://doi.org/10.5194/nhess-2022-63, 2022.</p> <p> </p> <p>The codes should be run in order A1-A7 to generate all the figures of the paper.</p> <p>For details please send an email to:</p> <p>angelica.tarpanelli@irpi.cnr.it</p> <p> </p>
Sentinel-3 NDVI ARD and Long Term Statistics (1999-2019) from the Copernicus Global Land Service over Lombardia
<p>Sentinel-3 NDVI Analysis Ready Data (ARD) (C_GLS_NDVI_20220101_20220701_Lombardia_S3_2.nc) product provided by the Copernicus Global Land Service [3]. The file C_GLS_NDVI_20220101_20220701_Lombardia_S3_2_masked.nc is derived from C_GLS_NDVI_20220101_20220701_Lombardia_S3_2.nc but values have been scaled (raw_value * ( 1/250) - 0.08) and values lower then -0.08 and greater than 0.92 have been removed (set to missing values).</p> <p>The original dataset can also be discovered through the OpenEO API[5] from the CGLS distributor VITO [4]. Access is free of charge but an <a href="https://aai.egi.eu/">EGI registration</a> is needed.</p> <p>The file called Italy.geojson has been created using the Global Administrative Unit Layers <a href="https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/9c35ba10-5649-41c8-bdfc-eb78e9e65654">GAUL G2015_2014</a> provided by FAO-UN (see <a href="https://data.apps.fao.org/map/catalog/srv/api/records/9c35ba10-5649-41c8-bdfc-eb78e9e65654/attachments/GAUL2015_Documentation.zip">Documentation</a>). It only contains information related to Italy.</p> <p> </p> <p>Further info about drought indexes can be found in the Integrated Drought Management Programme [5]</p> <p>[1] <a href="https://www.sciencedirect.com/science/article/abs/pii/027311779500079T">Application of vegetation index and brightness temperature for drought detection</a> [2] <a href="https://en.wikipedia.org/wiki/Normalized_difference_vegetation_index">NDVI</a> [3] <a href="https://land.copernicus.eu/global/index.html">Copernicus Global Land Service</a> [4] <a href="https://vito.be/en">Vito</a> [5] <a href="https://openeo.org/">OpenEO</a> [5] <a href="https://www.droughtmanagement.info/indices">Integrated Drought Management</a></p>
EOOffshore: Sentinel-1 Wind Data for the Irish Continental Shelf Region
<p><a href="https://eooffshore.github.io">EOOffshore</a> is a <a href="https://www.seai.ie/">Sustainable Energy Authority of Ireland (SEAI)</a> funded <a href="https://www.seai.ie/data-and-insights/seai-research/research-projects/details/building-upon-copernicus-earth-observation-services-to-augment-wind-measurement-coverage-of-the-oredp-offshore-renewable-energy-assessment-areas">project</a>, which commenced in June 2020 in the <a href="https://www.ucd.ie/physics/">School of Physics</a> in <a href="https://www.ucd.ie/">University College Dublin (UCD)</a>. It presents a case study that demonstrates the utility of the <a href="https://pangeo.io/">Pangeo</a> software ecosystem in the development of offshore wind speed and power density estimates, increasing wind measurement coverage of offshore renewable energy assessment areas in the <a href="https://www.marine.ie/Home/site-area/irelands-marine-resource/real-map-ireland">Irish Continental Shelf (ICS)</a> region. It has involved the creation of a new <a href="https://eooffshore.github.io/datasets.html">wind data catalog</a> for this region, consisting of a collection of analysis-ready, cloud-optimized (ARCO) datasets featuring up to 21 years of available in situ, reanalysis, and satellite observation wind data products.