研究论文
Global sea surface salinity via the synergistic use of SMAP satellite and HYCOM data based on machine learning
Eunna Jang, Young Jun Kim, Jungho Im, Young‐Gyu Park, Taejun Sung
Ulsan National Institute of Science and Technology Korea Institute of Ocean Science and Technology
来源Remote Sensing of Environment
年份2022
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3.93
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学术脉络
学科主题
物理Soil Moisture and Remote Sensing
Oceanographic and Atmospheric Processes · Hydrological Forecasting Using AI
参考文献 92
Software Design Description for the HYbrid Coordinate Ocean Model (HYCOM), Version 2.2
被引 101Alan J. Wallcraft, E. Joseph Metzger, Suzanne N. Carroll · 2009
Remotely sensed estimates of surface salinity in the Chesapeake Bay: A statistical approach
被引 88Erin Urquhart, Benjamin F. Zaitchik, Matthew J. Hoffman · Remote Sensing of Environment · 2012
SMOS salinity in the subtropical North Atlantic salinity maximum: 2. Two‐dimensional horizontal thermohaline variability
被引 37Nicolas Kolodziejczyk, Olga Hernandez, Jacqueline Boutin · Journal of Geophysical Research Oceans · 2015
此处列出前 3 条
引用本文 62
Artificial intelligence for geoscience: Progress, challenges, and perspectives
被引 348Tianjie Zhao, Sheng Wang, Chaojun Ouyang · The Innovation · 2024
Global Soil Salinity Estimation at 10 m Using Multi-Source Remote Sensing
被引 83Nan Wang, Songchao Chen, Jingyi Huang · Journal of Remote Sensing · 2024
A review of artificial intelligence in marine science
被引 62Tao Song, Cong Pang, Boyang Hou · Frontiers in Earth Science · 2023
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