High-Resolution Tropical Cyclone Rainfall Detection From C-Band SAR Imagery With Deep Learning

doi:10.1109/TGRS.2024.3445280

IOCAS-IR > 海洋环流与波动重点实验室

	High-Resolution Tropical Cyclone Rainfall Detection From C-Band SAR Imagery With Deep Learning
	Mu, Shanshan1,2,3 ; Li, Xiaofeng 4,5; Wang, Haoyu 1,2,3; Zheng, Gang 6; Perrie, William 7; Wang, Chong4,5
	2024
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	62 页码:15
通讯作者	Li, Xiaofeng([email protected])
摘要	This article introduces an innovative deep-learning approach for retrieving tropical cyclone (TC) rainfall information from C-band Sentinel-1 synthetic aperture radar (SAR) imagery. We collected 17 SAR images under TC conditions from 2016 to 2021 and matched them with synchronous observational Next Generation Weather Radar (NEXRAD) Level-III data, forming a dataset of 302689 data pairs for model development. The model inputs include SAR-measured physical parameters in normalized radar cross section (NRCS), texture features represented by the gray-level co-occurrence matrix (GLCM), and statistical parameters of VV-polarized NRCS. A deep-learning-based TC rain rate retrieval (TC3R) model, combining a convolutional network and a fully connected (FC) network, was developed to retrieve quantitative TC rainfall information effectively. The test results demonstrate that the TC3R model can offer reasonable and stable quantitative rainfall estimation, particularly effectively detecting areas with medium-to-heavy rainfall events (2.5-40 mm/h) in SAR images where the NRCS is significantly affected by rain. Furthermore, to offer valuable insights into the performance of the TC3R model, we analyzed results across TC events of different intensities as case studies. Our results show high structural similarity (SSIM) in rainfall patterns between SAR and NEXRAD across all cases, consistently achieving SSIM values above 0.67. Moreover, in areas where SAR signals are notably affected by rainfall, the SSIM index even exceeds 0.80. Finally, our model's performance was evaluated by comparing its results with the independent global precipitation measurement (GPM) data, demonstrating effective rainfall prediction, particularly for the primary spiral rain band, in the two cases analyzed.
关键词	Rain Radar polarimetry Precipitation Radar imaging Synthetic aperture radar Spaceborne radar C-band Deep learning rainfall synthetic aperture radar (SAR) imagery tropical cyclone (TC)
DOI	10.1109/TGRS.2024.3445280
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[U2006211]; National Natural Science Foundation of China[42221005]; National Key Research and Development Program[2022YFE0204600]; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB42000000]; Program of Laoshan Laboratory for Marine Science and Technology[LSKJ202204303]; Program of Laoshan Laboratory for Marine Science and Technology[LSKJ202202302]
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001303543600026
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS关键词	APERTURE RADAR IMAGES ; OCEAN ; FOOTPRINTS ; RETRIEVAL ; SEA
引用统计
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/198578
专题	海洋环流与波动重点实验室
通讯作者	Li, Xiaofeng
作者单位	1.Chinese Acad Sci, Inst Oceanog, Key Lab Ocean Observat & Forecasting, Qingdao 266071, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 3.Univ Chinese Acad Sci, Coll Marine Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Key Lab Ocean Observat & Forecasting, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 5.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China 6.State Ocean Adm, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou 310012, Peoples R China 7.Bedford Inst Oceanog, Fisheries & Oceans Canada, Dartmouth, NS B2Y 4A2, Canada
推荐引用方式 GB/T 7714	Mu, Shanshan,Li, Xiaofeng,Wang, Haoyu,et al. High-Resolution Tropical Cyclone Rainfall Detection From C-Band SAR Imagery With Deep Learning[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2024,62:15.
APA	Mu, Shanshan,Li, Xiaofeng,Wang, Haoyu,Zheng, Gang,Perrie, William,&Wang, Chong.(2024).High-Resolution Tropical Cyclone Rainfall Detection From C-Band SAR Imagery With Deep Learning.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62,15.
MLA	Mu, Shanshan,et al."High-Resolution Tropical Cyclone Rainfall Detection From C-Band SAR Imagery With Deep Learning".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024):15.