This product contains the atmospherically corrected water albedo product of a Sentinel-2 Level 1C scene. A water-land-cloud mask based on analysis of the atmospherically corrected scene on water, land and cloud specific spectral properties is also delivered.

The product is initially provided for the region of Hamburg.

The albedo product consists of the Sentinel-2 Level 1C bands 1, 2, 3, 4, 5, 6, 7, 8, 8a, 11, and 12 at 10 m spatial resolution, where a change of 1 digit is equivalent to a change of 0.0001 (0.01 %) in reflectance. The mask product has 10 m spatial resolution with values 0 (land), 1 (water), and 2 (cloud).

File format of value added product: ENVI BIL

Sentinel-2 is a wide-swath, high-resolution, multi-spectral imaging mission developed by ESA as part of the Copernicus Programme, supporting the Copernicus Land Monitoring services, including the monitoring of vegetation, soil and water cover, as well as the observation of inland waterways and coastal areas. The full Sentinel-2 mission comprises two polar-orbiting satellites in the same orbit, phased at 180° to each other.

Sensor: MSI (Multispectral Instrument)
Repeat rate: 5 days (with two satellites)
Launch dates: 23 June 2015 (Sentinel-2A), 07 March 2017 (Sentinel-2B)
Archiving start date: 27 June 2015
Mission Status: ongoing

Terms and conditions for the use of Sentinel data
https://scihub.copernicus.eu/twiki/pub/SciHubWebPortal/TermsConditions/T...

Sentinel-2 Mission Overview
https://sentinel.esa.int/web/sentinel/missions/sentinel-2

Sentinel-2 acquisition plans:
https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-2/acquisi...

Datum der letzten Änderung
03-05-2019 09:14:30

The processing of the Sentinel-2 Maritime Product is based on the Level-1C product.

This collection contains Sentinel-2 Level 2B vegetation indices (VI) for all of Germany for all acquired scenes since 01/2019. VIs (EVI, HA56, NDRE, NDVI, NDWI, PSRI and REIP) are calculated from Level 2A data in 10m spatial resolution. Products are available in tiles according to the ESA Sentinel-2 granule grid (UTM). This is a product of the AGRO-DE project (https://agro-de.info/).

For further information, please consult the product description document: https://code-de.org/download/agrode/S2_L2B_index/CODE-DE_Description_S2_...

Sensor: MSI (Multispectral Instrument)
Repeat rate: 5 days (with two satellites)
Launch dates: 23 June 2015 (Sentinel-2A), 07 March 2017 (Sentinel-2B)
Archiving start date: 27 June 2015
Mission Status: ongoing

Terms and conditions for the use of Sentinel data
https://scihub.copernicus.eu/twiki/pub/SciHubWebPortal/TermsConditions/T...

Sentinel-2 Mission Overview
https://sentinel.esa.int/web/sentinel/missions/sentinel-2

File format of measurement data: GeoTIFF

Sentinel-2 acquisition plans:
https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-2/acquisi...

Datum der letzten Änderung
11-10-2019 11:49:40

Input data: Sentinel-2 MSI - Level 2A Surface Reflectance (AGRO-DE)

The Global Urban Footprint® (GUF®) dataset is based on the radar (SAR) satellite imagery of the German satellites TerraSAR-X and TanDEM-X. By creating the GUF database, scientists at the German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR) have succeeded in using a newly developed method to generate a global raster map of the world’s built-up pattern in a so far unprecedented spatial resolution of about 12m per raster cell.

Using a fully automated processing system, a global coverage of more than 180,000 very high resolution SAR images (3m ground resolution) has been analyzed acquired between 2010 and 2013. Thereby, the backscatter amplitudes of the SAR data have been used in combination with derived textural information to delineate human settlements in a highly automated, complex decision-making process. The evaluation procedure based mainly on radar signals detects the characteristic vertical structures of human habitations – primarily built-up areas. In addition, auxiliary data such as digital elevation models have been included to improve the classification process. In total, over 20 million datasets were processed with a combined volume of about 320 terabytes. The final global maps show three coverage categories (e. g. in a B&W representation): Built-up areas (vertical structures only) in black, non-built-up surfaces in white, areas of no coverage by TSX/TDX satellites (NoData) as most parts of the oceans in grey.

The final product has been optimized for fast online access through web services by merging the 5° x 5° GUF tiles into a single global mosaic. Furthermore reduced resolution overviews have been generated with an interpolation algorithm, that computes the average value of all contribution pixels. The global mosaic uses PackBits compression to reduce file size.

(GUF® and Global Urban Footprint® are protected as trademarks.)

Datum der letzten Änderung
27-02-2019 01:35:31

The Global Urban Footprint® (GUF®) dataset is based on the radar (SAR) satellite imagery of the German satellites TerraSAR-X and TanDEM-X. Using a fully automated processing system, the so-called Urban Footprint Processor, a global coverage of more than 180,000 very high resolution SAR images (3m ground resolution) has been analyzed, mainly acquired between 2010 and 2013. Thereby, the backscatter amplitudes of the SAR data have been used in combination with derived textural information to delineate human settlements in a highly automated, complex decision-making process. In addition, auxiliary data such as digital elevation models have been included to improve the classification process. The data collection of satellite imagery was performed mainly between 2011 and 2012 (93 %), with single scenes with more recent acquisition dates (of 2013 / 2014) used to fill data gaps.
For more details see:
Esch, T., Marconcini, M., Felbier, A., Roth, A., Heldens, W., Huber, M., Schwinger, M., Taubenböck, H., Müller, A., Dech, S. (2013) Urban Footprint Processor – Fully Automated Processing Chain Generating Settlement Masks from Global Data of the TanDEM-X Mission. IEEE Geoscience and Remote Sensing Letters, Vol. 10, No. 6, November 2013. Pp. 1617-1621. ISSN 1545-598X, DOI 10.1109/LGRS.2013.2272953 (see: http://elib.dlr.de/83318/ )