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A review of remote sensing of suspended sediment concentration in the Yellow River Estuary

  • Xiaodong Bian
  • , Dingfeng Yu*
  • , Shenliang Chen
  • , Peng Li
  • , Yanguo Fan
  • , Chunyan Zhao
  • *此作品的通讯作者
  • Qilu University of Technology
  • Shandong Provincial Key Laboratory of Marine Monitoring Instrument Equipment Technology
  • National Engineering and Technological Research Center of Marine Monitoring Equipment
  • East China Normal University
  • China University of Petroleum (East China)

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

The Yellow River is one of the most sediment laden rivers in the world. It flows through the Loess Plateau and carries a large amount of sediment into the Bohai Sea. The sediment carried by the upper reaches of the Yellow River not only creates a large area of new land, but also affects the optical properties of the waters in the Yellow River Estuary. Suspended sediment content is one of the important parameters to characterize water quality. Many studies are devoted to the establishment of inversion models for quantitative inversion of suspended sediment concentration in the Yellow River Estuary. This paper summarizes the research progress of remote sensing retrieval of suspended sediment concentration in the Yellow River Estuary in recent years. The inversion algorithms for suspended sediment concentration in the Yellow River Estuary can be divided into three categories: empirical algorithm, semi analytical algorithm and analytical algorithm. The data sources for establishing the inversion model can be divided into measured spectral data and satellite data, in which the satellite data includes marine satellite data, Landsat data and meteorological satellite data. In view of the existing problems in the remote sensing inversion of suspended sediment concentration in the Yellow River Estuary, it is proposed that the future research should strengthen the spatiotemporal fusion of Multi-source Satellite data, develop the universal inversion model, and strengthen the application of machine learning algorithm and deep learning algorithm in this field.

源语言英语
主期刊名Eighth Symposium on Novel Photoelectronic Detection Technology and Applications
编辑Junhong Su, Lianghui Chen, Junhao Chu, Shining Zhu, Qifeng Yu
出版商SPIE
ISBN(电子版)9781510653115
DOI
出版状态已出版 - 2022
已对外发布
活动8th Symposium on Novel Photoelectronic Detection Technology and Applications - Kunming, 中国
期限: 7 12月 20219 12月 2021

出版系列

姓名Proceedings of SPIE - The International Society for Optical Engineering
12169
ISSN(印刷版)0277-786X
ISSN(电子版)1996-756X

会议

会议8th Symposium on Novel Photoelectronic Detection Technology and Applications
国家/地区中国
Kunming
时期7/12/219/12/21

联合国可持续发展目标

此成果有助于实现下列可持续发展目标:

  1. 可持续发展目标 14 - 水下生物
    可持续发展目标 14 水下生物

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