Intelligent Sparse2Dense Profile Reconstruction for Predicting Global Subsurface Chlorophyll Maxima

Yu, Yongjun; Huang, Baoxiang; Radenkovic, Milena; Wang, Tingting; Chen, Ge

Abstract

Subsurface chlorophyll maximum (SCM) is a crucial ecological indicator for marine ecosystems. Previous studies have indicated that this phenomenon is globally widespread. Although the biogeochemical argo assimilation results have yielded positive results, the sparse data prevents them from being effectively used in oceanographic operations. Considering the dependence of ocean parameter, a deep learning model termed AT-GRU based on Gated Recurrent Units is proposed. By incorporating an Attention Mechanism, the model can effectively address missing data in the BGC-Argo profiles, achieving the transition of data from sparse to dense (sparse2dense) and improving the accuracy of estimating subsurface Chla concentration. Specifically, the dataset of satellite remote sensing data and the associated BGC-Argo profiles is first established. AT-GRU is employed to reconstruct Chla concentration profiles from 1 to 300 m, utilizing several sources of ocean surface data. Next, an in-depth investigation is conducted to determine the characteristics of SCM. The objective is to enable wider research on SCM by analyzing vertical Chla profiles in four geographical locations. Finally, the general improvement of skill performance metrics, with R-squared reaching 0.84, demonstrates the feasibility of the proposed methodology through extensive experiments. Additionally, we apply AT-GRU to global surface satellite data from January 2023 and compare the results with numerical modeling data to further validate the performance. This study presents promising opportunities for leveraging artificial intelligence in subsurface oceanic phenomena with the idea of sparse2dense and holds significant implications for the field of marine ecology.

Citation

Yu, Y., Huang, B., Radenkovic, M., Wang, T., & Chen, G. (2024). Intelligent Sparse2Dense Profile Reconstruction for Predicting Global Subsurface Chlorophyll Maxima. IEEE Transactions on Geoscience and Remote Sensing, 62, Article 4211013. https://doi.org/10.1109/tgrs.2024.3464850

• Journal Article Type: Article
• Acceptance Date: Sep 17, 2024
• Online Publication Date: Sep 20, 2024
• Publication Date: Sep 20, 2024
• Deposit Date: Sep 28, 2024
• Publicly Available Date: Oct 2, 2024
• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Print ISSN: 0196-2892
• Electronic ISSN: 1558-0644
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 62
• Article Number: 4211013
• DOI: https://doi.org/10.1109/tgrs.2024.3464850
• Keywords: Oceans , Data models , Sea surface , Biological system modeling , Sensors , Spatial resolution , Numerical models , Bidirectional gated recurrent unit (Bi-GRU) , biogeochemical-Argo (BGC-Argo) , remote sensing , residual connection module , self-attention
• Public URL: https://nottingham-repository.worktribe.com/output/39731145
• Publisher URL: https://ieeexplore.ieee.org/document/10684819