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Prediction of chlorophyll a concentration using HJ-1 satellite imagery for Xiangxi Bay in Three Gorges Reservoir

Prediction of chlorophyll a concentration using HJ-1 satellite imagery for Xiangxi Bay in Three Gorges Reservoir

作     者:Dong-xing FAN Yu-ling HUANG Lin-xu SONG De-fu LIU Ge ZHANG Biao ZHANG 

作者机构:College of Hydraulic and Environmental EngineeringChina Three Gorges University College of Resources and Environment SciencesHubei University of Technology 

出 版 物:《Water Science and Engineering》 (水科学与水工程(英文版))

年 卷 期:2014年第7卷第1期

页      面:70-80页

核心收录:

学科分类:07[理学] 0713[理学-生态学] 

基  金:supported by the National Natural Science Foundation of China(Grants No.51009080 and 51179095) the Research Innovation Fund for Postgraduates in China Three Gorges University(Grant No.2012CX012) 

主  题:chlorophyll a concentration H J-1 satellite remote sensing prediction correlation analysis Xiangxi Bay Three Gorges Reservoir 

摘      要:Since the impoundment of the Three Gorges Reservoir in 2003, algal blooms have frequently been observed in it. The chlorophyll a concentration is an important parameter for evaluating algal blooms. In this study, the chlorophyll a concentration in Xiangxi Bay, in the Three Gorges Reservoir, was predicted using HJ-1 satellite imagery. Several models were established based on a correlation analysis between in situ measurements of the chlorophyll a concentration and the values obtained from satellite images of the study area from January 2010 to December 2011. Chlorophyll a concentrations in Xiangxi Bay were predicted based on the established models. The results show that the maximum correlation is between the reflectance of the band combination of B4/(B2+B3) and in situ measurements of chlorophyll a concentration. The root mean square errors of the predicted values using the linear and quadratic models are 18.49 mg/m3 and 18.52 mg/m3, respectively, and the average relative errors are 37.79% and 36.79%, respectively. The results provide a reference for water bloom prediction in typical tributaries of the Three Gorges Reservoir and contribute to large-scale remote sensing monitoring and water quality management.

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