Synthetic Aperture Radar for Geosciences

Author:

Meng Lingsheng12ORCID,Yan Chi1ORCID,Lv Suna2,Sun Haiyang2ORCID,Xue Sihan2,Li Quankun2,Zhou Lingfeng2,Edwing Deanna1,Edwing Kelsea1ORCID,Geng Xupu2,Wang Yiren2,Yan Xiao‐Hai1ORCID

Affiliation:

1. College of Earth, Ocean and Environment University of Delaware Newark DE USA

2. State Key Laboratory of Marine Environmental Science Xiamen University Xiamen China

Abstract

AbstractSynthetic 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.

Publisher

American Geophysical Union (AGU)

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