https://data.mundialis.de:/geonetwork/srv/api/records
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mundialis GeoNetwork catalogue
type: Catalog
label: mundialis GeoNetwork catalogue (mundialis)
open search description: eng/portal.opensearch
uri lookup endpoint: eng/rdf.search?any=
publisher: mundialis
themes: GEMET - INSPIRE themes, version 1.0
themes: Continents, countries, sea regions of the world.
themes: INSPIRE priority data set
themes: Classification of spatial data services
themes: Service Type
language: eng
dataset: Copernicus Digital Elevation Model (DEM) for Europe at 1000 meter resolution (EU-LAEA) derived from Copernicus Global 30 meter DEM dataset
dataset: Copernicus Digital Elevation Model (DEM) for Europe at 30 meter resolution derived from Copernicus Global 30 meter dataset
dataset: MODIS NDVI and EVI, 16-day time series for Europe at 1 km resolution
dataset: ERA5-Land weekly: Air temperature at 2 meter above surface, weekly time series for Europe at 1 km resolution (2016 - 2020)
dataset: ERA5-Land weekly: Surface temperature, weekly time series for Europe at 1 km resolution (2016 - 2020)
dataset: CLMS Water Bodies monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
dataset: mundialis
dataset: GEMET - INSPIRE themes, version 1.0
dataset: Continents, countries, sea regions of the world.
dataset: INSPIRE priority data set
dataset: Classification of spatial data services
dataset: Service Type
dataset: Elevation
dataset: Land cover
dataset: Europe
dataset: Meteorological geographical features
dataset: Atmospheric conditions
dataset: Human health and safety
dataset: zenodo.6211883
dataset: https://zenodo.org/record/6573852/files/MODIS_NDVI_v01.zip
dataset: https://zenodo.org/record/6573852/files/MODIS_NDVI_EVI.sld
dataset: https://zenodo.org/record/6573852/files/MODIS_NDVI_EVI.qml
dataset: https://zenodo.org/record/6573852/files/MODIS_EVI_v01.zip
dataset: https://zenodo.org/record/6559122/files/ERA5_t2m_weekly_avg_v01.zip
dataset: https://zenodo.org/record/6559122/files/ERA5_t2m_weekly_max_v01.zip
dataset: https://zenodo.org/record/6559122/files/ERA5_t2m_weekly_min_v01.zip
dataset: https://zenodo.org/record/6559122/files/era5_land_temperature_color_table.sld
dataset: https://zenodo.org/record/6559122/files/era5_land_temperature_color_table.qml
dataset: https://zenodo.org/record/7427010
dataset: https://zenodo.org/record/6559068/files/ERA5_ts_weekly_avg_v01.zip
dataset: https://zenodo.org/record/6559068/files/ERA5_ts_weekly_max_v01.zip
dataset: https://zenodo.org/record/6559068/files/ERA5_ts_weekly_min_v01.zip
dataset: https://zenodo.org/record/6559068/files/era5_land_temperature_color_table.sld
dataset: https://zenodo.org/record/6559068/files/era5_land_temperature_color_table.qml
dataset: https://zenodo.org/record/7426990
dataset: https://zenodo.org/record/7434376
dataset: https://zenodo.org/record/7432432
dataset: https://zenodo.org/record/7434447
dataset: https://zenodo.org/record/7434396
dataset: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_PROBAV_V1.0.1_4326_monthly_2019.zip
dataset: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_PROBAV_V1.0.1_4326_monthly_2020.zip
dataset: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_S2_V2.0.1_4326_monthly_2021.zip
dataset: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_S2_V2.0.1_4326_monthly_2022.zip
dataset: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_S2_V2.0.1_4326_monthly_2023.zip
dataset: zenodo.14987385
dataset: mundialis GmbH & Co. KG

mundialis
type: Organization

GEMET - INSPIRE themes, version 1.0
type: ConceptScheme
uri: https://data.mundialis.de:/geonetwork/srv/eng/thesaurus.download

Continents, countries, sea regions of the world.
type: ConceptScheme
uri: https://data.mundialis.de:/geonetwork/srv/eng/thesaurus.download

INSPIRE priority data set
type: ConceptScheme
uri: https://data.mundialis.de:/geonetwork/srv/eng/thesaurus.download

Classification of spatial data services
type: ConceptScheme
uri: https://data.mundialis.de:/geonetwork/srv/eng/thesaurus.download

