, Volume 642, Issue 1, pp 35–46 | Cite as

The development of new algorithms for remote sensing of snow conditions based on data from the catchment of Øvre Heimdalsvatn and the vicinity

  • Rune SolbergEmail author
  • Hans Koren
  • Jostein Amlien
  • Eirik Malnes
  • Dagrun Vikhamar Schuler
  • Nils Kristian Orthe


The catchment of Øvre Heimdalsvatn and the surrounding area was established as a site for snow remote sensing algorithm development, calibration and validation in 1997. Information on snow cover and snowmelt are important for understanding the timing and scale of many lake ecosystem processes. Field campaigns combined with data from airborne sensors and spaceborne high-resolution sensors have been used as reference data in experiments over many years. Several satellite sensors have been utilised in the development of new algorithms, including Terra MODIS and Envisat ASAR. The experiments have been motivated by operational prospects for snow hydrology, meteorology and climate monitoring by satellite-based remote sensing techniques. This has resulted in new time-series multi-sensor approaches for monitoring of snow cover area (SCA) and snow surface wetness (SSW). The idea was to analyse, on a daily basis, a time series of optical and radar satellite data in multi-sensor models. The SCA algorithm analyses each optical and synthetic aperture radar (SAR) image individually and combines them into a day product based on a set of confidence functions. The SSW algorithm combines information about the development of the snow surface temperature and the snow grain size (SGS) in a time-series analysis. The snow cover algorithm is being evaluated for application in a global climate monitoring system for snow variables. The successful development of these algorithms has led to operational applications of snow monitoring in Norway and Sweden, as well as enabling the prediction of the spring snowmelt flood and thus the initiation of many lake production processes.


Remote sensing Retrieval algorithms Fractional snow cover Snow surface wetness Snow surface temperature 



The authors wish to thank Rune Storvold, Stian Solbø and Tom Rune Lauknes, all at Norut, for their contributions to the fieldwork. Envisat data was provided by the European Space Agency through AOE 785. Financial contributions were received through European Commission projects (Snowtools, contract ENV4-CT96-0304; Envisnow, contract EVG1-CT-2001-00052; EuroClim, contract IST-2000-28766) and a Research Council of Norway project (Snowman, contract 143540/V30). Two unknown referees are greatly acknowledged for their suggestions and comments to improve the manuscript.


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Rune Solberg
    • 1
    Email author
  • Hans Koren
    • 1
  • Jostein Amlien
    • 1
  • Eirik Malnes
    • 2
  • Dagrun Vikhamar Schuler
    • 3
  • Nils Kristian Orthe
    • 4
  1. 1.Norwegian Computing Center (NR)OsloNorway
  2. 2.Northern Research Institute (Norut)TromsøNorway
  3. 3.Norwegian Meteorological InstituteOsloNorway
  4. 4.Norwegian Water Resources and Energy Directorate (NVE)OsloNorway

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