Blue–Red–NIR Model for Chlorophyll- a Retrieval in Hypersaline–Alkaline Water Using Landsat ETM+ Sensor

A conceptual three-band model has been proposed previously and efficiently used to retrieve the chlorophyll-a (C_chla) concentration (C_chla) in deeper water bodies. In this study, we have proposed an empirical C_chla estimation model using Landsat ETM+ image reflectance and laboratory-based C_chla measurements from hypersaline-alkaline shallow lake (HSASlake) water. This study aims to use remote sensing technique to determine the quantity and distribution of chlorophyll (as an indicator of cyanobacterial biomass) rendering an indirect estimate of food availability for flamingos and other aquatic animals, thus providing valuable information for their future conservation. Using proposed empirical method named blue-red- NIR model, it has been found that the C_chla ranges from 3.43 to 43.75 μg L^-1 with the mean Chl-a value of 5.45 μg L^-1, in the lake investigated. A variety of regression functions have been implemented for the single and multiband ratios. The best-fitted regression model was developed for the band combination of [R_rs^-1 (660) - R_rs^-1 (482)] × R_rs^-1 (825) having an R² of 0.88 and model errors of 0.93, 0.8, and 4.74 for standard error of estimate (SEE), Nash-Sutcliffe coefficient (E), and mean absolute percentage error (MAPE), respectively. Our finding evinces that the proposed blue-red-NIR model may be appraised as a robust solution for the estimation of C_chla in optically shallow waters, provided that the local inherent optical properties (IOPs) should be scrutinized and reinitialized.