Geoservice Maps List of Web Maps Contexts

Abstract:

TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements) is an Earth observation radar mission that consists of a SAR interferometer built by two almost identical satellites flying in close formation. With a typical separation between the satellites of 120 m to 500 m a global Digital Elevation Model (DEM) has been generated.

The TanDEM-X 90m DEM is a product variant of the global Digital Elevation Model (DEM) acquired in the frame of the German TanDEM-X mission between 2010 and 2014, and has a reduced pixel spacing of 3 arcseconds (arsec), which corresponds to 90m at the equator. It covers all Earth’s landmasses from pole to pole.

This map shows all 8 original TanDEM-X Information Layers as specified in the Product Specification:

• TDM90 Elevation (DEM): The DEM elevation values represent the ellipsoidal heights relative to the WGS84 ellipsoid in the WGS84-G1150 datum.
• TDM90 Height Error Map (HEM): The height error map (HEM) values represent for each DEM pixel the corresponding height error in form of the standard deviation.
• TDM90 Amplitute Min/Mean (AMP/AM2): The amplitude mosaic AMP and AM2 layers are a by-product generated for further processings, e.g. water body detection or DEM filtering. They represent the mean (AMP) and minimum value (AM2) of all calibrated amplitude values from the contributing DEM scenes (in general between 2 and up to 10 scenes).
• TDM90 Consistency Map (COM): The consistency mask (COM) indicates DEM pixels, which have height inconsistencies among the contributing DEM scenes.
• TDM90 Coverage Map (COV): The coverage map (COV) indicates how many valid height values from different DEM acquisitions were available for mosaicking.
• TDM90 Layover & Shadow Mask (LSM): The layover and shadow mask (LSM) is based on the SRTM-C DEM and the GLOBE DEM regarding the TanDEM-X geometry of each individual scene.
• TDM90 Water Indication Mask (WAM): The water indication mask (WAM) layer represents the water bodies indentified during processing.

Abstract:

The TanDEM-X Forest/Non-Forest Map is a project developed by the Microwaves and Radar Institute at the German Aerospace Center (DLR), within the activities of the TanDEM-X mission. The goal is the derivation of a global forest/non-forest classification mosaic from TanDEM-X bistatic interferometric synthetic aperture radar (InSAR) data, acquired for the generation of the global digital elevation model (DEM) in Stripmap single polarization (HH) mode.

The TanDEM-X Forest/Non-Forest Map (FNF) has been generated by processing and mosaicking more than 500,000 TanDEM-X bistatic images acquired from 2011 until 2015. The map has a spatial resolution of 50 m x 50 m. Forested and non-forested areas are depicted in green and white, respectively. Water bodies are depicted in blue and black is used for identifying urban areas and invalid pixels.

For further information, please consult the FNF50 Product Specification.

License:

License for the utilization of the TanDEM-X 50m FNF for Scientific Use (eng)
Lizenz für die Nutzung der TanDEM-X 50m Waldkarte für wissenschaftliche Zwecke (de)

Abstract:

This map contains elevation products at a spatial resolution of approximately 25 m x 25 m covering the globe between latitudes 60� North and 58� South. These products were generated from data collected by the German-Italian interferometric X-band radar system (X-SAR) onboard the Space Shuttle Endeavour during the Shuttle Radar Topography Mission (SRTM) between February 11 and February 22, 2000. The data have been processed into digital elevation models using radar interferometry, an innovative way for extracting surface height from the phase difference between two synthetic aperture radar datasets - a procedure somewhat resembling stereo viewing. The original tiled elevation products have been mosaicked into a single global elevation layer from which additional products have been derived. Shaded reliefs in grayscale and with color-coded elevation values provide a 3D impression of the global topography while a global error map, derived mainly from phase and baseline stability, helps to assess the relative accuracy of the measured elevation values.

Abstract:

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. 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 on a scale of 1:100,000. The first CLC data base CLC1990, which was finalised in the 1990s, consistently provided land use information comprising 44 classes, out of which 37 classes are relevant in Germany.

In the project CORINE Land Cover 2000 (CLC2000), an update of the database and a mapping of changes have been accomplished using the year 2000 as reference. The project CLC2000, which resulted in area-wide land use and land use change maps of Germany, was led by the German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR) on behalf of the Federal Environmental Agency (UBA). With CLC2000 a reliable, objective and comparable data base for the description of the current situation (at 2000) and the analysis of changes during the decade between 1990 and 2000 is available.

