Data Collections

A data collection is a group of earth observation (EO) datasets sharing exactly the same product specifications. All data sets belonging to a data collection were observed with the identical sensor and processed with an identical algorithm.

The following data collections are currently available in NextGEOSS:

126 data collections

  • ECDPC COVID-19

    Datasets: 1
    Pilots: Health
    Latest available public data on COVID-19 by ECDPC
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  • SCENT Kifisos Temperature

    Datasets: 337
    Pilots:
    Using SCENT Explore and SCENT Measure apps, 511 volunteers from the local community collected more than 5225 pieces of important information about Kifisos river parameters, such as measurements of air temperature.
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  • SCENT Kifisos Moisture

    Datasets: 337
    Pilots:
    Using SCENT Explore and SCENT Measure apps, 511 volunteers from the local community collected more than 5225 pieces of important information about Kifisos river parameters, such as measurements of soil moisture.
    Go to collection >
  • SCENT Kifisos Video

    Datasets: 111
    Pilots:
    Using SCENT Explore and SCENT Measure apps, 511 volunteers from the local community collected more than 5225 pieces of important information about Kifisos river parameters, such as water level and surface flow velocity.
    Go to collection >
  • SCENT Kifisos Image

    Datasets: 2929
    Pilots:
    Using SCENT Explore and SCENT Measure apps, 511 volunteers from the local community collected more than 5225 pieces of important information about Kifisos river parameters, such as images of land-cover/land-use.
    Go to collection >
  • SCENT Danube Temperature

    Datasets: 0
    Pilots:
    Using SCENT Explore and SCENT Measure apps, volunteers competed collecting important information about Danube Delta parameters, such as measurements of air temperature.
    Go to collection >
  • SCENT Danube Moisture

    Datasets: 0
    Pilots:
    Using SCENT Explore and SCENT Measure apps, volunteers competed collecting important information about Danube Delta parameters, such as measurements of soil moisture.
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  • SCENT Danube Video

    Datasets: 1552
    Pilots:
    Using SCENT Explore and SCENT Measure apps, volunteers competed collecting important information about Danube Delta parameters, such as as water level and surface flow velocity.
    Go to collection >
  • SCENT Danube Image

    Datasets: 6132
    Pilots:
    Using SCENT Explore and SCENT Measure apps, volunteers competed collecting important information about Danube Delta parameters, such as images of land-cover/land-use.
    Go to collection >
  • Sea ice and water classification in the Arctic, for CAATEX/INTAROS 2019 field experiment, using Sentinel-1 SAR. Extended Wide (EW) swath images at medium resolution (GRDM). Prior to classification, a thermal noise reduction algorithm is applied. A machine learning algorithm is then used to classify sea ice and open water in the noise corrected images. This data is made freely available by NERSC. User must display this citation in any publication or product using data: "These data were produced with support from the Horizon 2020 NextGEOSS project (Grant Agreement No 730329), and made freely available by NERSC (ref. Frode Monsen, Torill Hamre and Mohamed Babiker at NERSC)."
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  • Sea ice and water classification in the Arctic, for INTAROS 2018 field experiment, using Sentinel-1 SAR. Extended Wide (EW) swath images at medium resolution (GRDM). Prior to classification, a thermal noise reduction algorithm is applied. A machine learning algorithm is then used to classify sea ice and open water in the noise corrected images. This data is made freely available by NERSC. User must display this citation in any publication or product using data: "These data were produced with support from the Horizon 2020 NextGEOSS project (Grant Agreement No 730329), and made freely available by NERSC (ref. Frode Monsen, Torill Hamre and Mohamed Babiker at NERSC)."
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  • Sea ice and water classification in the Arctic using Sentinel-1. These datasets have been generated with support from the European Commission in the Horizon 2020 NextGEOSS project (grant agreement No 730329). The algorithms used for Sentinel-1 SAR noise removal and sea ice classification have been developed in the Research Council of Norway project SONARC (Project No 243608) and the Horizon 2020 SPICES project (grant agreement No 640161).
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  • Landsat-8 T1

    Datasets: 10138
    Pilots:
    Contains the highest quality Level-1 Precision Terrain (L1TP) data considered suitable for time-series analysis. The georegistration is consistent and within prescribed tolerances [<12m root mean square error (RMSE)].
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  • Landsat-8 T2

