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This paper is a research work intended to present a comprehensive water quality modeling for predicting three water quality parameters (Chlorophyll (a), Turbidity and Secchi Depth) in typical Inland lake environments (Hussain sagar and Umda sagar) using Hyperspectral Remote sensing technique. They are estimated through regression models by combining the field Spectro-radiometer reflectance values with concurrent in situ ground data (Analytical) collected in the study area and correlated and validated with the available Hyperspectral data (Hyperion).  A total of 180 in situ water sample and 900 spectral signatures were analysed during campaigns from 2010 to 2014 study period. The mean values of Chlorophyll-a varied between 6.983mgL -1 and 24.858mgL -1 , Turbidity varied between 16.583mgL -1 and 48.867mgL -1 and Secchi depth varied between 0.104mgL -1 and 0.375mgL -1 over the study period considering the two lakes during pre and post monsoon seasons. The band ratios of the reflected spectra at R670/R710, R710/R740 and R710/R550 are used for the development of the mathematical model of chlorophyll-a, Turbidity and Secchi depth respectively. The trained sets of the pixels extracted from the hyperspectral data for pure spectra are processed for preparing the water quality distribution maps. When subjected to multi-variant statistical tests of significance, the models have yielded satisfactory R 2 values. The model versus in situ analysis results demonstrated R 2 = 0.81% for Chlorophyll-a, R 2 = 0.81%  for Turbidity and R 2 = 0.78% for Secchi depth correlation and that of model versus satellite data exhibited R 2 = 0.60% for Chlorophyll-a, R 2 = 0.66% for Turbidity and R 2 = 0.65 %  for Secchi depth mean efficiency.

Hyper-spectral Remote Sensing of Water Quality Parameters in Lakes: A Case Study of Hyderabad City, Telangana State, India