Integrated in the European GMES activities, a further update of CORINE Land Cover was done in 37 European countries with the reference year 2006. The project CLC2006 in Germany was again performed by the German Remote Sensing Data Center, on behalf of the Federal Environment Agency (UBA). The update CORINE Land Cover 2006 for Germany is available since February 2010. Besides the status in 2006, an analysis of the changes between 2000 and 2006 is available. Read more at the DFD Corine Website

The CLC2000 project in Germany was executed using financial support by DG Regio and the German Federal Ministry on Environment, Nature Conservation and Nuclear Safety (BMU) on behalf of the German Federal Environmental Agency (UBA), project no. UBA FKZ 201 12 209. The CLC2006 project in Germany was under the responsibility of the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) and was performed on behalf of the Federal Environment Agency (UBA), project no. UBA FKZ 3707 12 200 and UBA FKZ 3708 12 200.

Abstract:

This land cover classfication 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.

Sensor: Sentinel-2 MSI
Period: 2015 - 2017
Product Version: 1.0

Image preprocessing: This dataset was created using a median mosaic of all collected Sentinel-2 Scenes up until April 30, 2017 with a cloud cover of less than 60%. From these a total of 226 spectral and textural image features were derived. Additionally imperviousness from buildings, roads and railways were included. A supervised machine learning classifier was trained with in-situ reference points from the 2015 Land Use and Coverage Area Frame Survey (LUCAS).

Digital Object Identifier (DOI): 10.15489/1ccmlap3mn39

Download: HTTP download via EOC Download Service

Attribution: The creation of this dataset was partly funded by the German Federal Environmental Foundation (DBU). Funding for the projects "meinGruen" and "SAUBER" (funding codes 19F2073B and 19F2064B, respectively) were granted by the German Federal Ministry of Transport and Digital Infrastructure (BMVI), and funding for the project "Monitoring des Stadtgrüns (Monitoring of urban green)" was granted by the German Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR). The dataset is produced and distributed by the DLR under the CC-BY-NC 4.0 license. This product contains processed and modified Copernicus Sentinel data (2015-2017).

Abstract:

The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key instrument aboard the American satellites Terra and Aqua. The MODIS-EU image mosaic is a seamless true color composite of all Terra and Aqua passes received at DLR during one day. Daily and Near Real Time (NRT) products are available. For the composite, MODIS channels 1, 4, 3 are used. The channels are re-projected, radiometrically enhanced, and seamlessly stitched to obtain a visually appealing result.

Terra passes from north to south across the equator in the morning, while Aqua passes the equator south to north in the afternoon. Both MODIS instruments are viewing the entire Earth surface every 1 to 2 days, acquiring data in 36 spectral bands. These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.

Abstract:

MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment (from http://modis.gsfc.nasa.gov/).

This mosaic has been generated from TERRA and AQUA products between 30 Sept. to 03 Oct. 2011

The MODIS data used in this product were obtained through the online Data Pool at the NASA Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota (https://lpdaac.usgs.gov/get_data).

Abstract:

The RapidEye RESA Germany Mosaic provides a nearly cloud-free view of the country's geography, natural resources, and infrastructure. It is composed of 374,240 sqkm of multi-spectral RapidEye imagery, acquired between April and October 2015. The product is being provided in the framework of the RapidEye Science Archive (RESA) agreement. Co-funded by the German Federal Government, the fleet of RapidEye satellites were launched from the Baikonur cosmodrome in Kazakhstan in 2008. The satellites are now owned by Planet Labs, Inc. The RapidEye Earth observation system comprises five satellites equipped with high-resolution optical sensors. With a spatial resolution of 6.5 m the 5-band instruments operate in the visible and near-infrared portions of the electromagnetic spectrum. With its high repetition rate the RapidEye constellation can image each point on the Earth's at least once per day.

For more information see the DLR Earth Observation projects page or the Planet Website.

