首页|SAR sensing of the atmosphere:stack-based processing for tropospheric and ionospheric phase retrieval
SAR sensing of the atmosphere:stack-based processing for tropospheric and ionospheric phase retrieval
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This paper is intended to summarize the research conducted during the first 2 years of the Dragon 5 project 59,332(geophysical and atmospheric retrieval from Synthetic Aperture Radar(SAR)data stacks over natural scenarios).Monitoring atmospheric phenomena,encompassing both tropospheric and ionospheric conditions,holds pivotal significance for various scientific and practical applications.In this paper,we present an exploration of advanced techniques for estimating tropospheric and ionospheric phase screens using stacks of Synthetic Aperture Radar(SAR)images.Our study delves into the current state-of-the-art in atmospheric monitor-ing with a focus on spaceborne SAR systems,shedding light on their evolving capabilities.For tropospheric phase screen estimation,we propose a novel approach that jointly estimates the tropospheric component from all the images.We discuss the methodology in detail,high-lighting its ability to recover accurate tropospheric maps.Through a series of quantitative case studies using real Sentinel-1 satellite data,we demonstrate the effectiveness of our technique in capturing tropospheric variability over different geographical regions.Concurrently,we delve into the estimation of ionospheric phase screens utilizing SAR image stacks.The intri-cacies of ionospheric disturbances pose unique challenges,necessitating specialized techni-ques.We dissect our approach,showcasing its capacity to mitigate ionospheric noise and recover precise phase information.Real data from the Sentinel-1 satellite are employed to showcase the efficacy of our method,unraveling ionospheric perturbations with improved accuracy.The integration of our techniques,though presented separately for clarity,collec-tively contributes to a comprehensive framework for atmospheric monitoring.Our findings emphasize the potential of SAR-based approaches in advancing our knowledge of atmospheric processes,thus fostering advancements in weather prediction,geophysics,and environmental management.
SARatmosphereionospheretroposphere
Marco Manzoni、Naomi Petrushevsky、Chuanjun Wu、Stefano Tebaldini、Andrea Virgilio Monti-Guarnieri、Mingsheng Liao
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Department of Electronics,Information,and Bioengineering,Politecnico di Milano,Milano,Italy
State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan,China
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