Service Type
type: ConceptScheme
uri: https://data.mundialis.de:/geonetwork/srv/eng/thesaurus.download

https://data.mundialis.de/geonetwork/srv/resources/records/e4c14079-1dd3-44e7-9b9b-a5f8593abfee
type: CatalogRecord
primary topic: zenodo.6211883
issued: 2023-12-15
modified: 2023-12-15
references: https://data.mundialis.de/geonetwork/srv/resources/records/e4c14079-1dd3-44e7-9b9b-a5f8593abfee/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/e4c14079-1dd3-44e7-9b9b-a5f8593abfee/formatters/xml
format: application/xml
  type: IMT
  label: XML

Copernicus Digital Elevation Model (DEM) for Europe at 1000 meter resolution (EU-LAEA) derived from Copernicus Global 30 meter DEM dataset
type: Dataset
abstract: The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters. The Copernicus DEM for Europe at 1000 meter resolution (EU-LAEA projection) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/). Processing steps: The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in https://gdal.org/drivers/raster/vrt.html format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized: gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt In order to reproject the data to EU-LAEA projection while reducing the spatial resolution to 1000 m, bilinear resampling was performed in GRASS GIS (using r.proj) and the pixel values were scaled with 1000 (storing the pixels as Integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using r.relief, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.
keyword: digital terrain model
keyword: elevation
keyword: Copernicus
keyword: DEM
keyword: DSM
keyword: geomorphometry
keyword: environment
keyword: Europe
keyword: remote sensing
keyword: radar
keyword: space
keyword: MOOD-H2020
keyword: Tularaemia
keyword: Lyme
keyword: TBE
keyword: WNV
keyword: USUTU
keyword: Mosquito borne Flaviviruses
theme: Elevation
theme: elevation
theme: geoscientificInformation
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-32.00 18.00, -32.00 82.00, 70.00 82.00, 70.00 18.00, -32.00 18.00))
temporal: 2010-01-01 / 2015-12-31
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
license: license
license: Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International (CC-BY-SA 4.0)
license: { "id": "cc-by-sa/4.0", "name": "Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International", "url": "http://creativecommons.org/licenses/by-sa/4.0/", "quelle": "Source: mundialis" }
license: EO data provided under COPERNICUS by the European Union and ESA. For more information see: https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/55df6882-8593-4123-b738-3558c20ad98c
distribution: zenodo.6211883
data quality: derived from Copernicus DEM GLO-30

https://data.mundialis.de/geonetwork/srv/resources/records/f576cda8-d598-478c-b8fe-ad2634c927e8
type: CatalogRecord
primary topic: dataset
issued: 2023-12-15
modified: 2023-12-15
references: https://data.mundialis.de/geonetwork/srv/resources/records/f576cda8-d598-478c-b8fe-ad2634c927e8/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/f576cda8-d598-478c-b8fe-ad2634c927e8/formatters/xml
format: application/xml
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  label: XML

