Reconstructing High-Resolution Ocean Subsurface and Interior Temperature and Salinity Anomalies From Satellite Observations
| Author(s) | Meng, Lingsheng | |
| Author(s) | Yan, Chi | |
| Author(s) | Zhuang, Wei | |
| Author(s) | Geng, Xupu | |
| Author(s) | Yan, Xiao-Hai | |
| Date Accessioned | 2022-04-11T15:48:51Z | |
| Date Available | 2022-04-11T15:48:51Z | |
| Publication Date | 2021-09-21 | |
| Description | Copyright 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This article was originally published in IEEE Transactions on Geoscience and Remote Sensing. The version of record is available at: https://doi.org/10.1109/TGRS.2021.3109979 | en_US |
| Abstract | Accurately retrieving ocean interior parameters from remote sensing observations is essential for ocean and climate studies because direct observations are sparse and costly. Furthermore, high-resolution structure of seawater properties is critical for understanding the oceanic processes and changes on multiple scales. Here, we designed a new method based on a deep neural network to retrieve subsurface temperature anomaly (STA) and subsurface salinity anomaly (SSA) in the Pacific Ocean at high (1/4°) and super (1/12°) horizontal resolution. We utilized multisource satellite-observed sea surface data (e.g., sea level, temperature, salinity, and wind vector) as inputs. The results revealed that our model retrieved the high- and super-resolution STA/SSA with high accuracy, and the model was reliable in a wide range of depths (near surface to 4000 m) and times (all months in 2014). Regarding the high-resolution STA (SSA) estimation, the average coefficient of determination ( R2 ) was 0.984 (0.966), and the average root-mean-squared error (RMSE) was 0.068 °C (0.016 psu). For the super-resolution STA, the average R2 was 0.988 and RMSE was 0.093 °C. Here, we established an effective technique that improved the resolution and accuracy of estimating the ocean interior parameters from satellite observation. The new technique provides some new insights into oceanic observation and dynamics. | en_US |
| Sponsor | This work was supported in part by the National Key Research and Development Program of China under Grant 2019YFA0606702; in part by the National Natural Science Foundation of China under Grant 91858202, Grant 41630963, and Grant 41776003; and in part by the State Oceanic Administration (SOA) Global Change and Air–Sea Interac- tion Project under Grant GASI-02-PAC-YGST2-02. | en_US |
| Citation | L. Meng, C. Yan, W. Zhuang, W. Zhang, X. Geng and X. -H. Yan, "Reconstructing High-Resolution Ocean Subsurface and Interior Temperature and Salinity Anomalies From Satellite Observations," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-14, 2022, Art no. 4104114, doi: 10.1109/TGRS.2021.3109979. | en_US |
| ISSN | 1558-0644 | |
| URL | https://udspace.udel.edu/handle/19716/30767 | |
| Language | en_US | en_US |
| Publisher | IEEE Transactions on Geoscience and Remote Sensing | en_US |
| Keywords | Deep learning | en_US |
| Keywords | high resolution | en_US |
| Keywords | ocean temperature and salinity | en_US |
| Keywords | parameter estimation | en_US |
| Keywords | remote sensing | en_US |
| Title | Reconstructing High-Resolution Ocean Subsurface and Interior Temperature and Salinity Anomalies From Satellite Observations | en_US |
| Type | Article | en_US |
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