Parameter selection and model research on remote sensing evaluation for nearshore water quality

LEI Guibin; ZHANG Ying; PAN Delu; WANG Difeng; FU Dongyang

doi:10.1007/s13131-016-0802-4

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Article Navigation > Acta Oceanologica Sinica > 2016 > 35(1): 114-117

LEI Guibin, ZHANG Ying, PAN Delu, WANG Difeng, FU Dongyang. Parameter selection and model research on remote sensing evaluation for nearshore water quality[J]. Acta Oceanologica Sinica, 2016, 35(1): 114-117. doi: 10.1007/s13131-016-0802-4

Citation:

LEI Guibin, ZHANG Ying, PAN Delu, WANG Difeng, FU Dongyang. Parameter selection and model research on remote sensing evaluation for nearshore water quality[J]. Acta Oceanologica Sinica, 2016, 35(1): 114-117. doi: 10.1007/s13131-016-0802-4

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Parameter selection and model research on remote sensing evaluation for nearshore water quality

doi: 10.1007/s13131-016-0802-4

1.
Guangdong Province Key Laboratory for Coastal Ocean Variation and Disaster Prediction (GLOD), Guangdong Ocean University, Zhanjiang, 524088, China
2.
Guangdong Province Key Laboratory for Coastal Ocean Variation and Disaster Prediction (GLOD), Guangdong Ocean University, Zhanjiang, 524088, China;State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China
3.
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China

Received Date: 2015-03-19
Rev Recd Date: 2015-09-08

Abstract

Abstract

Using remote sensing technology for water quality evaluation is an inevitable trend in marine environmental monitoring. However, fewer categories of water quality parameters can be monitored by remote sensing technology than the 35 specified in GB3097-1997 Marine Water Quality Standard. Therefore, we considered which parameters must be selected by remote sensing and how to model for water quality evaluation using the finite parameters. In this paper, focused on Leizhou Peninsula nearshore waters, we found N, P, COD, PH and DO to be the dominant parameters of water quality by analyzing measured data. Then, mathematical statistics was used to determine that the relationship among the five parameters was COD >DO >P >N >pH. Finally, five-parameter, fourparameter and three-parameter water quality evaluation models were established and compared. The results showed that COD, DO, P and N were the necessary parameters for remote sensing evaluation of the Leizhou Peninsula nearshore water quality, and the optimal comprehensive water quality evaluation model was the fourparameter model. This work may serve as a reference for monitoring the quality of other marine waters by remote sensing.

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References(13)

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