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MOOD-H2020

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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

  • Base epoch 2015 from the Collection 3 of annual, global 100m land cover maps. Other available (consolidated) epochs: 2016 2017 2018 2019 Produced by the global component of the Copernicus Land Service, derived from PROBA-V satellite observations and ancillary datasets. The maps include: - a main discrete classification with 23 classes aligned with UN-FAO's Land Cover Classification System, - a set of versatile cover fractions: percentage (%) of ground cover for the 10 main classes - - a forest type layer quality layers on input data density Online map viewer: https://lcviewer.vito.be

  • This global accessibility map enumerates land-based travel time to the nearest densely-populated area for all areas between 85 degrees north and 60 degrees south for a nominal year 2015. Densely-populated areas are defined as contiguous areas with 1,500 or more inhabitants per square kilometer or a majority of built-up land cover types coincident with a population centre of at least 50,000 inhabitants. This map was produced through a collaboration between the University of Oxford Malaria Atlas Project (MAP), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands. The underlying datasets used to produce the map, include roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce a “friction surface”, a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel. Least-cost-path algorithms (running in Google Earth Engine and, for high-latitude areas, in R) were used in conjunction with this friction surface to calculate the time of travel from all locations to the nearest city (by travel time). Cities were determined using the high-density-cover product created by the Global Human Settlement Project. Each pixel in the resultant accessibility map thus represents the modeled shortest time from that location to a city. Full Citation D.J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. (2018). Nature. doi:10.1038/nature25181.

  • 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

  • The Earth Observations Group (EOG) is producing a version 1 suite of average radiance composite images using nighttime data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB). Prior to averaging, the DNB data is filtered to exclude data impacted by stray light, lightning, lunar illumination, and cloud-cover. Cloud-cover is determined using the VIIRS Cloud Mask product (VCM). In addition, data near the edges of the swath are not included in the composites (aggregation zones 29-32). Temporal averaging is done on a monthly and annual basis. The version 1 series of monthly composites has not been filtered to screen out lights from aurora, fires, boats, and other temporal lights. However, the annual composites have layers with additional separation, removing temporal lights and background (non-light) values. The version 1 products span the globe from 75N latitude to 65S. The products are produced in 15 arc-second geographic grids and are made available in geotiff format as a set of 6 tiles. The tiles are cut at the equator and each span 120 degrees of latitude. Each tile is actually a set of images containing average radiance values and numbers of available observations. In the monthly composites, there are many areas of the globe where it is impossible to get good quality data coverage for that month. This can be due to cloud-cover, especially in the tropical regions, or due to solar illumination, as happens toward the poles in their respective summer months. Therefore, it is imperative that users of these data utilize the cloud-free observations file and not assume a value of zero in the average radiance image means that no lights were observed. The version 1 monthly series is run globally using two different configurations. The first excludes any data impacted by stray light. The second includes these data if the radiance vales have undergone the stray-light correction procedure (Reference). These two configurations are denoted in the filenames as "vcm" and "vcmsl" respectively. The "vcmsl" version, that includes the stray-light corrected data, will have more data coverage toward the poles, but will be of reduced quality. It is up to the users to determine which set is best for their applications. The annual versions are only made with the “vcm” version, excluding any data impacted by stray light. Filenaming convention: The version 1 composite products have 7 filename fields that are separated by an underscore "_". Internal to each field there can be an additional dash separator "-". These fields are followed by a filename extension. The fields are described below using this example filename: SVDNB_npp_20140501-20140531_global_vcmcfg_v10_c201502061154.avg_rade9 Field 1: VIIRS SDR or Product that made the composite "SVDNB" Field 2: satellite name "npp" Field 3: date range "20140501-20140531" Field 4: ROI "global" Field 5: config shortname "vcmcfg" Field 6: version "v10" is version 1.0 Field 7: creation date/time Extension: avg_rade9 The annual products can have other values for the config shortname (Field 5). They are: "vcm-orm" (VIIRS Cloud Mask - Outlier Removed) This product contains cloud-free average radiance values that have undergone an outlier removal process to filter out fires and other ephemeral lights. "vcm-orm-ntl" (VIIRS Cloud Mask - Outlier Removed - Nighttime Lights) This product contains the "vcm-orm" average, with background (non-lights) set to zero. "vcm-ntl" (VIIRS Cloud Mask - Nighttime Lights) This product contains the "vcm" average, with background (non-lights) set to zero. Data types/formats: To reach the widest community of users, files are delivered in compressed tarballs, each containing a set of 2 geotiffs. Files with extensions "avg_rade9" contain floating point radiance values with units in nanoWatts/cm2/sr. Note that the original DNB radiance values have been multiplied by 1E9. This was done to alleviate issues some software packages were having with the very small numbers in the original units. Files with extension "cf_cvg" are integer counts of the number of cloud-free coverages, or observations, that went in to constructing the average radiance image. Files with extension “cvg” are integer counts of the number of coverages or total observations available (regardless of cloud-cover). Credit: When using the data please credit the product generation to the Earth Observation Group, Payne Institute for Public Policy.

  • This data set contains the administrative boundaries at country level of the world and is based on the geometry from EBM v12.x. of EuroGeographics for the members of Eurogeographics, the Global Administrative Units Layer (2015) from FAO (UN) and geometry from the Turkish National Statistical Office. This dataset consists of 2 feature classes (regions, boundaries) per scale level and there are 6 different scale levels (100K, 1M, 3M, 10M, 20M and 60M). The public data set (1M - 60M) is available under the Download link indicated below. The full data set (100K - 60M) GISCO.CNTR_2016 is available via the EC restricted download link.

  • This data set contains the administrative boundaries at country level of the world and is based on the geometry from EBM v2020 (ReferenceDate 31.12.2018) of EuroGeographics for the members of Eurogeographics, and GISCO Countries 2020. This dataset consists of 2 feature classes (regions, boundaries) per scale level and there are 6 different scale levels (100K, 1M,3M, 10M, 20M and 60M). The public data set (1M - 60M) is available under the Download link indicated below. The full data set (100K - 60M) GISCO.CNTR_2020 is available via the EC restricted download link.

  • 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

  • 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 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.