Spatio-Temporal neighbors adaptive learning with two-point differences for ocean subsurface temperature reconstruction from 1960 to 2022
Long time series and accurate subsurface temperature data in the global ocean are essential for ocean warming and climate change studies. The sparse in situ observations in the pre-Argo era hinder the reconstruction of long-time series observational data for the global ocean. This study proposes a n...
| 出版年: | International Journal of Digital Earth |
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| 主要な著者: | , |
| フォーマット: | 論文 |
| 言語: | 英語 |
| 出版事項: |
Taylor & Francis Group
2025-08-01
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| 主題: | |
| オンライン･アクセス: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2500525 |