The Sentinel-2 fractional vegetation cover (fCover) product for the Netherlands was produced as part of the NextGEOSS project at the German Aerospace Center (DLR). The goal is to derive abundance maps from atmospherically corrected Sentinel-2 multispectral images for: photosynthetically active vegetation (PV); and for combined non-photosynthetically active vegetation (NPV) and bare soil (BS). The fCover product for the Netherlands has been generated by processing 10 cloud-free Sentinel-2 tiles which covered the country on 8 September 2016. The map has a spatial resolution of 60m x 60m. The Sentinel-2 scene classification layer was used to ensure that the spectral unmixing was only performed on areas of vegetation or soil. The abundance maps were made by performing MESMA unmixing on each pixel from an endmember library of PV and combined NPV + BS spectra. The purest pixels in a scene, called endmembers, were extracted using the Spatial-Spectral Endmember Extraction (SSEE) approach. The PV and NPV+BS endmembers were classified with a random forest approach and selected to form the spectral library. The spectral library was used in the µMESMA unmixing to get the PV and NPV+BS abundances.

This dataset includes the normalized difference vegetation index (NDVI) derived from Sentinel-2 imagery. Using the Google Earth Engine, all granules with a cloud cover below 60% were used as input. Cloudy pixels (referring to quality layer QA60) were masked as well. Eventually, a median mosaic was composed over the whole observation period. It was also used as input for a land cover classification (see: Land Cover DE - Sentinel-2 - Germany, 2015).

This land cover classification of Germany was created using Sentinel-2 imagery from the years 2015 to 2017 and LUCAS 2015 in-situ reference data (https://ec.europa.eu/eurostat/web/lucas). It contains seven land cover types: (1) artificial land, (2) open soil, (3) high seasonal vegetation, (4) high perennial vegetation, (5) low seasonal vegetation, (6) low perennial vegetation and (7) water with a spatial resolution of 10m x 10m. For further information, please see the following publication: https://doi.org/10.1016/j.jag.2020.102065

The objective of the pan-European project CORINE Land Cover (CLC) is the provision of a unique and comparable data set of land cover for Europe and the delivery of regular updates to register also the land cover and land use changes over time. It is part of the European Union programme CORINE (Coordination of Information on the Environment). The mapping of the land cover and land use was performed on the basis of satellite remote sensing images. The first CLC data base CLC1990, which was finalized in the 1990s, consistently provided land use information comprising 44 classes, out of which 37 classes are relevant in Germany. The first two updates for Europe were based on the reference years 2000 and 2006. For Germany, DLR-DFD was responsible for the creation of CLC2000 and CLC2006 on behalf of the Federal Environment Agency. In addition to the updated land cover, change datasets were also parts of the project. For deriving a meaningful CLC2000 change product, it became necessary to re-interprete parts of the satellite data of 1990 and to create a revised product, called CLC1990 (rev). Further details: https://www.dlr.de/en/eoc/research-transfer/projects-missions/corine-land-cover

The objective of the pan-European project CORINE Land Cover (CLC) is the provision of a unique and comparable data set of land cover for Europe and the delivery of regular updates to register also the land cover and land use changes over time. It is part of the European Union programme CORINE (Coordination of Information on the Environment). The mapping of the land cover and land use was performed on the basis of satellite remote sensing images. The first CLC data base CLC1990, which was finalized in the 1990s, consistently provided land use information comprising 44 classes, out of which 37 classes are relevant in Germany. The first two updates for Europe were based on the reference years 2000 and 2006. For Germany, DLR-DFD was responsible for the creation of CLC2000 and CLC2006 on behalf of the Federal Environment Agency. In addition to the updated land cover, change datasets were also parts of the project. For deriving a meaningful CLC2000 change product, it became necessary to re-interprete parts of the satellite data of 1990 and to create a revised product, called CLC1990 (rev). Further details: https://www.dlr.de/en/eoc/research-transfer/projects-missions/corine-land-cover

The objective of the pan-European project CORINE Land Cover (CLC) is the provision of a unique and comparable data set of land cover for Europe and the delivery of regular updates to register also the land cover and land use changes over time. It is part of the European Union programme CORINE (Coordination of Information on the Environment). The mapping of the land cover and land use was performed on the basis of satellite remote sensing images. The first CLC data base CLC1990, which was finalized in the 1990s, consistently provided land use information comprising 44 classes, out of which 37 classes are relevant in Germany. The first two updates for Europe were based on the reference years 2000 and 2006. For Germany, DLR-DFD was responsible for the creation of CLC2000 and CLC2006 on behalf of the Federal Environment Agency. In addition to the updated land cover, change datasets were also parts of the project. For deriving a meaningful CLC2000 change product, it became necessary to re-interprete parts of the satellite data of 1990 and to create a revised product, called CLC1990 (rev). Further details: https://www.dlr.de/en/eoc/research-transfer/projects-missions/corine-land-cover

