Item: Assessing the accuracy of sir-c snow cover classification
Title: Assessing the accuracy of sir-c snow cover classification
Proceedings: Proceedings of the 1996 International Snow Science Workshop, Banff, Canada
Authors: Thomas Albright, Jiancheng Shi, Jeff Dozier, Department of Geography, University of California, Institute for Computational Earth System Science, University of California, School of Environmental Science and Management, University of California
Abstract: Timely and accurate maps of snow covered area (SCA) are important to resource managers, planners, and scientists for applications ranging from avalanche hazard assessment to global climate studies. Optical sensors such as Landsat Thematic Mapper (TM) have already demonstrated their effectiveness at mapping SCA. Recently, much work has focused on the use of synthetic aperture radar (SAR) to accomplish this task, due to its high resolution, sun independent, all-weather capability. Though initial results are encouraging, an extensive assessment of the accuracy of these systems under a variety of sensor and target conditions needs to be performed. This study examines the accuracy of a Spaceborne Imaging Radar - C (SIR-C) algorithm for mapping SCA. We used a well verified Landsat TM fractional SCA image to validate SIR-C SeA images of Mammoth Mountain, CA, USA. We produced images showing the spatial distribution and magnitude of the errors. We also analyzed what surface conditions correlate with large errors in the SCA estimation. The SIR-C algorithm is accurate under some conditions but needs improvement in other areas. It does well in pure snow and snow free areas, but overall, it underestimates snow relative to the TM algorithm. The major source for this underestimation in this study is SIR-C's difficulty detecting snow in moderately vegetated areas.
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Keywords: snow covered area, classification, synthetic aperture radar, sir-c, accuracy
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