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  • Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. With a novel method [1] we fully reconstructed the daily global MODIS LST products MOD11A1/MYD11A1 (spatial resolution: 1 km). For this, we combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. Here we provide a time series of these reconstructed LST data aggregated as daily LST maps at overpass time (approx: 01:30 am, 10:30am, 1:30pm 10:30pm). [1] Metz M., Andreo V., Neteler M. (2017): A new fully gap-free time series of Land Surface Temperature from MODIS LST data. Remote Sensing, 9(12):1333. DOI: http://dx.doi.org/10.3390/rs9121333 The data are provided in GeoTIFF format. The Coordinate Reference System (CRS) is identical to the MOD11A1/MYD11A1 product (Sinusoidal) as provided by NASA. In WKT as reported by GDAL: PROJCRS["unnamed", BASEGEOGCRS["Unknown datum based upon the custom spheroid", DATUM["Not specified (based on custom spheroid)", ELLIPSOID["Custom spheroid",6371007.181,0, LENGTHUNIT["metre",1, ID["EPSG",9001]]]], PRIMEM["Greenwich",0, ANGLEUNIT["degree",0.0174532925199433, ID["EPSG",9122]]]], CONVERSION["unnamed", METHOD["Sinusoidal"], PARAMETER["Longitude of natural origin",0, ANGLEUNIT["degree",0.0174532925199433], ID["EPSG",8802]], PARAMETER["False easting",0, LENGTHUNIT["Meter",1], ID["EPSG",8806]], PARAMETER["False northing",0, LENGTHUNIT["Meter",1], ID["EPSG",8807]]], CS[Cartesian,2], AXIS["easting",east, ORDER[1], LENGTHUNIT["Meter",1]], AXIS["northing",north, ORDER[2], LENGTHUNIT["Meter",1]]] Acknowledgments: We are grateful to the NASA Land Processes Distributed Active Archive Center (LP DAAC) for making the MODIS LST data available. The dataset is based on MODIS Collection V006. Meaning of pixel values: The pixel values are coded in Kelvin * 50 Data type: raster, UInt16 Spatial resolution: 926.62543314 m Spatial extent Sinusoidal (W, S, E, N): 0, 4447802.079066, 2223901.039533, 6671703.118599 Spatial extent in EPSG:4326 (W, S, E, N): 0, 40, 40, 60

  • The map shows the Al Zaatari refugee camp in Jordan. It is situated approx. 12 km from the Syrian border and in close proximity to the city of Al Mafraq (10 km). The camp was set up on July 28, 2012, to shelter refugees fleeing the conflict in Syria. The map shows general characteristics of the camp infrastructure, including camp extent, location of shelters, containers and facility buildings, road infrastructure and the runway area. For a more detailed view parts of the camp area are also shown in the zoom boxes. The vector data have been digitized on the basis of WorldView-2 satellite data (0.5 m spatial resolution) acquired on January 03, 2013. The results have not been validated in the field. WorldView-2 satellite data acquired on January 03, 2013, is used as backdrop. The products elaborated for this Rapid Mapping Activity are realised to the best of our ability, within a very short time frame, optimising the material available. All geographic information has limitations due to the scale, resolution, date and interpretation of the original source materials. No liability concerning the content or the use thereof is assumed by the producer. The ZKI crisis maps are constantly updated.

  • This product comprises yearly composites and temporal statistics of selected vegetation indices (VI) for all of Germany from 2015 to today in 10m resolution, which were calculated using the DLR TimeScan processor. VIs (EVI, HA56, NDRE, NDVI, NDWI, PSRI and REIP) were calculated from Sentinel-2 Level 2A data at 10m spatial resolution produced by means of the DLR-PACO processor. Yearly compositing and temporal statistics are based on all valid and cloud-free observations per vegetation index. Derived variables per index are: minimum (min), maximum (max), mean, standard-deviation (sd), average absolute difference between observations (masd) as well as the number of cloud-free observations (n-cloudfree) and the total number of observations (n-obs). This is a product of the AGRO-DE project (https://agro-de.info/).

  • When a natural disaster or disease outbreak occurs there is a rush to establish accurate health care location data that can be used to support people on the ground. This has been demonstrated by events such as the Haiti earthquake and the Ebola epidemic in West Africa. As a result valuable time is wasted establishing accurate and accessible baseline data. Healthsites.io establishes this data and the tools necessary to upload, manage and make the data easily accessible. Global scope The Global Healthsites Mapping Project is an initiative to create an online map of every health facility in the world and make the details of each location easily accessible. Open data collaboration Through collaborations with users, trusted partners and OpenStreetMap we will capture and validate the location and contact details of every facility and make this data freely available under an Open Data License (ODBL). Accessible We will make the data accessible over the Internet through an API and other formats such as GeoJSON, Shapefile, KML, CSV. Focus on health care location data Our design philosophy is the long term curation and validation of health care location data. The healthsites.io map will enable users to discover what healthcare facilities exist at any global location and the associated services and resources.

  • INSPIRE theme Oceanographic Geographical Features.

  • INSPIRE theme Geology. Provision of the sediment distribution of the seabed in the North and Baltic Sea.

  • INSPIRE theme Elevation (bathymetry). Provision of the topography of the seabed in the North and Baltic Sea.

  • This product comprises monthly composites and temporal statistics of selected vegetation indices (VI) for all of Germany from 2015 to today in 10m resolution, which were calculated using the DLR TimeScan processor. VIs (EVI, HA56, NDRE, NDVI, NDWI, PSRI and REIP) were calculated from Sentinel-2 Level 2A data at 10m spatial resolution produced by means of the DLR-PACO processor. Monthly compositing and temporal statistics are based on all valid observations per vegetation index. Derived variables per index are: minimum, maximum, mean and standard-deviation as well as the number of valid observations. Products are available in tiles according to the ESA Sentinel 2 granule grid (UTM). This is a product of AGRO-DE project (https://agro-de.info/).

  • INSPIRE theme Production and Industrial Facilities

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