    Datasets: 34
    Pilots:
    Contains L1TP scenes not meeting Tier 1 criteria and all Systematic Terrain (L1GT) and Systematic (L1GS) scenes. Users interested in Tier 2 scenes can evaluate the L1TP RMSE and other properties to determine suitability for use in their applications and studies.
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  • Landsat-8 RT

    Datasets: 8941
    Pilots:
    Contains newly acquired Landsat 8 scenes, which require a period of evaluation and calibration adjustment after acquisition but are processed immediately based on preliminary calibration coefficients, assigned to the temporary RT Tier, and made available for download. When definitive calibration information becomes available, these scenes are reprocessed, assigned to the appropriate Tier 1 or Tier 2 category, and removed from the RT Tier (Temporary designation).
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  • CGS S1 GRD L1

    Datasets: 0
    Pilots:
    Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and projected to ground range using the Earth ellipsoid model WGS84. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range (but can be different for each IW/EW sub-swath).
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  • CGS S1 GRD SIGMA0 L1

    Datasets: 0
    Pilots:
    TBD
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  • CGS S1 SLC L1

    Datasets: 9
    Pilots:
    S1 datasets processed on VITO premises. Level-1 Single Look Complex (SLC) products consist of focused SAR data, geo-referenced using orbit and attitude data from the satellite, and provided in slant-range geometry. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track.
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  • MYD14A2 data are 8-day fire-mask composites at 1-kilometer resolution provided as a gridded level-3 product in the Sinusoidal projection. Science Data Sets include the fire-mask and algorithm quality assurance.
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  • MOD14A2 data are 8-day fire-mask composites at 1-kilometer resolution provided as a gridded level-3 product in the Sinusoidal projection. Science Data Sets include the fire-mask and algorithm quality assurance.
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  • The Level-4 MODIS global Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is a 8-day 500-meter resolution product on a Sinusoidal grid. Science Data Sets provided in the MOD15A2H include LAI, FPAR, a quality rating, and standard deviation for each variable.
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  • The Level-4 MODIS global Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is a 8-day 500-meter resolution product on a Sinusoidal grid. Science Data Sets provided in the MYD15A2H include LAI, FPAR, a quality rating, and standard deviation for each variable.
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  • The Terra/MODIS Gross Primary Productivity (GPP) product MOD17A2H is a cumulative composite of GPP values based on the radiation-use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. MOD17A2H is an 8-day composite at 1-km spatial resolution delivered as a gridded Level-4 product in Sinusoidal projection.
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  • The Terra/MODIS Gross Primary Productivity (GPP) product MOD17A3H is a cumulative composite of GPP values based on the radiation-use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. MOD17A3H is an annual composite at 500m spatial resolution delivered as a gridded Level-4 product in Sinusoidal projection.
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  • Global MOD13A2 data are provided every 16 days at 1-kilometer spatial resolution as a gridded level-3 product in the Sinusoidal projection. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes.
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  • Global MOD13A1 data are provided every 16 days at 500-meter spatial resolution as a gridded level-3 product in the Sinusoidal projection. Cloud-free global coverage is achieved by replacing clouds with the historical MODIS time series climatology record. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes.
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  • Global MOD13Q1 data are provided every 16 days at 250-meter spatial resolution as a gridded level-3 product in the Sinusoidal projection. Cloud-free global coverage is achieved by replacing clouds with the historical MODIS time series climatology record. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes.
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  • Global MYD13A2 data are provided every 16 days at 1-kilometer spatial resolution as a gridded level-3 product in the Sinusoidal projection. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes.
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  • Global MYD13A1 data are provided every 16 days at 500-meter spatial resolution as a gridded level-3 product in the Sinusoidal projection. Cloud-free global coverage is achieved by replacing clouds with the historical MODIS time series climatology record. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes.
    Go to collection >
  • Global MYD13Q1 data are provided every 16 days at 250-meter spatial resolution as a gridded level-3 product in the Sinusoidal projection. Cloud-free global coverage is achieved by replacing clouds with the historical MODIS time series climatology record. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes.
    Go to collection >
  • Average Flood Magnitude

    Datasets: 3134
    Pilots:
    Global merged daily and 4-day average flood magnitude datasets between 1997 and current. Highest sampling rate, global coverage. May have artifacts due to multi-sensor integration
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  • Average Flood Signal