</p> <p>The <a href="https://www.copernicus.eu/en/about-copernicus">European Union Copernicus Earth Observation (EO) programme</a> and services are based on data collected from EO satellites, in particular, the <a href="https://sentinels.copernicus.eu/web/sentinel/home">Sentinel satellite missions</a>. This includes the <a href="https://sentinel.esa.int/web/sentinel/missions/sentinel-1">Sentinel-1 mission</a>, which consists of C-band Synthetic Aperture Radar (SAR) imaging satellites in polar orbit. One of its main objectives is the provision of ocean monitoring services, where its <a href="https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-types-processing-levels/level-2">Level-2 Ocean (OCN)</a> products include an Ocean WInd field (OWI) component. This provides gridded estimates of wind speed and direction at 10 m above the surface, with a typical spatial resolution of 1 km. This particular catalog data set (<em>eooffshore_ics_level3_sentinel1_ocn.zarr.tar.gz</em>) contains 2015-2021 OCN wind products for the ICS region, which were retrieved from the <a href="https://scihub.copernicus.eu/">Copernicus Open Access Hub (COAH)</a> and the <a href="https://search.asf.alaska.edu/#/">Alaska Satellite Facility (ASF)</a>. The data set was used in the EOOffshore project outputs presented (<em><a href="https://meetingorganizer.copernicus.org/EGU22/EGU22-2746.html">Scalable Offshore Wind Analysis With Pangeo</a></em>) at the <em><a href="https://meetingorganizer.copernicus.org/EGU22/session/42046">Meeting Exascale Computing Challenges with Compression and Pangeo</a></em> <a href="https://www.egu22.eu/">2022 EGU General Assembly</a> session.</p> <p>Description and example usage of the Sentinel-1 data set in EOOffshore:</p> <ul> <li><a href="https://eooffshore.github.io/Sentinel-1_ICS_Wind_Data.html">Sentinel-1 Wind Data for Irish Continental Shelf region</a></li> <li><a href="https://eooffshore.github.io/Offshore_Wind_AOI.html">Offshore Wind in Irish Areas Of Interest</a></li> <li><a href="https://eooffshore.github.io/Comparison_Wind_Power.html">Comparison of Offshore Wind Speed Extrapolation and Power Density Estimation</a></li> </ul> <p>As requested by the <a href="https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice">Legal Notice on the use of Copernicus Sentinel Data and Service Information</a>, this data set:</p> <ul> <li>Contains modified Copernicus Sentinel data [2015 - 2021]</li> </ul>
InSAR stack of San Francisco Bay in California from Sentinel-1 descending track 42 processed with ARIA
<p>A stack of unwrapped interferograms in San Francisco Bay, California, USA from Sentinel-1descending track 42</p> <p>Processor: ARIA (processed using ISCE and prepared using <a href="https://github.com/aria-tools/ARIA-tools">ARIA-tools</a> as shown below)</p> <p>Tropospheric delay estimated from ERA5 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 1.2 (~5 GB):</strong><br> Time: 2015.03.01 - 2022.06.04, 189 acquisitions, 961 interferograms<br> Used ARIA-tools and MintPy commands:</p> <pre><code>ariaDownload.py -b '37.25 38.1 -122.6 -121.75' --track 42 ariaTSsetup.py -f 'products/*.nc' -b '37.25 38.1 -122.6 -121.75' --mask Download prep_aria.py -s ../stack/ -d ../DEM/SRTM_3arcsec.dem -i ../incidenceAngle/*.vrt -a ../azimuthAngle/*.vrt -w ../mask/watermask.msk</code></pre> <p><strong>Version 0.2 (~280 MB; for fast testing of code development)</strong><br> Time: 2016.01.31 - 2017.05.10, 23 acquisitions, 91 interferograms<br> Used ARIA-tools commands (access date Jun 18th, 2022):</p> <pre><code>ariaDownload.py -b '37.35 38.00 -122.45 -121.80' --track 42 --start 20160101 --end 20170510 ariaTSsetup.py -f 'products/*.nc' -b '37.35 38.00 -122.45 -121.80' --mask Download</code></pre> <p> </p>
Olive orchard stress assessment with supplementary Sentinel-2 data
