Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Monitoring water quality in reservoirs with IRS-1A-LISS-I

  • 86 Accesses

  • 12 Citations

Abstract

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.

This is a preview of subscription content, log in to check access.

References

  1. Alfoldi, T. T., and Munday, J. C. jr., 1978, Water quality analysis by digital chromaticity mapping of Landsat data,Can. J. Remote Sensing 4(2), 108–126.

  2. Biscaye, P. E., 1965, Mineralogy and sedimentation of recent deep sea clay in the Atlantic Ocean and adjacent seas and oceans,Geol. Soc. Am. Bull. 76, 803–832.

  3. Bukata, R. P., Bruton, J. E., and Jerome, J. H., 1983, Use of chromaticity in Remote measurements of water quality.Remote Sensing of Environment 13, 162–177.

  4. Carrol, D., 1970, Clay minerals a quide to their identification,Geol. Soc. Am, special paper 126:80.

  5. Choubey, V. K., 1990, Modelling sediment and dissolved load of Twwa reservoir and river (MP) by Remote Sensing techniques, PhD thesis, JNU, New Delhi.

  6. Davies-Colley, R. J. and Vant, W. N., 1988, Estimation of optical properties of water from secchi disc depth.Water. Resour. Bull. (AWRA) 24(6), 1329–1135.

  7. Gibbs, R. J., 1967, The geochemistry of the Amazon river system part I: The factors that control the salinity and the composition and concentration of the suspended solids,Geol. Soc. Am. Bull. 78, 1203–1232.

  8. Jerlov, N. G., 1976.Marine Optics, Elsevier, Amsterdam.

  9. Kerr, P. E., 1959,Optical Mineralogy, McGraw Hill, New York, pp. 163–168.

  10. Khorram, S., and Cheshire, H. M., 1985, Remote sensing of water quality in the Neuse river esturary, North Carolina,Photogrammetric Engineering and Remote Sensing 51, 329–341.

  11. Lindell, L. T., Steinvall., Johnson, O. M., and Classon, T. H., 1985, Mapping of coastal water turbidity using landsat imagery,Int. J. Remote Sensing 6(5), 629–642.

  12. McCauley, J. R., 1977. Reservoir water quality monitoring with orbital remote sensors, Ph D thesis, Department of Geology, University of Kansas.

  13. Marry, J. C., 1977. Airborne spectroradiometer data compared with ground water turbidity measurements at Lake Powell Utah, Cold Region Research and Engineering Laboratory, New Hampshire.

  14. Munday Jr. J. C., Alfoldi, T. T., and Amos, C. L., 1979, Bay of Fundy varification of a system for multidate Landsat measurement of suspended sediment,Satellite Hydrology (AWRA), June 1979, 622–640.

  15. Manu, L. and Robertson, C., 1990, Estimating suspended sediment concentrations from spectral reflectance data,Int. J. Remote Sensing 11(5), 913–920.

  16. Ramsey, E. W. and Jensen, J. R., 1990, The derivation of water volume reflectance from airborne MSS data using in situ water volume reflectance and a combined optimization technique and radiative transfer model,Int. J. Remote Sensing 11(6), 977–998.

  17. Ritchie, J. C. and Cooper, C. M., 1988, Suspended sediment concentrations estimated from Landsat NSS data,Int. J. Remote Sensing 9, 379–387.

  18. Subramanian, V., 1980. Mineralogical input of suspended matter by Indian rivers into the adjacent areas of the Indian ocean,Marine Geology 36, 29–34.

  19. Topliss, B. J., Amos, C. L., and Hill, P. R., 1990, Algorithm for remote sensing of high concentration, inorganic suspended sediment,Int. J. Remote Sensing 11(6), 947–966.

  20. Verdin, J. P., 1985, Monitoring water quality conditions in a large western reservoir with. Landsat imagery,Photogrammetric Eng. Remote Sensing 51(3), 343–353.

  21. Whitlock, C. H., Kuo, C. Y., and LeCroy, S. R., 1982, Criterion for the use of regression analysis for remote sensing of suspended sediment and pollutants,Remote Sensing of Environment 12, 151.

  22. Yarger, H. L., McCauley, J. R., James, G. W., Magnuson, L. M. and Richard, G., 1973, Water turbidity detection using ERTS-1 imagery,Proc. ERTS-1 Symposium, Washington, DC, pp. 651–660.

Download references

Author information

Rights and permissions

Reprints and Permissions

About this article

Cite this article

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

Download citation

Keywords

  • Water quality
  • reservoir water
  • suspended sediments
  • remote sensing
  • radiance
  • water quality mapping