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 3 arc seconds (ca. 90 meter) resolution derived from Copernicus Global 30 meter DEM dataset
dataset: Daily maps for global daylight length at 30 arc seconds resolution (2022)
dataset: ERA5-Land weekly: Air temperature at 2 meter above surface, weekly time series for Europe at 1 km resolution (2016 - 2020)
dataset: MODIS MNDWI, 8-day time series for Europe at 1 km resolution
dataset: Monthly gap-filled MODIS LST data (2003-2016)
dataset: Copernicus Digital Elevation Model (DEM) for Europe at 30 arc seconds (ca. 1000 meter) resolution derived from Copernicus Global 30 meter DEM dataset
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: Meteorological geographical features
dataset: Atmospheric conditions
dataset: Europe
dataset: Land cover
dataset: zenodo.6211701
dataset: zenodo.7249037
dataset: zenodo.6342776
dataset: zenodo.6342822
dataset: zenodo.6344012
dataset: zenodo.6344066
dataset: zenodo.6344125
dataset: https://zenodo.org/record/7427010
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: zenodo.6147830
dataset: https://zenodo.org/record/6573878/files/MODIS_MNDWI_v01.zip
dataset: https://zenodo.org/record/6573878/files/MODIS_MNDWI.sld
dataset: https://zenodo.org/record/6573878/files/MODIS_MNDWI.qml
dataset: zenodo.6211553
dataset: zenodo.14987502
dataset: mundialis GmbH & Co. KG
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/c1b4ca8b-81ce-40f1-9100-c7ff5ca44069
type: CatalogRecord
primary topic: zenodo.6211701
issued: 2023-12-15
modified: 2023-12-15
references: https://data.mundialis.de/geonetwork/srv/resources/records/c1b4ca8b-81ce-40f1-9100-c7ff5ca44069/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/c1b4ca8b-81ce-40f1-9100-c7ff5ca44069/formatters/xml
format: application/xml
  type: IMT
  label: XML

Copernicus Digital Elevation Model (DEM) for Europe at 3 arc seconds (ca. 90 meter) resolution 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 3 arcsec (0:00:03 = 0.00083333333 ~ 90 meter) 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 reduce the spatial resolution to 3 arc seconds, weighted resampling was performed in GRASS GIS (using r.resamp.stats -w) 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.6211701
data quality: derived from Copernicus DEM GLO-30

https://data.mundialis.de/geonetwork/srv/resources/records/97448e4f-e214-405a-95cc-6137f4379b6a
type: CatalogRecord
primary topic: dataset
issued: 2023-02-01
modified: 2023-02-01
references: https://data.mundialis.de/geonetwork/srv/resources/records/97448e4f-e214-405a-95cc-6137f4379b6a/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/97448e4f-e214-405a-95cc-6137f4379b6a/formatters/xml
format: application/xml
  type: IMT
  label: XML

