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Sustainability 2016, 8(8), 758; doi:10.3390/su8080758

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

1
School of Geology and Geomatics, Tianjin Chengjian University, Jinjing Road, Tianjin 300384, China
2
College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vincenzo Torretta
Received: 27 April 2016 / Revised: 13 July 2016 / Accepted: 29 July 2016 / Published: 6 August 2016
(This article belongs to the Section Sustainable Use of the Environment and Resources)
View Full-Text   |   Download PDF [5770 KB, uploaded 6 August 2016]   |  

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. View Full-Text
Keywords: chlorophyll-a; Landsat OLI; remote sensing retrieval; artificial neural network chlorophyll-a; Landsat OLI; remote sensing retrieval; artificial neural network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Guo, Q.; Wu, X.; Bing, Q.; Pan, Y.; Wang, Z.; Fu, Y.; Wang, D.; Liu, J. Study on Retrieval of Chlorophyll-a Concentration Based on Landsat OLI Imagery in the Haihe River, China. Sustainability 2016, 8, 758.

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