<p>This file contains ground truthing data from olive orchards in Halkidiki, N.Greece and Sentinel-2 data for the noted samples. The samples collected for ground truthing are polygons inside the borders of olive orchards in Halkidiki. Polygons or Sampling units contain information associated with biotic and abiotic stress-related assessments carried out by visual inspection and laboratory analysis of samples with ongoing symptoms. Assessments were recorded as percentages of symptoms observed in the total vegetation surface present in each sampling unit. Symptom percentages were attributed to three possible classes: Verticillium dahliae, Spilocaea oleaginea, Unidentified Stress Factors. These percentage values were used to characterize healthy trees and the incidence of V. dahliae, S. oleaginea and unidentified stress factors (USF) in the sample. USF was used for all other non-classified surveyed symptoms attributed to diseases, pests, or abiotic-related damage. Percentages were summed to compute the total stress present in each sampling unit. “Total stress” refers to the stress incidence value used together with different thresholds to create binary labels for each sample of “stressed” or “not stressed”.</p> <p>The polygon geographical information for each sample were recorded and stored in shapefile format using SW maps, a mobile mapping and GIS app.</p> <p>Sentinel-2 data was paired with each sample using the Feature Info Service (FIS) available from sentinel hub, now upgraded into the Statistical API tool ( & ). This API enables acquisition of statistics calculated based on satellite imagery without having to download images. In the Statistical API request the area of interest, time period, evalscript and statistical measures of interest can be calculated. The requested statistics are returned in the API response.</p> <p>Statistical API deployments:</p> <p><a href="https://creodias.sentinel-hub.com/api/v1/statistics">https://creodias.sentinel-hub.com/api/v1/statistics</a></p> <p><a href="https://services.sentinel-hub.com/api/v1/statistics">https://services.sentinel-hub.com/api/v1/statistics</a></p> <p><a href="https://services-uswest2.sentinel-hub.com/api/v1/statistics">https://services-uswest2.sentinel-hub.com/api/v1/statistics</a></p>
InSAR stack of the 2019 Ridgecrest, California earthquake sequence from Sentinel-1 descending track 71 processed with ASF HyP3
<p>A stack of unwrapped interferograms on Owens Valley, California for the <a href="https://earthquake.usgs.gov/earthquakes/eventpage/ci38457511/executive">2019 Ridgecrest earthquake sequence</a>.</p> <p>Sensor: Sentinel-1 descending track 71</p> <p>Time: 2019.06.10 - 2019.08.15, 7 acquisitions, 11 interferograms</p> <p>Processor: <a href="https://hyp3-docs.asf.alaska.edu/guides/insar_product_guide/">ASF HyP3</a> (GAMMA)</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>
waldmonitoring.ch: NDVI difference rasters for annual forest change in Switzerland (Sentinel 2 based): 2016 - 2023
<p><strong>NDVI difference rasters for annual forest change in Switzerland (Sentinel 2 based): 2016 - 2023<br></strong></p> <p><em><strong>Date format:</strong></em> GeoTIFF<br><em><strong>Data type: </strong></em>Int16 - Sixteen bit signed integer*<br><em><strong>Spatial Resolution</strong></em>: 10 x 10 m<br><em><strong>Spatial Extent: </strong></em>Switzerland and Liechtenstein, masked with swisstopo swissTLM3D Forest Mask (2021) <br><em><strong>Coordinate Reference System</strong></em>: EPSG:2056 - CH1903+ / LV95, Swiss. Obl. Mercator<br><br>*:<em> NDVI Difference Values (-1 to 1) are multiplied by 10'000 to allow using Integer 16 bit vs. Float 32 bit while maintaining a precision of 5 digits. The values have to be interpreted accordingly: -10'000 means an NDVI difference of -1, +10'000 an NDVI difference of +0.