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Sensing Sea Ice Based on Doppler Spread Analysis of Spaceborne GNSS-R Data

Authors :
Kegen Yu
Jens Wickert
Jun Geng
Maximilian Semmling
Yongchao Zhu
Tingye Tao
Xiaochuan Qu
Jingui Zou
Zhenxuan Li
Zhourun Ye
Source :
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 217-226 (2020)
Publication Year :
2020
Publisher :
Institute of Electrical and Electronics Engineers (IEEE), 2020.

Abstract

The spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) delay-Doppler map (DDM) data collected over ocean carry typical feature information about the ocean surface, which may be covered by open water, mixed water/ice, complete ice, etc. A new method based on Doppler spread analysis is proposed to remotely sense sea ice using the spaceborne GNSS-R data collected over the Northern and Southern Hemispheres. In order to extract useful information from DDM, three delay waveforms are defined and utilized. The delay waveform without Doppler shift is defined as central delay waveform (CDW), while the integration of delay waveforms of 20 different Doppler shift values is defined as integrated delay waveform (IDW). The differential waveform between normalized CDW (NCDW) and normalized IDW (NIDW) is defined as differential delay waveform (DDW), which is a new observable used to describe the difference between NCDW and NIDW, which have different Doppler spread characteristics. The difference is mainly caused by the roughness of reflected surface. First, a new data quality control method is proposed based on the standard deviation and root-mean-square error (RMSE) of the first 48 bins of DDW. Then, several different observables derived from NCDW, NIDW, and DDW are applied to distinguish sea ice from water based on their probability density function. Through validating against sea ice edge data from the Ocean and Sea Ice Satellite Application Facility, the trailing edge waveform summation of DDW achieves the best results, and its probabilities of successful detection are 98.22% and 96.65%, respectively, in the Northern and Southern Hemispheres.

Details

ISSN :
21511535 and 19391404
Volume :
13
Database :
OpenAIRE
Journal :
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Accession number :
edsair.doi.dedup.....fd3e61513204a113f54d83374a0b3f73
Full Text :
https://doi.org/10.1109/jstars.2019.2955175