Analysis of coastal wind speed retrieval from CYGNSS mission using artificial neural network

REMOTE SENSING OF ENVIRONMENT

卷:260

Article

Li, Xiaohui^1,2  Yang, Dongkai¹  Yang, Jingsong^2,3  Zheng, Gang^2,3  Han, Guoqi⁴  Nan, Yang⁵  Li, Weiqiang^6,7 

[1] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China

[2] Minist Nat Resources, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou 310012, Peoples R China

[3] Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China

[4] Fisheries & Oceans Canada, Inst Ocean Sci, Sidney, BC V8L 4B2, Canada

[5] Wuhan Univ, GNSS Res Ctr, Wuhan 430079, Peoples R China

[6] CSIC, Inst Space Sci ICE, Barcelona 08193, Spain

[7] Inst Estudis Espacials Catalunya IEEC, Barcelona 08034, Spain

关键词: Cyclone GNSS (CYGNSS);    Sea surface wind speed;    Coastal;    Artificial neural network (ANN);    Global navigation satellite system reflectometry (GNSS-R);   

DOI  :  10.1016/j.rse.2021.112454

来源: Elsevier

【 摘 要 】

This paper demonstrates the capability and performance of sea surface wind speed retrieval in coastal regions (within 200 km away from the coastline) using spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data from NASA's Cyclone GNSS (CYGNSS) mission. The wind speed retrieval is based on the Artificial Neural Network (ANN). A feedforward neural network is trained with the collocated CYGNSS Level 1B (version 2.1) observables and the wind speed from European Centre for Medium-range Weather Forecast Reanalysis 5th Generation (ECMWF ERA5) data in coastal regions. An ANN model with five hidden layers and 200 neurons in each layer has been constructed and applied to the validation set for wind speed retrieval. The proposed ANN model achieves good wind speed retrieval performance in coastal regions with a bias of -0.03 m/s and a RMSE of 1.58 m/s, corresponding to an improvement of 24.4% compared to the CYGNSS Level 2 (version 2.1) wind speed product. The ANN based retrievals are also compared to the ground truth measurements from the National Data Buoy Center (NDBC) buoys, which shows a bias of -0.44 m/s and a RMSE of 1.86 m/s. Moreover, the sensitivities of the wind speed retrieval performance to different input parameters have been analyzed. Among others, the geolocation of the specular point and the swell height can provide significant contribution to the wind speed retrieval, which can provide useful reference for more generic GNSS-R wind speed retrieval algorithms in coastal regions.

【 授权许可】

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