Abstract:

The GUF® maps show two land cover categories (e. g. in a B&W representation): Built-up areas (vertical structures only) in black and non-built-up surfaces in white; in addition, areas of no coverage by theTSX/TDX satellites (NoData) are coded in grey (most parts of the oceans). The focus on two categories clearly highlights the settlement patterns, improving the ability to analyze and compare them with other built-up areas across the world, in an urban or in a rural context. Unlike previous approaches, the fully automatic evaluation procedure detects the characteristic vertical structures of human habitations are primarily buildings. In contrast, areas used for infrastructure purposes, like roads, are not mapped. This is why broad urban canyons or expanses of greenery within the cities are shown as white corridors and patches.

Abstract:

The GUF® maps show two land cover categories (e. g. in a B&W representation): Built-up areas (vertical structures only) in black and non-built-up surfaces in white; in addition, areas of no coverage by theTSX/TDX satellites (NoData) are coded in grey (most parts of the oceans). The focus on two categories clearly highlights the settlement patterns, improving the ability to analyze and compare them with other built-up areas across the world, in an urban or in a rural context. Unlike previous approaches, the fully automatic evaluation procedure detects the characteristic vertical structures of human habitations are primarily buildings. In contrast, areas used for infrastructure purposes, like roads, are not mapped. This is why broad urban canyons or expanses of greenery within the cities are shown as white corridors and patches.

Abstract:

Information about extent, beginning, duration and melt of snow cover are important for climate research, hydrological applications, flood prediction and weather forecast. Climate change is influencing the characteristics and duration of snow cover, affecting landscape, hydrology, flora, fauna, and humans in equal measure. Therefore, precise information about the different snow parameters and their development over time are particularly important for various research fields.

The Global SnowPack is a dataset containing information about snow cover parameters on a global scale. Overall (September 1st - August 31st of the next calendar year), early season (September 1st - January 15th of the next calendar year), and late season (January 16th - August 31st) snow cover duration are included and allow detailed insights in the characteristics of this most relevant part of Earths cryosphere. The parameters are being derived from daily, operational MODIS snow cover products for every year since 2000. The negative effects of polar darkness and cloud coverage are compensated by applying several processing steps. Thereby, a unique global dataset can be provided that is characterized by its high accuracy, a spatial resolution of 500 meter and continuous future enhancements.

Abstract:

Information about extent, beginning, duration and melt of snow cover are important for climate research, hydrological applications, flood prediction and weather forecast. Climate change is influencing the characteristics and duration of snow cover, affecting landscape, hydrology, flora, fauna, and humans in equal measure. Therefore, precise information about the different snow parameters and their development over time are particularly important for various research fields.

The Global SnowPack is a dataset containing information about snow cover parameters on a global scale. Overall (September 1st - August 31st of the next calendar year), early season (September 1st - January 15th of the next calendar year), and late season (January 16th - August 31st) snow cover duration are included and allow detailed insights in the characteristics of this most relevant part of Earths cryosphere. The parameters are being derived from daily, operational MODIS snow cover products for every year since 2000. The negative effects of polar darkness and cloud coverage are compensated by applying several processing steps. Thereby, a unique global dataset can be provided that is characterized by its high accuracy, a spatial resolution of 500 meter and continuous future enhancements.

Abstract:

This map shows daily products of different trace gases measured by the Global Ozone Monitoring Experiment-2 (GOME-2) which was launched on board EUMETSAT's Meteorological Operational Satellites (MetOp-A, -B and -C) in October 2006, September 2012, and November 2018, respectively. A wide range of atmospheric trace constituents are measured, with the emphasis on global ozone distributions. Furthermore cloud properties and intensities of ultraviolet radiation are retrieved. DLR generates operational GOME-2/MetOp Level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Composition Monitoring (AC SAF).

Abstract:

This product contains tropospheric NO2 columns for Germany derived from Sentinel-5P/TROPOMI Level-1B data. The tropospheric NO2 data are vertical column densities with the unit "µmol/m2". Sentinel-5P observes Germany once per day at ~12:00 UTC (the measurement time is included in the netCDF data file). These daily observations are gridded onto a regular 2 km x 2 km UTM grid. Only tropospheric NO2 data for cloud-free Sentinel-5P measurements are provided (cloud fraction < ~0.2). The cloud fraction data from Sentinel-5P is included in this product as well.