    Datasets: 4181
    Pilots:
    Global merged daily and 4-day average flood signal datasets between 1997 and current. Highest sampling rate, global coverage. May have artifacts due to multi-sensor integration.
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  • EBAS NILU Data Archive

    Datasets: 500
    Pilots:
    Atmospheric composition data obtained at surface in situ stations associated to various international and national frameworks for long-term monitoring. Archived in EBAS database operated by NILU.
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  • Significant Wave Height Forecast

    Datasets: 41
    Pilots:
    Hourly Forecast of the significant wave height for mainland Portugal, updated to the next 42 hours
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  • Precipitation Forecast From AROME

    Datasets: 2
    Pilots:
    Precipitation forecast, for Portugal mainland, generated from AROME for the next 48hours. covering 36S-43N and 11.48W-5.5W
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  • Sea Wave Period Forecast

    Datasets: 16
    Pilots:
    Hourly Forecast of the wave period for mainland Portugal, updated to the next 42 hours
    Go to collection >
  • Sea Wave Direction Forecast

    Datasets: 41
    Pilots:
    Hourly Forecast of the wave direction for mainland Portugal, updated to the next 42 hours
    Go to collection >
  • Air Surface Temperature Forecast

    Datasets: 0
    Pilots:
    Surface Temperature forecast, for Portugal mainland, generated from AROME for the next 48hours. covering 36S-43N and 11.48W-5.5W
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  • Sea Surface Temperature Forecast

    Datasets: 0
    Pilots:
    Sea Surface Temperature forecast, for Portugal mainland and islands, generated from the European Centre data - European Centre for Medium-Range Weather Forecasts (ECMWF), for the next 48hours
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  • Mean Sea Level Pressure Forecast

    Datasets: 0
    Pilots:
    Mean sea level pressure forecast for Portugal mainland and islands regions, generated from data of the European Centre for Medium-Range Weather Forecasts (ECMWF), for the next 48hours
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  • Sea Surface Wind Forecast

    Datasets: 0
    Pilots:
    Sea Surface wind forecast for Portugal mainland and islands regions, generated from data of the European Centre for Medium-Range Weather Forecasts (ECMWF), for the next 48hours. This service is generated from the vector component u and v (m/s) of the European Centre data
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  • Surface Forecast From HYCOM

    Datasets: 20
    Pilots:
    Surface currents forecast from HYCOM with 1' horizontal resolution (Iberia-Biscay Region). Daily files with hourly timesteps
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  • Tidal Data

    Datasets: 93
    Pilots:
    Sea level from tide gauges: Viana do Castelo; Leixões; Nazaré; Peniche and Sines. A file is produced daily for each tide gauge with the last 24 hours with minutely timestep
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  • Data From Multiparametric Buoys

    Datasets: 77
    Pilots:
    Temperature, Wave and Meteo parameters from multiparametric buoys: Leixoes; Nazare and Faro. A file is produced daily for each buoy with the last 48 hours with hourly timestep
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  • Significant Wave Height forecast from SMARTWAVE with 0.5'' horizontal resolution (Port areas: Viana do Castelo; Povoa do Varzim; Aveiro; Figueira da Foz
    Go to collection >
  • Wave parameters forecast from SWAN with 2'' to 6'' horizontal resolution (14 areas in Portugal Mainland). A daily file is produced with 6-day forecast with a 6-hourly timestep
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  • Surface Currents From HF Radar

    Datasets: 40
    Pilots:
    Surface currents data from HF Radar system: Cascais-Cape Espichel. A daily file is produced by IH with hourly data for the last 24 hours, interpolated to a regular grid with a 1.5 km resolution
    Go to collection >
  • Cloudiness Forecast From AROME

    Datasets: 0
    Pilots:
    Cloudiness forecast, for Portugal mainland, generated from AROME for the next 48hours. covering 36S-43N and 11.48W-5.5W
    Go to collection >
  • Surface Wind Forecast From AROME

    Datasets: 0
    Pilots:
    Surface wind forecast, for Portugal mainland, generated from AROME for the next 48hours. covering 36S-43N and 11.48W-5.5W
    Go to collection >
  • Map of LoS Vector

    Datasets: 0
    Pilots:
    Map of the Line of Sight vector (North East Up coefficients).
    Go to collection >
  • Interferogram APS Global Model