The objective of the pan-European project CORINE Land Cover (CLC) is the provision of a unique and comparable data set of land cover for Europe and the delivery of regular updates to register also the land cover and land use changes over time. It is part of the European Union programme CORINE (Coordination of Information on the Environment). The mapping of the land cover and land use was performed on the basis of satellite remote sensing images. The first CLC data base CLC1990, which was finalized in the 1990s, consistently provided land use information comprising 44 classes, out of which 37 classes are relevant in Germany. The first two updates for Europe were based on the reference years 2000 and 2006. For Germany, DLR-DFD was responsible for the creation of CLC2000 and CLC2006 on behalf of the Federal Environment Agency. In addition to the updated land cover, change datasets were also parts of the project. For deriving a meaningful CLC2000 change product, it became necessary to re-interprete parts of the satellite data of 1990 and to create a revised product, called CLC1990 (rev). Further details: https://www.dlr.de/en/eoc/research-transfer/projects-missions/corine-land-cover

The objective of the pan-European project CORINE Land Cover (CLC) is the provision of a unique and comparable data set of land cover for Europe and the delivery of regular updates to register also the land cover and land use changes over time. It is part of the European Union programme CORINE (Coordination of Information on the Environment). The mapping of the land cover and land use was performed on the basis of satellite remote sensing images. The first CLC data base CLC1990, which was finalized in the 1990s, consistently provided land use information comprising 44 classes, out of which 37 classes are relevant in Germany. The first two updates for Europe were based on the reference years 2000 and 2006. For Germany, DLR-DFD was responsible for the creation of CLC2000 and CLC2006 on behalf of the Federal Environment Agency. In addition to the updated land cover, change datasets were also parts of the project. For deriving a meaningful CLC2000 change product, it became necessary to re-interprete parts of the satellite data of 1990 and to create a revised product, called CLC1990 (rev). Further details: https://www.dlr.de/en/eoc/research-transfer/projects-missions/corine-land-cover

Gridded Level 3 tropical tropospheric ozone column densities derived from the Metop/GOME-2-instruments. In the stratosphere – where the majority of the total O3 amount is located - O3 plays an vital role for the UV protection. In the troposphere O3 is generated by chemical processes caused by natural and anthropogenic emission of NO2 and volatile organic components (VOCs) (e.g. HCHO). Direct exposure to O3 is harmfull for humans and our environment. The total O3 column is retrieved from GOME solar back-scattered measurements in the UV wavelength region 325-335nm [using the DOAS method]. The tropical tropospheric ozone is retrieved by applying the convective cloud differential method (Valks et al., 2014 http://www.atmos-meas-tech.net/7/2513/2014/amt-7-2513-2014.html). This method estimates the stratospheric ozone column amount as the column above high reaching convective clouds in the tropics. The tropospheric column is determined by subtracting the stratospheric ozone column from the retrieved total column. The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Three instruments operate on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in 2006, 2012, and 2018, respectively. GOME-2 measures a range of atmospheric trace constituents, with the emphasis on global ozone distribution. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Composition Monitoring (AC-SAF).

The TimeScan product is based on the fully-automated analysis of comprehensive time-series acquisitions of Landsat data. Based on a user-specified definition of the required period of time, the region of interest and – optionally – the maximum cloud cover, the TimeScan processor starts with the collection of all available Landsat scenes that meet the user specification. Next, for each single scene masking of clouds, haze and shadow is conducted using the Fmask algorithm. Then, a total of 6 indices is calculated for those pixels of each single scene that have not been masked in the prior step. The set of indices includes the Normalized Difference Vegetation Index (NDVI), the Built-up Index (BI), the Modified Normalized Difference Water Index (MNDWI), the Normalized Difference Band-5 / Band-7 (ND57), the Normalized Difference Band-4 / Band-3 (ND43), and the Normalized Difference Band-3 / Band-2 (ND32). Finally, the TimeScan product is generated by calculating the temporal statistics (minimum, maximum, mean, standard deviation, mean slope) for each index over the defined period of time. Hence, in case of the defined 6 indices chosen, the TimeScan product will include a total of 30 bands (5 statistical features per index). As an additional band a quality layer is added which shows for each pixel the number of valid values (meaning times with no cloud/haze or shadow cover) that have been included in the statistics calculation.