Copernicus Digital Elevation Model (DEM) for Europe at 30 meter resolution derived from Copernicus Global 30 meter dataset
type: Dataset
abstract: Here we provide a mosaic of the Copernicus DEM 30m for Europe and the corresponding hillshade derived from the GLO-30 public instance of the Copernicus DEM. The CRS is the same as the original Copernicus DEM CRS: EPSG:4326. Note that GLO-30 Public provides limited coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters. The Copernicus DEM for Europe at 30 m in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/). Processing steps: The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in https://gdal.org/drivers/raster/vrt.html format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized: gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt The pixel values were scaled with 1000 (storing the pixels as integer values) for data volume reduction. In addition, a hillshade …
keyword: digital terrain model
keyword: elevation
keyword: Copernicus
keyword: DEM
keyword: DSM
keyword: geomorphometry
keyword: environment
keyword: Europe
keyword: remote sensing
keyword: radar
keyword: space
keyword: MOOD-H2020
keyword: Tularaemia
keyword: Lyme
keyword: TBE
keyword: WNV
keyword: USUTU
keyword: Mosquito borne Flaviviruses
theme: Elevation
theme: geoscientificInformation
theme: elevation
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-32.00 18.00, -32.00 82.00, 70.00 82.00, 70.00 18.00, -32.00 18.00))
temporal: 2010-01-01 / 2015-12-31
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
license: license
license: Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International (CC-BY-SA 4.0)
license: { "id": "cc-by-sa/4.0", "name": "Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International", "url": "http://creativecommons.org/licenses/by-sa/4.0/", "quelle": "Source: mundialis" }
license: EO data provided under COPERNICUS by the European Union and ESA. For more information see: https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/55df6882-8593-4123-b738-3558c20ad98c
data quality: derived from Copernicus DEM GLO-30 Public
title: Copernicus Digital Elevation Model (DEM) for Europe at 30 meter resolution (EU-LAEA) derived from Copernicus Global 30 meter dataset
abstract: The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters. The Copernicus DEM for Europe at 30 meter resolution (EU-LAEA projection) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/). Processing steps: The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in https://gdal.org/drivers/raster/vrt.html format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized: gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt In order to reproject the data to EU-LAEA projection, bilinear resampling was performed in GRASS GIS (using r.proj) and the pixel values were scaled with 1000 (storing the pixels as Integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using r.relief, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files. Note that GLO-30 Public provides limited coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the C…
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-32.00 18.00, -32.00 82.00, 70.00 82.00, 70.00 18.00, -32.00 18.00))
title: Decadal time series of spatially enhanced relative humidity for Europe at 1000 m resolution (2000 - 2021) derived from ERA5-Land data
abstract: Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Processing steps: The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds. 4. add the interpolated differences to CHELSA Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021. Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997): maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta)) actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td)) relative humidity = actual water pressure / maximum water pressure The resulting relative humidity has been aggregated to decadal averages. …
keyword: relative humidity
keyword: geospatial analysis
keyword: meteorology
keyword: ERA5-Land
keyword: CHELSA
theme: Atmospheric conditions
theme: Meteorological geographical features
theme: climatologyMeteorologyAtmosphere
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-36.00 18.00, -36.00 82.00, 70.00 82.00, 70.00 18.00, -36.00 18.00))
temporal: 2000-01-01 / 2021-07-31
license: Creative Commons Namensnennung – 4.0 International (CC BY 4.0)
license: { "id": "cc-by/4.0", "name": "Creative Commons Namensnennung – 4.0 Internationa", "url": "http://creativecommons.org/licenses/by/4.0/", "quelle": "Source: mundialis GmbH & Co. KG" }
distribution: https://zenodo.org/record/7427010
data quality: derived from ERA5-Land reanalysis data, spatially enhanced by image fusion with CHELSA data
title: Daily time series of spatially enhanced relative humidity for Europe at 1000 m resolution (2000 - 2021) derived from ERA5-Land data
abstract: Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Processing steps: The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds. 4. add the interpolated differences to CHELSA Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021. Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997): maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta)) actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td)) relative humidity = actual water pressure / maximum water pressure Data provided is the daily averages of relative humidity. Resultant value…
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-36.00 18.00, -36.00 82.00, 70.00 82.00, 70.00 18.00, -36.00 18.00))
license: { "id": "cc-by/4.0", "name": "Creative Commons Namensnennung – 4.0 International", "url": "http://creativecommons.org/licenses/by/4.0/", "quelle": "Source: mundialis GmbH & Co. KG" }
distribution: https://zenodo.org/record/7426990
distribution: https://zenodo.org/record/7434376
distribution: https://zenodo.org/record/7432432
distribution: https://zenodo.org/record/7434447
distribution: https://zenodo.org/record/7434396
title: ERA5-Land daily: Total precipitation (2000 - 2020)
abstract: Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Total precipitation: 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. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. The original ERA5-Land dataset (period: 2000 - 2020) has been reprocessed to: - aggregate ERA5-Land hourly data to daily data (minimum, mean, maximum) - while increasing the resolution from the native ERA5-Land resolution of 0.1 degree (~ 9 km) to 30 arc-sec (~ 1 km) by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. Th…
keyword: precipitation
keyword: Leptospirosis
theme: Europe
theme: environment
theme: health
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-52.00 30.00, -52.00 72.00, 49.00 72.00, 49.00 30.00, -52.00 30.00))
temporal: 2000-01-01 / 2020-12-31
license: intellectualPropertyRights
license: Licence to Use Copernicus Products (Version 1.2, Nov. 2019): "All users of Copernicus Products must provide clear and visible attribution to the Copernicus programme. The Licensee will communicate to the public the source of the Copernicus Products by crediting the Copernicus Climate Change and Atmosphere Monitoring Services" (full licence available here: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf
license: { "id": "other-open", "name": "Licence to Use Copernicus Products", "url": "https://ads.atmosphere.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf", "quelle": "Source: Copernicus Programme" }
distribution: zenodo.14987385
data quality: Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.