<br><br></em></p> <p>The NDVI difference rasters for annual forest change in Switzerland are created by using Sentinel 2 based NDVI composites (Normalized Difference Vegetation Index). The code for the generating method can be found in the <a href="https://github.com/HAFL-WWI/Digital-Forest-Monitoring/tree/main/methods/use-case1">waldmonitoring-repository</a>, the method itself is also described and translated in further detail in the <a href="https://wiki.waldmonitoring.ch/index.php/Use_Case_1_-_J%C3%A4hrliche_Waldver%C3%A4nderungen">corresponding waldmonitoring-wiki</a>: For the automatic detection of areas of change, the differences between two years were examined using the NDVI. In order to automatically filter out cloudy images, the maximum NDVI value of all available images of the summer months (June - August) was used for each pixel (10 x 10 m). During this time, practically all the vegetation is green. This results in almost cloud-free, annual raster images with the maximum NDVI ("NDVI maximum composite"). The difference between two years is formed from these composites. The difference values accordingly reflect the strength of the change. </p> <p>As an example for interpretation, values of -0.1 or smaller (closer to -1.0) indicate strong forest changes (e.g. clearing), whereas positive values indicate vegetation regeneration or re-greening of previously unvegetated areas. Using a threshold value (we suggest -0.06 for forest applications), areas with considerable negative change can be separated out and be vectorized (converted to polygons) to create a dataset that can be queried.</p>
A Semi-Automatic Classification Approach for River Shape Extraction from Sentinel-2 Imagery
<p>To extract the river from satellite imagery, at first we have to classify the waterbody from satellite imagery. Then we will differentiate the river from waterbody. We have used three different techniques to classify the waterbody from satellite imagery. At first, pixel based iso-cluster unsupervised classification was used to classify the waterbody in Sentinel 2 imagery. We excluded the supervised classification in decided methodology as we are interested about automatic process of river extraction. Then we have used Segment mean shift classification tool as image segmentation techniques. Indices based NDWI (Normalize difference water indices) classification was also used in this research.</p>
Determination of rapeseed areas based on Sentinel-2 data
<p>Determination of rapeseed areas on the basis of Sentinel-2 data. The areas were determined using supervised classifications.</p> <p>Useful presentation:</p> <p>http://fabspace.pl/wp-content/uploads/2017/11/2_Klasyfikacja.pdf</p>
Assessment of the condition of winter crops before winter dormancy on the basis of Sentinel-2 data; season 2018
<p>NDVI determined on the basis of images of Sentinel-2 from the dates 15 and 18.10.2018, were used to study the assessment of the winter crop before winter dormancy. Data were used to assess the degree of development and density of plants. The data was used to study the correlation with Planet.</p>
Monitoring of the Vistula estuary into the Baltic Sea using Sentinel-2 data
<p>Senitnel-2 data and dedicated presentations were used during the Daily Animation. The aim of the exercise was to familiarize the participants with the structure of Senitnel-2 data and creating RGB compositions in SNAP and QGIS software.</p> <p>Links to the presentation:</p> <p>http://fabspace.pl/wp-content/uploads/2017/11/RGB-w-QGIS.pdf</p> <p>http://fabspace.pl/wp-content/uploads/2017/11/RGB-w-SNAP.pdf</p> <p> </p>
Surface deformation of the Mw 6.4 and Mw 7.1 Ridgecrest earthquakes measured from subpixel correlation of Copernicus Sentinel-2 optical images
<p>Surface deformation of the Mw 6.4 and Mw 7.1 Ridgecrest earthquakes measured from subpixel correlation of Copernicus Sentinel-2 optical images </p>
Seamless 30 meter Sentinel-2 L2A Pan-European seasonal cloudless mosaics from winter 2018 to spring 2020
