Assessment of Empirical Algorithms for Shallow Water Bathymetry Using Multi-Spectral Imagery of Pearl River Delta Coast, China
文献类型：期刊论文
| 作者 | Wei, Chunzhu1,2; Zhao, Qianying1; Lu, Yang3; Fu, Dongjie4 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2021-08-01 |
| 卷号 | 13期号:16页码:17 |
| 关键词 | bathymetry Landsat 8 Sentinel-2 Google Earth Engine retrieval algorithm |
| DOI | 10.3390/rs13163123 |
| 通讯作者 | Wei, Chunzhu(weichzh@mail.sysu.edu.cn) |
| 英文摘要 | Pearl River Delta (PRD), as one of the most densely populated regions in the world, is facing both natural changes (e.g., sea level rise) and human-induced changes (e.g., dredging for navigation and land reclamation). Bathymetric information is thus important for the protection and management of the estuarine environment, but little effort has been made to comprehensively evaluate the performance of different methods and datasets. In this study, two linear regression models-the linear band model and the log-transformed band ratio model, and two non-linear regression models-the support vector regression model and the random forest regression model-were applied to Landsat 8 (L8) and Sentinel-2 (S2) imagery for bathymetry mapping in 2019 and 2020. Results suggested that a priori area clustering based on spectral features using the K-means algorithm improved estimation accuracy. The random forest regression model performed best, and the three-band combinations outperformed two-band combinations in all models. When the non-linear models were applied with three-band combination (red, green, blue) to L8 and S2 imagery, the Root Mean Square Error (Mean Absolute Error) decreased by 23.10% (35.53%), and the coefficient of determination (Kling-Gupta efficiency) increased by 0.08 (0.09) on average, compared to those using the linear regression models. Despite the differences in spatial resolution and band wavelength, L8 and S2 performed similarly in bathymetry estimation. This study quantified the relative performance of different models and may shed light on the potential combination of multiple data sources for more timely and accurate bathymetry mapping. |
| WOS关键词 | MULTIBEAM ECHO-SOUNDER ; SATELLITE IMAGERY ; DEPTH ; DERIVATION ; TIDES ; MAPS |
| 资助项目 | National Natural Science Foundation of China[42001178] ; National Natural Science Foundation of China[41930646] ; Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)[311021018] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000689881100001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China ; Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/164978]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wei, Chunzhu |
| 作者单位 | 1.Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou 510275, Peoples R China 2.Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519080, Peoples R China 3.Univ Southampton, Geog & Environm, Southampton SO17 1BJ, Hants, England 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wei, Chunzhu,Zhao, Qianying,Lu, Yang,et al. Assessment of Empirical Algorithms for Shallow Water Bathymetry Using Multi-Spectral Imagery of Pearl River Delta Coast, China[J]. REMOTE SENSING,2021,13(16):17. |
| APA | Wei, Chunzhu,Zhao, Qianying,Lu, Yang,&Fu, Dongjie.(2021).Assessment of Empirical Algorithms for Shallow Water Bathymetry Using Multi-Spectral Imagery of Pearl River Delta Coast, China.REMOTE SENSING,13(16),17. |
| MLA | Wei, Chunzhu,et al."Assessment of Empirical Algorithms for Shallow Water Bathymetry Using Multi-Spectral Imagery of Pearl River Delta Coast, China".REMOTE SENSING 13.16(2021):17. |
入库方式： OAI收割
来源：地理科学与资源研究所
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