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      Semi-Empirical Algorithm for Wind Speed Retrieval from Gaofen-3 Quad-Polarization Strip Mode SAR Data

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          Abstract

          Synthetic aperture radar (SAR) is a suitable tool to obtain reliable wind retrievals with high spatial resolution. The geophysical model function (GMF), which is widely employed for wind speed retrieval from SAR data, describes the relationship between the SAR normalized radar cross-section (NRCS) at the copolarization channel (vertical-vertical and horizontal-horizontal) and a wind vector. SAR-measured NRCS at cross-polarization channels (horizontal-vertical and vertical-horizontal) correlates with wind speed. In this study, a semi-empirical algorithm is presented to retrieve wind speed from the noisy Chinese Gaofen-3 (GF-3) SAR data with noise-equivalent sigma zero correction using an empirical function. GF-3 SAR can acquire data in a quad-polarization strip mode, which includes cross-polarization channels. The semi-empirical algorithm is tuned using acquisitions collocated with winds from the European Center for Medium-Range Weather Forecasts. In particular, the proposed algorithm includes the dependences of wind speed and incidence angle on cross-polarized NRCS. The accuracy of SAR-derived wind speed is around 2.10 m s −1 root mean square error, which is validated against measurements from the Advanced Scatterometer onboard the Metop-A/B and the buoys from the National Data Buoy Center of the National Oceanic and Atmospheric Administration. The results obtained by the proposed algorithm considering the incidence angle in a GMF are relatively more accurate than those achieved by other algorithms. This work provides an alternative method to generate operational wind products for GF-3 SAR without relying on ancillary data for wind direction.

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          Author and article information

          Journal
          JOUC
          Journal of Ocean University of China
          Science Press and Springer (China )
          1672-5182
          20 December 2019
          01 February 2020
          : 19
          : 1
          : 23-35
          Affiliations
          1Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316000, China
          2Natural Sciences, University of Stirling, Stirling FK9 4LA, UK
          3Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China
          4Key Laboratory of Space Ocean Remote Sensing and Application, National Satellite Ocean Application Service, Beijing 100081, China
          Author notes
          *Corresponding author: SHAO Weizeng, E-mail: shaoweizeng@ 123456zjou.edu.cn
          Article
          s11802-019-4215-9
          10.1007/s11802-019-4215-9
          Copyright © Ocean University of China, Science Press and Springer-Verlag GmbH Germany 2020.

          The copyright to this article, including any graphic elements therein (e.g. illustrations, charts, moving images), is hereby assigned for good and valuable consideration to the editorial office of Journal of Ocean University of China, Science Press and Springer effective if and when the article is accepted for publication and to the extent assignable if assignability is restricted for by applicable law or regulations (e.g. for U.S. government or crown employees).

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          Self URI (journal-page): https://www.springer.com/journal/11802

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