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Identification of marine algal blooms by SDGSAT-1 multispectral imagery

文献类型：期刊论文

作者Jiang, Shanshan4,5,6; Guan, Rongda4,5,6; Xing, Qianguo4,5,6; Li, Jie4,5,6; Hou, Yingzhuo4,5,6; Zhang, Wenjing3,5,6; Li, Lin5,6; Arif, Maham4,5,6; Li, Jinghu4,5,6; Tang, Yunwei1,2
刊名REMOTE SENSING OF ENVIRONMENT
出版日期2026-05-01
卷号337页码:15
关键词SDGSAT-1 Green macroalgae of Ulva prolifera Sargassum Red and green Noctiluca scintillans Black water Algal blooms Remote sensing
ISSN号0034-4257
DOI10.1016/j.rse.2026.115340
通讯作者Xing, Qianguo(qgxing@yic.ac.cn)
英文摘要With the increasing algal blooms in global oceans, high resolution satellite remote sensing of various types of marine algal blooms is getting more emergent for supporting the sustainable development of ocean and coastal zone. This study explored the usage of the Sustainable Development Science Satellite 1 (SDGSAT-1) Multispectral Imager (MII) in detecting and differentiating different types of algal blooms across three marine regions: China's coastal waters, the Indian Ocean, and the Atlantic Ocean. In this work, sea surface reflectance of floating algal blooms (Ulva prolifera, Sargassum, red/green Noctiluca scintillans) and non-floating, light-absorbing red tides (black water) were retrieved to build the spectral dataset for further analysis. On the basis of all the seven bands of SDGSAT-1 MII with the featured bands of the deep blue (400 nm) and the red-edge (776 nm), a spectrally interpretable decision tree model was developed to achieve automated extraction and classification of five types of algal blooms, showing more reliable performance than traditional spectral indices in the tests with Ulva prolifera blooms. Meanwhile, a machine learning model with the same SDGSAT-1 MII dataset was developed and identification of these algal blooms was achieved; however, it is less robust than the decision tree model in the extraction of black water in coastal optically-complex waters. In addition, results of applying the two models to Sentinel-2 MSI without 400 nm band show decreased ability in identification of the five algal blooms, however there is potential of integrating SDGSAT-1 MII with other similar satellite sensors in monitoring marine algal blooms. This study can provide the data and technical support for using SDGSAT-1 MII to monitor different algal blooms and to promote the progress of coastal algal blooms remote sensing on the basis of high resolution multispectral imagery.
WOS关键词PHYTOPLANKTON BLOOMS ; CHLOROPHYLL-A ; YELLOW SEA ; TIDE ; RED ; COASTAL ; GREEN ; COLOR ; COMMUNITIES ; RADIANCE
WOS研究方向Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001709637400001
资助机构National Natural Science Foundation of China ; Shandong Province Innovation Technology Guidance Plan ; Seed Project of Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences
源URL[http://ir.yic.ac.cn/handle/133337/42261]  
专题中国科学院牟平海岸带环境综合试验站
通讯作者Xing, Qianguo
作者单位1.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China
2.Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China
3.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Muping Coastal Environm Res Stn, Yantai 264003, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Shandong Key Lab Coastal Zone Environm Proc & Ecol, Yantai 264003, Peoples R China
6.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China
推荐引用方式
GB/T 7714		 Jiang, Shanshan,Guan, Rongda,Xing, Qianguo,et al. Identification of marine algal blooms by SDGSAT-1 multispectral imagery[J]. REMOTE SENSING OF ENVIRONMENT,2026,337:15.
APA Jiang, Shanshan.,Guan, Rongda.,Xing, Qianguo.,Li, Jie.,Hou, Yingzhuo.,...&Tang, Yunwei.(2026).Identification of marine algal blooms by SDGSAT-1 multispectral imagery.REMOTE SENSING OF ENVIRONMENT,337,15.
MLA Jiang, Shanshan,et al."Identification of marine algal blooms by SDGSAT-1 multispectral imagery".REMOTE SENSING OF ENVIRONMENT 337(2026):15.

入库方式： OAI收割

来源：烟台海岸带研究所

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