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  • Overview: rgb: Landsat RGB time-series, derived by the median pixel values obtained between June 25 and September 12 on a specific year. Traceability (lineage): This dataset is a seasonally aggregated and gapfilled version of the Landsat GLAD analysis-ready data product presented by Potapov et al., 2020 ( https://doi.org/10.3390/rs12030426 ). Scientific methodology: The Landsat GLAD ARD dataset was aggregated and harmonized using the eumap python package (available at https://eumap.readthedocs.io/en/latest/ ). The full process of gapfilling and harmonization is described in detail in Witjes et al., 2022 (in review, preprint available at https://doi.org/10.21203/rs.3.rs-561383/v3 ). Usability: The usability of this dataset is demonstrated and discussed in Witjes et al., 2022 (in review). A second publication is planned that will quantify the usability of the gapfilling process for LULC classification. Uncertainty quantification: nan Data validation approaches: This dataset has not been validated Completeness: The dataset is completely gapfilled; a QA layer is available to distinguish measured pixels from gapfilled pixels. Consistency: There are significant differences in the amount of gapfilling required for different regions. For example, data for the winter months in Sweden are almost completely gapfilled due to snow and cloud cover. Positional accuracy: The rasters have a spatial resolution of 30m Temporal accuracy: Each of the 8 Landsat bands is represented by 12 raster layers: 3 percentiles of values for each of 4 seasons. Thematic accuracy: The dataset consists of 8 Landsat bands.

  • Overview: era5.copernicus: precipitation daily sums from 2000 to 2020 resampled with CHELSA to 1 km resolution Traceability (lineage): The data sources used to generate this dataset are ERA5-Land hourly data from 1950 to present (Copernicus Climate Data Store) and CHELSA monthly climatologies. Scientific methodology: The methodology used for downscaling follows established procedures as used by e.g. Worldclim and CHELSA. Usability: The substantial improvement of the spatial resolution together with the high temporal resolution of one day further improve the usability of the original ERA5 Land time series product which is useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states. Uncertainty quantification: The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. Data validation approaches: Validation of the ERA5 Land ddataset against multiple in-situ datasets is presented in the reference paper (Muñoz-Sabater et al., 2021). Completeness: The dataset covers the entire Geo-harmonizer region as defined by the landmask raster dataset. However, some small islands might be missing if there are no data in the original ERA5 Land dataset. Consistency: 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. Positional accuracy: 1 km spatial resolution Temporal accuracy: Daily maps for the years 2020-2020. Thematic accuracy: The raster values represent cumulative daily precipitation in mm x 10.

  • Overview: natura: Protected areas rasterized from NATURA 2000 (A, B and C site categories) and OSM (IUCN Ia, IUCN Ib, IUCN 2, IUCN 3, IUCN 4, IUCN 5, IUCN 6 and others categories), first to 10m spatial resolution and after downsampled to 30m by spatial average. The overlap areas are indicated in a new category. Traceability (lineage): The IUCN status was extracted from crowdsourced data obtained from OpenStreetMap through geofabrik.de aggregated based on labels assigned to the volunteered geographical information objects. Scientific methodology: nan Usability: nan Uncertainty quantification: nan Data validation approaches: This dataset has not been validated. Completeness: Volunteered geographical information often more complete in regions with more active contributors. It is likely that this dataset contains many omission errors in regions of Europe where OpenStreetMap is used less intensively. Consistency: Volunteered geographical information often more complete in regions with more active contributors. It is likely that this dataset contains many omission errors in regions of Europe where OpenStreetMap is used less intensively. Positional accuracy: The rasters have a spatial resolution of 30m Temporal accuracy: The maps are based on an extract from 2020. Thematic accuracy: The 30m pixels of each OSM extract map have values ranging from 0-100, indicating the density aggregated from 10m pixels where rasterized objects burned the value 100 in a 0-value raster.

