CLASSIFICATION OF ALGAL BLOOM TYPES FROM REMOTE SENSING REFLECTANCE

A technique for classification of phytoplankton bloom types from remote sensing reflectance is described in this paper. Several minor algal bloom events were sighted and their characteristics reflectance signatures were collected using a handheld spectrometer during a series of sea-truth water sampling campaigns carried out from Dec 1996 to Dec 1999 in coastal waters around Singapore. Reflectance spectra of two additional algal bloom classes were also collected during two field trips to the Manila Bay. In order to assess the potential of utilizing satellite ocean color sensors for algal bloom detection and classification, reflectance data for the SeaWiFS and future MERIS sensor spectral bands were simulated from the in-situ radiance data. An algorithm based on the singular value decomposition (SVD) technique was then applied for classification of algal bloom types from the simulated satellite sensor reflectance data. The results show that all the eight algal bloom classes can be distinguished from the clear sea water reference sample. The average accuracy of classification using this technique for all the classes are 98.6% (for MERIS) and 96.6% (for SeaWiFS), in comparison to 87.5% (MERIS) and 73.8% (SeaWiFS) if the reflectance values are used.