cl_maintenanceAndUpdateFrequency

daily

1299 record(s)

 

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  • The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key instrument aboard the Terra (EOS AM-1) and Aqua (EOS PM-1) satellites. The MODIS-EU image mosaic is a seamless true color composite of all Terra and Acqua passes received at DLR during a single day. Daily and Near Real Time (NRT) products are available. For the composite, MODIS channels 1, 4, 3 are used. The channels are re-projected, radiometrically enhanced, and seamlessly stitched to obtain a visually appealing result. Terra passes from north to south across the equator in the morning, while Aqua passes the equator south to north in the afternoon. Both MODIS instruments are viewing the entire Earth surface every 1 to 2 days, acquiring data in 36 spectral bands.

  • The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission to monitoring and characterise Earth’s environment on a global scale. EnMAP measures and models key dynamic processes of Earth’s ecosystems by extracting geochemical, biochemical and biophysical parameters that provide information on the status and evolution of various terrestrial and aquatic ecosystems. The mission’s main objective is to study and decipher coupled environmental processes and to assist and promote the sustainable management of Earth’s resources. This collection includes Level 0 quicklook images of the mission. For more information, please see the mission website: https://www.enmap.org/

  • The product is automatically derived from Sentinel-3 (OLCI) satellite imagery in near-real time. It is an incremental product, meaning that the retrieved results are updated as soon as new input data becomes available over a timespan of ten days. Besides the fire perimeter, and detection time each feature contains information about the severity of the burning.

  • ---- The bulletin collects TEMP reports: FM 35 (TEMP, Upper-level pressure, temperature, humidity and wind report from a fixed land station). (Refer to WMO No.306 - Manual on Codes for the definition of WMO international codes) ---- The USLV10 TTAAii Data Designators decode (2) as: T1 (U): Upper air data. T2 (S): Upper level pressure, temperature, humidity and wind (Part A). A1A2 (LV): Latvia. (2: Refer to WMO No.386 - Manual on the GTS - Attachment II.5) ---- The bulletin collects reports from stations: Skriveri ---- WMO No.9 - Volume C1 'Remarks' field: TEMPORARILY SUSPENDED

  • ---- The bulletin collects TEMP reports: FM 35 (TEMP, Upper-level pressure, temperature, humidity and wind report from a fixed land station). (Refer to WMO No.306 - Manual on Codes for the definition of WMO international codes) ---- The UKLV10 TTAAii Data Designators decode (2) as: T1 (U): Upper air data. T2 (K): Upper level pressure, temperature, humidity and wind (Part B). A1A2 (LV): Latvia. (2: Refer to WMO No.386 - Manual on the GTS - Attachment II.5) ---- The bulletin collects reports from stations: Skriveri ---- WMO No.9 - Volume C1 'Remarks' field: TEMPORARILY SUSPENDED

  • ---- The bulletin collects TEXT reports. ---- The VMLV40 TTAAii Data Designators decode (2) as: T1 (V): National data. A1A2 (LV): Latvia. (2: Refer to WMO No.386 - Manual on the GTS - Attachment II.5) ---- The bulletin collects reports from stations: Kolka, Ventspils, Skulte and Liepaja

  • The product is automatically derived from Aqua/Terra (MODIS) satellite imagery in near-real time. It is an incremental product, meaning that the retrieved results are updated as soon as new input data becomes available over a timespan of ten days. Besides the fire perimeter and detection time, each feature contains information about the severity of the burning.

  • This product shows globally the daily snow cover extent (SCE). The snow cover extent is the result of the Global SnowPack processor's interpolation steps and all data gaps have been filled. Snow cover extent is updated daily and processed in near real time (3 days lag). In addition to the near real-time product (NRT_SCE), the entire annual data set is processed again after the end of a calendar year in order to close data gaps etc. and the result is made available as a quality-tested SCE product. There is also a quality layer for each day (SCE_Accuracy), which reflects the quality of the snow determination based on the time interval to the next "cloud-free" day, the time of year and the topographical/geographical location. 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

  • GeoMIS 2.0 - official Discovery Service (CSW) for the GDI-Th

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