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: Decadal time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2021) derived from ERA5-Land data
dataset: ERA5-Land monthly: Total precipitation, 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: 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: Atmospheric conditions
dataset: Meteorological geographical features
dataset: Land use
dataset: Land cover
dataset: World
dataset: Human health and safety
dataset: Elevation
dataset: zenodo.6147830
dataset: LICENSE.html
dataset: germany 2016 2019 MAJA cd total v02.sld
dataset: germany 2016 2019 MAJA cd total v02.qml
dataset: Daily gap-filled MODIS LST data (2003-2019)
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/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: zenodo.6146383
dataset: https://zenodo.org/record/7427021
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/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
  type: IMT
  label: XML

Decadal time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2021) 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 - 07/2021. Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997): maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta)) actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td)) relative humidity = actual water pressure / maximum water pressure The resulting relative humidity has been aggregated to decadal averages. …
keyword: relative humidity
keyword: geospatial analysis
keyword: 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 / 2021-07-31
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.6147830
data quality: derived from ERA5-Land reanalysis data, spatially enhanced by image fusion with CHELSA data
title: Landcover classification map of Germany 2019 based on Sentinel-2 data
abstract: This landcover map was produced as an intermediate result in the course of the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data. This classification is based on a time-series of monthly averaged, atmospherically corrected Sentinel-2 tiles (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). It consists of the following landcover classes: 10: forest 20: low vegetation 30: water 40: built-up 50: bare soil 60: agriculture Potential training and validation areas were automatically extracted using spectral indices and their temporal variability from the Sentinel-2 data itself as well as the following auxiliary datasets: - OpenStreetMap (Map data copyrighted OpenStreetMap contributors and available from htttps://www.openstreetmap.org) - Copernicus HRL Imperviousness Status Map 2018 (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA)) - S2GLC Land Cover Map of Europe 2017 (Malinowski et al. 2020: Automated Production of Land Cover/Use Map of Europe Based on Sentinel-2 Imagery. Remote Sens. 2020, 12(21), 3523; https://doi.org/10.3390/rs12213523) - Germany NUTS administrative areas 1:250000 (© GeoBasis-DE / BKG 2020 / dl-de/by-2-0 / https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html) - Contains modified Copernicus Sentinel data (2019), processed by mundialis Processing was performed for blocks of federal states and individual maps were mosaicked afterwards. For each class 100,000 pixels from the potential training a…
keyword: Sentinel-2
keyword: Classification
keyword: Land Cover
keyword: mFUND
keyword: MAJA
keyword: Infrastuktur
keyword: Umwelt
keyword: Regionen und Städte
keyword: mfund-projekt:incora
keyword: mfund-fkz:19F2079C
keyword: incora
keyword: Germany
theme: Land use
theme: Land cover
temporal: 2019-01-01 / 2019-12-31
accrual periodicity: irregular
license: Data licence Germany - attribution - version 2.0 or later (DL-DE->BY-2.0) | Datenlizenz Deutschland - Namensnennung - Version 2.0 oder neuer
license: { "id": "dl-by-de/2.0", "name": "Datenlizenz Deutschland Namensnennung 2.0", "url": "https://www.govdata.de/dl-de/by-2-0", "quelle": "Source: mundialis GmbH & Co. KG" }
distribution: LICENSE.html
data quality: derived from Sentinel-2 MSI - Level 3A-WASP
title: Landcover classification map of Germany 2020 based on Sentinel-2 data
abstract: This landcover map was produced with a classification method developed in the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data. This classification is based on a time-series of monthly averaged, atmospherically corrected Sentinel-2 tiles (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). It consists of the following landcover classes: 10: forest 20: low vegetation 30: water 40: built-up 50: bare soil 60: agriculture Potential training and validation areas were automatically extracted using spectral indices and their temporal variability from the Sentinel-2 data itself as well as the following auxiliary datasets: - OpenStreetMap (Map data copyrighted OpenStreetMap contributors and available from htttps://www.openstreetmap.org) - Copernicus HRL Imperviousness Status Map 2018 (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA)) - S2GLC Land Cover Map of Europe 2017 (Malinowski et al. 2020: Automated Production of Land Cover/Use Map of Europe Based on Sentinel-2 Imagery. Remote Sens. 2020, 12(21), 3523; https://doi.org/10.3390/rs12213523) - Germany NUTS administrative areas 1:250000 (© GeoBasis-DE / BKG 2020 / dl-de/by-2-0 / https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html) - Contains modified Copernicus Sentinel data (2020), processed by mundialis Processing was performed for blocks of federal states and individual maps were mosaicked afterwards. For each class 100,000 pixels from the potential training ar…
temporal: 2020-01-01 / 2020-12-31
title: Change Detection map of Germany 2016-2019 based on Sentinel-2 data
abstract: This change map was produced as an intermediate result in the course of the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data. The map indicates land cover changes between the years 2016 and 2019. It is a difference map from two classifications based on Sentinel-2 MAJA data (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). More information on the two basis classifications can be found here: https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/db130a09-fc2e-421d-95e2-1575e7c4b45c https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/36512b46-f3aa-4aa4-8281-7584ec46c813 To keep only significant changes in the change detection map, the following postprocessing steps are applied to the initial difference raster: - Modefilter (3x3) to eliminate isolated pixels and edge effects - Information gain in a 4x4 window compares class distribution within the window from the two timesteps. High values indicate that the class distribution in the window has changed, and thus a change is likely. Gain ranges from 0 to 1, all changes < 0.5 are omitted. - Change areas < 1ha are removed The resulting map has the following nomenclature: 0: No Change 1: Change from low vegetation to forest 2: Change from water to forest 3: Change from built-up to forest 4: Change from bare soil to forest 5: Change from agriculture to forest 6: Change from forest to low vegetation 7: Change from water to low vegetation 8: Change from built-up to low vegetation 9: Change from bare soil to low vegetat…
keyword: Change Detection
keyword: Settlement Dynamics
temporal: 2016-01-01 / 2019-12-31
distribution: germany 2016 2019 MAJA cd total v02.sld
distribution: germany 2016 2019 MAJA cd total v02.qml
title: Daily gap-filled MODIS LST data (2003-2019)
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 MOD11A1/MYD11A1 (spatial resolution: 1 km). For this, we combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. The data are provided in GeoTIFF format. The Coordinate Reference System (CRS) is identical to the MOD11A1/MYD11A1 product (Sinusoidal) as provided by NASA. In WKT as reported by GDAL: PROJCRS["unnamed", BASEGEOGCRS["Unknown datum based upon the custom spheroid", DATUM["Not specified (based on custom spheroid)", ELLIPSOID["Custom spheroid",6371007.181,0, LENGTHUNIT["metre",1, ID["EPSG",9001]]]], PRIMEM["Greenwich",0, ANGLEUNIT["degree",0.0174532925199433, ID["EPSG",9122]]]], CONVERSION["unnamed", METHOD["Sinusoidal"], PARAMETER["Longitude of natural origin",0, ANGLEUNIT["degree",0.0174532925199433], ID["EPSG",8802]], PARAMETER["False easting",0, LENGTHUNIT["Meter",1], ID["EPSG",8806]], PARAMETER["False northing",0, LENGTHUNIT["Meter",1], ID["EPSG",8807]]], CS[Cartesian,2], AXIS["easting",east, ORDER[1], LENGTHUNIT["Meter",1]], AXIS["northing",north, ORDER[2], LENGTHUNIT["Meter",1]]] Acknowledgments: We are grateful to the NASA Land Processes Distributed Active Archive Center (LP DAAC) for making the MODIS LST data available. The dataset is based on MODIS Collection V006. Meaning of pixel values: The pixel values are coded in Kelvin * 50 Data type: raster, UInt16 Spatial resolution: 926.62543314 m Spatial extent Sinusoidal (W, S, E, N): 0, 4447802.079066, 2223901.039533, 6671703.118599 Spatial extent in EPSG:4326 (W, S, E, N): 0, 40, 40, 60 [1] Metz M., Andreo V., Neteler M. (2017): A new fully gap…
keyword: land surface temperature
theme: World

