Estimating the mixed layer depth of the global ocean by combining multisource remote sensing and spatiotemporal deep learning

Author:

Su Hua12,Tang Zhiwei12,Qiu Junlong12,Wang An12,Yan Xiao-Hai3

Affiliation:

1. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, The Academy of Digital China, Fuzhou University, Fuzhou, People’s Republic of China

2. National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou, People’s Republic of China

3. Center for Remote Sensing, College of Earth, Ocean and Environment, University of Delaware, Newark, DE, USA

Funder

National Natural Science Foundation of China

Natural Science Foundation for Distinguished Young Scholars of Fujian Province of China

Publisher

Informa UK Limited

Reference56 articles.

1. Estimation of ocean subsurface thermal structure from surface parameters: A neural network approach

2. Argo products - gridded: 1-degree x 1-degree gridded on mixed layer. 2020. “International Pacific Research Center.” http://apdrc.soest.hawaii.edu/datadoc/argo_iprc_gridded.php (Accessed on 2022-11-20).

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