Comparison of IRS-IB LISS-IIA pixel array sizes for estimating suspended solids concentration in Hussain Sagar lake, Hyderabad, India—a statistical approach

Abstract

The objective of the study is to select the best possible array size of Indian Remote Sensing Satellite (IRS-IB) linear imaging self scanning (LISS-IIA) digital data for the estimation of the suspended solids concentration on a surface water body. For this purpose a lake namely Hussain Sagar in Hyderabad (India) has been considered. The lake water samples were collected on 21 February 1992 in concurrence with the date of IRS-IB overpass. These water samples have been analysed to determine the suspended solids concentration at predetermined sample locations. Different pixel array sizes of IRS-IB LISS-IIA digital data has been analysed for the selection of the size of the pixel array for the estimation of water quality variables. This selection has been conducted by using various statistical methods such as analysis of variance, paired t-test and linear regression techniques. Analysis of variance and paired t-test are basically used for the selection of minimum pixel array size and linear regression techniques have been used for the selection of the best favourable band and pixel array for the estimation of suspended solids concentration. The relations between digital data and measured values of suspended solids concentrations have been quantified using simple linear and multiple regression. The possible combinations of bands, i.e., model 1, model 2 are developed. From possible combinations model 1 has been chosen for the estimation of suspended solids concentration based on the highest coefficient of determination (R ²) lowest standard error of estimate and F-ratio (four times greater than critical F-ratio (F_cr). Based on the results of this study it is observed that the statistical approach has a strong potential for the application of remote sensing data for quantification of suspended solids concentration.