https://data.mundialis.de/geonetwork/srv/resources/records/36512b46-f3aa-4aa4-8281-7584ec46c813
type: CatalogRecord
primary topic: dataset
issued: 2023-02-28
modified: 2023-02-28
references: https://data.mundialis.de/geonetwork/srv/resources/records/36512b46-f3aa-4aa4-8281-7584ec46c813/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/36512b46-f3aa-4aa4-8281-7584ec46c813/formatters/xml
format: application/xml
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https://data.mundialis.de/geonetwork/srv/resources/records/9246503f-6adf-460b-a31e-73a649182d07
type: CatalogRecord
primary topic: dataset
issued: 2023-02-28
modified: 2023-02-28
references: https://data.mundialis.de/geonetwork/srv/resources/records/9246503f-6adf-460b-a31e-73a649182d07/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/9246503f-6adf-460b-a31e-73a649182d07/formatters/xml
format: application/xml
  type: IMT
  label: XML

https://data.mundialis.de/geonetwork/srv/resources/records/5ef8565b-ea72-4f34-985b-71ab41959230
type: CatalogRecord
primary topic: dataset
issued: 2021-10-25
modified: 2021-10-25
references: https://data.mundialis.de/geonetwork/srv/resources/records/5ef8565b-ea72-4f34-985b-71ab41959230/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/5ef8565b-ea72-4f34-985b-71ab41959230/formatters/xml
format: application/xml
  type: IMT
  label: XML

https://data.mundialis.de/geonetwork/srv/resources/records/8f4ed617-1783-48fb-a0b6-6e817a0fc7ee
type: CatalogRecord
primary topic: dataset
issued: 2023-12-15
modified: 2023-12-15
references: https://data.mundialis.de/geonetwork/srv/resources/records/8f4ed617-1783-48fb-a0b6-6e817a0fc7ee/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/8f4ed617-1783-48fb-a0b6-6e817a0fc7ee/formatters/xml
format: application/xml
  type: IMT
  label: XML

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))

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
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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/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
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/4ccb09d0-99c1-4509-9cda-9195b5755ceb/formatters/xml
format: application/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
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accrual periodicity: asNeeded
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license: otherRestrictions
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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/
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