332: Scree, cliffs, rock outcrops, including areas of active erosion, rocks and reef flats situated above the high-water mark, inland salt planes.

Overview: 422: Sections of salt marsh exploited for the production of salt by evaporation, active or in processof abandonment, distinguishable from marsh by parcellation or embankment systems. Traceability (lineage): This dataset was produced with a machine learning framework with several input datasets, specified in detail in Witjes et al., 2022 (in review, preprint available at https://doi.org/10.21203/rs.3.rs-561383/v3 ) Scientific methodology: The single-class probability layers were generated with a spatiotemporal ensemble machine learning framework detailed in Witjes et al., 2022 (in review, preprint available at https://doi.org/10.21203/rs.3.rs-561383/v3 ). The single-class uncertainty layers were calculated by taking the standard deviation of the three single-class probabilities predicted by the three components of the ensemble. The HCL (hard class) layers represents the class with the highest probability as predicted by the ensemble. Usability: The HCL layers have a decreasing average accuracy (weighted F1-score) at each subsequent level in the CLC hierarchy. These metrics are 0.83 at level 1 (5 classes):, 0.63 at level 2 (14 classes), and 0.49 at level 3 (43 classes). This means that the hard-class maps are more reliable when aggregating classes to a higher level in the hierarchy (e.g. 'Discontinuous Urban Fabric' and 'Continuous Urban Fabric' to 'Urban Fabric'). Some single-class probabilities may more closely represent actual patterns for some classes that were overshadowed by unequal sample point distributions. Users are encouraged to set their own thresholds when postprocessing these datasets to optimize the accuracy for their specific use case. Uncertainty quantification: Uncertainty is quantified by taking the standard deviation of the probabilities predicted by the three components of the spatiotemporal ensemble model. Data validation approaches: The LULC classification was validated through spatial 5-fold cross-validation as detailed in the accompanying publication. Completeness: The dataset has chunks of empty predictions in regions with complex coast lines (e.g. the Zeeland province in the Netherlands and the Mar da Palha bay area in Portugal). These are artifacts that will be avoided in subsequent versions of the LULC product. Consistency: The accuracy of the predictions was compared per year and per 30km*30km tile across europe to derive temporal and spatial consistency by calculating the standard deviation. The standard deviation of annual weighted F1-score was 0.135, while the standard deviation of weighted F1-score per tile was 0.150. This means the dataset is more consistent through time than through space: Predictions are notably less accurate along the Mediterrranean coast. The accompanying publication contains additional information and visualisations. Positional accuracy: The raster layers have a resolution of 30m, identical to that of the Landsat data cube used as input features for the machine learning framework that predicted it. Temporal accuracy: The dataset contains predictions and uncertainty layers for each year between 2000 and 2019. Thematic accuracy: The maps reproduce the Corine Land Cover classification system, a hierarchical legend that consists of 5 classes at the highest level, 14 classes at the second level, and 44 classes at the third level. Class 523: Oceans was omitted due to computational constraints.

311: Slope of broad-leaved forest derived by OLS regression over the probabilities values (2000—2019). The std. error of the model was considered as uncertainty.

nuts: Administrative areas according to NUTS and OSM, rasterized to provide one single hierarchical code as pixel value for each county in EU.

312: R2 of OLS regression calculated over the probabilities values (2000—2019) for coniferous forest.

Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. With a novel method [1] we fully reconstructed the daily global MODIS LST products MOD11C1 and MYD11C1 (spatial resolution: 3 arc-min, i.e. approximately 5.6 km at the equator). For this, we combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. Here we provide a time series of these reconstructed LST data aggregated as monthly average, minimum and maximum LST maps. [1] Metz M., Andreo V., Neteler M. (2017): A new fully gap-free time series of Land Surface Temperature from MODIS LST data. Remote Sensing, 9(12):1333. DOI: http://dx.doi.org/10.3390/rs9121333 The data available here for download are the reconstructed global MODIS LST products MOD11C1/MYD11C1 at a spatial resolution of 3 arc-min (approximately 5.6 km at the equator; see https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table), aggregated to monthly data. The data are provided in GeoTIFF format. The Coordinate Reference System (CRS) is identical to the MOD11C1/MYD11C1 product as provided by NASA. In WKT as reported by GDAL: GEOGCS["Unknown datum based upon the Clarke 1866 ellipsoid", DATUM["Not specified (based on Clarke 1866 spheroid)", SPHEROID["Clarke 1866",6378206.4,294.9786982138982, AUTHORITY["EPSG","7008"]]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]] Acknowledgments: We are grateful to the NASA Land Processes Distributed Active Archive Center (LP DAAC) for making the MODIS LST data available. The dataset is based on MODIS Collection V006. File name abbreviations: avg = average of daily averages min = minimum of daily minima max = maximum of daily maxima Meaning of pixel values: The pixel values are coded in degree Celsius * 100 (hence, to obtain °C divide the pixel values by 100.0).

pm2.5: Monthly PM2.5 maps aggregated from the daily PM2.5 predictions.

corine: Landcover sample points

111: Areas mainly occupied by dwellings and buildings used by administrative/public utilities, including their connected areas (associated lands, approach road network, parking lots).

The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for the archive and distribution of NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Digital Elevation Model (DEM) version 1 (NASADEM_SC) dataset, which provides global slope and curvature elevation data at 1 arc second spacing. NASADEM data products were derived from original telemetry data from the Shuttle Radar Topography Mission (SRTM), a collaboration between NASA and the National Geospatial-Intelligence Agency (NGA), as well as participation from the German and Italian space agencies. SRTM’s primary focus was to generate a near-global DEM of the Earth using radar interferometry. It was a primary component of the payload on space shuttle Endeavour during its STS-99 mission, which was launched on February 11, 2000, and flew for 11 days. In addition to Terra Advanced Spaceborne Thermal and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) Version 3 data, NASADEM also relied on Ice, Cloud, and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) ground control points of its lidar shots to improve surface elevation measurements that led to improved geolocation accuracy. Other reprocessing improvements include the conversion to geoid reference and the use of GDEMs and Advanced Land Observing Satellite Panchromatic Remote-sensing instrument for Stereo Mapping (PRISM) AW3D30 DEM, and interpolation for void filling. NASADEM are distributed in 1° by 1° tiles and consist of all land between 60° N and 56° S latitude. This accounts for about 80% of Earth’s total landmass. NASADEM_SC data product layers include slope, aspect angle, profile curvature, plan curvature, and an updated SRTM water body dataset (water mask). A low-resolution browse image showing slope is also available for each NASADEM_SC granule.