<p>Seasonal composites of <a href="https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html">Sentinel-2 L2A</a> imagery created as part of the <a href="https://opendatascience.eu/geo-harmonizer/">Geo-harmonizer project</a>, containing median of the blue, green, red, NIR, SWIR1 and SWIR2 bands, as well as pixel counts per season, produced in the ETRS89-extended / LAEA Europe (<a href="https://epsg.io/3035">EPSG:3035</a>) spatial reference system. Mosaics were produced from winter 2017 to spring 2020, with the imaging intervals per season being:</p> <ul> <li>winter: 02/12 of previous year to 20/03</li> <li>spring: 21/03 to 24/06</li> <li>summer: 25/06 to 12/09</li> <li>fall: 13/09 to 01/12</li> </ul> <p>Seamlessness of the composites was achieved through overlapping pixel averaging weighted by distance from the suborbital track.</p> <p>The data are provided as UINT8 values and were scaled with a common threshold (13712) chosen to minimize compression loss across the dataset. Data at the original (UINT16) scale can be obtained as follows:</p> <p><span>\(x_{\text{uint16}} = 13712 {x_{\text{uint8}} \over 254}\)</span></p> <p>For any additional questions regarding the data please contact the authors at <a href="mailto:multione@multione.hr?subject=S2L2A%20Europe%20mosaics">multione[at]multione.hr</a>.</p>
High-resolution sea ice drift and deformation example data derived from Sentinel-1 in the Arctic Ocean during MOSAiC
<p>This data set contains two high-resolution sea ice drift and deformation fields from 30/31 December 2019 and 20/21 June 2021. They were acquired in the Transpolar Drift along the drift track of the research campaign "Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). Drift fields were calculated from Sentinel-1, HH polarization SAR images acquired in enhanced wide mode. These had a pixel resolution of 50 m in Polar Stereographic North projection (latitude of true scale: 70 N, center longitude: 45 W). We used an ice tracking algorithm introduced by Thomas et al. (2008, 2011) and modified by Hollands and Dierking (2011) to derive drift from sequential pairs. The time step between two sequential images is approximately one day. The resulting drift data set was defined on a regular grid with a spatial resolution of 700 m. Outliers in the velocity data were reduced by a 3x3 point running median filter covering an area of 2.1x2.1 km. For the deformation estimates, we calculated deformation using a linear approximation based on Green's Theorem that relates the double integral over a plane to the line integral along a simple curve surrounding the plane. We discretized the curve applying the trapezoid method that linearly interpolates velocity between the vertices of the grid cells. This work contains modified Copernicus Sentinel data (2020)</p> <p>Related publications:</p> <p><strong>von Albedyll, L., Haas, C., and Dierking, W.</strong>: Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations, The Cryosphere, 15, 2167–2186, <a href="https://doi.org/10.5194/tc-15-2167-2021">https://doi.org/10.5194/tc-15-2167-2021</a>, 2021.</p> <p><strong>Hollands, Thomas; Dierking, Wolfgang (2011):</strong> Performance of a multiscale correlation algorithm for the estimation of sea-ice drift from SAR images: initial results. <em>Annals of Glaciology</em>, <strong>52(57)</strong>, 311-317, <a href="https://doi.org/10.3189/172756411795931462">https://doi.org/10.3189/172756411795931462</a></p> <p><strong>Thomas, Mani; Geiger, Cathleen A; Kambhamettu, Chandra (2008):</strong> High resolution (400 m) motion characterization of sea ice using ERS-1 SAR imagery. <em>Cold Regions Science and Technology</em>, <strong>52(2)</strong>, 207-223, <a href="https://doi.org/10.1016/j.coldregions.2007.06.006">https://doi.org/10.1016/j.coldregions.2007.06.006</a></p> <p><strong>Thomas, Mani; Kambhamettu, Chandra; Geiger, Cathleen A (2011):</strong> Motion Tracking of Discontinuous Sea Ice. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <strong>49(12)</strong>, 5064-5079, <a href="https://doi.org/10.1109/TGRS.2011.2158005">https://doi.org/10.1109/TGRS.2011.2158005</a></p>
Water analysis dataset > Water Sentinels pilot project - ACTION project
