https://data.mundialis.de:/geonetwork/srv/api/records
type: PagedCollection
type: PartialCollectionView
last page: 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: MODIS MNDWI, 8-day time series for Europe at 1 km resolution
dataset: ERA5-Land daily: Total precipitation (2000 - 2020)
dataset: ERA5-Land monthly: Total precipitation, monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
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: ERA5-Land weekly: Air temperature at 2 meter above surface, weekly time series for Europe at 1 km resolution (2016 - 2020)
dataset: MODIS NDWI, monthly aggregated time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
dataset: MODIS NDVI, monthly aggregated 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: Land cover
dataset: Europe
dataset: Meteorological geographical features
dataset: Human health and safety
dataset: Atmospheric conditions
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.14987385
dataset: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2019.zip
dataset: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2020.zip
dataset: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2021.zip
dataset: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2022.zip
dataset: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2023.zip
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: zenodo.6146383
dataset: zenodo.6147830
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: 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/records/14764686/files/MODIS_NDWI_4326_monthly_2019.zip
dataset: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2020.zip
dataset: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2021.zip
dataset: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2022.zip
dataset: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2023.zip
dataset: https://zenodo.org/records/12188840/files/MODIS_NDVI_4326_monthly_2019.zip
dataset: https://zenodo.org/records/12188840/files/MODIS_NDVI_4326_monthly_2020.zip
dataset: https://zenodo.org/records/12188840/files/MODIS_NDVI_4326_monthly_2021.zip
dataset: https://zenodo.org/records/12188840/files/MODIS_NDVI_4326_monthly_2022.zip
dataset: https://zenodo.org/records/12188840/files/MODIS_NDVI_4326_monthly_2023.zip
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/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

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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/d5334e64-bae5-4894-ad02-fc4286a9e500
type: CatalogRecord
primary topic: dataset
issued: 2025-03-07
modified: 2025-03-07
references: https://data.mundialis.de/geonetwork/srv/resources/records/d5334e64-bae5-4894-ad02-fc4286a9e500/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/d5334e64-bae5-4894-ad02-fc4286a9e500/formatters/xml
format: application/xml
  type: IMT
  label: XML

ERA5-Land daily: Total precipitation (2000 - 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. 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: MOOD-H2020
keyword: Tularaemia
keyword: Leptospirosis
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((-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
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: 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.
title: Monthly time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2023) 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 - 12/2023. 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 monthly averages. …
keyword: relative humidity
keyword: geospatial analysis
keyword: environment
keyword: meteorology
keyword: ERA5-Land
keyword: CHELSA
keyword: Europe
keyword: Lyme
keyword: TBE
keyword: WNV
keyword: USUTU
theme: Atmospheric conditions
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 / 2023-12-31
license: license
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: zenodo.6146383
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 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 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))
temporal: 2000-01-01 / 2021-07-31
distribution: zenodo.6147830

https://data.mundialis.de/geonetwork/srv/resources/records/6e9f4ea1-7111-4505-8165-a5857e486584
type: CatalogRecord
primary topic: zenodo.12189668
issued: 2024-07-02
modified: 2024-07-02
references: https://data.mundialis.de/geonetwork/srv/resources/records/6e9f4ea1-7111-4505-8165-a5857e486584/formatters/xml
format: text/html
  type: IMT
  label: HTML

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format: application/xml
  type: IMT
  label: XML

