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: MODIS NDWI, monthly aggregated time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
dataset: Monthly time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2023) derived from ERA5-Land data
dataset: CLMS Water Bodies monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
dataset: MODIS LST Day and Night, monthly aggregated time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
dataset: Copernicus Digital Elevation Model (DEM) for Europe at 1000 meter resolution (EU-LAEA) 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: Land cover
dataset: Human health and safety
dataset: Atmospheric conditions
dataset: Meteorological geographical features
dataset: Elevation
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: 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: zenodo.6146383
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/records/14762335/files/MODIS_LST_4326_monthly_2019.zip
dataset: https://zenodo.org/records/14762335/files/MODIS_LST_4326_monthly_2020.zip
dataset: https://zenodo.org/records/14762335/files/MODIS_LST_4326_monthly_2021.zip
dataset: https://zenodo.org/records/14762335/files/MODIS_LST_4326_monthly_2022.zip
dataset: https://zenodo.org/records/14762335/files/MODIS_LST_4326_monthly_2023.zip
dataset: https://zenodo.org/record/7427021
dataset: zenodo.6211883
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: zenodo.6147830
dataset: https://zenodo.org/record/7427010
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/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
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/e4941c0b-27c0-4522-857f-5e02d719721e/formatters/xml
format: application/xml
  type: IMT
  label: XML

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/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
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/b9ce7dba-4130-428d-96f0-9089d8b9f4a5/formatters/xml
format: application/xml
  type: IMT
  label: XML

Monthly time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2023) derived from ERA5-Land data
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. 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: MOOD-H2020
keyword: Tularaemia
keyword: Lyme
keyword: TBE
keyword: WNV
keyword: USUTU
keyword: Mosquito borne Flaviviruses
theme: Atmospheric conditions
theme: Meteorological geographical features
theme: geoscientificInformation
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 / 2023-12-31
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
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: Monthly time series of spatially enhanced relative humidity for Europe at 1000 m resolution (2000 - 2022) 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: https://zenodo.org/record/7427021
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))
temporal: 2000-01-01 / 2021-07-31
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: 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))
distribution: zenodo.6147830
title: Decadal time series of spatially enhanced relative humidity for Europe at 1000 m 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: https://zenodo.org/record/7427010

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
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/cbc4aad4-bde6-4ef7-8079-6df637bc729a/formatters/xml
format: application/xml
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  label: XML

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/4ccb09d0-99c1-4509-9cda-9195b5755ceb
type: CatalogRecord
primary topic: zenodo.14762335
issued: 2025-01-31
modified: 2025-01-31
references: https://data.mundialis.de/geonetwork/srv/resources/records/4ccb09d0-99c1-4509-9cda-9195b5755ceb/formatters/xml
format: text/html
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  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/4ccb09d0-99c1-4509-9cda-9195b5755ceb/formatters/xml
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MODIS LST Day and Night, monthly aggregated time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
type: Dataset
abstract: Land Surface Temperature (LST) from MODIS data for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023). Source data: - MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1 km SIN Grid (MOD11A2 v061): https://lpdaac.usgs.gov/products/mod11a2v061/ The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MOD11A2) Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding MOD11A1 LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. For the time period January 2019 - December 2023, the LST layer of the original data has been processed. Both day (10:30 am) and night (10:30 pm) overpasses have been processed separately. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers. 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 LST. Only missing values were replaced by modelled values. The 8-day data are then aggregated to monthly temporal resolution using the average and reprojected to Latitude-Longitude/WGS84. File naming: lst_day/night_monthly_YYYY_MM_30arcsec.tif e.g.: lst_day_monthly_2023_12_30arcsec.tif The date within the filename are year and month of aggregated timestamp. Pixel values: LST * 10 Scaled to Integer, example: value 327 = 32.7 Units are degree Celsius. Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 28N south: 14N west: 18W east: 4W Temporal extent: January 2019 - December 2023 Sp…
keyword: MOOD-H2020
keyword: Land Surface Temperature
keyword: LST
keyword: Rift Valley Fever
keyword: RVF
keyword: Mauritania
theme: Human health and safety
theme: Meteorological geographical features
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/14762335/files/MODIS_LST_4326_monthly_2019.zip
distribution: https://zenodo.org/records/14762335/files/MODIS_LST_4326_monthly_2020.zip
distribution: https://zenodo.org/records/14762335/files/MODIS_LST_4326_monthly_2021.zip
distribution: https://zenodo.org/records/14762335/files/MODIS_LST_4326_monthly_2022.zip
distribution: https://zenodo.org/records/14762335/files/MODIS_LST_4326_monthly_2023.zip
data quality: Derived from: MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1 km SIN Grid (MOD11A2 v061): https://lpdaac.usgs.gov/products/mod11a2v061/ Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers. The time series has been gapfilled with a temporal and a spatial approach.

https://data.mundialis.de/geonetwork/srv/resources/records/ab06ed25-84af-43c9-b1c3-57e3b6ad8d29
type: CatalogRecord
primary topic: dataset
issued: 2024-07-11
modified: 2024-07-11
references: https://data.mundialis.de/geonetwork/srv/resources/records/ab06ed25-84af-43c9-b1c3-57e3b6ad8d29/formatters/xml
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https://data.mundialis.de/geonetwork/srv/resources/records/ab06ed25-84af-43c9-b1c3-57e3b6ad8d29/formatters/xml
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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
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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/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/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
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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
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https://data.mundialis.de/geonetwork/srv/resources/records/ddbbc194-7000-4ff3-9d41-8d99ad90bc6c/formatters/xml
format: application/xml
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Land cover
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

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

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

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https://zenodo.org/record/6573878/files/MODIS_MNDWI.qml
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https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2020.zip
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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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zenodo.6146383
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mundialis GmbH & Co. KG
type: Organization