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A new technique for quantifying algal bloom, floating/emergent and submerged vegetation in eutrophic shallow lakes using Landsat imagery

  • Published:2023-03 Issue: Volume:287 Page:113480
  • ISSN:0034-4257
  • Container-title:Remote Sensing of Environment
  • language:en
  • Short-container-title:Remote Sensing of Environment

Author:

Luo Juhua,Ni Guigao,Zhang Yunlin,Wang Kang,Shen Ming,Cao Zhigang,Qi Tianci,Xiao Qitao,Qiu Yinguo,Cai Yongjiu,Duan Hongtao

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Geology,Soil Science

Reference72 articles.

1. Remotely sensed vegetation indices: Theory and applications for crop management. Rivista Italiana di Agrometeorologia.;Basso,2004

2. A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes;Cao;Remote Sens. Environ.,2020

3. High-frequency observation of floating algae from AHI on Himawari-8;Chen;Remote Sens. Environ.,2019

4. A review of assessing the accuracy of classifications of remotely sensed data;Congalton;Remote Sens. Environ.,1991

5. A physically-based transformation of thematic mapper Data–-the TM tasseled cap;Crist;IEEE Trans. Geosci. Remote Sens.,1984

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