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Synthetic Aperture Radar for Geosciences

地质学 合成孔径雷达 范围(计算机科学) 遥感 计算机科学 雷达 地球物理学 数据科学 系统工程 地球科学 工程类 电信 程序设计语言
作者
Lingsheng Meng,Chi Yan,Suna Lv,Haiyang Sun,Shimin Xue,Q. Li,Lingfeng Zhou,Deanna Edwing,Kelsea Edwing,Xiao‐Hai Yan,Yiren Wang,Xiao‐Hai Yan
出处
期刊:Reviews of Geophysics [Wiley]
卷期号:62 (3)
标识
DOI:10.1029/2023rg000821
摘要

Abstract Synthetic Aperture Radar (SAR) has emerged as a pivotal technology in geosciences, offering unparalleled insights into Earth's surface. Indeed, its ability to provide high‐resolution, all‐weather, and day‐night imaging has revolutionized our understanding of various geophysical processes. Recent advancements in SAR technology, that is, developing new satellite missions, enhancing signal processing techniques, and integrating machine learning algorithms, have significantly broadened the scope and depth of geosciences. Therefore, it is essential to summarize SAR's comprehensive applications for geosciences, especially emphasizing recent advancements in SAR technologies and applications. Moreover, current SAR‐related review papers have primarily focused on SAR technology or SAR imaging and data processing techniques. Hence, a review that integrates SAR technology with geophysical features is needed to highlight the significance of SAR in addressing challenges in geosciences, as well as to explore SAR's potential in solving complex geoscience problems. Spurred by these requirements, this review comprehensively and in‐depth reviews SAR applications for geosciences, broadly including various aspects in air‐sea dynamics, oceanography, geography, disaster and hazard monitoring, climate change, and geosciences data fusion. For each applied field, the scientific advancements produced because of SAR are demonstrated by combining the SAR techniques with characteristics of geophysical phenomena and processes. Further outlooks are also explored, such as integrating SAR data with other geophysical data and conducting interdisciplinary research to offer comprehensive insights into geosciences. With the support of deep learning, this synergy will enhance the capability to model, simulate, and forecast geophysical phenomena with greater accuracy and reliability.
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