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Improved Envisat Altimetry Ice Sheet Elevation Change Data Processing Algorithms Using Repeat-Track Analysis

delete2016-08-01
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PRE
AI
X
Xiaoli Su *
C
C. K. Shum
C
Chung‐Yen Kuo
Y
Yuchan Yi
DOI:10.1109/LGRS.2016.2567486delete
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摘要

摘要

En 中文
Repeat-track analysis is commonly utilized to generate elevation change time series from satellite radar altimetry over ice sheets. It requires surface gradient (SG) correction due primarily to orbital drifts and radar-related empirical corrections caused by radar scatters from ice surface and potential subsurface. In this letter, two approaches, namely, the use of a digital elevation model (DEM) and the modified repeat-track analysis, which uses the accumulated Envisat altimetry profiles, are applied to correct the SG over both Greenland ice sheet (GrIS) and Antarctic ice sheet (AIS). By comparing the root mean square (rms) of elevation change time series after SG correction, the percentage of data (rms < 1 m) obtained by using modified repeat-track analysis is found to be 85% and 88% for the GrIS and AIS, respectively, as opposed to 45% and 44% if the DEM method is used. Furthermore, three cases are studied to assess empirical corrections for elevation retrieved from both ice-1 and ice-2 algorithms over the AIS. We conclude that the modified repeat-track analysis is more effective to remove topographic induced error. For the ice-2 algorithm, waveform shape parameters are needed in addition to applying corrections from changes in backscatter coefficients. The trend of elevation changes from the ice-1 algorithm with only backscatter analysis agrees with that from the ice-2 algorithm with corrections from backscatter coefficient changes and waveform shape parameters. This study could provide a potential data processing recipe for generating improved satellite radar altimetry elevation time series over ice sheets.
Keyword:
Continental ice sheets
elevation change
Envisat altimetry
radar backscatters
repeat-track analysis
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期刊

IEEE Geoscience and Remote Sensing Magazine 封面图
IEEE Geoscience and Remote Sensing Magazine
IF:
16.4
论文数:
1.0W
被引数:
5.1K

机构

U
University System of Ohio
学者数:
15.5W
论文数: 13.0W
被引数: 200
C
chinese academy of sciences
学者数:
56.7W
论文数: 45.0W
被引数: 704
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