Global SnowPack - MODIS - Mean

This product shows the mean snow cover duration (SCDmean), which is updated each year and consists of the arithmetic mean for the entire time series since the hydrological year 2001. The hydrological year begins in the meteorological autumn (October 1 of the previous year in the northern hemisphere or March 1 of the reference year in the southern hemisphere) and ends with the meteorological summer (northern hemisphere: August 31 of the reference year; southern hemisphere: February 28/29 of the following year). Analogous to the annual products for snow cover duration, the entire year as well as the early season (until mid-winter) and the late season (from mid-winter) are taken into account here.

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

Citation proposal
. Global SnowPack - MODIS - Mean. https://gdk.gdi-de.org/geonetwork/srv/api/records/02392381-5bb0-4b28-a49d-e742fe44820b

Simple

Date ( Creation ): 2022-03-01T00:00:00

Edition

Identifier: https://geoservice.dlr.de/catalogue/srv/metadata/02392381-5bb0-4b28-a49d-e742fe44820b

Presentation form: Digital map

Other citation details

Status: ongoing

Point of contact

German Aerospace Center (DLR) - ( )

Point of contact

DFD-LAX -

Maintenance and update frequency: yearly

GEMET - INSPIRE themes, version 1.0 ( Theme )

Land cover

Spatial scope

global

Keywords

MODIS
Global Snow Pack
mean snow cover duration
mean snow cover duration late season
mean snow cover duration early season
snow
SCDmean
SCDEmean
SCDLmean
opendata
inspireidentifiziert

Use limitation: Nutzungseinschränkungen: Das DLR ist nicht haftbar für Schäden, die sich aus der Nutzung ergeben. / Use Limitations: DLR not liable for damage resulting from use.

Access constraints: Other restrictions

Use constraints: Other restrictions

Other constraints: Nutzungsbedingungen: Lizenz, https://creativecommons.org/licenses/by/4.0 / Terms of use: License, https://creativecommons.org/licenses/by/4.0

Other constraints

{"id": "cc-by/4.0",

"name": "Creative Commons Namensnennung - Attribution 4.0 International (CC BY 4.0)",

"url": "http://dcat-ap.de/def/licenses/cc-by/4.0",

"quelle": "Copyright DLR (2022)"}

Spatial representation type: Grid

Denominator: 920000

Metadata language: English

Character set: UTF8

Topic category

Climatology, meteorology, atmosphere

Number of dimensions: 2

Dimension name: Column

Dimension size: 86400

Resolution: 460 m

Dimension name: Row

Dimension size: 43200

Resolution: 460 m

Cell geometry: Area

Transformation parameter availability: false

Distribution format

GeoTIFF ()

OnLine resource: GSP_SCD_MEAN
Global SnowPack - Mean Snow Cover Duration (SCDmean)

OnLine resource: GSP_SCDE_MEAN
Global SnowPack - Mean Snow Cover Duration Early Season (SCDE)

OnLine resource: GSP_SCDL_MEAN
Global SnowPack - Mean Snow Cover Duration Late Season (SCDL)

OnLine resource: https://geoservice.dlr.de/eoc/land/wms?SERVICE=WMS%26REQUEST=GetCapabilities

OnLine resource: EOC Geoservice map context
EOC Geoservice map context

OnLine resource: EOC Download Service
EOC Download Service

OnLine resource: Global SnowPack - EOC News
Global SnowPack - EOC News

Hierarchy level: Dataset

Domain consistency

Measure identification: INSPIRE / Conformity_001

Conformance result

Date ( Publication ): 2010-12-08

Explanation: See the referenced specification.

Pass: true

Statement

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., and 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, No. 11. pp. 844-853

Description

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.