This repository contains the datasets and codes used in the research article "Revealing European-wide ecosystem strategies to drought from space".

The LAI data presented here is derived from the GEOV2 LAI product, provided by the European Union’s Copernicus Land Monitoring Service. Original data accessed from: https://land.copernicus.eu/global/products/lai.

The Sentinel-1C satellite was launched December 5, 2024. Sentinel-1C is the the latest satellite to be added to the Sentinel-1 constellation. The Sentinel-1 satellites (Sentinel-1A, Sentinel-1B, and Sentinel-1C) are sun-synchronous polar-orbiting satellites that operate day and night performing C-band synthetic aperture radar (SAR) imaging. The Sentinel-1 satellites operate in four imaging modes with different resolutions (down to 5 meters) and coverage (up to 400 kilometers). The Sentinel-1 satellites provide dual polarization capability and short revisit times.

Sentinel-1C Level 0 products consist of compressed and unprocessed instrument source packets, with additional annotations and auxiliary information to support processing. Level 0 products are the basis from which all other high level products are produced. They are compressed using Flexible Dynamic Block Adaptive Quantization (FDBAQ) which provides a variable bit rate coding that increases the number of bits allocated to bright scatterers. For the data to be usable, it will need to be decompressed and processed using focusing software.

Level 0 data includes noise, internal calibration and echo source packets as well as orbit and attitude information.

The data products in this collection mirror the Sentinel-1C products provided through the Copernicus Data Space Ecosystem.

Sentinel-2, 10, 20, and 60m Multispectral, Multitemporal, 13-band imagery is rendered on-the-fly and available for visualization. This imagery layer pulls directly from the Sentinel-2 on AWS collection and is updated daily with new imagery.This imagery layer can be applied across a number of industries, scientific disciplines, and management practices. Some applications include, but are not limited to, land cover and environmental monitoring, climate change, deforestation, disaster and emergency management, national security, plant health and precision agriculture, forest monitoring, watershed analysis and runoff predictions, land-use planning, tracking urban expansion, highlighting burned areas and estimating fire severity.Geographic CoverageGlobalContinental land masses from 65.4° South to 72.1° North, with these special guidelines:All coastal waters up to 20 km from the shoreAll islands greater than 100 km2All EU islandsAll closed seas (e.g. Caspian Sea)The Mediterranean SeaTemporal CoverageThe revisit time for each point on Earth is every 5 days.This layer is updated daily with new imagery.This imagery layer includes a rolling collection of imagery acquired within the past 14 months.The number of images available will vary depending on location.Product LevelThis service provides Level-1C Top of Atmosphere imagery.Alternatively, Sentinel-2 Level-2A is also available.Image Selection/FilteringThe most recent and cloud free images are displayed by default.Any image available within the past 14 months can be displayed via custom filtering.Filtering can be done based on attributes such as Acquisition Date, Estimated Cloud Cover, and Tile ID.Tile_ID is computed as [year][month][day]T[hours][minutes][seconds]_[UTMcode][latitudeband][square]_[sequence]. More…Visual RenderingDefault rendering is Natural Color (bands 4,3,2) with Dynamic Range Adjustment (DRA).The DRA version of each layer enables visualization of the full dynamic range of the images.Rendering (or display) of band combinations and calculated indices is done on-the-fly from the source images via Raster Functions.Various pre-defined Raster Functions can be selected or custom functions created.Available renderings include: Agriculture with DRA, Bathymetric with DRA, Color-Infrared with DRA, Natural Color with DRA, Short-wave Infrared with DRA, Geology with DRA, NDMI Colorized, Normalized Difference Built-Up Index (NDBI), NDWI Raw, NDWI - with VRE Raw, NDVI – with VRE Raw (NDRE), NDVI - VRE only Raw, NDVI Raw, Normalized Burn Ratio, NDVI Colormap.Multispectral BandsBandDescriptionWavelength (µm)Resolution (m)1Coastal aerosol0.433 - 0.453602Blue0.458 - 0.523103Green0.543 - 0.578104Red0.650 - 0.680105Vegetation Red Edge0.698 - 0.713206Vegetation Red Edge0.733 - 0.748207Vegetation Red Edge0.773 - 0.793208NIR0.785 - 0.900108ANarrow NIR0.855 - 0.875209Water vapour0.935 - 0.9556010SWIR – Cirrus1.365 - 1.3856011SWIR-11.565 - 1.6552012SWIR-22.100 - 2.28020Additional NotesOverviews exist with a spatial resolution of 150m and are updated every quarter based on the best and latest imagery available at that time.To work with source images at all scales, the ‘Lock Raster’ functionality is available.NOTE: ‘Lock Raster’ should only be used on the layer for short periods of time, as the imagery and associated record Object IDs may change daily.This ArcGIS Server dynamic imagery layer can be used in Web Maps and ArcGIS Desktop as well as Web and Mobile applications using the REST based Image services API.Images can be exported up to a maximum of 4,000 columns x 4,000 rows per request.Data SourceSentinel-2 imagery is the result of close collaboration between the (European Space Agency) ESA, the European Commission and USGS. Data is hosted by the Amazon Web Services as part of their Registry of Open Data. Users can access the imagery from Sentinel-2 on AWS , or alternatively access EarthExplorer or the Copernicus Data Space Ecosystem to download the scenes.For information on Sentinel-2 imagery, see Sentinel-2.

