气候变化领域集成服务门户(CCPortal): Analysis of coastal wind speed retrieval from CYGNSS mission using artificial neural network

CCPortal

DOI	10.1016/j.rse.2021.112454
	Analysis of coastal wind speed retrieval from CYGNSS mission using artificial neural network
	Li X.; Yang D.; Yang J.; Zheng G.; Han G.; Nan Y.; Li W.
发表日期	2021
ISSN	00344257
卷号	260
英文摘要	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. © 2021 Elsevier Inc.
英文关键词	Artificial neural network (ANN); Coastal; Cyclone GNSS (CYGNSS); Global navigation satellite system reflectometry (GNSS-R); Sea surface wind speed
语种	英语
scopus关键词	Coastal zones; Communication satellites; Feedforward neural networks; NASA; Reflection; Reflectometers; Salinity measurement; Speed; Storms; Surface waters; Weather forecasting; Wind; Artificial neural network; Coastal; Coastal regions; Cyclone GNSS; Global navigation satellite system reflectometry; Global Navigation Satellite Systems; Neural-networks; Reflectometry; Sea-surface wind speed; Wind speeds retrieval; Global positioning system
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178843
作者单位	School of Electronic and Information Engineering, Beihang University, Beijing, 100191, China; State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, 310012, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China; Fisheries and Oceans Canada, Institute of Ocean Sciences, Sidney, BC V8L 4B2, Canada; GNSS Research Center, Wuhan University, Wuhan, 430079, China; Institute of Space Sciences (ICE, CSIC), Barcelona, 08193, Spain; Institut d'Estudis Espacials de Catalunya (IEEC), Barcelona, 08034, Spain
推荐引用方式 GB/T 7714	Li X.,Yang D.,Yang J.,et al. Analysis of coastal wind speed retrieval from CYGNSS mission using artificial neural network[J],2021,260.
APA	Li X..,Yang D..,Yang J..,Zheng G..,Han G..,...&Li W..(2021).Analysis of coastal wind speed retrieval from CYGNSS mission using artificial neural network.Remote Sensing of Environment,260.
MLA	Li X.,et al."Analysis of coastal wind speed retrieval from CYGNSS mission using artificial neural network".Remote Sensing of Environment 260(2021).