  • Overview: Aerosol Optical Depth (AOD) at 550 nm Traceability (lineage): The data source used to generate this dataset are daily Aerosol Optical Depth (AOD) at 550 nm with 1 km spatial resolution from the MCD19A2 product of MODIS collection 6 for the years 2018-2020 (https://lpdaac.usgs.gov/products/mcd19a2v006/), daily modeled AOD at 550 nm with 80 km spatial resolution from Copernicus Atmosphere Monitoring Service (CAMS) for the years 2018-2020, and elevations from the worldwide digital surface model of the Japan Aerospace Exploration Agency (JAXA) (https://www.eorc.jaxa.jp/ALOS/). NASA’s Aerosols Robotic Network (AERONET) ground measurements were used to validate this dataset. Scientific methodology: The outcome of the scheme was a geo-harmonized atmospheric dataset for aerosol optical depth (GHADA) at 550 nm with 1 km spatial resolution and full coverage over Europe. Daily AOD maps were created by training an optimized space-time extra trees model for each year 2018-2020. The results have shown that our trained models reach a prediction accuracy up to 95% when predicting the missing values in the MODIS MCD19A2 product. Usability: AOD maps can be used for future air quality studies concerning Europe. Uncertainty quantification: nan Data validation approaches: GHADA was validated using AOD measurements from AERONET stations across Europe. Completeness: The raster files cover the entire Geo-harmonizer region. Consistency: nan Positional accuracy: 1 km spatial resolution Temporal accuracy: Daily maps for 3 years, 2018-2020. Thematic accuracy: nan

  • Overview: ndvi: NDVI time-series, derived from the Landsat quarterly temporal composites. Traceability (lineage): This dataset is a seasonally aggregated and gapfilled version of the Landsat GLAD analysis-ready data product presented by Potapov et al., 2020 ( https://doi.org/10.3390/rs12030426 ). Scientific methodology: The Landsat GLAD ARD dataset was aggregated and harmonized using the eumap python package (available at https://eumap.readthedocs.io/en/latest/ ). The full process of gapfilling and harmonization is described in detail in Witjes et al., 2022 (in review, preprint available at https://doi.org/10.21203/rs.3.rs-561383/v3 ). Usability: The usability of this dataset is demonstrated and discussed in Witjes et al., 2022 (in review). A second publication is planned that will quantify the usability of the gapfilling process for LULC classification. Uncertainty quantification: nan Data validation approaches: This dataset has not been validated Completeness: The dataset is completely gapfilled; a QA layer is available to distinguish measured pixels from gapfilled pixels. Consistency: There are significant differences in the amount of gapfilling required for different regions. For example, data for the winter months in Sweden are almost completely gapfilled due to snow and cloud cover. Positional accuracy: The rasters have a spatial resolution of 30m Temporal accuracy: Each of the 8 Landsat bands is represented by 12 raster layers: 3 percentiles of values for each of 4 seasons. Thematic accuracy: The dataset consists of 8 Landsat bands.

  • Overview: pm2.5: Number of pixels used in aggregating monthly PM2.5 maps. Traceability (lineage): The data source used to generate this dataset are daily Aerosol Optical Depth (AOD) at 550 nm with 1 km spatial resolution from the MCD19A2 product of MODIS collection 6 for the years 2018-2020 (https://lpdaac.usgs.gov/products/mcd19a2v006/), daily modeled AOD at 550 nm with 80 km spatial resolution from Copernicus Atmosphere Monitoring Service (CAMS) for the years 2018-2020, and elevations from the worldwide digital surface model of the Japan Aerospace Exploration Agency (JAXA) (https://www.eorc.jaxa.jp/ALOS/). NASA’s Aerosols Robotic Network (AERONET) ground measurements were used to validate this dataset. Scientific methodology: The outcome of the scheme was a geo-harmonized atmospheric dataset for aerosol optical depth (GHADA) at 550 nm with 1 km spatial resolution and full coverage over Europe. Daily AOD maps were created by training an optimized space-time extra trees model for each year 2018-2020. The results have shown that our trained models reach a prediction accuracy up to 95% when predicting the missing values in the MODIS MCD19A2 product. Usability: AOD maps can be used for future air quality studies concerning Europe. Uncertainty quantification: nan Data validation approaches: GHADA was validated using AOD measurements from AERONET stations across Europe. Completeness: The raster files cover the entire Geo-harmonizer region. Consistency: nan Positional accuracy: 1 km spatial resolution Temporal accuracy: Daily maps for 3 years, 2018-2020. Thematic accuracy: nan

  • Areas planted with vines, vineyard parcels covering >50% and determining the land use of the area.