<p><a href="http://www.ocean-alive.org/en/water-sentinels">WATER SENTINELS</a> is a citizen-science pilot by Ocean Alive to promote water quality at Sado estuary.</p> <p>This dataset presents data and metadata for water quality assessment on water samples collected at Sado estuary by citizens during the 6-month pilot. The samples were analyzed by students and researchers at Setúbal School of Technology (Polytechnic Institute of Setúbal). </p> <p>The data collected by citizens at the field was location (GPS coordinates), temperature and water transparency (using the secchi disc method). In the laboratory researchers evaluated pH, salinity, conductivity, and concentration of ammonia, nitrate, phosphate, suspended solids (SST) and organic matter (SOM and DOM).</p> <p>WATER SENTINELS is one of the pilots supported by <a href="https://actionproject.eu/water-sentinels/"><strong>ACTION</strong></a> project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 824603.</p>
Horizontal and vertical velocities in Ho Chi Minh city by Sentinel-1 radar interferometry
<p>Ho Chi Minh City (HCMC), the most crowded city and economic hub of Viet Nam, has been experiencing land subsidence over the past decades. This effort aims to contribute the spatial distribution of subsidence in HCMC in its horizontal and vertical components using synthetic aperture radar interferometry (InSAR) time series. To this purpose, an advanced Persistent Scatterers and Distributed Scatterers (PSDS) InSAR technique was applied to two European Space Agency (ESA) Sentinel-1 datasets consisting of 96 ascending and 202 descending images, acquired from 2014 to 2020 over the HCMC area. The combination of ascending and descending satellite passes is used to decompose the light of sight velocities into horizontal east-west and vertical components. The obtained results revealed that subsidence is most pronounced in the areas along the Sai Gon River, in the northwest-southeast axis, and in the southwest of the city, with a maximum value of 80 mm/yr, which is in accordance with the findings of the literature. The amplitude of east-west horizontal velocities is relatively small and large-scale eastward movement can be observed in the west of the city at a rate of 3-5 mm/yr.</p> <p>File "Dinh_HCMUD_v1.tif" is the 50-m vertical velocity in mm/year. Negative velocities represent movement subsidence.</p> <p>File "Dinh_HCMEW_v1.tif" is the 50-m east-west horizontal velocity in mm/year. Positive velocities represent movement Eastward.</p> <p>For more details on the technique, the reader can be found in [1].</p> <p>[1] Ho Tong Minh, D.; NGO, Y.; Lê, T.T.; Le, T.C.; Bui, H.S.; Vuong, Q.V.; Le Toan, T. Quantifying Horizontal and Vertical Movements in Ho Chi Minh City by Sentinel-1 Radar Interferometry. <em>Preprints</em> <strong>2020</strong>, 2020120382. Available: https://www.preprints.org/manuscript/202012.0382/v2</p> <p> </p> <p> </p>
Sample version 2.0 ITS_LIVE Sentinel-1 image pair product of ice velocity in Greenland
<p>21 samples of version 2.0 ITS_LIVE Sentinel-1 image pair product of ice velocity in three test regions of Greenland Ice Sheet</p> <p>Sensor: Sentinel-1A/B</p> <p>Processor: <a href="https://github.com/isce-framework/isce2">ISCE</a> v2.4.1 (topsApp -> <a href="https://github.com/leiyangleon/Geogrid">Geogrid</a> v1.4.0 -> <a href="https://github.com/nasa-jpl/autoRIFT">autoRIFT</a> v1.4.0)</p> <p>Project: NASA MEaSUREs project <a href="https://its-live.jpl.nasa.gov">ITS_LIVE</a></p> <p>Region 1 (69.13N, 50.88W; Jakobshavn Isbræ Glacier): 7 ascending image pairs</p> <p>Region 2 (77.61N, 42.79W; central north of interior Greenland): 3 ascending image pairs</p> <p>Region 3 (72.48N, 35.87W; central south of interior Greenland): 10 descending image pairs and 1 ascending image pair</p> <p>This serves as a supplementary dataset for the companion journal article submitted to Earth System Science Data (to appear soon).</p> <p> </p> <p>Acknowledgement:</p> <p>This effort was funded by the NASA MEaSUREs program in contribution to the Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project (<a href="https://its-live.jpl.nasa.gov/">https://its-live.jpl.nasa.gov/</a>) and through Alex Gardner’s participation in the NASA NISAR Science Team</p>
