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Remote Sens. 2015, 7(9), 11731-11752; doi:10.3390/rs70911731

A Weighted Algorithm Based on Normalized Mutual Information for Estimating the Chlorophyll-a Concentration in Inland Waters Using Geostationary Ocean Color Imager (GOCI) Data

1,2,†
,
1,2,* and 3,4,5,†
1
International Institute for Earth System Science, Nanjing University, Nanjing 210023, China
2
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China
3
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
4
Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China
5
Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra, Magaly Koch and Prasad S. Thenkabail
Received: 6 August 2015 / Revised: 30 August 2015 / Accepted: 8 September 2015 / Published: 14 September 2015
(This article belongs to the Special Issue Remote Sensing of Water Resources)
View Full-Text   |   Download PDF [5342 KB, uploaded 17 September 2015]   |  

Abstract

Due to the spatiotemporal variations of complex optical characteristics, accurately estimating chlorophyll-a (Chl-a) concentrations in inland waters using remote sensing techniques remains challenging. In this study, a weighted algorithm was developed to estimate the Chl-a concentrations based on spectral classification and weighted matching using normalized mutual information (NMI). Based on the NMI algorithm, three water types (Class 1 to Class 3) were identified using the in situ normalized spectral reflectance data collected from Taihu Lake. Class-specific semi-analytic algorithms for the Chl-a concentrations were established based on the GOCI data. Next, weighted factors, which were used to determine the matching probabilities of different water types, were calculated between the GOCI data and each water type using the NMI algorithm. Finally, Chl-a concentrations were estimated using the weighted factors and the class-specific inversion algorithms for the GOCI data. Compared to the non-classification and hard-classification algorithms, the accuracies of the weighted algorithms were higher. The mean absolute error and root mean square error of the NMI weighted algorithm decreased to 22.63% and 9.41 mg/m3, respectively. The results also indicated that the proposed algorithm could reduce discontinuous or jumping effects associated with the hard-classification algorithm. View Full-Text
Keywords: normalized mutual information; chlorophyll-a; optical classification; weighted algorithm; GOCI normalized mutual information; chlorophyll-a; optical classification; weighted algorithm; GOCI
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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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Bao, Y.; Tian, Q.; Chen, M. A Weighted Algorithm Based on Normalized Mutual Information for Estimating the Chlorophyll-a Concentration in Inland Waters Using Geostationary Ocean Color Imager (GOCI) Data. Remote Sens. 2015, 7, 11731-11752.

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