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Journal of Sensors
Volume 2017, Article ID 4820905, 15 pages
Research Article

New Approach for Snow Cover Detection through Spectral Pattern Recognition with MODIS Data

1Division of Earth Environmental System Science, Major of Spatial Information Engineering, Pukyong National University, Daeyeon-3 Nam-Gu, Busan 608-737, Republic of Korea
2Korea Aerospace Research Institute (KARI), 169-84 Gwahak-ro, Yuseong-gu, Daejeon 305-806, Republic of Korea
3Division of Earth Environmental System Science, Major of Environmental Atmosphere Sciences, Pukyong National University, Daeyeon-3 Nam-Gu, Busan 608-737, Republic of Korea

Correspondence should be addressed to Kyung-Soo Han;

Received 23 April 2017; Revised 25 July 2017; Accepted 28 August 2017; Published 21 November 2017

Academic Editor: Saro Lee

Copyright © 2017 Kyeong-Sang Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Snow cover plays an important role in climate and hydrology, at both global and regional scales. Most previous studies have used static threshold techniques to detect snow cover, which can lead to errors such as misclassification of snow and clouds, because the reflectance of snow cover exhibits variability and is affected by several factors. Therefore, we present a simple new algorithm for mapping snow cover from Moderate Resolution Imaging Spectroradiometer (MODIS) data using dynamic wavelength warping (DWW), which is based on dynamic time warping (DTW). DTW is a pattern recognition technique that is widely used in various fields such as human action recognition, anomaly detection, and clustering. Before performing DWW, we constructed 49 snow reflectance spectral libraries as reference data for various solar zenith angle and digital elevation model conditions using approximately 1.6 million sampled data. To verify the algorithm, we compared our results with the MODIS swath snow cover product (MOD10_L2). Producer’s accuracy, user’s accuracy, and overall accuracy values were 92.92%, 78.41%, and 92.24%, respectively, indicating good overall classification accuracy. The proposed algorithm is more useful for discriminating between snow cover and clouds than threshold techniques in some areas, such as those with a high viewing zenith angle.