    Datasets: 0
    Pilots:
    Interferometric Atmospheric Phase Screen derived from Global Atmospheric Model.
    Go to collection >
  • LoS Displacement Timeseries

    Datasets: 0
    Pilots:
    Displacement Time Series along the sensor Line of Sight.
    Go to collection >
  • Spatial Coherence

    Datasets: 0
    Pilots:
    Interferometric SAR Spatial coherence.
    Go to collection >
  • Wrapped Interferogram

    Datasets: 14
    Pilots:
    Differential SAR Interferogram (Phase and Amplitude) in the wrapped domain.
    Go to collection >
  • Unwrapped Interferogram

    Datasets: 0
    Pilots:
    Unwrapped Differential SAR Interferogram (Phase and Amplitude).
    Go to collection >
  • NextGEOSS Sentinel-2 NDVI

    Datasets: 0
    Pilots:
    The SENTINEL-2 Normalized Difference Vegetation Index (NDVI) is a proxy to quantify the vegetation amount. It is defined as NDVI=(NIR-Red)/(NIR+Red) where NIR corresponds to the reflectance in the near infrared band , and Red to the reflectance in the red band. It is closely related to FAPAR and is little scale dependent.
    Go to collection >
  • NextGEOSS Sentinel-2 LAI

    Datasets: 0
    Pilots:
    LAI was defined by CEOS as half the developed area of the convex hull wrapping the green canopy elements per unit horizontal ground. This definition allows accounting for elements which are not flat such as needles or stems. LAI is strongly non linearly related to reflectance. Therefore, its estimation from remote sensing observations will be scale dependent over heterogeneous landscapes. When observing a canopy made of different layers of vegetation, it is therefore mandatory to consider all the green layers. This is particularly important for forest canopies where the understory may represent a very significant contribution to the total canopy LAI. The derived LAI corresponds therefore to the total green LAI, including the contribution of the green elements of the understory. The resulting NEXTGEOSS SENTNEL 2 LAI products are relatively consistent with the actual LAI for low LAI values and 'non-forest' surfaces; while for forests, particularly for needle leaf types, significant departures with the true LAI are expected.
    Go to collection >
  • NextGEOSS Sentinel-2 FCOVER

    Datasets: 164
    Pilots:
    Fraction of vegetation Cover (FCOVER) corresponds to the gap fraction for nadir direction. It is used to separate vegetation and soil in energy balance processes, including temperature and evapotranspiration. It is computed from the leaf area index and other canopy structural variables and does not depend on variables such as the geometry of illumination as compared to FAPAR. For this reason, it is a very good candidate for the replacement of classical vegetation indices for the monitoring of green vegetation. Because of the linear relationship with radiometric signal, FCOVER will be only marginally scale dependent. Note that similarly to LAI and FAPAR, only the green elements will be considered, either belonging both to the overstory and understory.
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  • NextGEOSS Sentinel-2 FAPAR

    Datasets: 1
    Pilots:
    FAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy.The FAPAR value results directly from the radiative transfer model in the canopy which is computed instantaneously. It depends on canopy structure, vegetation element optical properties and illumination conditions. FAPAR is very useful as input to a number of primary productivity models which run at the daily time step. Consequently, the product definition should correspond to the daily integrated FAPAR value that can be approached by computation of the clear sky daily integrated FAPAR values as well as the FAPAR value computed for diffuse conditions. The SENTINEL 2 FAPAR product corresponds to the instantaneous black-sky around 10:15 which is a close approximation of the daily integrated black-sky FAPAR value. The FAPAR refers only to the green parts of the canopy.
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  • DEIMOS-2 Pan-sharpened Level-1C

    Datasets: 18
    Pilots:
    DEIMOS-2 PSH-L1C is a four-band image, resulting from adding the information of each multispectral band to the panchromatic band. The products are calibrated and radiometrically corrected, manually orthorectified and resampled to a map grid.
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  • DEIMOS-2 Pan-sharpened Level-1B

