A large-scale framework for deriving tidal flat topography from SWOT data
文献类型：期刊论文
| 作者 | Xu, Hao14; Xu, Nan12,13; Li, Wenyu11; Tan, Kai1; Chen, Chunpeng1; Li, Huan15; Zhan, Lucheng16; Xin, Pei17; Yao, Jiaqi18; Li, Peng19 |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2026-03-01 |
| 卷号 | 334页码:115237 |
| 关键词 | Tidal flat Topography Coastal Satellite altimetry Surface water and ocean topography (SWOT) Sea level rise Intertidal |
| ISSN号 | 0034-4257 |
| DOI | 10.1016/j.rse.2026.115237 |
| 产权排序 | 18 |
| 文献子类 | Article |
| 英文摘要 | Tidal flat topography is a fundamental attribute affecting inundation dynamics, sediment transport, and ecosystem functioning, yet accurate and spatially consistent large-scale monitoring remains challenging. Here, we leveraged satellite altimetry from the Surface Water and Ocean Topography (SWOT) mission to develop a novel, large-scale framework for deriving tidal flat topography from SWOT data, and demonstrated its capability by generating a high-accuracy, national-scale elevation dataset for China. By combining a percentile-based aggregation of multi-temporal water-surface elevation observations with a tide-constrained, adaptive best-quantile (best-q) reconstruction strategy, followed by linear interpolation for gap filling, we improved both vertical accuracy and spatial completeness. Validation against airborne LiDAR, GNSS-RTK surveys, and ICESat-2 photon data demonstrates robust performance across diverse coastal settings, achieving RMSE = 0.34-0.47 m and R2 = 0.81-0.88 at a horizontal resolution of 100 m. Compared with existing large-scale digital elevation models (DEMs), the SWOT-derived topography not only improves vertical accuracy by over 80% but also providing substantially more complete spatial coverage of tidal flat elevations. Spatial analyses reveal pronounced latitudinal gradients, with higher tidal flats concentrated in low-latitude regions and extensive low-lying flats dominating northern estuarine and deltaic systems. This study establishes a scalable framework for tidal-flat elevation retrieval and provides a foundational dataset to support coastal monitoring and sustainable management. |
| URL标识 | 查看原文 |
| WOS关键词 | TEMPORAL VARIATIONS ; ACCURACY ASSESSMENT ; COASTLINE CHANGES ; TIME-SERIES ; CHINA ; DELTA ; ATTENUATION ; EVOLUTION ; IMPACTS ; MAP |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001664905800002 |
| 出版者 | ELSEVIER SCIENCE INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219612]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Xu, Nan |
| 作者单位 | 1.East China Normal Univ, State Key Lab Estuarine & Coastal Res, 500 Dongchuan Rd, Shanghai 200241, Peoples R China; 2.Guangdong Lab Artificial Intelligence & Digital Ec, Shenzhen 518107, Peoples R China 3.Guilin Univ Technol, Coll Geomat & Geoinformat, Guilin 541006, Peoples R China; 4.Nanjing Normal Univ, Sch Marine Sci & Engn, Nanjing 210023, Peoples R China; 5.Shandong Jianzhu Univ, Sch Surveying & Geoinformat, Jinan 250101, Peoples R China; 6.Univ Tokyo, Grad Sch Frontier Sci, Kashiwa 2778561, Japan; 7.Heriot Watt Univ, Sch Energy Geosci Infrastruct & Soc, Edinburgh, Scotland; 8.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; 9.Tsinghua Univ, Inst Global Change Studies, Dept Earth Syst Sci, Minist Educ,Key Lab Earth Syst Modeling, Beijing 100084, Peoples R China; 10.Univ Hong Kong, Dept Geog, Dept Earth Sci, Pokfulam, Hong Kong 999077, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Xu, Hao,Xu, Nan,Li, Wenyu,et al. A large-scale framework for deriving tidal flat topography from SWOT data[J]. REMOTE SENSING OF ENVIRONMENT,2026,334:115237. |
| APA | Xu, Hao.,Xu, Nan.,Li, Wenyu.,Tan, Kai.,Chen, Chunpeng.,...&Li, Qingquan.(2026).A large-scale framework for deriving tidal flat topography from SWOT data.REMOTE SENSING OF ENVIRONMENT,334,115237. |
| MLA | Xu, Hao,et al."A large-scale framework for deriving tidal flat topography from SWOT data".REMOTE SENSING OF ENVIRONMENT 334(2026):115237. |
入库方式： OAI收割
来源：地理科学与资源研究所
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