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55 Estimation of Snow Extent and Snow Properties

Part 5. Remote Sensing

  1. Dorothy K Hall1,
  2. Richard EJ Kelly2,
  3. James L Foster1,
  4. Alfred TC Chang1,†

Published Online: 15 APR 2006

DOI: 10.1002/0470848944.hsa062

Encyclopedia of Hydrological Sciences

Encyclopedia of Hydrological Sciences

How to Cite

Hall, D. K., Kelly, R. E., Foster, J. L. and Chang, A. T. 2006. Estimation of Snow Extent and Snow Properties. Encyclopedia of Hydrological Sciences. 5:55.

Author Information

  1. 1

    NASA/Goddard Space Flight Center, Greenbelt, MD, US

  2. 2

    University of Maryland, Baltimore, MD, US

  1. Deceased May 26, 2004

Publication History

  1. Published Online: 15 APR 2006

Abstract

Important advances have been made in the measurement of seasonal snow cover since the advent of satellite remote sensing in the mid 1960s. Data from the visible, near-infrared, infrared, and microwave portions of the electromagnetic spectrum have proven useful for measuring different properties of snow. In terms of snow mapping, sensors employing visible and near-infrared wavelengths are now capable of accurately and reliably measuring snow-cover extent with a spatial resolution of up to 250 m on a daily basis, and even higher resolution for less-frequent coverage. Passive-microwave data, available since the 1970s, have been utilized for measuring snow extent, depth and snow-water equivalent (SWE), though at a coarse spatial resolution compared to visible data, while active-microwave sensors such as scatterometers, provide valuable information on snowpack ripening. Capabilities of synthetic-aperture radar (SAR) data for snow-cover studies are still being explored, however, bands on current satellite SAR sensors are not ideal for measuring snow cover. Remote sensing data of snow cover are now well suited for use in hydrologic and general-circulation models. Inclusion of remotely-sensed data significantly enhances our understanding of the Earth's weather and climate, and decadal-scale climate change. Future improvements include refinement of snow-cover extent measurements, minimizing SWE errors, and improving our ability to ingest remote sensing data of snow into models.