    Datasets: 18
    Pilots:
    DEIMOS-2 PSH-L1B is a four-band image, resulting from adding the information of each multispectral band to the panchromatic band. The products are calibrated and radiometrically corrected, but not resampled. The geometric information is contained in a rational polynomial.
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  • DEIMOS-2 PM4-L1B is a five-band image containing the panchromatic and multispectral products packaged together, with band co-registration. The products are calibrated and radiometrically corrected, but not resampled. The geometric information is contained in a rational polynomial.
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  • Biodiversity auxiliary datasets
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  • eMODIS Remote Sensing Phenology

    Datasets: 0
    Pilots:
    The Remote Sensing Phenology (RSP) collection is a set of nine annual phenological metrics for the conterminous United States. Researchers at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center utilize data gathered by satellite sensors to track seasonal changes in vegetation. These datasets are provided by the sensor Moderate Resolution Imaging Spectroradiometer (MODIS) carried aboard National Aeronautics and Space Administration (NASA) Terra and Aqua satellites.
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  • AVHRR Remote Sensing Phenology

    Datasets: 0
    Pilots:
    The Remote Sensing Phenology (RSP) collection is a set of nine annual phenological metrics for the conterminous United States. Researchers at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center utilize data gathered by satellite sensors to track seasonal changes in vegetation. These datasets are provided by the sensor Advanced Very High Resolution Radiometer (AVHRR) from National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites.
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  • Flood Hazard Europe/Global

    Datasets: 13
    Pilots:
    The map depicts flood prone areas in Europe for flood events with 10-year return period. Cell values indicate water depth (in m). The map can be used to assess flood exposure and risk of population and assets. NOTE: this dataset is based on JRC elaborations and is not an official flood hazard map.
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  • European Distribution of the specie Fagus sylvatica for the years 2020, 2050 and 2080, based on different models such as ENS, CCCMA, CSIRO, HADCM3 (Habitat Suitability future).
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  • The GLASS Leaf Area Index (LAI) product, a global LAI product with long time series, derived from MODIS land surface reflectance (MOD09A1), and released by the Center for Global Change Data Processing and Analysis of Beijing Normal University. The GLASS LAI product has a temporal resolution of 8 days and is available from 1982 to 2015.
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  • The GLASS Leaf Area Index (LAI) product, a global LAI product with long time series, generated from AVHRR reflectance, and released by the Center for Global Change Data Processing and Analysis of Beijing Normal University. The GLASS LAI product has a temporal resolution of 8 days and is available from 1982 to 2015.
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  • Open Land Use Map

    Datasets: 118719
    Pilots:
    The main idea is to put Open Land Use dataset and also its metadata (from micka.lesprojekt.cz) in RDF format into Virtuoso and explore SPARQL queries that would combine data with metadata. For instance: Show me the datasets (municipalities) where more than 50% of the area is covered by residential areas and data were collected not later than 5 years ago? This query combines some metadata (such as year of data collection and municipality which data covers) with data itself (object features with residential land use). For automatization of such queries it is necessary to have both data and metadata available for querying and interconnected. In ideal case the output will be endpoint where it will be possible to query both OLU data and metadata, some model queries and possibly some visualization of query results.
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  • Proba-V Level-2A (100M)

    Datasets: 0
    Pilots:
    PROBA-V Level2A - 100M segments contain the Level 1C (P product) data projected on a uniform 100m grid.
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  • Proba-V S5-TOC NDVI (100M)

    Datasets: 0
    Pilots:
    Synthesis products with Top of Atmosphere (TOA) reflectances composited over defined time frame of 5 days for 100m of spatial resolution.
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  • Proba-V S5-TOA (100M)

    Datasets: 0
    Pilots:
    Synthesis products with Top of Atmosphere (TOA) reflectances composited over defined time frame of 5 days for 100m of spatial resolution.
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  • Proba-V S5-TOC (100M)

    Datasets: 0
    Pilots:
    Synthesis products with Top of Canopy (TOC) reflectances composited over defined time frame of 5 days for 100m of spatial resolution.
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  • Proba-V S1-TOC NDVI (100M)

    Datasets: 0
    Pilots:
    Synthesis products with Top of Atmosphere (TOA) reflectances composited over defined time frame of 1 day for 100m of spatial resolution containing only Normalized Difference Vegetation Index (NDVI)
    Go to collection >
  • Proba-V S1-TOA (100M)

    Datasets: 0
    Pilots:
    Synthesis products with Top of Atmosphere (TOA) reflectances composited over defined time frame of 1 day for 100m of spatial resolution.
    Go to collection >
  • Proba-V S1-TOC (100M)