https://data.mundialis.de/geonetwork/srv/resources/records/9a382836-47a9-4dac-ad62-f0021e455ab8
type: CatalogRecord
primary topic: dataset
issued: 2023-12-15
modified: 2023-12-15
references: https://data.mundialis.de/geonetwork/srv/resources/records/9a382836-47a9-4dac-ad62-f0021e455ab8/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/9a382836-47a9-4dac-ad62-f0021e455ab8/formatters/xml
format: application/xml
  type: IMT
  label: XML

https://data.mundialis.de/geonetwork/srv/resources/records/4ea3b1e9-d3d0-4c3f-afb1-6445cec3a89d
type: CatalogRecord
primary topic: zenodo.6573852
issued: 2024-07-02
modified: 2024-07-02
references: https://data.mundialis.de/geonetwork/srv/resources/records/4ea3b1e9-d3d0-4c3f-afb1-6445cec3a89d/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/4ea3b1e9-d3d0-4c3f-afb1-6445cec3a89d/formatters/xml
format: application/xml
  type: IMT
  label: XML

MODIS NDVI and EVI, 16-day time series for Europe at 1 km resolution
type: Dataset
abstract: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from MODIS data for Europe at 1 km resolution. Source data: - MODIS/Terra Vegetation Indices 16-Day L3 Global 500 m SIN Grid (MOD13A1 v006): https://lpdaac.usgs.gov/products/mod13a1v006/ - MODIS/Aqua Vegetation Indices 16-Day L3 Global 500 m SIN Grid (MYD13A1 v006): https://lpdaac.usgs.gov/products/myd13a1v006/ The MOD/MYD13A1 Version 6 product provide Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. For the time periods October 2016 - March 2017 and August 2020 - April 2021, the original data has been reprojected to ETRS89-extended / LAEA Europe and aggregated to a 1 km grid. The temporal resolution is 16 days. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers and appear as "no data". File naming: productCode.acquisitionDate[A (YYYYDDD)]_mosaic_spatialResolution_frequency_VI.tif example: MOD13A1.A2020305_mosaic_1000m_16_days_NDVI.tif The date is Year and Day of Year. Values are NDVI/EVI * 10000. Example: Value 6473 = 0.6473
keyword: NDVI
keyword: EVI
keyword: Vegetation Index
keyword: Europe
keyword: MOOD-H2020
keyword: Lyme
keyword: TBE
keyword: WNV
keyword: USUTU
theme: Land cover
theme: geoscientificInformation
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-52.00 30.00, -52.00 72.00, 49.00 72.00, 49.00 30.00, -52.00 30.00))
temporal: 2016-10-01 / 2017-03-31
temporal: 2020-08-01 / 2021-04-30
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
license: license
license: All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge.
license: All LP DAAC current data and products acquired through the LP DAAC have no restrictions on reuse, sale, or redistribution. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For full information on LP DAAC Data Citation and Policies, see: https://lpdaac.usgs.gov/data/data-citation-and-policies/
distribution: https://zenodo.org/record/6573852/files/MODIS_NDVI_v01.zip
distribution: https://zenodo.org/record/6573852/files/MODIS_NDVI_EVI.sld
distribution: https://zenodo.org/record/6573852/files/MODIS_NDVI_EVI.qml
distribution: https://zenodo.org/record/6573852/files/MODIS_EVI_v01.zip
data quality: Derived from MODIS Terra/Aqua Vegetation Indices 16-Day L3 Global 500 m SIN Grid. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers.

