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| DOI | 10.1080/17538947.2024.2332374 |
| Estimating the mixed layer depth of the global ocean by combining multisource remote sensing and spatiotemporal deep learning | |
| 发表日期 | 2024 |
| ISSN | 1753-8947 |
| EISSN | 1753-8955 |
| 起始页码 | 17 |
| 结束页码 | 1 |
| 卷号 | 17期号:1 |
| 英文摘要 | Estimating the ocean mixed layer depth (MLD) is crucial for studying the atmosphere-ocean interaction and global climate change. Satellite observations can accurately estimate the MLD over large scales, effectively overcoming the limitation of sparse in situ observations and reducing uncertainty caused by estimation based on in situ and reanalysis data. However, combining multisource satellite observations to accurately estimate the global MLD is still extremely challenging. This study proposed a novel Residual Convolutional Gate Recurrent Unit (ResConvGRU) neural networks, to accurately estimate global MLD along with multisource remote sensing data and Argo gridded data. With the inherent spatiotemporal nonlinearity and dependence of the ocean dynamic process, the proposed method is effective in spatiotemporal feature learning by considering temporal dependence and capturing more spatial features of the ocean observation data. The performance metrics show that the proposed ResConvGRU outperforms other well-used machine learning models, with a global determination coefficient (R2) and a global root mean squared error (RMSE) of 0.886 and 17.83 m, respectively. Overall, the new deep learning approach proposed is more robust and advantageous in data-driven spatiotemporal modeling for retrieving ocean MLD at the global scale, and significantly improves the estimation accuracy of MLD from remote sensing observations. |
| 英文关键词 | Mixed layer depth; remote sensing observations; residual convolutional gate recurrent unit; global ocean |
| 语种 | 英语 |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| WOS类目 | Geography, Physical ; Remote Sensing |
| WOS记录号 | WOS:001189476400001 |
| 来源期刊 | INTERNATIONAL JOURNAL OF DIGITAL EARTH
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/295763 |
| 作者单位 | Fuzhou University; Fuzhou University; University of Delaware |
| 推荐引用方式 GB/T 7714 | . Estimating the mixed layer depth of the global ocean by combining multisource remote sensing and spatiotemporal deep learning[J],2024,17(1). |
| APA | (2024).Estimating the mixed layer depth of the global ocean by combining multisource remote sensing and spatiotemporal deep learning.INTERNATIONAL JOURNAL OF DIGITAL EARTH,17(1). |
| MLA | "Estimating the mixed layer depth of the global ocean by combining multisource remote sensing and spatiotemporal deep learning".INTERNATIONAL JOURNAL OF DIGITAL EARTH 17.1(2024). |
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