  • Overview: era5.copernicus: surface temperature daily maxima from 2000 to 2020 resampled with CHELSA to 1 km resolution Traceability (lineage): The data sources used to generate this dataset are ERA5-Land hourly data from 1950 to present (Copernicus Climate Data Store) and CHELSA monthly climatologies. Scientific methodology: The methodology used for downscaling follows established procedures as used by e.g. Worldclim and CHELSA. Usability: The substantial improvement of the spatial resolution together with the high temporal resolution of one day further improve the usability of the original ERA5 Land time series product which is useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states. Uncertainty quantification: The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. Data validation approaches: Validation of the ERA5 Land ddataset against multiple in-situ datasets is presented in the reference paper (Muñoz-Sabater et al., 2021). Completeness: The dataset covers the entire Geo-harmonizer region as defined by the landmask raster dataset. However, some small islands might be missing if there are no data in the original ERA5 Land dataset. Consistency: 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. Positional accuracy: 1 km spatial resolution Temporal accuracy: Daily maps for the years 2020-2020. Thematic accuracy: The raster values represent minimum, mean, and maximum daily surface temperature in degrees Celsius x 10.

  • Overview: era5.copernicus: air temperature daily maxima from 2000 to 2020 resampled with CHELSA to 1 km resolution Traceability (lineage): The data sources used to generate this dataset are ERA5-Land hourly data from 1950 to present (Copernicus Climate Data Store) and CHELSA monthly climatologies. Scientific methodology: The methodology used for downscaling follows established procedures as used by e.g. Worldclim and CHELSA. Usability: The substantial improvement of the spatial resolution together with the high temporal resolution of one day further improve the usability of the original ERA5 Land time series product which is useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states. Uncertainty quantification: The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. Data validation approaches: Validation of the ERA5 Land ddataset against multiple in-situ datasets is presented in the reference paper (Muñoz-Sabater et al., 2021). Completeness: The dataset covers the entire Geo-harmonizer region as defined by the landmask raster dataset. However, some small islands might be missing if there are no data in the original ERA5 Land dataset. Consistency: 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. Positional accuracy: 1 km spatial resolution Temporal accuracy: Daily maps for the years 2020-2020. Thematic accuracy: The raster values represent minimum, mean, and maximum daily air temperature 2m above ground in degrees Celsius x 10.

  • Overview: era5.copernicus: surface temperature daily minima from 2000 to 2020 resampled with CHELSA to 1 km resolution Traceability (lineage): The data sources used to generate this dataset are ERA5-Land hourly data from 1950 to present (Copernicus Climate Data Store) and CHELSA monthly climatologies. Scientific methodology: The methodology used for downscaling follows established procedures as used by e.g. Worldclim and CHELSA. Usability: The substantial improvement of the spatial resolution together with the high temporal resolution of one day further improve the usability of the original ERA5 Land time series product which is useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states. Uncertainty quantification: The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. Data validation approaches: Validation of the ERA5 Land ddataset against multiple in-situ datasets is presented in the reference paper (Muñoz-Sabater et al., 2021). Completeness: The dataset covers the entire Geo-harmonizer region as defined by the landmask raster dataset. However, some small islands might be missing if there are no data in the original ERA5 Land dataset. Consistency: 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. Positional accuracy: 1 km spatial resolution Temporal accuracy: Daily maps for the years 2020-2020. Thematic accuracy: The raster values represent minimum, mean, and maximum daily surface temperature in degrees Celsius x 10.