    Datasets: 0
    Pilots:
    Synthesis products with Top of Canopy (TOC) reflectances composited over defined time frame of 1 day for 100m of spatial resolution.
    Go to collection >
  • Proba-V S10-TOC NDVI (333M)

    Datasets: 0
    Pilots:
    Synthesis products with Top of Canopy (TOC) reflectances composited over defined time frame of 10 days for 333m of spatial resolution, containing only Normalized Difference Vegetation Index (NDVI).
    Go to collection >
  • PROBAV_S10-TOC_333M_V001

    Datasets: 0
    Pilots:
    Synthesis products with Top of Canopy (TOC) reflectances composited over defined time frame of 10 days for 333m of spatial resolution.
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  • Proba-V S1-TOA (333M)

    Datasets: 0
    Pilots:
    Synthesis products with Top of Atmosphere (TOA) reflectances composited over defined time frame of 1 day for 333m of spatial resolution.
    Go to collection >
  • Proba-V S1-TOC (333M)

    Datasets: 0
    Pilots:
    Synthesis products with Top of Canopy (TOC) reflectances composited over defined time frame of 1 day for 333m of spatial resolution.
    Go to collection >
  • Proba-V Level-1C

    Datasets: 683
    Pilots:
    Raw data which is geo-located and radiometrically calibrated to Top Of Atmosphere (TOA) reflectance values.
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  • Proba-V Level-2A (1KM)

    Datasets: 683
    Pilots:
    PROBA-V Level2A - 1KM segments contain the Level 1C (P product) data projected on a uniform 1Km grid.
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  • Proba-V S10-TOC NDVI (1KM)

    Datasets: 355
    Pilots:
    Synthesis products with Top of Canopy (TOC) reflectances composited over defined time frame of 10 days for 1Km of spatial resolution, containing only Normalized Difference Vegetation Index (NDVI).
    Go to collection >
  • Proba-V S10-TOC (1KM)

    Datasets: 355
    Pilots:
    Synthesis products with Top of Canopy (TOC) reflectances composited over defined time frame of 10 days for 1Km of spatial resolution.
    Go to collection >
  • Proba-V S1-TOA (1KM)

    Datasets: 2675
    Pilots:
    Synthesis products with Top of Atmosphere (TOA) reflectances composited over defined time frame of 1 day for 1Km of spatial resolution.
    Go to collection >
  • Proba-V S1-TOC (1KM)

    Datasets: 2675
    Pilots:
    Synthesis products with Top of Canopy (TOC) reflectances composited over defined time frame of 1 day for 1Km of spatial resolution.
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  • Daily global concentrations of atmospheric sulphur dioxide mass. These data are crucial for monitoring atmospheric pollutants to keep a check on the health of the Earth's atmosphere.
    Go to collection >
  • Daily global concentrations of atmospheric sulphur dioxide. These data are crucial for monitoring atmospheric pollutants to keep a check on the health of the Earth's atmosphere.
    Go to collection >
  • Daily global concentrations of tropospheric nitrogen dioxide. These data are crucial for monitoring atmospheric pollutants to keep a check on the health of the Earth's atmosphere.
    Go to collection >
  • Daily global concentrations of atmospheric nitrogen dioxide. These data are crucial for monitoring atmospheric pollutants to keep a check on the health of the Earth's atmosphere.
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  • MetOP-A GOME-2 Ozone (O3)

