0aaf17e5-ffd2-4729-92b9-c75ce9239a70 Dataseries German Aerospace Center (DLR) geoservice@dlr.de 2024-06-20T13:07:19 ISO 19115-1:2014/19139 2003/Cor.1:2006 2 86400 460 43200 460 false EPSG:4326 Global SnowPack - MODIS - Daily 2022-03-01T00:00:00 https://geoservice.dlr.de/catalogue/srv/metadata/0aaf17e5-ffd2-4729-92b9-c75ce9239a70 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 DFD-LAX Global-SnowPack@dlr.de German Aerospace Center (DLR) geoservice@dlr.de https://geoservice.dlr.de/catalogue/srv/api/records/0aaf17e5-ffd2-4729-92b9-c75ce9239a70/attachments/gspdaily_ql.png large_thumbnail https://geoservice.dlr.de/catalogue/srv/api/records/0aaf17e5-ffd2-4729-92b9-c75ce9239a70/attachments/gspdaily_ql_s.png thumbnail Land cover GEMET - INSPIRE themes, version 1.0 2008-06-01 geonetwork.thesaurus.external.theme.inspire-theme Global Spatial scope 2019-05-22 MODIS Global Snow Pack snow cover extent near realtime snow cover extent snow cover extent accuracy snow SCE NRT_SCE SCE_Accuarcy inspireidentifiziert Nutzungseinschränkungen: Das DLR ist nicht haftbar für Schäden, die sich aus der Nutzung ergeben. / Use Limitations: DLR not liable for damage resulting from use. Es gelten keine Zugriffsbeschränkungen Nutzungsbedingungen: Lizenz, https://creativecommons.org/licenses/by/4.0 / Terms of use: License, https://creativecommons.org/licenses/by/4.0 {"id": "cc-by-4.0", "name": "Creative-Commons - Attribution 4.0 International (CC BY 4.0)", "url": "http://dcat-ap.de/def/licenses/cc-by/4.0", "quelle": "Copyright DLR (2022)"} 920000 climatologyMeteorologyAtmosphere -180.00 180.00 -90.00 90.00 2000-02-24T00:00:00 GeoTIFF https://geoservice.dlr.de/eoc/land/wms? OGC:WMS GSP_SCE_P1D Global SnowPack - Snow Cover Extent (SCE) https://geoservice.dlr.de/eoc/land/wms? OGC:WMS GSP_SCENRT_P1D Global SnowPack - Snow Cover Extent Near Real-Time (SCE NRT) https://geoservice.dlr.de/eoc/land/wms?SERVICE=WMS%26REQUEST=GetCapabilities OGC:WMS-http-get-capabilities https://geoservice.dlr.de/web/maps/eoc:gsp:daily WWW:LINK-1.0-http--link EOC Geoservice map context EOC Geoservice map context https://download.geoservice.dlr.de/GSP/files/daily WWW:LINK-1.0-http--link EOC Download Service EOC Download Service https://www.dlr.de/eoc/desktopdefault.aspx/tabid-18220/29005_read-77046 WWW:LINK-1.0-http--link Global SnowPack - EOC News Global SnowPack - EOC News Conformity_001 INSPIRE Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 See the referenced specification. true Created from MODIS daily snow cover products MOD10A1, MYD10A1 provided by the National Snow and Ice Datacenter NSIDC (https://nsidc.org/) Processing steps include combination of observations recorded by Aqua and Terra, temporal interpolation of 3 successive days, snow line identification, and a seasonal filter. More details are available in the publication "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" Single day snow cover datasets MOD10A1 and MYD10A1 were first cleared of any clouds/data gaps/polar darkness areas using 4 different interpolation techniques, then merged to produce the seasonal products. Accuracy of the seasonal product ranges between 77% and 85%, depending on the location and the duration of data gaps caused by clouds or darkness.