The Sentinel-1C satellite was launched December 5, 2024. Sentinel-1C is the the latest satellite to be added to the Sentinel-1 constellation. The Sentinel-1 satellites (Sentinel-1A, Sentinel-1B, and Sentinel-1C) are sun-synchronous polar-orbiting satellites that operate day and night performing C-band synthetic aperture radar (SAR) imaging. The Sentinel-1 satellites operate in four imaging modes with different resolutions (down to 5 meters) and coverage (up to 400 kilometers). The Sentinel-1 satellites provide dual polarization capability and short revisit times.

Sentinel-1C Level 0 products consist of compressed and unprocessed instrument source packets, with additional annotations and auxiliary information to support processing. Level 0 products are the basis from which all other high level products are produced. They are compressed using Flexible Dynamic Block Adaptive Quantization (FDBAQ) which provides a variable bit rate coding that increases the number of bits allocated to bright scatterers. For the data to be usable, it will need to be decompressed and processed using focusing software.

Level 0 data includes noise, internal calibration and echo source packets as well as orbit and attitude information.

The data products in this collection mirror the Sentinel-1C products provided through the Copernicus Data Space Ecosystem.

Algorithm description

This is a prototype product for a new Copernicus Service element which is being developed in the course of FOCCUS. The algorithm computes percent cover of emerging aquatic vegetation (seagrass and macroalgae) in intertidal regions of the German Bight based on temporal aggregates of the Normalised Difference Vegetation Index (NDVI). Sentinel-2 data are atmospherically corrected using sen2cor and pixels identified as water or clouds using IdePix are masked. Images are processed and aggregated using the Copernicus Data Space Ecosystem. Retrieved NDVI values are temporally aggregated per pixel around the time of maximum seagrass cover in July/August/September. Aggregated NDVI values are translated into vegetation percent cover based on an empirical relationship.

Limitations

Data availability depends on water level at the time of satellite acquisitions and cloud cover and may therefore vary spatially and temporally. The algorithm does not enable differentiation between different types of emerging aquatic vegetation (e.g., macroalgae, seagrass, diatoms) and does not consider vegetation health. This information would be subject of further development based on the delivered products.

Corine Land Cover 2018 (CLC2018) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 2018.

CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe.

CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (LCC), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring.

Algorithm description

This is a prototype product for a new Copernicus Service element which is being developed in the course of FOCCUS. The algorithm computes the presence/absence of submerged aquatic vegetation (SAV; seagrass and macroalgae) in subtidal regions of the Balearic Islands based on temporally aggregated Sentinel-2 imagery and EMODNET bathymetry. Sentinel-2 data are atmospherically corrected using ACOLITE and water pixels are identified using IdePix. Images are processed and aggregated using the Copernicus Data Space Ecosystem. Aggregate images are classified as SAV (0) or non-SAV (1) using machine learning methods. Results are clipped to water depths shallower than 25 m and cleaned up using a 3x3 pixel median filter.

Limitations

Data availability depends on the number of usable observations (clouds, water clarity, glint) and may therefore vary spatially and temporally. The quality of the products depends on proper flagging by IdePix classification and other masks applied. The algorithm does not differentiate between different types of submerged aquatic vegetation (e.g., macroalgae or seagrass) and does not consider vegetation health. A known issue is are misclassifications of optically deep water in some areas.