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Remote Sens. 2015, 7(9), 11602-11620; doi:10.3390/rs70911602

Assessment of Snow Status Changes Using L-HH Temporal-Coherence Components at Mt. Dagu, China

1
School of Resources and Environment, University of Electronic and Science Technology of China (UESTC), 2006 Xiyuan Avenue, West Hi-tech Zone, Chengdu 611731, China
2
Department of Geography, Planning, and Environment, East Carolina University, Greenville, NC 27858, USA
3
Institute of Remote Sensing Big Data, Big Data Research Center of UESTC, 2006 Xiyuan Avenue, West Hi-tech Zone, Chengdu 611731, China
4
Department of Management and Information Systems, East Carolina University, Greenville, NC 27858, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Richard Gloaguen and Prasad S. Thenkabail
Received: 30 March 2015 / Revised: 31 August 2015 / Accepted: 2 September 2015 / Published: 11 September 2015
View Full-Text   |   Download PDF [1528 KB, uploaded 11 September 2015]   |  

Abstract

Multitemporal Phased Array type L-band Synthetic Aperture Radar (PALSAR) horizontally transmitted and horizontally received (HH) coherence data was decomposed into temporal-coherence, spatial-coherence, and thermal noise components. The multitemporal data spanned between February and May of 2008, and consisted of two pairs of interferometric SAR (InSAR) images formed by consecutive repeat passes. With the analysis of ancillary data, a snow increase process and a snow decrease process were determined. Then, the multiple temporal-coherence components were used to study the variation of thawing and freezing statuses of snow because the components can mostly reflect the temporal change of the snow that occurred between two data acquisitions. Compared with snow mapping results derived from optical images, the outcomes from the snow increase process and the snow decrease process reached an overall accuracy of 71.3% and 79.5%, respectively. Being capable of delineating not only the areas with or without snow cover but also status changes among no-snow, wet snow, and dry snow, we have developed a critical means to assess the water resource in alpine areas. View Full-Text
Keywords: change detection; interferometric SAR (InSAR); multitemporal coherence data; snow cover and status change; temporal-coherence component change detection; interferometric SAR (InSAR); multitemporal coherence data; snow cover and status change; temporal-coherence component
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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. (CC BY 4.0).

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MDPI and ACS Style

Wang, Y.; Wang, L.; Li, H.; Yang, Y.; Yang, T. Assessment of Snow Status Changes Using L-HH Temporal-Coherence Components at Mt. Dagu, China. Remote Sens. 2015, 7, 11602-11620.

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