Sea surface wind speed retrieval from Sentinel-1 HH polarization data using conventional and neural network methods

Tingting Qin; Tong Jia; Qian Feng; Xiaoming Li

doi:10.1007/s13131-020-1682-1

Volume 40 Issue 1

Feb. 2021

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Article Navigation > Acta Oceanologica Sinica > 2021 > 40(1): 13-21

GUO Jingsong, ZHANG Zhixin, XIA Changshui, GUO Binghuo. Seasonal characteristics and forcing mechanisms of the surface Kuroshio branch intrusion into the South China Sea[J]. Acta Oceanologica Sinica, 2019, 38(1): 13-21. doi: 10.1007/s13131-017-1132-x

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Tingting Qin, Tong Jia, Qian Feng, Xiaoming Li. Sea surface wind speed retrieval from Sentinel-1 HH polarization data using conventional and neural network methods[J]. Acta Oceanologica Sinica, 2021, 40(1): 13-21. doi: 10.1007/s13131-020-1682-1

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Sea surface wind speed retrieval from Sentinel-1 HH polarization data using conventional and neural network methods

doi: 10.1007/s13131-020-1682-1

Tingting Qin^{1, 2},
Tong Jia^{2, 3},
Qian Feng⁴,
Xiaoming Li^{2
,
,}

1.
College of Surveying, Mapping and Geoinformation, Guilin University of Technology, Guilin 541006, China
2.
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
3.
University of Chinese Academy of Sciences, Beijing 101408, China
4.
Key Laboratory of Space Ocean Remote Sensing and Applications, National Satellite Ocean Application Service, Beijing 100081, China

Funds: The National Key Research and Development Program under contract Nos 2016YFC1402703 and 2018YFC1407100.

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Corresponding author: E-mail: lixm@radi.ac.cn
Received Date: 2020-01-05
Accepted Date: 2020-05-26

Available Online: 2021-04-21

Publish Date: 2021-01-25

Abstract

Abstract

Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed (SSWS) from HH-polarized Sentinel-1 (S1) SAR images. The Polarization Ratio (PR) models combined with the CMOD5.N Geophysical Model Function (GMF) is used for SSWS retrieval from the HH-polarized SAR data. We compared different PR models developed based on previous C-band SAR data in HH-polarization for their applications to the S1 SAR data. The recently proposed CMODH, i.e., retrieving SSWS directly from the HH-polarized S1 data is also validated. The results indicate that the CMODH model performs better than results achieved using the PR models. We proposed a neural network method based on the backward propagation (BP) neural network to retrieve SSWS from the S1 HH-polarized data. The SSWS retrieved using the BP neural network model agrees better with the buoy measurements and ASCAT dataset than the results achieved using the conventional methods. Compared to the buoy measurements, the bias, root mean square error (RMSE) and scatter index (SI) of wind speed retrieved by the BP neural network model are 0.10 m/s, 1.38 m/s and 19.85%, respectively, while compared to the ASCAT dataset the three parameters of training set are –0.01 m/s, 1.33 m/s and 15.10%, respectively. It is suggested that the BP neural network model has a potential application in retrieving SSWS from Sentinel-1 images acquired at HH-polarization.
- Sentinel-1,
- HH-polarization,
- sea surface wind speed,
- retrieval methods

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References

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