High-Resolution Tropical Cyclone Rainfall Detection From C-Band SAR Imagery With Deep Learning
文献类型：期刊论文
| 作者 | Mu, Shanshan4,5,6 ; Li, Xiaofeng1,7; Wang, Haoyu4,5,6; Zheng, Gang2; Perrie, William3; Wang, Chong1,7
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| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
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| 出版日期 | 2024 |
| 卷号 | 62页码:15 |
| 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) |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2024.3445280 |
| 通讯作者 | Li, Xiaofeng(xiaofeng.li@ieee.org) |
| 英文摘要 | 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. |
| WOS关键词 | APERTURE RADAR IMAGES ; OCEAN ; FOOTPRINTS ; RETRIEVAL ; SEA |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:001303543600026 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/198577]  |
| 专题 | 海洋研究所_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, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China 2.State Ocean Adm, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou 310012, Peoples R China 3.Bedford Inst Oceanog, Fisheries & Oceans Canada, Dartmouth, NS B2Y 4A2, Canada 4.Chinese Acad Sci, Inst Oceanog, Key Lab Ocean Observat & Forecasting, Qingdao 266071, Peoples R China 5.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 6.Univ Chinese Acad Sci, Coll Marine Sci, Beijing 100049, Peoples R China 7.Chinese Acad Sci, Key Lab Ocean Observat & Forecasting, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China |
| 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. |
入库方式： OAI收割
来源：海洋研究所
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