    Datasets: 213
    Pilots:
    Daily global concentrations of atmospheric ozone. These data are crucial for monitoring atmospheric pollutants to keep a check on the health of the Earth's atmosphere.
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  • This product is a 6 hourly NRT L4 global total velocity field at 0m and 15m. It consists of the zonal and meridional velocity at a 6h frequency and at 1/4 degree regular grid produced on a daily basis. These total velocity fields are obtained by combining CMEMS NRT satellite Geostrophic Surface Currents and modelled Ekman current at the surface and 15m depth (using ECMWF NRT wind).
    Go to collection >
  • Daily global ocean analysis and forecast system at 1/12 degree providing 10 days of 3D global ocean forecasts. These datasets include hourly mean surface fields for sea level height, temperature and currents (eastward sea water velocity, northward sea water velocity.
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  • Daily products processed by the DUACS multimission altimeter data processing system. The geostrophic currents are derived from sla (geostrophic velocities anomalies, ugosa and vgosa variables) and from adt (absolute geostrophic velicities, ugos and vgos variables
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  • Daily Arctic Ocean physics analysis to provide 10 days of forecast of the 3D physical ocean, including temperature, salinity, sea ice concentration, sea ice thickness, sea ice velocity and sea ice type.
    Go to collection >
  • Daily sea ice concentration at 10km resolution in polar stereographic and EASE grid projections covering the Southern Hemisphere.
    Go to collection >
  • Daily sea ice concentration at 10km resolution in polar stereographic and EASE grid projections covering the Northern Hemisphere.
    Go to collection >
  • Daily analysis of sea surface temperature (SST), based on measurements from several satellite and in situ SST datasets, for the global ocean and some lakes.
    Go to collection >
  • Sentinel-3 Synergy Level-2 V10

    Datasets: 45
    Pilots:
    1 km VEGETATION-Like product (~VGT-S10) 10 day synthesis surface reflectance and NDVI.
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  • Sentinel-3 Synergy Level-2 VG1

    Datasets: 593
    Pilots:
    1 km VEGETATION-Like product (~VGT-S1) 1 day synthesis surface reflectance and NDVI.
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  • Sentinel-3 Synergy Level-2 VGK

    Datasets: 0
    Pilots:
    Surface reflectance over Land– used as input for VG-S product.
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  • Sentinel-3 Synergy Level-2 VGP

    Datasets: 613
    Pilots:
    1 km VEGETATION-Like product (~VGT-P) - TOA Reflectance.
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  • Sentinel-3 Synergy Level-2 SYN

    Datasets: 5328
    Pilots:
    Surface Reflectance and Aerosol parameters over Land.
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  • Sentinel-3 Synergy Level-1B

    Datasets: 0
    Pilots:
    Correspondence and collocation grids between OLCI/SLSTR L1b product and SYN Level 2 grid.
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  • Sentinel-5P RPRO Level-2

    Datasets: 0
    Pilots:
    The Sentinel-5 Precursor mission is dedicated to monitoring our atmosphere, using the TROPOspheric Monitoring Instrument (TROPOMI). The The Level-2 products are geolocated total columns of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide, formaldehyde and methane, geolocated tropospheric columns of ozone, geolocated vertical profiles of ozone, geolocated cloud and aerosol information (e.g. absorbing aerosol index and aerosol layer height). For reprocessing activities (RPRO) there are no time constraints. Reprocessing of Sentinel-5 Precursor products will be performed when major product upgrades are considered necessary.
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  • Sentinel-5P NRTI Level-2

    Datasets: 125
    Pilots:
    The Sentinel-5 Precursor mission is dedicated to monitoring our atmosphere, using the TROPOspheric Monitoring Instrument (TROPOMI). The The Level-2 products are geolocated total columns of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide, formaldehyde and methane, geolocated tropospheric columns of ozone, geolocated vertical profiles of ozone, geolocated cloud and aerosol information (e.g. absorbing aerosol index and aerosol layer height). For near real time processing (NRT) the availability of products must be within 3 hours after sensing.
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  • Sentinel-5P OFFL Level-2

    Datasets: 648
    Pilots:
    The Sentinel-5 Precursor mission is dedicated to monitoring our atmosphere, using the TROPOspheric Monitoring Instrument (TROPOMI). The The Level-2 products are geolocated total columns of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide, formaldehyde and methane, geolocated tropospheric columns of ozone, geolocated vertical profiles of ozone, geolocated cloud and aerosol information (e.g. absorbing aerosol index and aerosol layer height). For offline processing (OFFL), the data availability depends on the product.
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  • Sentinel-5P OFFL Level-1B

