Retrieving Ocean Surface Winds and Waves from Augmented Dual-Polarization Sentinel-1 SAR Data Using Deep Convolutional Residual Networks

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Author(s)Xue, Sihan
Author(s)Meng, Lingsheng
Author(s)Geng, Xupu
Author(s)Sun, Haiyang
Author(s)Edwing, Deanna
Author(s)Yan, Xiao-Hai
Date Accessioned2024-01-10T18:06:04Z
Date Available2024-01-10T18:06:04Z
Publication Date2023-08-11
DescriptionThis article was originally published in Atmosphere. The version of record is available at: https://doi.org/10.3390/atmos14081272. © 2023 by the authors. Licensee MDPI, Basel, Switzerland.
AbstractSea surface winds and waves are very important phenomena that exist in the air–sea boundary layer. With the advent of climate change, cascade effects are bringing more attention to these phenomena as warmer sea surface temperatures bring about stronger winds, thereby altering global wave conditions. Synthetic aperture radar (SAR) is a powerful sensor for high-resolution surface wind and wave observations and has accumulated large quantities of data. Furthermore, deep learning methods have been increasingly utilized in geoscience, especially the inversion of ocean information from SAR imagery. Here, we propose a method to invert various parameters of ocean surface winds and waves using Sentinel-1 SAR IW mode data. To ensure this method is more robust and scalable, we augmented the input data with dual-polarized SAR imagery, an incident angle, and a more constrained homogeneity test. This method adopts a deeper structure in order to retrieve more wind and wave parameters, and the use of residual networks can accelerate training convergence and improve regression accuracy. Using 1600 training samples filtered by a novel homogeneity test and with significant wave heights between 0 and 10 m, results from error parameters including the root mean square error (RMSE), scatter index (SI), and correlation coefficient (COR) show the great performance of this proposed method. The RMSE is 0.45 m, 0.76 s, and 1.90 m/s for the significant wave height, mean wave period, and wind speed, respectively. Furthermore, the temporal variation and spatial distribution of the estimates are consistent with China–France Oceanography Satellite (CFOSAT) observations, buoy measurements, WaveWatch3 regional model data, and ERA5 reanalysis data.
SponsorThis work is supported by the National Key R & D Program of China (2019YFA0606702), the National Natural Science Foundation of China (Grant Numbers: 91858202, 41630963, and 41776003), and Industry–University Cooperation and Collaborative Education Projects (202102245034). Xiao-Hai Yan has been supported by the NSF (IIS-2123264) and NASA (80NSSC20M0220). The authors would also like to thank Fujian Haisi Digital Technology Co., Ltd. and Fujian Tendering Purchasing Group Co., Ltd. for supporting this research.
CitationXue, Sihan, Lingsheng Meng, Xupu Geng, Haiyang Sun, Deanna Edwing, and Xiao-Hai Yan. 2023. "Retrieving Ocean Surface Winds and Waves from Augmented Dual-Polarization Sentinel-1 SAR Data Using Deep Convolutional Residual Networks" Atmosphere 14, no. 8: 1272. https://doi.org/10.3390/atmos14081272
ISSN2073-4433
URLhttps://udspace.udel.edu/handle/19716/33784
Languageen_US
PublisherAtmosphere
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
Keywordsconvolutional residual networks
Keywordssea surface wind
Keywordsocean wave
KeywordsSAR
KeywordsSentinel-1
TitleRetrieving Ocean Surface Winds and Waves from Augmented Dual-Polarization Sentinel-1 SAR Data Using Deep Convolutional Residual Networks
TypeArticle
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