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  • This collection contains monthly mean surface NO2 concentrations for Germany derived from Sentinel-5P/TROPOMI data. The Sentinel-5P NO2 data is generated by DLR and provided in the framework of the mFUND-Project "S-VELD". The surface NO2 data are concentrations with the unit "μg/m3". Sentinel-5P observes Germany once per day at ~12:00 UTC and only cloud-free measurements (cloud fraction less than ~0.2) are used. The Sentinel-5P surface NO2 data within each month are averaged and gridded onto a regular UTM grid. The number of measurements used in the calculation of the averaged value are included in this collection as well.

  • SWACI is a research project of DLR supported by the State Government of Mecklenburg-Vorpommern. Radio signals, transmitted by modern communication and navigation systems may be heavily disturbed by space weather hazards. Thus, severe temporal and spatial changes of the electron density in the ionosphere may significantly degrade the signal quality of various radio systems which even may lead to a complete loss of the signal. By providing specific space weather information, in particular now- and forecast of the ionospheric state, the accuracy and reliability of impacted communication and navigation systems shall be improved. NmF2 Maps and 3D electron density distribution: According to the pioneer work of Sir E. Appleton the vertical structure of the terrestrial ionosphere may be devided into different layers (D, E, F1, F2) with different physical characteristics. The layers are primarily characterized by its height and peak electron density. Typical plasma frequencies /electron densities are in the order of 10 MHz /1012 electrons / m3. Shown are here global maps of the peak electron density of the F2 layer and related electron density maps at selected altitudes. The figures provide a first estimation of the three dimensional electron density distributions around the globe. For more details see http://swaciweb.dlr.de/data-and-products/public/nmf2/?L=1.

  • The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. ROCINN takes the OCRA cloud fraction as input and uses a neural network training scheme to invert GOME / GOME-2 reflectivities in and around the O2-A band. VLIDORT [Spurr (2006)] templates of reflectances based on full polarization scattering of light are used to train the neural network. ROCINN retrieves cloud-top pressure and cloud-top albedo. The cloud optical thickness is computed using libRadtran [Mayer and Kylling (2005)] radiative transfer simulations taking as input the cloud-top albedo retrieved with ROCINN. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/

  • The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational BrO (Bromine monoxide) total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. For more details please refer to https://atmos.eoc.dlr.de/app/missions/gome2

  • 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/ Spectral high resolution measurements allow to assess different water constituents in optically complex case-2 waters (IOCCG, 2000). The main groups of constituents are Chlorophyll, corresponding to living phytoplankton, suspended minerals or sediments and dissolved organic matter. They are characterised by their specific inherent optical properties, in particular scattering and absorption spectra. The Baltic Sea Water Constituents product was developed in a co-operative effort of DLR (Remote Sensing Technology Institute IMF, German Remote Sensing Data Centre DFD), Brockmann Consult (BC) and Baltic Sea Research Institute (IOW) in the frame of the MAPP project (MERIS Application and Regional Products Projects). The data are processed on a regular (daily) basis using ESA standard Level-1 and -2 data as input and producing regional specific value added Level-3 products. The regular data reception is realised at DFD ground station in Neustrelitz. 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.

  • This product is a shape file of all detected forest patches in the Paraguayan Chaco that are larger than 10 hectars fort he years 2000, 2010, and 2020. Every forest patch contains information on its perimeter, size, shape, and core area. By looking at all forest patches together, an impression can be gained of the fragmentation of the forest in the Paraguayan Chaco. Proximity is a measure of fragmentation. Areas of large and close by forest patches show high proximity values while isolated patches or patchest hat are only surrounded by small forest patches, have a small proximity. The Core area index quantifies the share of core area in the entire forest patch area. Thereby, corea area is the area of a forest patch with at least 500m distance to the edge of the forest. The Shape index is calculated from perimeter and area of a patch. The fragementation of a forest often has the effect that the ratio between area and perimeter is affected. The edge lengths become longer while the surface area becomes smaller.

