Using remote sensing to estimate snow depth and snow water equivalence

Lee, C, Jones, S and Bellman, C 2009, 'Using remote sensing to estimate snow depth and snow water equivalence', in Ostendorf, B., Baldock, P., Bruce, D., Burdett, M. and P. Corcoran (ed.) Proceedings of the Surveying & Spatial Sciences Institute Biennial International Conference, Adelaide Convention Centre, Adelaide, Australia, 28 September - 2 October 2009, pp. 829-843.


Document type: Conference Paper
Collection: Conference Papers

Title Using remote sensing to estimate snow depth and snow water equivalence
Author(s) Lee, C
Jones, S
Bellman, C
Year 2009
Conference name 2009 Surveying & Spatial Sciences Institute Biennial International Conference
Conference location Adelaide Convention Centre, Adelaide, Australia
Conference dates 28 September - 2 October 2009
Proceedings title Proceedings of the Surveying & Spatial Sciences Institute Biennial International Conference
Editor(s) Ostendorf, B., Baldock, P., Bruce, D., Burdett, M. and P. Corcoran
Publisher Surveying and Spatial Sciences Institute
Place of publication Adelaide, Australia
Start page 829
End page 843
Total pages 15
Abstract Snow is an important source of water. However, data is often lacking on the water content (snow water equivalence - SWE), extent and depth of the seasonal snow cover. This paper presents results from a research project that determined the amount of water in a snow pack from snow depth and SWE in an areal manner using remote sensing and photogrammetry. Five field campaigns were undertaken within the Falls Creek Ski Resort, over 18 months, to establish a framework of positional points, collect snow data and acquire aerial imagery. A GPS survey was completed concurrent with the image capture of snow-covered terrain to obtain appropriate validation points of the snow surface and to coordinate photo control. Spectral reflectance measurements, supported by manual sampling of the snow surface, were collected in conjunction with snow surface GPS measurements. Digital photogrammetric methods were used to create digital elevation models (DEMs) of the snow surface and the terrain. The digital aerial photography was acquired with a ground sample distance of 8 cm. Derived DEM values have been consistently higher than GPS elevations (mean differences of 15 cm; standard deviation of 8 cm). Spectral reflectance signatures were used to infer the properties of snow, such as snow grain size. A prominent absorption feature (~1030 nm) of the snow reflectance was found to be highly correlated with grain size (R = 0.71, p <0.05) allowing for the estimation of SWE, at least for the snow surface. The potential of this methodology is to allow for the automated estimation of both snow depth and water content at a landscape scale.
Subjects Photogrammetry and Remote Sensing
ISBN 9780958136686
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