气候变化领域集成服务门户(CCPortal): Wind direction retrieval from Sentinel-1 SAR images using ResNet

CCPortal

DOI	10.1016/j.rse.2020.112178
	Wind direction retrieval from Sentinel-1 SAR images using ResNet
	Zanchetta A.; Zecchetto S.
发表日期	2021
ISSN	00344257
卷号	253
英文摘要	This paper introduces a novel approach to estimate the wind direction over the sea from Synthetic Aperture Radar (SAR) images without any external information. The method employs deep residual network (ResNet), a variant of Convolutional Neural Network, to obtain high resolution (2 km by 2 km) aliased wind direction fields. Forty-seven SAR images of the European Space Agency satellites Sentinel-1 have been processed with ResNet, previously trained with other fifteen images. The areas of interest are the Mediterranean Sea and the Persian Gulf, two regional seas where the SAR images often present complex patterns associated to the wind field spatial structure reporting traces of the interaction with coastal orography, hence valuable test sites to evaluate the performance of the methodology here proposed. Statistical analysis was carried out comparing the SAR-derived wind directions with those from ECMWF atmospheric model, ASCAT scatterometer and in-situ gauges. It reports biases β of -1.1°, 2.4° and -4.6° respectively, and centered root mean square difference cRMSd<21°, consistent with the benchmark obtained comparing scatterometer with ECMWF wind directions over the areas imaged by SAR (β 2.1°, cRMSd =19°). These results are relevant because they include the coastal data not accounted in the benchmark. Analysis of selected cases showed that SAR-derived wind fields reproduce meteorological situations characterized by strong divergence. Notably, our ResNet is able to estimate the wind direction even in the absence of wind streaks on the SAR images and in presence of convective turbulence structures, atmospheric lee waves, and ships. Furthermore, the model is also able to derive the wind field over small areas, as the example of Venice lagoon has shown. Detailed analysis of selected cases raised the issue of the lack of data with true spatial resolution of ≈2 km and within half hour from the satellite pass time necessary for exhaustive comparisons. © 2020 Elsevier Inc.
英文关键词	Coastal areas; Convolutional neural network; Deep residual network; Synthetic aperture radar; Wind direction
语种	英语
scopus关键词	Convolutional neural networks; Meteorological instruments; Space-based radar; Synthetic aperture radar; Atmospheric model; Coastal orography; Convective turbulence; European Space Agency; External informations; Root mean square differences; Spatial resolution; Synthetic aperture radar (SAR) images; Radar imaging; in situ measurement; satellite imagery; scatterometer; Sentinel; spatial resolution; synthetic aperture radar; wind direction; wind field; Arabian Sea; Indian Ocean; Italy; Mediterranean Sea; Persian Gulf; Veneto; Venezia [Veneto]; Venice Lagoon; Satellites
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/179034
作者单位	Department of Music, Hong Kong Baptist University, Sing Tao Building, Kowloon Tong, Hong Kong SAR, Hong Kong; Istituto di Scienze Polari, Consiglio Nazionale delle Ricerche, Corso Stati Uniti, 5, Padova, 35127, Italy; Department of Electrical Engineering, Persian Gulf University, Shahid Mahini St., Bushehr, 7516913817, Iran
推荐引用方式 GB/T 7714	Zanchetta A.,Zecchetto S.. Wind direction retrieval from Sentinel-1 SAR images using ResNet[J],2021,253.
APA	Zanchetta A.,&Zecchetto S..(2021).Wind direction retrieval from Sentinel-1 SAR images using ResNet.Remote Sensing of Environment,253.
MLA	Zanchetta A.,et al."Wind direction retrieval from Sentinel-1 SAR images using ResNet".Remote Sensing of Environment 253(2021).