Environmental Management

, Volume 42, Issue 3, pp 511–522 | Cite as

Landsat ETM+ Images in the Estimation of Seasonal Lake Water Quality in Boreal River Basins

  • Kari KallioEmail author
  • Jenni Attila
  • Pekka Härmä
  • Sampsa Koponen
  • Jouni Pulliainen
  • Ulla-Maija Hyytiäinen
  • Timo Pyhälahti


We investigated the use of Landsat ETM+ images in the monitoring of turbidity, colored dissolved organic matter (CDOM), and Secchi disk transparency (ZSD) in lakes of two river basins located in southern Finland. The ETM+ images were acquired in May, June, and September 2002 and were corrected for atmospheric disturbance using the simplified method of atmospheric correction (SMAC) model. The in situ measurements consisted of water sampling in the largest lake of the region, routine monitoring results for the whole study area, and ZSD observations made by volunteers. The ranges of the water quality variables in the dataset were as follows: turbidity, 0.6–25 FNU; absorption coefficient of CDOM at 400 nm, 1.0–12.2 m−1; ZSD, 0.5–5.5 m; and chlorophyll a concentration, 2.4–80 μg L−1. The estimation accuracies of the image-specific empirical algorithms expressed as relative errors were 23.0% for turbidity, 17.4% for CDOM, and 21.1% for ZSD. If concurrent in situ measurements had not been used for algorithm training, the average error would have been about 37%. The atmospheric correction improved the estimation accuracy only slightly compared with the use of top-of-atmospheric reflectances. The accuracy of the water quality estimates without concurrent in situ measurements could have been improved if in-image atmospheric parameters had been available. The underwater reflectance simulations of the ETM+ channel wavelengths using water quality typical for Finnish lakes (data from 1113 lakes) indicated that region-specific algorithms may be needed in other parts of the country, particularly in the case of ZSD. Despite the limitations in the spectral and radiometric resolutions, ETM+ imagery can be an effective aid, particularly in the monitoring and management of small lakes (<1 km2), which are often not included in routine monitoring programs.


Landsat ETM+ Water quality Monitoring Lakes Boreal 



This study was partly funded by the Karjaanjoki LIFE project (INNOWA, LIFE00 ENV/FIN/000668).


