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 Sensing

Ying Bao³  Qingjiu Tian³  Min Chen¹  Deepak R. Mishra²  Magaly Koch² 

[1] Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China; E-Mail:;International Institute for Earth System Science, Nanjing University, Nanjing 210023, China; E-Mail;International Institute for Earth System Science, Nanjing University, Nanjing 210023, China; E-Mail:

关键词: normalized mutual information;    chlorophyll-a;    optical classification;    weighted algorithm;    GOCI;   

DOI  :  10.3390/rs70911731

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.