  • The Global WaterPack is a dataset containing information about open surface water cover parameters on a global scale. The water detection is derived from daily, operational MODIS datasets for every year since 2003. The negative effects of polar darkness and cloud coverage are compensated by applying interpolation processing steps. Thereby, a unique global dataset can be provided that is characterized by its high temporal resolution of one day and a spatial resolution of 250 meter. This collection includes yearly composites of the dataset with information on how often a pixel was detected as open surface water with pixel values between 0 and 365 (366 for leap years). Furthermore, a reliability layer provides information on the quality of each Global WaterPack pixel.

  • This product shows the snow cover duration for a hydrological year. Its beginning differs from the calendar year, since some of the precipitation that falls in late autumn and winter falls as snow and only drains away when the snow melts in the following spring or summer. The meteorological seasons are used for subdivision and the hydrological year begins in autumn and ends in summer. The snow cover duration is made available for three time periods: the snow cover duration for the entire hydrological year (SCD), the early snow cover duration (SCDE), which extends from autumn to midwinter (), and the late snow cover duration (SCDL), which in turn extends over the period from mid-winter to the end of summer. For the northern hemisphere SCD lasts from September 1st to August 31st, for the southern hemisphere it lasts from March 1st to February 28th/29th. The SCDE lasts from September 1st to January 14th in the northern hemisphere and from March 1st to July 14th in the southern hemisphere. The SCDL lasts from January 15th to August 31st in the northern hemisphere and from July 15th to February 28th/29th in the southern hemisphere. The “Global SnowPack” is derived from daily, operational MODIS snow cover product for each day since February 2000. Data gaps due to polar night and cloud cover are filled in several processing steps, which provides a unique global data set characterized by its high accuracy, spatial resolution of 500 meters and continuous future expansion. It consists of the two main elements daily snow cover extent (SCE) and seasonal snow cover duration (SCD; full and for early and late season). Both parameters have been designated by the WMO as essential climate variables, the accurate determination of which is important in order to be able to record the effects of climate change. Changes in the largest part of the cryosphere in terms of area have drastic effects on people and the environment. For more information please also refer to: Dietz, A.J., Kuenzer, C., Conrad, C., 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, 3879–3902. https://doi.org/10.1080/01431161.2013.767480 Dietz, A.J., Kuenzer, C., Dech, S., 2015. Global SnowPack: a new set of snow cover parameters for studying status and dynamics of the planetary snow cover extent. Remote Sensing Letters 6, 844–853. https://doi.org/10.1080/2150704X.2015.1084551 Dietz, A.J., Wohner, C., Kuenzer, C., 2012. European Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products. Remote Sensing 4. https://doi.org/10.3390/rs4082432 Dietz, J.A., Conrad, C., Kuenzer, C., Gesell, G., Dech, S., 2014. Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data. Remote Sensing 6. https://doi.org/10.3390/rs61212752 Rößler, S., Witt, M.S., Ikonen, J., Brown, I.A., Dietz, A.J., 2021. Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers. Geosciences 11, 130. https://doi.org/10.3390/geosciences11030130

  • This collection contains monthly mean surface PM2.5 concentrations for Germany and parts of the surrounding countries. PM2.5 surface concentrations are derived from Aqua/MODIS and Sentinel-3A/SLSTR AOD data and provided as merged MODIS/SLSTR product. The data is generated by DLR and provided in the framework of the mFUND-Project "S-VELD". The surface PM2.5 data are concentrations with the unit "µg/m3". The satellites Aqua (NASA) and Sentinel-3 (Copernicus) observe Germany on a daily basis. PM2.5 concentrations were derived on a daily basis from the two AOD products separately and combined to a merged MODIS/SLSR surface PM2.5 product. The data within each month are averaged and gridded onto a regular UTM grid. As AOD measurements are strongly depending on cloud conditions, the spatial coverage can be limited, especially in the winter months.

  • 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/

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