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 70 km x 70 km IRS LISS-IV mono data provide a cost effective solution for mapping tasks up to 1:25'000 scale.
This collection contains synthesized Sentinel-2 Level 3A surface reflectances for Germany on a monthly basis computed by the WASP processor (which utilizes L2A products derived from the MAJA processor). During the Level 3A processing, atmospherically corrected data from a predefined time interval are collected, weighted based on temporal distance and integrated to a new data set with the aim of removing clouds. Thereby, monthly Sentinel-2 Level 3A composites are provided for whole Germany. Please see https://logiciels.cnes.fr/en/content/maja for additional information on the MAJA product. Further Sentinel-2 Level 3A data computed using MAJA are available on the following website: https://theia.cnes.fr
The hyperspectral instrument DESIS (DLR Earth Sensing Imaging Spectrometer) is one of four possible payloads of MUSES (Multi-User System for Earth Sensing), which is mounted on the International Space Station (ISS). DLR developed and delivered a Visual/Near-Infrared Imaging Spectrometer to Teledyne Brown Engineering, which was responsible for integrating the instrument. Teledyne Brown designed and constructed, integrated and tested the platform before delivered to NASA. Teledyne Brown collaborates with DLR in several areas, including basic and applied research for use of data. DESIS is operated in the wavelength range from visible through the near infrared and enables precise data acquisition from Earth's surface for applications including fire-detection, change detection, maritime domain awareness, and atmospheric research. Three product types can be ordered, which are Level 1B (systematic and radiometric corrected), Level 1C (geometrically corrected) and Level 2A (atmospherically corrected). The spatial resolution is about 30m on ground. DESIS is sensitive between 400nm and 1000nm with a spectral resolution of about 3.3nm. DESIS data are delivered in tiles of about 30x30km. For more information concerning DESIS the reader is referred to https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-13614/
F-SAR, “Flugzeug-SAR”, is an airborne high-resolution imaging radar (Synthetic Aperture Radar) sensor presently operated by the German Aerospace Center (DLR), Microwaves and Radar Institute (HR) since November 02, 2006. It is operated on a Dornier Do-228 aircraft from altitudes of 2000 to 6000 m above ground in five different center frequency bands (X,C,S,L,P). Wavelengths range from 3 cm, 5 cm, 9 cm, 23 cm to 67 cm. Ka-band (1 cm wavelength) is planned to be added. Up to four center-frequencies (X,S,L,P) or (X,C,L,P) can be operated simultaneously per overflight. All frequencies are fully polarimetric (HH,HV,VV,VH) and have full repeat-pass capabilities. Single-pass interferometry in along-track (ATI) and across-track mode is available in X-band (ATI and/or XTI) and S-band (XTI). Data are processed up to three different levels: RGI (Radar Geometry Image product), INF (Repeat-pass-interferometric product) and GTC (Geocoded and Terrain-Corrected product). Resolutions range from 25 cm (X-band) to 1.5 m (P-band) in both azimuth and range direction. Data acquisition modes are typically “stripmap”, “repeat-pass” (two parallel tracks), “tomography” (several parallel tracks), ”circular” (one circle) or “circular-tomography” (several vertically distributed circles). Individually planned experiments can also be supported. For more information concerning F-SAR data, the reader is referred to: www.dlr.de/hr/f-sar
This collection contains radar image products of the German national TerraSAR-X mission acquired in ScanSAR mode. ScanSAR imaging allows for a spatial resolution of up to 18.5 m at a scene size of 100 km (across swath) x 150-1650 km (in orbit direction) in regular ScanSAR mode (4 beams) and up to 270 km (across swath) x 200-1500 km (in orbit direction) in Wide ScanSAR mode (6 beams). TerraSAR-X is a sun-synchronous polar-orbiting, all-weather, day-and-night X-band radar earth observation mission realized in the frame of a public-private partnership between the German Aerospace Center (DLR) and Airbus Defence and Space. For more information concerning the TerraSAR-X mission, the reader is referred to: https://www.dlr.de/content/de/missionen/terrasar-x.html
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. IRS LISS-III data are well suited for agricultural and forestry monitoring tasks. Because of their simultaneous acquisition with IRS PAN data and the availability of a synthetic blue band, LISS-III data are ideal for colouring IRS PAN products.