Daily maps for global daylight length at 30 arc seconds resolution (2022)
type: Dataset
abstract: Overview: Daily maps for global daylight length, calculated for the year 2022. Processing steps: For each day within the year 2022, the photoperiod (sunshine hours on flat terrain) are calculated using the SOLPOS algorithm developed by the National Renewable Energy Laboratory (NREL), USA. Resultant values have been converted from hours to minutes. File naming scheme (DDD = day within year) (min is abbreviation for minute): daylight_min_2022_DDD.tif Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 90 south: -90 west: -180 east: 180 Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: Daily Pixel values: unit: minutes Software used: GDAL 3.2.2 and GRASS GIS 8.2.0 Processed by: mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/) Reference: National Renewable Energy Laboratory (NREL): SOLPOS 2.0 sun position algorithm (https://www.nrel.gov/grid/solar-resource/solpos.html)
keyword: daylength
keyword: photoperiod
keyword: environment
keyword: meteorology
keyword: MOOD-H2020
keyword: Global
theme: Meteorological geographical features
theme: geoscientificInformation
thumbnail: add thumbnail in sidebar
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-180.00 -90.00, -180.00 90.00, 180.00 90.00, 180.00 -90.00, -180.00 -90.00))
temporal: 2022-01-01 / 2022-12-31
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 GmbH & Co. KG"}
distribution: zenodo.7249037
relation: optionally add identifier of reference dataset here
data quality: calculated with SOLPOS 2.0 sun position algorithm (https://www.nrel.gov/grid/solar-resource/solpos.html) developed by National Renewable Energy Laboratory (NREL)
title: Daily time series of spatially enhanced relative humidity for Europe at 30 arc seconds 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. This set provid…
keyword: relative humidity
keyword: geospatial analysis
keyword: ERA5-Land
keyword: CHELSA
keyword: Europe
keyword: Tularaemia
keyword: Lyme
keyword: TBE
keyword: WNV
keyword: USUTU
keyword: Mosquito borne Flaviviruses
theme: Atmospheric conditions
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 International", "url": "http://creativecommons.org/licenses/by/4.0/", "quelle": "Source: mundialis GmbH & Co. KG" }
distribution: zenodo.6342776
distribution: zenodo.6342822
distribution: zenodo.6344012
distribution: zenodo.6344066
distribution: zenodo.6344125
data quality: derived from ERA5-Land reanalysis data, spatially enhanced by image fusion with CHELSA data
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. …
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 Internationa", "url": "http://creativecommons.org/licenses/by/4.0/", "quelle": "Source: mundialis GmbH & Co. KG" }
distribution: https://zenodo.org/record/7427010
title: Decadal time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2021) derived from ERA5-Land data
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))
distribution: zenodo.6147830
title: ERA5-Land daily: Surface temperature (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. 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. 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 spatial 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. 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 Data available is the daily average, minimum and maximum of surface temperature. Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief) Original ERA5-Land dataset license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-cop…
keyword: surface temperature
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.14987502
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/eb4a552e-6782-415f-81ea-89ea1a584635
type: CatalogRecord
primary topic: dataset
issued: 2025-03-07
modified: 2025-03-07
references: https://data.mundialis.de/geonetwork/srv/resources/records/eb4a552e-6782-415f-81ea-89ea1a584635/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/eb4a552e-6782-415f-81ea-89ea1a584635/formatters/xml
format: application/xml
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  label: XML

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
references: https://data.mundialis.de/geonetwork/srv/resources/records/ddbbc194-7000-4ff3-9d41-8d99ad90bc6c/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/ddbbc194-7000-4ff3-9d41-8d99ad90bc6c/formatters/xml
format: application/xml
  type: IMT
  label: XML

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
references: https://data.mundialis.de/geonetwork/srv/resources/records/223c3eab-4a64-48c4-8b1e-e85a53d116df/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/223c3eab-4a64-48c4-8b1e-e85a53d116df/formatters/xml
format: application/xml
  type: IMT
  label: XML

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/782cdbf5-bfda-4813-a5d7-336ebbc241bb
type: CatalogRecord
primary topic: dataset
issued: 2024-07-02
modified: 2024-07-02
references: https://data.mundialis.de/geonetwork/srv/resources/records/782cdbf5-bfda-4813-a5d7-336ebbc241bb/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/782cdbf5-bfda-4813-a5d7-336ebbc241bb/formatters/xml
format: application/xml
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  label: XML

https://data.mundialis.de/geonetwork/srv/resources/records/b4bfcab8-7dad-4bf2-961e-a1615c2ac67f
type: CatalogRecord
primary topic: zenodo.6573878
issued: 2023-12-15
modified: 2023-12-15
references: https://data.mundialis.de/geonetwork/srv/resources/records/b4bfcab8-7dad-4bf2-961e-a1615c2ac67f/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/b4bfcab8-7dad-4bf2-961e-a1615c2ac67f/formatters/xml
format: application/xml
  type: IMT
  label: XML

