Modeling of Chlorophyll-a concentration for the coastal waters of Hong Kong

In coastal waters, accurate remote sensing retrieval of Chlorophyll-a (Chl-a) is challenging. In a spatially complex urban coastal region like Hong Kong, the development of a single Chl-a estimation algorithm over whole region is unrealistic. In such case the best strategy will be to develop an individual algorithm for each water type to precisely estimate Chl-a concentration. Therefore, to define the effective water zones in the region, Fuzzy c-Means (FCM) clustering was applied to surface reflectance derived from the first four bands of the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) and HJ-1 A/B Charge Couple Device (CCD) sensors for 76 Hong Kong Environmental Protection Department (EPD) water monitoring stations. The FCM clustering results suggested the existence of five optically different water types in the region. Cluster specific algorithms were then developed for the retrieval of Chl-a concentrations using Neural Network (NN) and Regression Modeling (RM) techniques. Twenty seven Landsat TM/ETM+ (January 2000-December 2012) and thirty HJ-1 A/B CCD (September 2008-December 2012) cloud free images paired with in situ Chl-a data were used for development and validation of the NNs and RMs. The performance of the cluster specific NNs and RMs suggested that NN can efficiently estimate and map Chl-a concentrations with greater confidence as compared to band ratio algorithms developed using regression modeling. Overall, the validation results showed a correlation of 0.63 to 0.85 between the NN estimated and in situ measured Chl-a concentrations compared to a correlation of 0.26 to 0.54 between the RM estimated and in situ measured Chl-a concentrations.