This product displays for Hexachlorobenzene, positions with values counts that have been measured per matrix for each year and are present in EMODnet regional contaminants aggregated datasets, v2024. The product displays positions for every available year.

The High Resolution Layer Grassland (GRA) raster product provides a binary status layer of grassland/non-grassland mask. This grassy and non-woody vegetation baseline product includes all kinds of grasslands: managed grassland, semi-natural grassland and natural grassy vegetation. It does not include temporary grasslands, which are masked out using the corresponding Ploughing indicator (PLOUGH), indicating on the number of years since a pixel was last ploughed. This dataset is provided annually starting in 2017 with 10 meter rasters (fully conformant with the EEA reference grid) in 100 x 100 km tiles covering the EEA38 countries. High Resolution Layer Grasslands product is part of the European Union’s Copernicus Land Monitoring Service. Confidence layer available for the dataset. This dataset includes data from the French Overseas Territories (DOMs)

This product displays for Lead, positions with values counts that have been measured per matrix and are present in EMODnet regional contaminants aggregated datasets, v2022. The product displays positions for all available years.

This product displays for Anthracene, positions with values counts that have been measured per matrix and are present in EMODnet regional contaminants aggregated datasets, v2024. The product displays positions for all available years.

This product displays for Cadmium, positions with percentages of all available data values per group of animals that are present in EMODnet regional contaminants aggregated datasets, v2024. The product displays positions for all available years.

The pan-European Very High Resolution (VHR) Image Mosaic 2021 is a seamless mosaic of the VHR 2021 dataset. The input data consists of a mix of Pleiades, SuperView, Worldview, Kompsat-3, Kompsat-4, Geoeye, SPOT, Deimos-2, Vision-1 and TripleSat images. To enhance the appearance of the input imagery, a histogram stretch was applied, cutting off the lowest and highest 0.1 percent of the histogram values and stretching the remaining values to fit the 16-bit pixel depth. For each input image, only selected areas were used to create the mosaic, and the rest was masked out to exclude areas with clouds and their shadows. Color balance was achieved using a second-order method, which modifies all input pixels toward a set of multiple points derived from a two-dimensional polynomial parabolic surface, ensuring a seamless mosaic. For several water bodies, especially large lakes, the final result exhibited a patchy surface pattern due to presence of sun glint on the satellite images. A post-processing methodology was implemented to recalculate the digital values to produce a seamless appearance surface of some of these major lakes: Vänern and Vättern (Sweden), Oulu (Finland), Peipus (Estonia), Geneva (Switzerland/France), Constance (Switzerland/Germany/Austria), Garda and Bolsena (Italy), Skadar (Montenegro/Albania), Prespa (North Macedonia/Albania/Greece), Beysehir, Iznik and Van (Turkey). The applied methodology consisted of creating individual mosaics with the images comprising only the mentioned lakes (a mosaic per lake), calculating Normalized Difference Vegetation Index (NDVI) for shoreline extraction, and color balancing each mosaic individually with all land surfaces masked out, using only pixels belonging to the water category. This approach allowed smoothing the patchy surfaces of the above-mentioned lakes considering statistics solely from the water pixels, ensuring a more uniform appearance. To enhance the visualization of the entire dataset at larger scales (greater than 1:500.000), the mosaic displays pan-European overviews generated from the pan-European Very High Resolution 2018 Image Mosaic. The updated VHR 2021 version is visualized only at scales below 1:500.000. The mosaic primarily is used as input data in the production of various Copernicus Land Monitoring Service (CLMS) datasets and services, such as land cover maps and high-resolution layers on land cover characteristic. It can be also useful for CLMS users for visualizations and classifications on land. The input imagery for the creation of the mosaic is provided by ESA. Due to license restrictions, the VHR Image Mosaic 2021 is only available as a web map service (WMS), and not for data download.

The European Environment Agency (EEA) is an agency of the European Union (EU) tasked with providing independent information on the environment. Established in 1990 and headquartered in Copenhagen, Denmark, the EEA serves as a key source of environmental data, assessments, and reports for policymakers and the public across Europe. With a focus on improving environmental decision-making and promoting sustainable development, the agency plays a vital role in monitoring, analyzing, and communicating environmental trends and challenges. At its core, the EEA aims to support the development, implementation, and evaluation of EU environmental policies. It achieves this mission through a range of activities, including data collection, analysis, and reporting. The agency collaborates with national environmental agencies in EU member states, as well as other international organizations, to gather and harmonize environmental data from across Europe. This wealth of information is then used to produce high-quality assessments and reports on various environmental topics. One of the primary functions of the EEA is to provide regular assessments of the state of Europe's environment. These assessments cover a wide range of issues, including air and water quality, biodiversity, climate change, and resource use. By compiling and analyzing data from different sources, the agency produces comprehensive reports that highlight key environmental trends, identify emerging challenges, and assess progress towards environmental objectives. These assessments are invaluable tools for policymakers, helping them to make informed decisions and prioritize actions to protect and improve the environment. In addition to assessing the state of the environment, the EEA also plays a crucial role in monitoring the effectiveness of environmental policies and measures. The agency tracks the implementation of EU environmental legislation and policies, assessing their impact on the ground. By evaluating the success or shortcomings of these policies, the EEA provides valuable feedback to policymakers, helping them to refine and strengthen environmental governance at the European level. Furthermore, the EEA acts as a hub for environmental information and knowledge exchange. The agency maintains several databases and online platforms, such as the European Environmental Data Centre (EEDC) and the European Environment Information and Observation Network (EIONET), which provide access to a wealth of environmental data, maps, and indicators. These resources are freely available to policymakers, researchers, NGOs, and the public, supporting evidence-based decision-making and fostering greater transparency and accountability in environmental governance. In this page you can find the Web Services of the European Environment Agency

The High Resolution Layer Cropping Patterns - Secondary Crop Type (CPSCT) raster product indicates if a cover crop was present within the respective calendar year and further segregates the types of cover crop into: short summer, long summer, short winter and long winter cover crop. This dataset is provided annually starting in 2017 with 10 meter rasters (fully conformant with the EEA reference grid) in 100 x 100 km tiles covering the EEA38 countries. High Resolution Layer Croplands product is part of the European Union’s Copernicus Land Monitoring Service. Confidence layer available for the dataset. This dataset includes data from the French Overseas Territories (DOMs)

Number of days with an average air temperature above 10 degrees (tas>10 °C) for the period 1991-2020, at a 1x1 km spatial resolution. Based on daily temperature normals.

Average difference between maximum and minimum temperature (approx. 2 meters above the ground) for year or season, for the period 1991-2020, at a 1x1 km spatial resolution.