ERA5-Land monthly: Total precipitation, monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
type: Dataset
abstract: ERA5-Land total precipitation monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023) Source data: 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. 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…
keyword: MOOD-H2020
keyword: Precipitation
keyword: Rift Valley fever
keyword: RVF
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: 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/records/12189669/files/ERA5_land_prectot_4326_monthly_2019.zip
distribution: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2020.zip
distribution: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2021.zip
distribution: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2022.zip
distribution: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2023.zip
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/a98fa2dd-6787-4eeb-b51a-ab4c9ae731be
type: CatalogRecord
primary topic: zenodo.6559068
issued: 2023-12-15
modified: 2023-12-15
references: https://data.mundialis.de/geonetwork/srv/resources/records/a98fa2dd-6787-4eeb-b51a-ab4c9ae731be/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/a98fa2dd-6787-4eeb-b51a-ab4c9ae731be/formatters/xml
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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/b9ce7dba-4130-428d-96f0-9089d8b9f4a5
type: CatalogRecord
primary topic: dataset
issued: 2024-07-11
modified: 2024-07-11
references: https://data.mundialis.de/geonetwork/srv/resources/records/b9ce7dba-4130-428d-96f0-9089d8b9f4a5/formatters/xml
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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
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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
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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/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
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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
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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/e4941c0b-27c0-4522-857f-5e02d719721e
type: CatalogRecord
primary topic: zenodo.14764686
issued: 2025-01-31
modified: 2025-01-31
references: https://data.mundialis.de/geonetwork/srv/resources/records/e4941c0b-27c0-4522-857f-5e02d719721e/formatters/xml
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MODIS NDWI, monthly aggregated time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
type: Dataset
abstract: Normalized Difference Water Index (NDWI) from MODIS data for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023). Source data: - MODIS/Terra Surface Reflectance 8-Day L3 Global 500 m SIN Grid (MOD09A1 v061): https://lpdaac.usgs.gov/products/mod09a1v061/ The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 Version 6.1 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 period January 2019 - December 2023, the NDWI has been calculated from the Terra MOD09A1 Version 6.1 product. The layers B02 (near infrared) and B06 (shortwave infrared) of the original data have been processed. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layer. The time series has been gapfilled with a temporal and a spatial approach. Gaps in the time series were filled with a harmonic analysis of time series using six frequencies to also model relatively short-term changes in NDWI. Only missing values were replaced by modelled values. NDWI was calculated as the normalized difference of the bands B02 (near infrared) and B06 (shortwave infrared) with: NDWI = (B02 – B06) / (B02 + B06). This NDWI represents vegetation water content. The 8-day data are then aggregated to monthly temporal resolution using the average and reprojected to Latitude-Longitude/WGS84. File naming: ndwi_monthly_YYYY_MM_30arcsec.tif e.g.: ndwi_monthly_2023_12_30arcsec.tif The date within the filename are year an…
keyword: MOOD-H2020
keyword: Normalized Difference Water Index
keyword: NDWI
keyword: Rift Valley Fever
keyword: RVF
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: 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/records/14764686/files/MODIS_NDWI_4326_monthly_2019.zip
distribution: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2020.zip
distribution: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2021.zip
distribution: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2022.zip
distribution: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2023.zip
data quality: Derived from: MODIS/Terra Surface Reflectance 8-Day L3 Global 500 m SIN Grid (MOD09A1 v061): https://lpdaac.usgs.gov/products/mod09a1v061/ NDWI has been calculated from the Terra MOD09A1 Version 6.1 product. The layers B02 (near infrared) and B06 (shortwave infrared) of the original data have been processed. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layer. The time series has been gapfilled with a temporal and a spatial approach.

https://data.mundialis.de/geonetwork/srv/resources/records/0590e642-fe89-4d5f-a665-ddabe722a04d
type: CatalogRecord
primary topic: zenodo.12188839
issued: 2025-01-31
modified: 2025-01-31
references: https://data.mundialis.de/geonetwork/srv/resources/records/0590e642-fe89-4d5f-a665-ddabe722a04d/formatters/xml
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MODIS NDVI, monthly aggregated time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
type: Dataset
abstract: Normalized Difference Vegetation Index (NDVI) from MODIS data for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023). Source data: - MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid (MOD13A2 v061): https://lpdaac.usgs.gov/products/mod13a2v061/ The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MOD13A2) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) 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 period January 2019 - December 2023, the NDVI layer of the original data has been processed. 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". These 16-Day data are then aggregated to monthly temporal resolution using the maximum and reprojected to Latitude-Longitude/WGS84. File naming: ndvi_filt_YYYY_MM_01T00_00_00.tif e.g.: ndvi_filt_2023_12_01T00_00_00.tif The date within the filename is year and month of aggregated timestamp. Pixel values: NDVI * 10000 Scaled to Integer, example: value 6473 = 0.6473 Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 28N south: 14N west: 18W east: 4W Temporal extent: January 2019 - December 2023 Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: monthly Software used: GRASS GIS …
keyword: MOOD-H2020
keyword: Vegetation Index
keyword: NDVI
keyword: Rift Valley Fever
keyword: RVF
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: 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/records/12188840/files/MODIS_NDVI_4326_monthly_2019.zip
distribution: https://zenodo.org/records/12188840/files/MODIS_NDVI_4326_monthly_2020.zip
distribution: https://zenodo.org/records/12188840/files/MODIS_NDVI_4326_monthly_2021.zip
distribution: https://zenodo.org/records/12188840/files/MODIS_NDVI_4326_monthly_2022.zip
distribution: https://zenodo.org/records/12188840/files/MODIS_NDVI_4326_monthly_2023.zip
data quality: Derived from MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid (MOD13A2 v061): https://lpdaac.usgs.gov/products/mod13a2v061/. 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".

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

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

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

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https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2021.zip
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https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2022.zip
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https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2023.zip
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https://zenodo.org/records/12188840/files/MODIS_NDVI_4326_monthly_2019.zip
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https://zenodo.org/records/12188840/files/MODIS_NDVI_4326_monthly_2020.zip
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format: WWW:DOWNLOAD-1.0-http--download

https://zenodo.org/records/12188840/files/MODIS_NDVI_4326_monthly_2021.zip
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https://zenodo.org/records/12188840/files/MODIS_NDVI_4326_monthly_2022.zip
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https://zenodo.org/records/12188840/files/MODIS_NDVI_4326_monthly_2023.zip
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mundialis GmbH & Co. KG
type: Organization