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  • The TimeScan product is based on the fully-automated analysis of comprehensive time-series acquisitions of Landsat data. Based on a user-specified definition of the required period of time, the region of interest and – optionally – the maximum cloud cover, the TimeScan processor starts with the collection of all available Landsat scenes that meet the user specification. Next, for each single scene masking of clouds, haze and shadow is conducted using the Fmask algorithm. Then, a total of 6 indices is calculated for those pixels of each single scene that have not been masked in the prior step. The set of indices includes the Normalized Difference Vegetation Index (NDVI), the Built-up Index (BI), the Modified Normalized Difference Water Index (MNDWI), the Normalized Difference Band-5 / Band-7 (ND57), the Normalized Difference Band-4 / Band-3 (ND43), and the Normalized Difference Band-3 / Band-2 (ND32). Finally, the TimeScan product is generated by calculating the temporal statistics (minimum, maximum, mean, standard deviation, mean slope) for each index over the defined period of time. Hence, in case of the defined 6 indices chosen, the TimeScan product will include a total of 30 bands (5 statistical features per index). As an additional band a quality layer is added which shows for each pixel the number of valid values (meaning times with no cloud/haze or shadow cover) that have been included in the statistics calculation.

  • 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 revisit capability of only 5 days and the products coverage size of 370 km x 370 km make AWiFS products a valuable source for application fields such forestry and environmental monitoring

  • 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 H2O 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. The total H2O column is retrieved from GOME solar backscattered measurements in the red wavelength region (614-683.2 nm), using the Differential Optical Absorption Spectroscopy (DOAS) method. 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. 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-top pressure for GOME scenes is derived from the cloud-top height provided by ROCINN and an appropriate pressure profile. 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/

  • 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 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. 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/

  • Every day, three successive NOAA-AVHRR scenes are used to derive a synthesis product in stereographic projection known as the "Normalized Difference Vegetation Index" for Europe and North Africa. It is calculated by dividing the difference in technical albedos between measurements in the near infrared and visible red part of the spectrum by the sum of both measurements. This value provides important information about the "greenness" and density of vegetation. Weekly and monthly thematic synthesis products are also derived from this daily operational product, at each step becoming successively free of clouds. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/

  • 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 (SCD) for the whole hydrological year (Sept. 1st of a given year until Aug. 31st of the next 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 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 daily maps.

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