The RapidEye Earth observation system comprised five satellites equipped with high-resolution optical sensors. Co-funded by the German Federal Government, the fleet of satellites was launched from the Baikonur cosmodrome in Kazakhstan in 2008. RapidEye is owned by Planet Labs, Inc.. It has been operated by Planet Labs Germany GmbH until the constellation was retired in March 2020. With all 5 satellites arranged in one orbit the RapidEye constellation was capable of taking images of the Earth's surface at high repeat rates with a maximum of 5 million km² per day. With a spatial resolution of 6.5m the 5-band sensors onboard the RapidEye satellites operated in the visible and near-infrared portions of the electromagnetic spectrum. For more information see: https://www.dlr.de/rd/en/desktopdefault.aspx/tabid-2440/3586_read-5336/ The PlanetScope data of this collection has been purchased by the German Space Agency with funds from the Ministry of Economy and is available for Germany-based researchers for scientific use. The data collection is maintained by the German Satellite Data Archive (D-SDA) of DLR’s Earth Observation Center and can be accessed via the EOWEB Geoportal. The RapidEye Basic Scene (L1B) product is radiometrically- and sensor-corrected, providing imagery as seen from the spacecraft without correction for any geometric distortions inherent in the imaging process, and is not mapped to a cartographic projection. The imagery data is accompanied by all spacecraft telemetry necessary for the processing of the data into a geo-corrected form. For more details see: https://assets.planet.com/docs/Planet_Combined_Imagery_Product_Specs_letter_screen.pdf
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/
The World Settlement Footprint WSF 2015 version 2 (WSF2015 v2) is a 10m resolution binary mask outlining the extent of human settlements globally for the year 2015. Specifically, the WSF2015 v2 is a pilot product generated by combining multiple datasets, namely: • The WSF2015 v1 derived at 10m spatial resolution by means of 2014-2015 multitemporal Landsat-8 and Sentinel-1 imagery (of which ~217K and ~107K scenes have been processed, respectively); https://doi.org/10.1038/s41597-020-00580-5 • The High Resolution Settlement Layer (HRSL) generated by the Connectivity Lab team at Facebook through the employment of 2016 DigitalGlobe VHR satellite imagery and publicly released at 30m spatial resolution for 214 countries; https://arxiv.org/pdf/1712.05839.pdf • The novel WSF2019 v1 derived at 10m spatial resolution by means of 2019 multitemporal Sentinel-1 and Sentinel-2 imagery (of which ~ 1.2M and ~1.8M scenes have been processed, respectively); https://doi.org/10.1553/giscience2021_01_s33 The WSF2015 v1 demonstrated to be highly accurate, outperforming all similar existing global layers; however, the use of Landsat imagery prevented a proper detection of very small structures, mostly due to their reduced scale. Based on an extensive qualitative assessment, wherever available the HRSL layer shows instead a systematic underestimation of larger settlements, whereas it proves particularly effective in identifying smaller clusters of buildings down to single houses, thanks to the employment of 2016 VHR imagery. The WSF2015v v2 has been then generated by: i) merging the WSF2015 v1 and HRSL (after resampling to 10m resolution and disregarding the population density information attached); and ii) masking the outcome by means of the WSF2019 product, which exhibits even higher detail and accuracy, also thanks to the use of Sentinel-2 data and the proper employment of state-of-the-art ancillary datasets (which allowed, for instance, to effectively mask out all roads globally from motorways to residential).
This dataset includes the normalized difference vegetation index (NDVI) derived from Sentinel-2 imagery. Using the Google Earth Engine, all granules with a cloud cover below 60% were used as input. Cloudy pixels (referring to quality layer QA60) were masked as well. Eventually, a median mosaic was composed over the whole observation period. It was also used as input for a land cover classification (see: Land Cover DE - Sentinel-2 - Germany, 2015).
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