MODIS MNDWI, 8-day time series for Europe at 1 km resolution
type: Dataset
abstract: Modified Normalized Difference Water Index (MNDWI) from MODIS data for Europe at 1 km resolution. Source data: - MODIS/Terra Surface Reflectance 8-Day L3 Global 500 m SIN Grid (MOD09A1 v006): https://lpdaac.usgs.gov/products/mod09a1v006/ The corresponding MODIS/Aqua product (MYD09A1 v006) could not be used due to the fact that the Aqua satellite has a number of broken detectors resulting in unreliable data for band 6 (SWIR) measurements. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 Version 6 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. 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 8 days. Bad quality pixels (cloud, cloud shadow, dead detector, solar zenith angle too large, etc.) 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: MOD09A1.A2016353_mosaic_1000m_8_days_MNDWI.tif The date is Year and Day of Year. Values are MNDWI * 10000. Example: Value -5099 = -0.5099
keyword: MNDWI
keyword: Water Index
keyword: Europe
keyword: MOOD-H2020
keyword: Avian Flu
keyword: Tularaemia
keyword: Leptospirosis
keyword: Lyme
keyword: TBE
keyword: WNV
keyword: USUTU
keyword: Mosquito borne Flaviviruses
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/6573878/files/MODIS_MNDWI_v01.zip
distribution: https://zenodo.org/record/6573878/files/MODIS_MNDWI.sld
distribution: https://zenodo.org/record/6573878/files/MODIS_MNDWI.qml
data quality: Derived from MODIS Terra Surface Reflectance 8-Day L3 Global 500 m SIN Grid. Bad quality pixels (cloud, cloud shadow, dead detector, solar zenith angle too large, etc.) have been masked using the provided quality assurance (QA) layers.

https://data.mundialis.de/geonetwork/srv/resources/records/c0009947-e04e-4f5e-b9cb-79ae1f90608a
type: CatalogRecord
primary topic: Dataset
issued: 2023-12-15
modified: 2023-12-15
references: https://data.mundialis.de/geonetwork/srv/resources/records/c0009947-e04e-4f5e-b9cb-79ae1f90608a/formatters/xml
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https://data.mundialis.de/geonetwork/srv/resources/records/c0009947-e04e-4f5e-b9cb-79ae1f90608a/formatters/xml
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Monthly gap-filled MODIS LST data (2003-2016)
type: Dataset
abstract: 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 val…
keyword: land 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((-180.00 -90.00, -180.00 90.00, 180.00 90.00, 180.00 -90.00, -180.00 -90.00))
temporal: 2003-01-01 / 2016-03-12
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
granularity: 3
language: eng
license: otherRestrictions
license: license
license: Open Data Commons Open Database License v1.0 (ODbL)
license: { "id": "odbl", "name": "Open Data Commons Open Database License (ODbL)", "url": "http://www.opendefinition.org/licenses/odc-odbl", "quelle": "Source: mundialis GmbH & Co. KG" }
distribution: Monthly gap-filled MODIS LST data (2003-2016)
data quality: Dataset has been gap-filled from the original MODIS LST products MOD11C1/MYD11C1 at a spatial resolution of 3 arc-min

https://data.mundialis.de/geonetwork/srv/resources/records/948c3313-9957-4581-a238-812439d44397
type: CatalogRecord
primary topic: zenodo.6211553
issued: 2023-12-15
modified: 2023-12-15
references: https://data.mundialis.de/geonetwork/srv/resources/records/948c3313-9957-4581-a238-812439d44397/formatters/xml
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https://data.mundialis.de/geonetwork/srv/resources/records/948c3313-9957-4581-a238-812439d44397/formatters/xml
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Copernicus Digital Elevation Model (DEM) for Europe at 30 arc seconds (ca. 1000 meter) resolution 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 30 arcsec (0:00:30 = 0.0083333333 ~ 1000 meter) 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 a 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 reduce the spatial resolution to 30 arc seconds, weighted resampling was performed in GRASS GIS (using r.resamp.stats) 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: 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
distribution: zenodo.6211553
data quality: derived from Copernicus DEM GLO-30

https://data.mundialis.de/geonetwork/srv/resources/records/a72f738b-6704-4d00-98d7-a83f61346560
type: CatalogRecord
primary topic: dataset
issued: 2025-03-07
modified: 2025-03-07
references: https://data.mundialis.de/geonetwork/srv/resources/records/a72f738b-6704-4d00-98d7-a83f61346560/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/a72f738b-6704-4d00-98d7-a83f61346560/formatters/xml
format: application/xml
  type: IMT
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Elevation
type: Concept
in scheme: GEMET - INSPIRE themes, version 1.0

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

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

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

zenodo.6211701
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Monthly gap-filled MODIS LST data (2003-2016)
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zenodo.6211553
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mundialis GmbH & Co. KG
type: Organization

mundialis GmbH & Co KG
type: Organization