Product Version: 1.0
Sensor: TROPOMI/Sentinel-5P
Period: February 2018 - June 2020 (orbit/daily)
Coverage: Germany and surrounding areas
Horizontal resolution: 2 km x 2 km

Attribution: This is an outcome of the S-VELD project, which is funded by the Federal Ministry of Transport and Digital Infrastructure (FKZ: 19F2065A) in the framework of the mFUND-Programme. It is produced and distributed by the DLR under the CC-BY-NC 4.0 license. This product contains processed and modified Copernicus Sentinel data (2018-2019).

Download: HTTP download via EOC Download Service

S-VELD S5P Trop. NO2 (Monthly): Click Here

More Information of S-VELD products can be found in:
Readme File
User Manual

Abstract:

This product contains monthly mean tropospheric NO2 columns for Germany derived from Sentinel-5P/TROPOMI Level-1B data. The tropospheric NO2 data are vertical column densities with the unit "µmol/m2". Sentinel-5P observes Germany once per day at ~12:00 UTC and only cloud-free measurements (cloud fraction < ~0.2) are used. The Sentinel-5P tropospheric NO2 data within each month are averaged and gridded onto a regular 2 km x 2 km UTM grid. The number of measurements used in the calculation of the averaged value are included in this product as well.

Product Version: 1.0
Sensor: TROPOMI/Sentinel-5P
Period: February 2018 - May 2020 (monthly mean)
Coverage: Germany and surrounding areas
Horizontal resolution: 2 km x 2 km

Attribution: This is an outcome of the S-VELD project, which is funded by the Federal Ministry of Transport and Digital Infrastructure (FKZ: 19F2065A) in the framework of the mFUND-Programme. It is produced and distributed by the DLR under the CC-BY-NC 4.0 license. This product contains processed and modified Copernicus Sentinel data (2018-2019).

Download: HTTP download via EOC Download Service

S-VELD S5P Trop. NO2 (Daily): Click Here

More Information of S-VELD products can be found in:
Readme File
User Manual

Abstract:

AVHRR - Monthly Mediterranean Sea Surface Temperature (SST) is an AVHRR subset from 1993 to 2014 integrated to facilitate valuable time series exploitation of historic data. This subset represents the Monthly Average of Sea Surface Temperature in degrees celsius, being possible to display the average temperature by selecting the first day of each month. Temperature ranges in Mediterranean Sea oscillate around 10 -21 degrees celsius in winter and 18-30 in summer. For more specifications how SST values are derived. See the EOWEB Short Guide for details.

Abstract:

Abstract:

Abstract:

The Soil Composite Mapping Processor (SCMaP) is a new approach designed to make use of per-pixel compositing to overcome the issue of limited soil exposure due to vegetation. Primary products are reflectance composites that will allow for a long term assessment of exposed soils. Further products include the distribution of exposed soils and statistical information related to soil use and intensity. The resulting reflectance soil composites correlate well with existing soil maps and the underlying geological structure.

Version: V 1.0
Sensor: Landsat 4, 5, 7
Period: 1984 - 2014 (6 x 5-year composites)
Image preprocessing: Cloud-removal (FMask), ATM-Correction + Haze removal (ATCOR), Reprojection, Image filtering
Reference: Rogge, D., Bauer, A., Zeidler, J., Müller, A., Esch, T., Heiden, U. (2018). Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984-2014). Remote Sensing of Environment, 205, Seiten 1-17. DOI: 10.1016/j.rse.2017.11.004 ISSN 0034-425.

Abstract:

The Soil Composite Mapping Processor (SCMaP) is a new approach designed to make use of per-pixel compositing to overcome the issue of limited soil exposure due to vegetation. Primary products are reflectance composites that will allow for a long term assessment of exposed soils. Further products include the distribution of exposed soils and statistical information related to soil use and intensity. The resulting reflectance soil composites correlate well with existing soil maps and the underlying geological structure.

Version: V 1.0
Sensor: Landsat 4, 5, 7
Period: 1984 - 2014
Image preprocessing: Cloud-removal (FMask), ATM-Correction + Haze removal (ATCOR), Reprojection, Image filtering
Reference: Rogge, D., Bauer, A., Zeidler, J., Müller, A., Esch, T., Heiden, U. (2018). Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984-2014). Remote Sensing of Environment, 205, Seiten 1-17. DOI: 10.1016/j.rse.2017.11.004 ISSN 0034-425.