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A Self-Attention-Based Deep Learning Model for Estimating Global Phytoplankton Pigment Profiles

文献类型：期刊论文

作者Yang, Yi1,2; Li, Xiaolong2; Li, Xiaofeng1
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期2024
卷号62页码:15
关键词Phytoplankton Pigments Sea measurements Satellites Ocean temperature Deep learning Temperature measurement ocean color phytoplankton pigments satellite vertical profiles
ISSN号0196-2892
DOI10.1109/TGRS.2024.3435044
通讯作者Li, Xiaofeng(xiaofeng.li@ieee.org)
英文摘要Characterizing global phytoplankton pigment profiles in the ocean is crucial for comprehending phytoplankton dynamics. This study develops a self-attention-based deep learning model (Pigmentsformer) to estimate nine types of global phytoplankton pigment profiles in the top 300-m ocean. Pigmentsformer employed 15-day sequences of ocean-color data to capture the dynamic changes in phytoplankton growth. The model was trained on an extensive collection of matchups, including 33 975 samples between global high-performance liquid chromatography (HPLC) in situ measurements, ocean-color satellite measurements, and the environment fields from the reanalysis dataset. Validation of the model employs a tenfold leave-one-out cross-validation (LOOCV) approach. The coefficient of determination (R-2) between in situ HPLC and Pigmentsformer-estimated concentrations ranges from 0.67 to 0.87, with the mean absolute error (MAE) ranging from 0.01 to 0.33 mg center dot m(-3) for nine types of pigments. The backpropagation technique reveals that among all predictors, optical properties are paramount when estimating total chlorophyll-a (TChla) at the surface, with ocean current being the most influential environmental factor. However, as depth increases, the effect of environmental variables SSH and temperature exceed that of optical properties and ocean current. An examination of 20 years of model-generated phytoplankton size classes (PSCs) was conducted to explore the correlation between changes in phytoplankton communities and the El Nino-Southern Oscillation (ENSO) in the Equatorial Pacific. The location of the maximum phytoplankton layer has a positive relationship with Nino 3.4 index (R =0.70 for micro-phytoplankton, R =0.68 for nano-phytoplankton, and R = 0.45 for pico-phytoplankton) within the Equatorial Pacific of the Nino 3.4 region.
WOS关键词REMOTE-SENSING REFLECTANCE ; CHLOROPHYLL-A ; COMMUNITY STRUCTURE ; SIZE CLASSES ; VARIABILITY ; WATERS ; COLOR ; VALIDATION ; ALGORITHM ; NUTRIENTS
资助项目National Natural Science Foundation of China[42221005] ; National Natural Science Foundation of China[42076200] ; National Natural Science Foundation of China[U2006211] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB42000000] ; Natural Science Foundation of Shandong Province[ZR2020MD083] ; Key Research and Development Program of Shandong Province[2022CXPT020]
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001294099800012
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://ir.qdio.ac.cn/handle/337002/199597]  
专题海洋研究所_海洋环流与波动重点实验室
通讯作者Li, Xiaofeng
作者单位1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Observat & Forecasting, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China
2.Univ Chinese Acad Sci, Coll Marine Sci, Beijing 10049, Peoples R China
推荐引用方式
GB/T 7714		 Yang, Yi,Li, Xiaolong,Li, Xiaofeng. A Self-Attention-Based Deep Learning Model for Estimating Global Phytoplankton Pigment Profiles[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2024,62:15.
APA Yang, Yi,Li, Xiaolong,&Li, Xiaofeng.(2024).A Self-Attention-Based Deep Learning Model for Estimating Global Phytoplankton Pigment Profiles.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62,15.
MLA Yang, Yi,et al."A Self-Attention-Based Deep Learning Model for Estimating Global Phytoplankton Pigment Profiles".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024):15.

入库方式： OAI收割

来源：海洋研究所

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