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.
Highlights
Lake eutrophication, which is characterized by algal blooms, has become one of the most serious environmental issues around the world [1,2]
The primary objectives of this study were: (1) to identify the optical water types using the criterion function normalized mutual information (NMI) based on the in situ spectral reflectance; (2) to estimate the Chl-a concentration using a weighted algorithm based on weighting factors and to assess the accuracy of the proposed algorithm by comparison with the non-classification and hard classification algorithms; and (3) to demonstrate the potential of the proposed algorithm using
Based on the in situ normalized reflectance spectra collected from Taihu Lake, three water types were identified by the clustering method, which was implemented by the similarity measure (NMI)
Summary
Lake eutrophication, which is characterized by algal blooms, has become one of the most serious environmental issues around the world [1,2]. Optical properties of Case II waters (i.e., turbid coastal and inland waters) are influenced by complex optical components such as phytoplankton, total suspended matter (TSM) and colored, dissolved organic matter (CDOM); the blue-green algorithms may not be suitable for this type of water [8]. Due to this problem, effective semi-empirical algorithms using the near-infrared-red (NIR) and red spectral reflectances have been established to estimate Chl-a in Case II waters. Commonly used algorithms, such as two-band and three-band algorithms, have been widely used for Case II waters in different regions [8,9,10,11]
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