  1. Brivio PA, Giardino C, Zilioli E (2001) Determination of chlorophyll concentration changes in Lake Garda using an image-based radiative transfer code for Landsat TM images. International Journal of Remote Sensing 22:487–502CrossRefGoogle Scholar
  2. Cox RM, Forsythe RD, Vaughan GE, Olmsted LL (1998) Assessing water quality in Catawba River reservoirs using Landsat thematic mapper satellite data. Lake and Reservoir Management 14:405–416Google Scholar
  3. Dekker AG (1993) Detection of optical water quality parameters for eutrophic waters by high resolution remote sensing. PhD thesis, Vrije Universiteit, AmsterdamGoogle Scholar
  4. Dekker AG, Vos RJ, Peters SWM (2002) Analytical algorithms for lake water TSM estimation for retrospective analysis of TM and SPOT sensor data. International Journal of Remote Sensing 23:15–36CrossRefGoogle Scholar
  5. EN 27027 (1994) Water quality—determination of turbidity. European Committee for StandardizationGoogle Scholar
  6. Gordon HR, Brown OB, Jacobs MM (1975) Computed relationships between the inherent and apparent optical properties of a flat, homogenous ocean. Applied Optics 14:417–427CrossRefGoogle Scholar
  7. Hilsenrath E, Cebula RP, Deland MT, Laamann K, Taylor S, Wellemeyer C, Bhartia PK (1995) Calibration of the NOAA-11 solar backscatter ultraviolet (SBUV/2) ozone data set from 1989 to 1993 using in-flight calibration data and SSBUV. Journal of Geophysical Research 100:1351–1366CrossRefGoogle Scholar
  8. Hirtle H, Rencz A (2003) The relation between spectral reflectance and dissolved organic carbon in lake water: Kejimkujik National Park, Nova Scotia, Canada. International Journal of Remote Sensing 23:953–967CrossRefGoogle Scholar
  9. ISO 10260 (1992) Water quality—measurement of biochemical parameters—spectrometric determination of the chlorophyll a concentration. International Organization for StandardizationGoogle Scholar
  10. Jerlov NG (1976) Marine optics. Elsevier, AmsterdamCrossRefGoogle Scholar
  11. Kallio K, Kutser T, Hannonen T, Koponen S, Pulliainen J, Vepsäläinen J, Pyhälahti T (2001). Retrieval of water quality variables from airborne spectrometry of various lake types in different seasons. The Science of the Total Environment 268:59–77 CrossRefGoogle Scholar
  12. Kallio K (2006) Optical properties of Finnish lakes estimated with simple bio-optical models and water quality monitoring data. Nordic Hydrology 37:183–204Google Scholar
  13. Kallio K, Pulliainen J, Ylöstalo P (2005) MERIS, MODIS and ETM channel configurations in the estimation of lake water quality from subsurface reflectance with semi-analytical and empirical algorithms. Geophysica 41:31–55Google Scholar
  14. Kirk J (1984) Dependence of relationship between inherent and apparent optical properties of water on solar altitude. Limnology and Oceanography 29:350–356CrossRefGoogle Scholar
  15. Kloiber SM, Brezonik PL, Olmanson LG, Bauer ME (2002a) A procedure for regional lake water clarity assessment using Landsat multispectral data. Remote Sensing of Environment 82:38–47CrossRefGoogle Scholar
  16. Kloiber SM, Brezonik PL, Bauer ME (2002b) Application of Landsat imagery to regional-scale assessment of lake clarity. Water Research 36:4330–4340CrossRefGoogle Scholar
  17. Kutser T, Pierson D, Tranvik L, Reinart A, Sobek S, Kallio K (2005a) Estimating the colored dissolved organic matter absorption coefficient in lakes using satellite remote sensing. Ecosystems 8:709–720CrossRefGoogle Scholar
  18. Kutser T, Pierson DC, Kallio KY, Reinart A, Sobek S (2005b) Mapping lake CDOM by satellite remote sensing. Remote Sensing of Environment 94:535–540CrossRefGoogle Scholar
  19. MacFarlane N, Robinson IS (1984) Atmospheric correction of Landsat MSS data for a multidate suspended sediment algorithm. International Journal of Remote Sensing 5:561–576CrossRefGoogle Scholar
  20. Rahman H, Dedieu G (1994) SMAC: a simplified method for the atmospheric correction of satellite measurement s in the solar spectrum. International Journal of Remote Sensing 15:123–143CrossRefGoogle Scholar
  21. Thiemann S, Kaufmann H (2000) Determination of chlorophyll content and trophic state of lakes using field spectrometer and IRS-1C satellite data in the Mecklenburg Lake District, Germany. Remote Sensing of Environment 73:227–235CrossRefGoogle Scholar
  22. Vincent RK, Qin X, McKay RML, Miner J, Czajkowski K, Savino J, Bridgeman T (2004) Phycocyanin detection from Landsat TM data for mapping cyanobacterial blooms in Lake Erie. Remote Sensing of Environment 89:381–392CrossRefGoogle Scholar
  23. Vuoristo H (1998) Water quality classification of Finnish inland waters. European Water Management 1:35–41Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Kari Kallio
    • 1
    Email author
  • Jenni Attila
    • 1
  • Pekka Härmä
    • 1
  • Sampsa Koponen
    • 2
  • Jouni Pulliainen
    • 3
  • Ulla-Maija Hyytiäinen
    • 4
  • Timo Pyhälahti
    • 1
  1. 1.Finnish Environment InstituteHelsinkiFinland
  2. 2.Laboratory of Space TechnologyHelsinki University of TechnologyHelsinkiFinland
  3. 3.Finnish Meteorological InstituteSodankylaFinland
  4. 4.Municipality of VihtiNummelaFinland

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