Global estimation of phytoplankton pigment concentrations from satellite data using a deep-learning-based model
文献类型：期刊论文
| 作者 | Li, Xiaolong3,4 ; Yang, Yi2,3,4; Ishizaka, Joji1; Li, Xiaofeng3,4
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| 刊名 | REMOTE SENSING OF ENVIRONMENT
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| 出版日期 | 2023-08-15 |
| 卷号 | 294页码:16 |
| ISSN号 | 0034-4257 |
| 关键词 | Phytoplankton pigments Remote sensing Deep learning Satellite Global Ocean Phytoplankton community El Nino |
| DOI | 10.1016/j.rse.2023.113628 |
| 通讯作者 | Li, Xiaofeng(lixf@qdio.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | REMOTE-SENSING REFLECTANCE ; CHLOROPHYLL-A ; COMMUNITY STRUCTURE ; ABSORPTION-SPECTRA ; SIZE ; HPLC ; CLASSIFICATION ; ALGORITHMS ; CHEMTAX ; WATERS |
| 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 |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCIENCE INC |
| WOS记录号 | WOS:001018440800001 |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/182586]  |
| 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.Nagoya Univ, Inst Space Earth Environm Res, Nagoya, Japan 2.Univ Chinese Acad Sci, Coll Marine Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao, Peoples R China 4.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China |
| 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. |
入库方式： OAI收割
来源：海洋研究所
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