Abstract
Remote sensing is a valuable tool for monitoring water quality parameters in inland and coastal waters. The prime objective of present research was to investigate the accuracy and limitations of hyperspectral remotely sensed data for water quality monitoring. The in situ hyperspectral spectroradimeter data of Altamaha River, Georgia, USA, and the St. Marys River, Georgia, USA was collected below the water surface. The pronounced difference was observed between the subsurface spectral reflectance of different sampling points within the same water body. The spectral signatures were found to be strongly correlated with the optically active constituents present within the water body. The collected hyperspectral and in situ water quality data were analyzed to develop the models for estimation of total suspended sediment (TSS), colored dissolved organic matter (CDOM), chlorophyll-a and turbidity. The band ratio algorithms were developed by means of collected remotely sensed hyperspectral data. The developed regression models showed good correlation with the water quality parameters. It is imperative to comprehensively understand the spectral nature, spectral response to individual water quality parameters, and the effect of influencing factors on the reflected signals. The research work demonstrates the operational feasibility of remotely sensed data for monitoring water quality parameters.