HR: 1330h
AN: C43A-03    [Abstracts]
TI: Differentiation of Land-Based Snow and Ice by the Moderate Resolution Imaging Spectroradiometer (MODIS)
AU: * Casey, K A
EM: Kimberly.A.Casey.1@gsfc.nasa.gov
AF: Science Systems and Applications Inc., 10210 Greenbelt Road, Suite 600, Lanham, MD 20706 United States
AU: * Casey, K A
EM: Kimberly.A.Casey.1@gsfc.nasa.gov
AF: Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218 United States
AU: Hall, D K
AF: NASA Goddard Space Flight Center, Code 614.1, Greenbelt, MD 20771 United States
AB: The extent of snow and ice over land is a key factor in the global radiation budget and the hydrologic cycle, and is essential information for regional- and global-scale climate modeling. Remote sensing of land cover via satellite provides an opportunity for automated measurement of snow and ice over expanded spatial areas and is amenable to study using moderate-resolution sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS consists of 36 discrete spectral bands from the visible through the thermal infrared parts of the electromagnetic spectrum and three spatial resolutions of 250m (bands 1-2), 500m (bands 3-7), and 1km (bands 8-36), and daily or near-daily temporal coverage. The first MODIS sensor is aboard NASA's Earth Observing System (EOS) Terra spacecraft, which was launched in December 1999. The EOS Aqua satellite was launched in May 2002, and carries the second MODIS sensor. Five years of MODIS data are now available and are useful for the creation of time series data sets. The spectral bands from MODIS of greatest potential in discrimination between snow and bare (non-snow covered) ice on land surfaces are shown to be useful for development of automated algorithms to distinguish land-based snow and bare ice based on their optical properties. An advantage of automated algorithms is the consistency of results within the time series and therefore the reduction of subjectivity in identifying climatologically-significant trends. Preliminary results of automated algorithms to distinguish snow and bare ice show that it is very difficult to separate snow and ice, for example on Greenland, using classification techniques. Clouds also present a challenge in discrimination of snow and bare ice over land. However, progress has been made and results showing a time series of data for an area in southwestern Greenland will be presented.
UR: http://modis-snow-ice.gsfc.nasa.gov
DE: 1827 Glaciology (1863)
DE: 1863 Snow and ice (1827)
SC: Cryosphere [C]
MN: 2005 Joint Assembly