Study on Retrieval of Chlorophyll-a Concentration Based on Landsat OLI Imagery in the Haihe River, China

Qiaozhen Guo, Xiaoxu Wu, Qixuan Bing, Yingyang Pan, Zhiheng Wang, Ying Fu, Dongchuan Wang and Jianing Liu
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Qiaozhen Guo: School of Geology and Geomatics, Tianjin Chengjian University, Jinjing Road, Tianjin 300384, China
Xiaoxu Wu: College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
Qixuan Bing: School of Geology and Geomatics, Tianjin Chengjian University, Jinjing Road, Tianjin 300384, China
Yingyang Pan: School of Geology and Geomatics, Tianjin Chengjian University, Jinjing Road, Tianjin 300384, China
Zhiheng Wang: School of Geology and Geomatics, Tianjin Chengjian University, Jinjing Road, Tianjin 300384, China
Ying Fu: School of Geology and Geomatics, Tianjin Chengjian University, Jinjing Road, Tianjin 300384, China
Dongchuan Wang: School of Geology and Geomatics, Tianjin Chengjian University, Jinjing Road, Tianjin 300384, China
Jianing Liu: College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China

Sustainability, 2016, vol. 8, issue 8, 1-15

Abstract: The optical complexity of urban waters makes the remote retrieval of chlorophyll-a (Chl-a) concentration a challenging task. In this study, Chl-a concentration was retrieved using reflectance data of Landsat OLI images. Chl-a concentration in the Haihe River of China was obtained using mathematical regression analysis (MRA) and an artificial neural network (ANN). A regression model was built based on an analysis of the spectral reflectance and water quality sampling data. Remote sensing inversion results of Chl-a concentration were obtained and analyzed based on a verification of the algorithm and application of the models to the images. The analysis results revealed that the two models satisfactorily reproduced the temporal variation based on the input variables. In particular, the ANN model showed better performance than the MRA model, which was reflected in its higher accuracy in the validation. This study demonstrated that Landsat Operational Land Imager (OLI) images are suitable for remote sensing monitoring of water quality and that they can produce high-accuracy inversion results.

Keywords: chlorophyll-a; Landsat OLI; remote sensing retrieval; artificial neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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