An attempt has been made to quantify the relationship between the variation in IRS-IA-LISS-I (Indian Remote Sensing Satellite-1A Linear Imaging Self-Scanning System) radiance data and field measured change in secchi disc depth. Secchi disc depth was measured for 47 predetermined sampling locations on reservoir surface water. At extinction depth (secchi depth), water samples were collected from all the sampling locations. Suspended sediments of eight locations representing various reaches of the reservoir were selected for mineralogical, particle size and optical properties analysis. The LISS-I radiance value in band 1 (0.45–0.52µm) band 2 (0-52–0.59 µm) and band 3 (0.62–0.68 µm) were used in a regression analysis. The absorption infrared band 4 (0.77–0.86 µm) was not included in the analysis. In these, the dependable variable was secchi depth (SD) and the LISS-I-radiance data was the estimator variable. Forty-seven data sets of 20 October 1988 from Tawa reservoir surface water were used to obtain an estimator equation for SD. The verification of the estimator equation was tested by applying it to a data set of 21 measurements of 28 September 1988 for this reservoir. The coefficient of correlation between observed and estimated values for the 28 September 1988 data set wasr=0.92 for SD, indicating that the equation could accurately predict the water clarity (SD) for this reservoir on new occasions from IRS-IA-LISS-I spectral data. It is shown that mineral composition and optical properties of suspended sediments influence the reflected radiance of water quality. It is concluded that IRS-IA-LISS-I data provide a useful means of mapping water quality in reservoir.
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Choubey, V.K. Monitoring water quality in reservoirs with IRS-1A-LISS-I. Water Resour Manage 8, 121–136 (1994). https://doi.org/10.1007/BF00872432
- Water quality
- reservoir water
- suspended sediments
- remote sensing
- water quality mapping