Introduction to GlobSnow Snow Extent products with considerations for accuracy assessment

  • Sari Metsämäki
  • Jouni Pulliainen
  • Miia Salminen
  • Kari Luojus
  • Andreas Wiesmann

Publication details

  • Journal: Remote Sensing of Environment, vol. 156, p. 96–108, 2015
  • International Standard Numbers:
    • Printed: 0034-4257
    • Electronic: 1879-0704
  • Link:

The European Space Agency's Data User Element (DUE) project GlobSnow was established to create a global database of Snow Extent and Snow Water Equivalent. The Snow Extent (SE) product portfolio provided within ESA DUE GlobSnow (2008–2014) is introduced and described, with a special focus on the Daily Fractional Snow Cover (DFSC) of the SE version 2.0 and its successor 2.1 released in 2013–2014. The fractional snow retrieval uses the SCAmod method designed ecpecially to enable accurate snow mapping including forests. The basics of the methodology are presented, as well as the cloud screening method applied in SE production. Considerations for future validations together with discussion on some current issues and potential inaccuracies are presented. One focus of the investigation is on the representativeness of reference FSC generated from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM +) data, with a particular interest in forested areas. Two methods for reference data generation are investigated. When comparing the GlobSnow Daily Fractional Snow Cover to these reference data, we try to identify how the comparison reflects the possible inaccuracies of the DFSC and to define the conditions where the reference data are not representative. It is obvious that the evaluation result strongly depends on the quality of the reference data, and that the two methods investigated cannot provide representative reference data for dense forests. For fully snow-covered dense conifer forest area in Finland, a Root Mean Squared Error of 20–30% was obtained from comparisons although DFSC indicated full snow cover correctly. These first evaluations would indicate a good performance of GlobSnow SE products in forests; however, this does not necessarily show up in validations due to the non-representativeness of the reference data. It is also concluded that GlobSnow SE products are sensitive to the representativeness of the applied SCAmod parameters and that FSC overestimations may occur in dense forests. GlobSnow SE products are available at