    Datasets: 0
    Pilots:
    The Sentinel-5 Precursor mission is dedicated to monitoring our atmosphere, using the TROPOspheric Monitoring Instrument (TROPOMI). The Level-1B products are geo-located and radiometrically corrected top of the atmosphere Earth radiances in all spectral bands, as well as solar irradiances. For offline processing (OFFL), the data availability depends on the product.
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  • SENTINEL-3 SLSTR Level-2 LST product provides land surface parameters generated on the wide 1 km measurement grid.
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  • SENTINEL-3 SLSTR Level-1 product provides radiances and brightness temperatures for each pixel in a regular image grid, each view and each SLSTR channel, plus annotations data associated with SLSTR pixels.
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  • SENTINEL-3 OLCI level-2 land product provides land and atmospheric geophysical parameters computed for reduced Resolution.
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  • SENTINEL-3 OLCI level-2 land product provides land and atmospheric geophysical parameters computed for full Resolution.
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  • SENTINEL-3 OLCI Level-1 product provides radiances for each pixel in the instrument grid, each view and each OLCI channel, plus annotation data associated to OLCI pixels. The output of this product is during EO processing mode for Reduced Resolution.
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  • Sentinel-3 OLCI Level-1 Full Resolution

    Datasets: 12800
    Pilots:
    SENTINEL-3 OLCI Level-1 product provides radiances for each pixel in the instrument grid, each view and each OLCI channel, plus annotation data associated to OLCI pixels. The output of this product is during EO processing mode for Full Resolution.
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  • Sentinel-3 SRAL Level-2 Water

    Datasets: 0
    Pilots:
    SENTINEL-3 is the first Earth Observation Altimetry mission to provide 100% SAR altimetry coverage where LRM is maintained as a back-up operating mode. This is a product of Level 2 processing and geographical coverage over water.
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  • Sentinel-3 SRAL Level-2 Land

    Datasets: 9474
    Pilots:
    SENTINEL-3 is the first Earth Observation Altimetry mission to provide 100% SAR altimetry coverage where LRM is maintained as a back-up operating mode. This is a product of Level 2 processing and geographical coverage over land.
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  • Sentinel-3 SRAL Level-1 SRA

    Datasets: 20616
    Pilots:
    SENTINEL-3 is the first Earth Observation Altimetry mission to provide 100% SAR altimetry coverage where LRM is maintained as a back-up operating mode. This is a Level 1 SRA product.
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  • SENTINEL-3 is the first Earth Observation Altimetry mission to provide 100% SAR altimetry coverage where LRM is maintained as a back-up operating mode. This is a Level 1 Calibration product.
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  • Sentinel-2 Level-2A

    Datasets: 210609
    Pilots:
    The Sentinel-2 Level-2A products are Bottom-of-atmosphere reflectances in cartographic geometry (prototype product). These products are generated using Sentinel-2 Toolbox and the data volume is 600MB for each 100x100 km².
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  • Sentinel-2 Level-1C

    Datasets: 926613
    Pilots:
    The Sentinel-2 Level-1C products are Top-of-atmosphere reflectances in cartographic geometry. These products are systematically generated and the data volume is 500MB for each 100x100 km².
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  • Sentinel-1 Level-0 (RAW)

    Datasets: 0
    Pilots:
    The Sentinel-1 Level-0 products consist of the sequence of Flexible Dynamic Block Adaptive Quantization (FDBAQ) compressed unfocused SAR raw data. For the data to be usable, it will need to be decompressed and processed using focusing software.
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  • Sentinel-1 Level-2 (OCN)

    Datasets: 41100
    Pilots:
    The Sentinel-1 Level-2 OCN products include components for Ocean Swell spectra (OSW) providing continuity with ERS and ASAR WV and two new components: Ocean Wind Fields (OWI) and Surface Radial Velocities (RVL). The OSW is a two-dimensional ocean surface swell spectrum and includes an estimate of the wind speed and direction per swell spectrum. The OWI is a ground range gridded estimate of the surface wind speed and direction at 10 m above the surface derived from internally generated Level-1 GRD images of SM, IW or EW modes. The RVL is a ground range gridded difference between the measured Level-2 Doppler grid and the Level-1 calculated geometrical Doppler.
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  • Sentinel-1 Level-1 (GRD)

    Datasets: 91333
    Pilots:
    The Sentinel-1 Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model. Phase information is lost. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle at the cost of reduced geometric resolution.
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  • Sentinel-1 Level-1 (SLC)

    Datasets: 70628
    Pilots:
    The Sentinel-1 Level-1 Single Look Complex (SLC) products consist of focused SAR data geo-referenced using orbit and attitude data from the satellite and provided in zero-Doppler slant-range geometry. The products include a single look in each dimension using the full TX signal bandwidth and consist of complex samples preserving the phase information.
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