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    Northern Italy Land Surface Temperature 1km daily Celsius gap-filled dataset LST daily avg, 2010 - 2018, reconstructed format: GRASS GIS raster format ZLIB compressed stored as a GRASS GIS 7 location/mapset Projection: EU LAEA (EPSG:3035) Reference: Metz, M.; Andreo, V.; Neteler, M. A New Fully Gap-Free Time Series of Land Surface Temperature from MODIS LST Data. Remote Sens. 2017, 9, 1333. https://doi.org/10.3390/rs9121333 Data provided by: mundialis GmbH & Co. KG Koelnstrasse 99 53111 Bonn, Germany https://www.mundialis.de

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    Overview: The Essential Climate Variables for assessment of climate variability from 1979 to present dataset contains a selection of climatologies, monthly anomalies and monthly mean fields of Essential Climate Variables (ECVs) suitable for monitoring and assessment of climate variability and change. Selection criteria are based on accuracy and temporal consistency on monthly to decadal time scales. The ECV data products in this set have been estimated from climate reanalyses ERA-Interim and ERA5, and, depending on the source, may have been adjusted to account for biases and other known deficiencies. Data sources and adjustment methods used are described in the Product User Guide, as are various particulars such as the baseline periods used to calculate monthly climatologies and the corresponding anomalies. Surface air temperature: This variable is the temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Spatial resolution: 0:15:00 (0.25°) Temporal resolution: monthly Temporal extent: 1979 - present Data unit: °C * 10 Data type: Int16 CRS as EPSG: EPSG:4326 Processing time delay: one month

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    Overview: The Essential Climate Variables for assessment of climate variability from 1979 to present dataset contains a selection of climatologies, monthly anomalies and monthly mean fields of Essential Climate Variables (ECVs) suitable for monitoring and assessment of climate variability and change. Selection criteria are based on accuracy and temporal consistency on monthly to decadal time scales. The ECV data products in this set have been estimated from climate reanalyses ERA-Interim and ERA5, and, depending on the source, may have been adjusted to account for biases and other known deficiencies. Data sources and adjustment methods used are described in the Product User Guide, as are various particulars such as the baseline periods used to calculate monthly climatologies and the corresponding anomalies. Sum of monthly precipitation: This variable is the accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. Spatial resolution: 0:15:00 (0.25°) Temporal resolution: monthly Temporal extent: 1979 - present Data unit: mm * 10 Data type: UInt32 CRS as EPSG: EPSG:4326 Processing time delay: one month

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    Overview: The Essential Climate Variables for assessment of climate variability from 1979 to present dataset contains a selection of climatologies, monthly anomalies and monthly mean fields of Essential Climate Variables (ECVs) suitable for monitoring and assessment of climate variability and change. Selection criteria are based on accuracy and temporal consistency on monthly to decadal time scales. The ECV data products in this set have been estimated from climate reanalyses ERA-Interim and ERA5, and, depending on the source, may have been adjusted to account for biases and other known deficiencies. Data sources and adjustment methods used are described in the Product User Guide, as are various particulars such as the baseline periods used to calculate monthly climatologies and the corresponding anomalies. Surface air relative humidity: The ratio of the partial pressure of water vapour to the equilibrium vapour pressure of water at the same temperature near the surface. Spatial resolution: 0:15:00 (0.25°) Temporal resolution: monthly Temporal extent: 1979 - present Data unit: percent * 10 Data type: UInt8 CRS as EPSG: EPSG:4326 Processing time delay: one month

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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

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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 MOD11C1 and MYD11C1 (spatial resolution: 3 arc-min, i.e. approximately 5.6 km at the equator). 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 monthly average, minimum and maximum LST maps. [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 available here for download are the reconstructed global MODIS LST products MOD11C1/MYD11C1 at a spatial resolution of 3 arc-min (approximately 5.6 km at the equator; see https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table), aggregated to monthly data. The data are provided in GeoTIFF format. The Coordinate Reference System (CRS) is identical to the MOD11C1/MYD11C1 product as provided by NASA. In WKT as reported by GDAL: GEOGCS["Unknown datum based upon the Clarke 1866 ellipsoid", DATUM["Not specified (based on Clarke 1866 spheroid)", SPHEROID["Clarke 1866",6378206.4,294.9786982138982, AUTHORITY["EPSG","7008"]]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]] 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. File name abbreviations: avg = average of daily averages min = minimum of daily minima max = maximum of daily maxima Meaning of pixel values: The pixel values are coded in degree Celsius * 100 (hence, to obtain °C divide the pixel values by 100.0).

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    The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Digital Elevation Model (DEM) version 1 (NASADEM_SC) dataset, which provides global slope and curvature elevation data at 1 arc second spacing. NASADEM data products were derived from original telemetry data from the Shuttle Radar Topography Mission (SRTM), a collaboration between NASA and the National Geospatial-Intelligence Agency (NGA), as well as participation from the German and Italian space agencies. SRTM’s primary focus was to generate a near-global DEM of the Earth using radar interferometry. It was a primary component of the payload on space shuttle Endeavour during its STS-99 mission, which was launched on February 11, 2000, and flew for 11 days. In addition to Terra Advanced Spaceborne Thermal and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) Version 3 data, NASADEM also relied on Ice, Cloud, and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) ground control points of its lidar shots to improve surface elevation measurements that led to improved geolocation accuracy. Other reprocessing improvements include the conversion to geoid reference and the use of GDEMs and Advanced Land Observing Satellite Panchromatic Remote-sensing instrument for Stereo Mapping (PRISM) AW3D30 DEM, and interpolation for void filling. NASADEM are distributed in 1° by 1° tiles and consist of all land between 60° N and 56° S latitude. This accounts for about 80% of Earth’s total landmass. NASADEM_SC data product layers include slope, aspect angle, profile curvature, plan curvature, and an updated SRTM water body dataset (water mask). A low-resolution browse image showing slope is also available for each NASADEM_SC granule.

  • 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.

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    The Land Cover Map of Europe 2017 is a product resulting from the Phase 2 of the S2GLC project. The final map has been produced on the CREODIAS platform with algorithms and software developed by CBK PAN. Classification of over 15 000 Sentinel-2 images required high level of automation that was assured by the developed software. The legend of the resulting Land Cover Map of Europe 2017 consists of 13 land cover classes. The pixel size of the map equals 10 m, which corresponds to the highest spatial resolution of Sentinel-2 imagery. Its overall accuracy was estimated to be at the level of 86% using approximately 52 000 validation samples distributed across Europe. Related publication: https://doi.org/10.3390/rs12213523

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    Major hydrological basins and their sub-basins. This dataset divides the African continent according to its hydrological characteristics. The dataset consists of the following information:- numerical code and name of the major basin (MAJ_BAS and MAJ_NAME); - area of the major basin in square km (MAJ_AREA); - numerical code and name of the sub-basin (SUB_BAS and SUB_NAME); - area of the sub-basin in square km (SUB_AREA); - numerical code of the sub-basin towards which the sub-basin flows (TO_SUBBAS) (the codes -888 and -999 have been assigned respectively to internal sub-basins and to sub-basins draining into the sea)