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High accuracy water level estimation using super-resolution Sentinel-1 data

delete2026-02-12
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OA
AI
Y
Yan Jia
Q
Quan Liu
H
Hongjie He *
金双根 cover
金双根 (Shuanggen Jin) *
C
Chunqiao Song
K
Kyle Gao
Z
Zebiao Wu
DOI:10.1016/j.jag.2026.105156delete
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Abstract

Abstract

En 中文
• First adaptation of Real-ESRGAN to Sentinel-1 SAR for precise water mapping. • SAR-optimized feature extraction boosts resolution and detection robustness. • Enables accurate, high-resolution water level estimation without extra data.
Keywords:
Machine learning
River
Super-resolution
Sentinel-1
Water level
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Journal

International Journal of Applied Earth Observation and Geoinformation cover
International Journal of Applied Earth Observation and Geoinformation
IF:
8.6
Papers:
5.1K
Citations:
2.4W

Organization

H
henan polytechnic university
Scholars:
1.2W
Papers: 7.2K
Citations: 5
E
east china normal university
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3.1W
Papers: 2.1W
Citations: 25
U
university of cape town
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2.6K
Papers: 1.3K
Citations: 0
N
Nanjing University of Posts and Telecommunications
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2.4K
Papers: 969
Citations: 1.2W
C
Chinese Academy of Sciences
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3.9W
Papers: 1.5W
Citations: 58.4W
U
University of Waterloo
Scholars:
2.2W
Papers: 2.3W
Citations: 3.3W
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