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  • The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of Lake Constance derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred to http://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdf This product provides daily maps.

  • 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 and the delivery of regular updates to register also the land cover and land use changes over time. 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. The first CLC data base CLC1990, which was finalized in the 1990s, consistently provided land use information comprising 44 classes, out of which 37 classes are relevant in Germany. The first two updates for Europe were based on the reference years 2000 and 2006. For Germany, DLR-DFD was responsible for the creation of CLC2000 and CLC2006 on behalf of the Federal Environment Agency. In addition to the updated land cover, change datasets were also parts of the project. For deriving a meaningful CLC2000 change product, it became necessary to re-interprete parts of the satellite data of 1990 and to create a revised product, called CLC1990 (rev). Further details: http://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-11882/20871_read-48836/

  • Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. The satellite has two panchromatic cameras that were especially designed for in flight stereo viewing. However, this collection contains the monoscopic data.

  • Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 23.5 km x 23.5 km IRS LISS-IV multispectral data provide a cost effective solution for mapping tasks up to 1:25'000 scale.

  • This product shows Snow Cover Duration Early Season (SCDES). SCDES represents the SCD between September 1st and January 15th of a given hydrological year. 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, early season, and late season snow cover duration are included and allow detailed insights in the characteristics of this most relevant part of Earth’s 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. For more information please also refer to: Dietz, A. J., C. Kuenzer, and S. Dech. 2015: Global SnowPack – “A new set of snow cover parameters to study status and dynamics of the planetary snow cover extent.“ accepted for publication in Remote Sensing Letters. Dietz, A. J., C. Conrad, C. Kuenzer, G. Gesell, and S. Dech. 2014. “Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data.” Remote Sensing 6 (12): 12752–75. doi:10.3390/rs61212752. Dietz, A. J., C. Kuenzer, and C. Conrad. 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 (11): 3879–3902. Dietz, A. J., C. Wohner, and C. Kuenzer. 2012. “European Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products.” Remote Sensing 4 (8): 2432–54. doi:10.3390/rs4082432.

  • 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 x 50m. 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 more information, please visit: https://www.dlr.de/hr/en/desktopdefault.aspx/tabid-12538/21873_read-50027/

  • The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of Lake Constance derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred to http://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdf This product provides 10-days maps.

  • 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 degree 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.)

  • The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of the North Sea derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred to http://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdf This product provides monthly maps.

  • This product consists of global gap free Leaf area index (LAI) time series, based on MERIS full resolution Level 1B data. It is produced as a series of 10-day composites in geographic projection at 300m spatial resolution. The processing chain comprises geometric correction, radiometric correction and pixel identification, LAI calculation with the BEAM MERIS vegetation processor, re-projection to a global grid, and temporal aggregation selecting the measurement closest to the mean value. After the LAI pre-processing we applied time series analysis to fill data gaps and filter outliers using the technique of harmonic analysis in combination with mean annual and multiannual phenological data. Data gaps are caused by clouds, sensor limitations due to the solar zenith angle (less than 10 degrees), topography and intermittent data reception. We applied our technique for the whole period of observation (Jul 2002 - Mar 2012). Validation, was performed using VALERI and BigFoot data.

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