Annual land cover maps of Germany based on Sentinel-2 MSI Level 3A (WASP) data
<p>Overview:<br> This annual land cover product is available for the years 2016, 2019, 2020, 2021 for the whole of Germany. It was generated based on Sentinel-2 MSI L3A WASP Data provided by DLR (https://geoservice.dlr.de/data-assets/4hcq6dgkj648.html). For a complete description of the classification procedure please refer to<br> Riembauer, G.; Weinmann, A.; Xu, S.; Eichfuss, S.; Eberz, C.; Neteler, M.: Germany-wide Sentinel-2 based land cover classification and change detection for settlement and infrastructure monitoring. In: Proceedings of the 2021 conference on Big Data from Space (doi:10.2760/125905), 2021.</p> <p>Source data:</p> <ul> <li>Satellite data <ul> <li>German Aerospace Center (DLR): Sentinel-2 MSI - Level 3A (MAJA/WASP Tiles) - Germany, DOI: 10.15489/4hcq6dgkj648</li> </ul> </li> <li>Auxiliary data <ul> <li>European Union, Copernicus Land Monitoring Service, European Environment Agency (EEA), <strong>Copernicus High Resolution Layer: Imperviousness Status Map, 2018 </strong>(https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/imperviousness-density-2018)</li> <li><strong>OpenStreetMap</strong> Planet dump retrieved from https://planet.osm.org, https://www.openstreetmap.org</li> <li><strong>S2GLC Map of Europe</strong> (R. Malinowski, S. Lewiński, M. Rybicki, E. Gromny, M. Jenerowicz, M. Krupiński, A. Nowakowski, C. Wojtkowski, M. Krupiński, E. Krätzschmar, and P. Schauer, "Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery," Remote Sensing, vol. 12, no. 21, p. 3523, 2020.)</li> </ul> </li> </ul> <p>File naming:<br> classification_map_germany_[year].tif example: classification_map_germany_2020.tif</p> <p>Projection + EPSG code:<br> WGS 84 / UTM zone 32N (EPSG: 32632)</p> <p>Spatial extent:<br> north: 55:03:38.646483N<br> south: 47:08:24.738401N<br> west: 5:33:47.816647E<br> east: 15:34:24.108516E</p> <p>Spatial resolution:<br> 10 m</p> <p>Format: COG (Cloud-Optimized GeoTIFF)</p> <p>Pixel values:<br> 10: forest<br> 20: low vegetation<br> 30: water<br> 40: built-up<br> 50: bare soil<br> 60: agriculture</p> <p>Temporal coverage:<br> Years 2016, 2019, 2020, 2021</p> <p>Software used:<br> GRASS 7.8, actinia</p> <p>Original dataset license:<br> The Sentinel-2 level 3A data produced and distributed by DLR are based on Copernicus Sentinel-2 level 1C data, which are subject to the following license: https://theia.cnes.fr/atdistrib/documents/TC_Sentinel_Data_31072014.pdf One of the following citations is mandatory for using the provided MAJA/WASP L3A product: German Aerospace Center (DLR): Sentinel-2 MSI - Level 3A (MAJA/WASP Tiles) - Germany, DOI: 10.15489/4hcq6dgkj648 or Contains modified Copernicus Sentinel data, processed by DLR, licensed under CC-BY 4.0</p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p>
Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 1-band NDWI images of coasts.
<p><em><strong>Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 1-band NDWI images of coasts.</strong></em></p> <p>These Residual-UNet model data are based on 1-band NDWI images of coasts and associated labels.</p> <p>Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7344571</p> <p>Classes: {0=water, 1=whitewater, 2=sediment, 3=other}</p> <p>File descriptions</p> <p>For each model, there are 5 files with the same root name:</p> <p>1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p><br> References</p> <p>*Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym</p> <p>** Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571</p> <p> </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.