https://data.mundialis.de/geonetwork/srv/resources/records/223c3eab-4a64-48c4-8b1e-e85a53d116df
type: CatalogRecord
primary topic: zenodo.6559122
issued: 2023-12-15
modified: 2023-12-15
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ERA5-Land weekly: Air temperature at 2 meter above surface, weekly time series for Europe at 1 km resolution (2016 - 2020)
type: Dataset
abstract: Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Air temperature (2 m): 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. Processing steps: The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA The spatially enhanced daily ERA5-Land data has been aggregated on a weekly basis starting from Saturday for the time period 2016 - 2020. Data available is the weekly average of daily averages, the weekly minimum of daily minima and the weekly maximum of daily maxima of air temperature (2 m). File naming: Average of daily average: era5_land_t2m_avg_weekly_YYYY_MM_DD.tif Max of daily max: era5_land_t2m_max_weekly_YYYY_MM_DD.tif Min of daily min: era5_land_t2m_min_w…
keyword: air temperature
keyword: MOOD-H2020
keyword: Tularaemia
keyword: Lyme
keyword: TBE
keyword: WNV
keyword: USUTU
keyword: Mosquito borne Flaviviruses
theme: Europe
theme: Meteorological geographical features
theme: environment
theme: geoscientificInformation
theme: health
theme: climatologyMeteorologyAtmosphere
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-32.00 18.00, -32.00 82.00, 61.00 82.00, 61.00 18.00, -32.00 18.00))
temporal: 2016-01-01 / 2020-12-31
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
license: intellectualPropertyRights
license: Licence to Use Copernicus Products (Version 1.2, Nov. 2019): "All users of Copernicus Products must provide clear and visible attribution to the Copernicus programme. The Licensee will communicate to the public the source of the Copernicus Products by crediting the Copernicus Climate Change and Atmosphere Monitoring Services" (full licence available here: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf
license: { "id": "other-open", "name": "Licence to Use Copernicus Products", "url": "https://ads.atmosphere.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf", "quelle": "Source: Copernicus Programme" }
distribution: https://zenodo.org/record/6559122/files/ERA5_t2m_weekly_avg_v01.zip
distribution: https://zenodo.org/record/6559122/files/ERA5_t2m_weekly_max_v01.zip
distribution: https://zenodo.org/record/6559122/files/ERA5_t2m_weekly_min_v01.zip
distribution: https://zenodo.org/record/6559122/files/era5_land_temperature_color_table.sld
distribution: https://zenodo.org/record/6559122/files/era5_land_temperature_color_table.qml
data quality: Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.

https://data.mundialis.de/geonetwork/srv/resources/records/ddbbc194-7000-4ff3-9d41-8d99ad90bc6c
type: CatalogRecord
primary topic: dataset
issued: 2023-12-15
modified: 2023-12-15
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type: CatalogRecord
primary topic: zenodo.6559068
issued: 2023-12-15
modified: 2023-12-15
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ERA5-Land weekly: Surface temperature, weekly time series for Europe at 1 km resolution (2016 - 2020)
type: Dataset
abstract: Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Surface temperature: Temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Processing steps: The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA The spatially enhanced daily ERA5-Land data has been aggregated on a weekly basis (starting from Saturday) for the time period 2016 - 2020. Data available is the weekly average of daily averages, the weekly minimum of daily minima and the weekly maximum of daily maxima of surface temperature. File naming: Average of daily average: era5_land_ts_avg_weekly_YYYY_MM_DD.tif Max of daily max: …
keyword: surface temperature
keyword: MOOD-H2020
keyword: Tularaemia
keyword: Lyme
keyword: TBE
keyword: WNV
keyword: USUTU
keyword: Mosquito borne Flaviviruses
theme: Europe
theme: Meteorological geographical features
theme: environment
theme: geoscientificInformation
theme: health
theme: climatologyMeteorologyAtmosphere
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-32.00 18.00, -32.00 82.00, 61.00 82.00, 61.00 18.00, -32.00 18.00))
temporal: 2016-01-01 / 2020-12-31
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
license: intellectualPropertyRights
license: Licence to Use Copernicus Products (Version 1.2, Nov. 2019): "All users of Copernicus Products must provide clear and visible attribution to the Copernicus programme. The Licensee will communicate to the public the source of the Copernicus Products by crediting the Copernicus Climate Change and Atmosphere Monitoring Services" (full licence available here: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf
license: { "id": "other-open", "name": "Licence to Use Copernicus Products", "url": "https://ads.atmosphere.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf", "quelle": "Source: Copernicus Programme" }
distribution: https://zenodo.org/record/6559068/files/ERA5_ts_weekly_avg_v01.zip
distribution: https://zenodo.org/record/6559068/files/ERA5_ts_weekly_max_v01.zip
distribution: https://zenodo.org/record/6559068/files/ERA5_ts_weekly_min_v01.zip
distribution: https://zenodo.org/record/6559068/files/era5_land_temperature_color_table.sld
distribution: https://zenodo.org/record/6559068/files/era5_land_temperature_color_table.qml
data quality: Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.

