Satellite based observations for seasonal snow cover detection and characterisation in Australia

Kathryn J. Bormann*, Matthew McCabe, Jason P. Evans

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

A new daily snow cover dataset was developed using Moderate resolution Imaging Spectroradiometer (MODIS) Level-1B products for the Australian alpine region over the period 2000-2010 at 500. m resolution. The dataset has been evaluated during clear-sky conditions using snow detection estimates derived from Landsat Thematic Mapper (TM) data and has been compared to the MOD10_L2 snow cover products. The ability to customise the snow detection threshold is one of the benefits of developing the Melt Area Detection Index (MADI) approach for regional conditions. The dataset provides a new satellite based observational record that may be used to characterise spatial and temporal development of Australian snow cover extent and duration. The new snow cover observations provide insight into snow characteristics in this region where significant declines in snow cover extent, season duration and a shift towards earlier snow melt date are observed. Shifts towards early season melt dates are observed for snow at 1580. m and above. This includes areas which are pertinent to snow recreation activities in the region. Season length declines are attributed to earlier seasonal snowmelt rather than later season onset and may be linked to observed warming trends in the area. The MODIS based approach can be applied to other regions and other sensors to assist in evaluating snow modelling efforts and improve water resource management and snow hydrology based investigations.

Original languageEnglish (US)
Pages (from-to)57-71
Number of pages15
JournalRemote Sensing of Environment
Volume123
DOIs
StatePublished - Aug 1 2012

Keywords

  • Australia
  • MODIS
  • Regional scale
  • Snow detection
  • Snow trends

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

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