The Potential Soil Erosion Risk map gives an overview of the exposure of arable soils to soil loss due to surface runoff and splash erosion in Germany. It is based on pedological, relief and climatic factors. The map was created by using the long-term model USLE (Universal Soil Loss Equation). The method is published in the DIN 19708:2005-02 and in the documentation of Ad-hoc-AG Boden (representing the soil experts of the geological services of the German federal states). For the application with soil maps, the method was adapted by the Federal Institute for Geosciences and Natural Resources (BGR).The landuse stratified soil map of Germany at scale 1:1,000,000 was used as pedological input to the model. The relief data was derived from the DEM50 of the BKG. The mean annual precipitation data of the period 1961-1990 (DWD) is used to model the erosivity of rainfall. The land use information is derived from CORINE land cover data set (2006).

The Web Map Service (WMS) shows the distribution of typical soil types (soil texture) in the topsoils of Germany. Typical is used in the term of areally dominating. The map visualizes the results of the project that are documented in a BGR report (Bodenarten der Böden Deutschlands; BGR Archiv, Nr. 0127305). The soil texture data from the analysis of the particle size distribution for 16,132 sites in Germany were classified after the legend units of land use-stratified soil map of Germany 1: 1,000,000 (BÜK1000N V2.3) and mean soil texture were calculated. Considering the large heterogeneity in the data and the resulting uncertaintly in the precision for a site the depiction of the obtained soil texture is presented at the level of the soil types group, according to the German soil classification system (KA5).

INSPIRE theme Oceanographic Geographical Features.

The Map of Mineral Resources of Germany 1: 1,000,000 (BSK1000) provides the basic information on the spatial distribution of energy resources and mineral raw materials (‘stones and earth’, industrial minerals and ores) in Germany. The mineral resources are summarized in commodity groups, represented as different colored units. The map is published by the Federal Institute for Geosciences and Natural Resources in cooperation with the National Geological Surveys.

The Potential Wind Erosion Risk map gives an overview of the exposure of arable soils to soil loss due to deflation in Germany. It is based on pedological and climatic factors. The method to predict the soil erosion risk is published in the DIN 19706:2002 and in the documentation of Ad-hoc-AG Boden (representing the soil experts of the geological services of the German federal states). For the application with soil maps, the method was adapted by the Federal Institute for Geosciences and Natural Resources (BGR).The land use stratified soil map of Germany at scale 1:1,000,000 was used as pedological input to the model. The mean annual wind speed at 10 meters above ground level of the period 1980-2000 (DWD) is used as well. The land use information is derived from CORINE land cover data set (2006).

IceLines (Ice Shelf and Glacier Front Time Series) is an automated calving front monitoring service providing monthly ice shelf front time series of major Antarctic ice shelves. The provided time series allows to discover the dynamics of ice shelf front changes and calving events. The front positions are automatically derived from Sentinel-1 data based on a deep neuronal network called HED-U-Net. The time series covers the timespan 2014 to today (partly limited due to Sentinel-1 data availability). Incorrectly extracted fronts are truncated which might lead to gaps in the time series especially between December to March due to strong surface melt. Annual averages are calculated based on the extracted monthly fronts (excluding the summer months) and provide more robust results due to temporal aggregation

INSPIRE theme Elevation (bathymetry). Provision of the topography of the seabed in the North and Baltic Sea.

This product shows the snow cover duration for a hydrological year. Its beginning differs from the calendar year, since some of the precipitation that falls in late autumn and winter falls as snow and only drains away when the snow melts in the following spring or summer. The meteorological seasons are used for subdivision and the hydrological year begins in autumn and ends in summer. The snow cover duration is made available for three time periods: the snow cover duration for the entire hydrological year (SCD), the early snow cover duration (SCDE), which extends from autumn to midwinter (), and the late snow cover duration (SCDL), which in turn extends over the period from mid-winter to the end of summer. For the northern hemisphere SCD lasts from September 1st to August 31st, for the southern hemisphere it lasts from March 1st to February 28th/29th. The SCDE lasts from September 1st to January 14th in the northern hemisphere and from March 1st to July 14th in the southern hemisphere. The SCDL lasts from January 15th to August 31st in the northern hemisphere and from July 15th to February 28th/29th in the southern hemisphere. The “Global SnowPack” is derived from daily, operational MODIS snow cover product for each day since February 2000. Data gaps due to polar night and cloud cover are filled in several processing steps, which provides a unique global data set characterized by its high accuracy, spatial resolution of 500 meters and continuous future expansion. It consists of the two main elements daily snow cover extent (SCE) and seasonal snow cover duration (SCD; full and for early and late season). Both parameters have been designated by the WMO as essential climate variables, the accurate determination of which is important in order to be able to record the effects of climate change. Changes in the largest part of the cryosphere in terms of area have drastic effects on people and the environment. For more information please also refer to: Dietz, A.J., Kuenzer, C., Conrad, C., 2013. Snow-cover variability in central Asia between 2000 and 2011 derived from improved MODIS daily snow-cover products. International Journal of Remote Sensing 34, 3879–3902. https://doi.org/10.1080/01431161.2013.767480 Dietz, A.J., Kuenzer, C., Dech, S., 2015. Global SnowPack: a new set of snow cover parameters for studying status and dynamics of the planetary snow cover extent. Remote Sensing Letters 6, 844–853. https://doi.org/10.1080/2150704X.2015.1084551 Dietz, A.J., Wohner, C., Kuenzer, C., 2012. European Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products. Remote Sensing 4. https://doi.org/10.3390/rs4082432 Dietz, J.A., Conrad, C., Kuenzer, C., Gesell, G., Dech, S., 2014. Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data. Remote Sensing 6. https://doi.org/10.3390/rs61212752 Rößler, S., Witt, M.S., Ikonen, J., Brown, I.A., Dietz, A.J., 2021. Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers. Geosciences 11, 130. https://doi.org/10.3390/geosciences11030130

INSPIRE theme Planned Land Use (Marine Spatial Planning). Provision of marine spatial planning exclusive economic zone of Germany.