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A hybrid remote sensing approach for estimating chemical oxygen demand concentration in optically complex waters: A case study in inland lake waters in eastern China.
Cai, Xiaolan; Li, Yunmei; Lei, Shaohua; Zeng, Shuai; Zhao, Zhilong; Lyu, Heng; Dong, Xianzhang; Li, Junda; Wang, Huaijing; Xu, Jie; Zhu, Yuxin; Wu, Luyao; Cheng, Xin.
Affiliation
  • Cai X; School of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing 210023, China.
  • Li Y; School of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing 210023, China. Electronic address: liyunmei@njnu.edu.cn.
  • Lei S; State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China.
  • Zeng S; School of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing 210023, China.
  • Zhao Z; School of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing 210023, China.
  • Lyu H; School of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing 210023, China.
  • Dong X; School of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing 210023, China.
  • Li J; School of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing 210023, China.
  • Wang H; School of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing 210023, China.
  • Xu J; Yangtze River Basin Ecological Environment Monitoring and Scientific Research Center, Yangtze River Basin Ecological Environment Supervision and Administration Bureau, Ministry of Ecological Environment, Wuhan 430010, China.
  • Zhu Y; School of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing 210023, China.
  • Wu L; School of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing 210023, China.
  • Cheng X; School of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing 210023, China.
Sci Total Environ ; 856(Pt 1): 158869, 2023 Jan 15.
Article in En | MEDLINE | ID: mdl-36152846
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Lakes / Remote Sensing Technology Country/Region as subject: Asia Language: En Journal: Sci Total Environ Year: 2023 Document type: Article Affiliation country: China
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Lakes / Remote Sensing Technology Country/Region as subject: Asia Language: En Journal: Sci Total Environ Year: 2023 Document type: Article Affiliation country: China