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Antarctic snow melt detection based on the synergy of SSM/I and QuikSCAT

Published online by Cambridge University Press:  25 July 2017

Xinwu Li ,
Xingdong Wang ,
Cheng Wang  and
Lu Zhang
Xinwu Li
Affiliation:
Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
Xingdong Wang*
Affiliation:
Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, 9 Dengzhuang South Road, Haidian District, Beijing 100094, China Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, Peoples Republic of China
Cheng Wang
Affiliation:
Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
Lu Zhang
Affiliation:
Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
*
*Corresponding author: [email protected]
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Abstract

Microwave radiometer SSM/I (Special Sensor Microwave Imager) data and scatterometer QuikSCAT (Quick Scatterometer) data have been widely used for near-surface snow melt detection based on their sensitivity to liquid water present in snow. The SSM/I data have high reliability and the QuikSCAT data have high spatial resolution. In order to improve the accuracy of Antarctic near-surface snow melt detection, we propose a new method based on the synergy of SSM/I and QuikSCAT data, i.e. the snow melt physical model incorporates the complementary advantages of both datasets. Based on comparisons with temperature data from three automatic weather stations, the proposed algorithm improved the accuracy of snow melt detection. The algorithm could also be applied to other regions, which would provide further evidence to support its use and additional data to document changes in the Antarctic due to global climate change.

Keywords

microwave radiometermicrowave scatterometerphysical modelpolar regionssynergistic algorithm
Type
Physical Sciences
Information
Antarctic Science , Volume 29 , Issue 6 , December 2017 , pp. 561 - 568
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Copyright
© Antarctic Science Ltd 2017 

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