https://data.mundialis.de/geonetwork/srv/resources/records/c3bfc5ce-03dd-4cb7-9600-4a5587733c80
type: CatalogRecord
primary topic: dataset
issued: 2023-12-15
modified: 2023-12-15
references: https://data.mundialis.de/geonetwork/srv/resources/records/c3bfc5ce-03dd-4cb7-9600-4a5587733c80/formatters/xml
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https://data.mundialis.de/geonetwork/srv/resources/records/cbc4aad4-bde6-4ef7-8079-6df637bc729a
type: CatalogRecord
primary topic: zenodo.12189682
issued: 2024-07-02
modified: 2024-07-02
references: https://data.mundialis.de/geonetwork/srv/resources/records/cbc4aad4-bde6-4ef7-8079-6df637bc729a/formatters/xml
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CLMS Water Bodies monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
type: Dataset
abstract: Water Bodies from Copernicus Land Monitoring Service (CLMS) as monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023) Source data: - CLMS: Water Bodies 2014-2020 (raster 300 m), global, 10-daily – version 1: https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m - CLMS: Water Bodies 2020-present (raster 300 m), global, monthly – version 2: https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m Water is fundamental to life on Earth. Water quality, including aspects like turbidity and trophic state, is vital for assessing a water body's ecological well-being and its suitability for drinking. Understanding the water's surface temperature is key for monitoring climate change and can influence weather patterns. Tracking water levels in lakes and rivers helps in flood prediction, irrigation planning, and hydroelectric power generation. The presence and extent of ice on lakes and rivers can have significant implications for regional climates, ecosystems, and human activities. Moreover, the surface extent of water bodies, whether permanent or ephemeral, informs land management across various sectors. In an era marked by environmental change, these metrics offer insights into sustainable water resource management. The Water Bodies product group aims to address these critical issues by providing tailored datasets to users which are applicable across a wide array of sectors. It includes Lake Surface Water Temperature, providing real-time and historical data; Lake Water Quality in various resolutions; Water Bodies datasets for surface extent; Lake and River Water Level information; the River and Lake Ice Extent product for ice presence; and the Aggregated River and Lake Ice Extent product, showing percent ice coverage. These products support applications like food security, public health safeguarding, climate studies, and responsible water management practices. Processing steps: To cov…
keyword: MOOD-H2020
keyword: Water
keyword: Water bodies
keyword: RVF
keyword: Rift Valley fever
keyword: Mauritania
theme: Human health and safety
theme: geoscientificInformation
theme: health
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-18.00 14.00, -18.00 28.00, -4.00 28.00, -4.00 14.00, -18.00 14.00))
temporal: 2019-01-01 / 2023-12-31
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
license: intellectualPropertyRights
license: Generated using European Union's Copernicus Land Monitoring Service information
distribution: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_PROBAV_V1.0.1_4326_monthly_2019.zip
distribution: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_PROBAV_V1.0.1_4326_monthly_2020.zip
distribution: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_S2_V2.0.1_4326_monthly_2021.zip
distribution: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_S2_V2.0.1_4326_monthly_2022.zip
distribution: https://zenodo.org/records/12189683/files/CLMS_WB300_GLOBE_S2_V2.0.1_4326_monthly_2023.zip
data quality: Dataset has been processed from: - CLMS: Water Bodies 2014-2020 (raster 300 m), global, 10-daily – version 1: https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m - CLMS: Water Bodies 2020-present (raster 300 m), global, monthly – version 2: https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m

https://data.mundialis.de/geonetwork/srv/resources/records/d5334e64-bae5-4894-ad02-fc4286a9e500
type: CatalogRecord
primary topic: dataset
issued: 2025-03-07
modified: 2025-03-07
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Elevation
type: Concept
in scheme: GEMET - INSPIRE themes, version 1.0

Land cover
type: Concept
in scheme: GEMET - INSPIRE themes, version 1.0

Europe
type: Concept
in scheme: Continents, countries, sea regions of the world.

Meteorological geographical features
type: Concept
in scheme: GEMET - INSPIRE themes, version 1.0

Atmospheric conditions
type: Concept
in scheme: GEMET - INSPIRE themes, version 1.0

Human health and safety
type: Concept
in scheme: GEMET - INSPIRE themes, version 1.0

zenodo.6211883
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Copernicus DEM 30m for Europe
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Copernicus DEM 30m for Europe
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zenodo.14987385
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mundialis GmbH & Co. KG
type: Organization