Global estimation of phytoplankton pigment concentrations from satellite data using a deep-learning-based model

doi:10.1016/j.rse.2023.113628

IOCAS-IR > 海洋环流与波动重点实验室

	Global estimation of phytoplankton pigment concentrations from satellite data using a deep-learning-based model
	Li, Xiaolong1,2 ; Yang, Yi 1,2,3; Ishizaka, Joji 4; Li, Xiaofeng 1,2
	2023-08-15
发表期刊	REMOTE SENSING OF ENVIRONMENT
ISSN	0034-4257
卷号	294 页码:16
通讯作者	Li, Xiaofeng([email protected])
摘要	Based on a global matchup between satellite observations and high performance liquid chromatography (HPLC) measurements, we developed a deep-learning-based model (DL-PPCE model) for globally estimating concentrations of 17 different phytoplankton pigments. The model adopted a fusion architecture of residual and pyramid networks to achieve robust estimation performance. The model inputs include three different data types: essential ocean color parameters, satellite-derived environmental parameters, and the slope of above-surface remote-sensing reflectance (R-rs). We compared the model performances with various input parameters to determine the most effective inputs. The results showed that R-rs in the essential ocean color parameters and sea surface temperature (SST) in the environmental parameters were the most critical input parameters. The estimation of phytoplankton pigment concentrations was validated against HPLC data using the leave-one-out crossvalidation method. Except for three pigments, 19'-butanoyloxy-fucoxanthin, prasinoxanthin, and lutein, the estimated pigment concentrations and in-situ observations were strongly correlated for all other pigments (an average relative root-mean-square error of 0.59, R-2 >= 0.60, and regression slopes close to 1). In addition, a time series analysis was performed on the MODIS retrieved global pigment concentrations during 2003-2021 using the established DL-PPCE model to explore the relationship between the distribution of phytoplankton groups and El Nino in the western equatorial Pacific. Our findings revealed that the prokaryotes-dominated area extended eastward from180 degrees E to 150 degrees W during the 2015/2016 El Nino event. From 2003 to 2021, prokaryotic abundance was positively correlated with El Nino intensity ( R = 0.65, P << 0.01) but negatively correlated with the abundance of the entire phytoplankton community ( R = -0.53, P << 0.01). These results demonstrate that the DLPPCE model presents a novel approach for estimating the concentration of 17 pigments worldwide, and the estimated pigment concentrations are advantageous for analyzing the phytoplankton community dynamics on a large spatiotemporal scale.
关键词	Phytoplankton pigments Remote sensing Deep learning Satellite Global Ocean Phytoplankton community El Nino
DOI	10.1016/j.rse.2023.113628
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[42076200]; Joint Project of the National Natural Science Foundation of China-Shandong Province[U2006211]; CAS (Chinese Academy of Sciences) Program[YJKYYQ20200063]; CAS (Chinese Academy of Sciences) Program[Y9KY04101L]; Natural Science Foundation of Shandong Province[ZR2020MD083]; Key Research and Development Program of Shandong Province[2022CXPT020]; China Scholarship Fund[201904910027]
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001018440800001
出版者	ELSEVIER SCIENCE INC
WOS关键词	REMOTE-SENSING REFLECTANCE ; CHLOROPHYLL-A ; COMMUNITY STRUCTURE ; ABSORPTION-SPECTRA ; SIZE ; HPLC ; CLASSIFICATION ; ALGORITHMS ; CHEMTAX ; WATERS
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/182587
专题	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, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China 2.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao, Peoples R China 3.Univ Chinese Acad Sci, Coll Marine Sci, Beijing, Peoples R China 4.Nagoya Univ, Inst Space Earth Environm Res, Nagoya, Japan
第一作者单位	中国科学院海洋研究所; 中国科学院海洋大科学研究中心
通讯作者单位	中国科学院海洋研究所; 中国科学院海洋大科学研究中心
推荐引用方式 GB/T 7714	Li, Xiaolong,Yang, Yi,Ishizaka, Joji,et al. Global estimation of phytoplankton pigment concentrations from satellite data using a deep-learning-based model[J]. REMOTE SENSING OF ENVIRONMENT,2023,294:16.
APA	Li, Xiaolong,Yang, Yi,Ishizaka, Joji,&Li, Xiaofeng.(2023).Global estimation of phytoplankton pigment concentrations from satellite data using a deep-learning-based model.REMOTE SENSING OF ENVIRONMENT,294,16.
MLA	Li, Xiaolong,et al."Global estimation of phytoplankton pigment concentrations from satellite data using a deep-learning-based model".REMOTE SENSING OF ENVIRONMENT 294(2023):16.