Volume 43 Issue 10
Oct.  2018
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ZHU Yongchao, ZOU Jingui, YU Kegen. A New Sea Ice Distribution Detection Method Using GNSS Reflected Signals[J]. Geomatics and Information Science of Wuhan University, 2018, 43(10): 1472-1477. DOI: 10.13203/j.whugis20160539
Citation: ZHU Yongchao, ZOU Jingui, YU Kegen. A New Sea Ice Distribution Detection Method Using GNSS Reflected Signals[J]. Geomatics and Information Science of Wuhan University, 2018, 43(10): 1472-1477. DOI: 10.13203/j.whugis20160539
shu

A New Sea Ice Distribution Detection Method Using GNSS Reflected Signals

  • 1.

    School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

  • 2.

    Key Laboratory of Precise Engineering and Industry Surveying, NASG, Wuhan 430079, China

Funds: 

The National Natural Science Foundation of China 41574031

More Information
  • Author Bio:

    ZHU Yongchao, PhD candidate, specializes in the study of global navigation satellite system (GNSS) applications. E-mail: ychzhu@whu.edu.cn

  • Corresponding author:

    ZOU Jingui, PhD, professor. E-mail: jgzou@sgg.whu.edu.cn

  • Received Date: September 04, 2017
  • Published Date: October 04, 2018
    Graphical Abstract
    • Abstract
  • Global navigation satellite system reflectometry (GNSS-R) delay-doppler map (DDM) obtained with TechDemoSat-1 (TDS-1) is used to detect ice presence over Greenland region of Arctic. DDM of sea ice shows less spreading than those of open water as the scattered GNSS signals follow the coherenty scattering model and diffuse scattering model over ice and water respectively. The transition from seawater to sea ice could lead to decrease of the pixel number of DDM. A pixel number ratio of adjacent DDM based detection scheme is proposed through employing a threshold method to distinguish sea ice and sea water, and to analyze sea ice distribution of Greenland region. Sea ice distribution of Greenland with time is analyzed through using multi-period data from TDS-1. The performance of the proposed method is assessed with a mathematical statistical approach through comparing with ground-truth sea ice data from the National Snow and Ice Data Center, USA. The effectiveness of this approach is validated with a probability of detection up to 98.76%-99.21%, and the overall probability of detection is 99.09%.
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    • References (14)
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