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LINKING MODIS IMAGERY WITH TURBIDITY AND TSS TIME
SERIES DATA GENERATION OVER LAKE TANA, ETHIOPIA
Essayas K. Ayana*1,3, William D. Philpot2 and Tammo S. Steenhuis1,3
1Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA
2Department of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA
3School of Civil and Water Resources Engineering, Bahir Dar University, Bahir Dar, Ethiopia
INTRODUCTION
Various conservation programs are being implemented in
developing countries with the potential benefit of reduced
sediment inflow into fresh water lakes. These claims are hard to
verify due to prohibitive costs of continuous stream sediment
load sampling and analysis. Remote sensing can potentially aid
in monitoring sediment concentration. Various regression models
have been developed using remotely sensed images (Hu et al.,
2004, Chen et al., 2007, Wang and Lu, 2010). However these
regression models are not universal and hence cannot be
applied to estimate the same parameters elsewhere. The
relationship between in situ water quality parameters and their
corresponding reflectance measurement are almost always sitespecific (Liu et al. 2003).
TSS TIME SERIES GENERATION
•The Getis–Ord Gi* statistic data mining technique (Getis and Ord 2010) is used to
select the representative “muddiest pixel”
•Regression equation established for TSS is applied on the “muddiest pixel” to
estimate the maximum TSS for the given day
•A 10 years (2000-2009) TSS time series data is generated
METHODS
•Moderate Resolution Imaging Spectroradiometer (MODIS)
250m resolution images used (LPDAAC 2010)
•Bulk water samples and GPS coordinates of sampling locations
are collected during the satellite overpass time over Lake Tana
•Total suspended solids and turbidity measured
•Reflectance values of the sampling locations are extracted
APPLICATION FOR WATER QUALITY MODELING
•A SWAT model is calibrated and validated using the MODIS image generated 10
years TSS time series.
•Nash–Sutcliffe efficiencies of 0.39 for calibration period and 0.32 for validation.
•Multiple regression analysis was performed on two set of
measurements (taken 27 November 2010 and 13 May 2011)
NSE=0.39
•Third data set (collected November 7, 2011) is used for
validation
NSE=0.32
RESULTS
•water reflectance in the NIR band fits best resulting in a linear
correlation with measured TSS (R2=0.95) and turbidity
(R2=0.89)
CONCLUSION
•Secchi depth correlate with NIR reflectance exponentially
(R2=0.74)
•The root mean square error (RMSE) in using these equations
was 16.5 mg l-1 ,15.6 NTU, and 0.11 m to measured TSS,
turbidity and Secchi depth respectively
REFERENCES
•Single band relations are found to be most accurate to measure turbidity, TSS and
Secchi depth
•MODIS images are a potential cost effective tool to monitor suspended sediment
concentration and to obtain past history of concentrations which will help to evaluate
the effect of best management practices
•Results showed that MODIS images are not sensitive enough to detect turbidity
variations below 60 NTU.
1.
CHEN, Z., HU, C. & MULLER-KARGER, F. 2007. Monitoring turbidity in Tampa Bay using MODIS/Aqua 250-m imagery. Remote Sensing of Environment, 109, 207-220.
2.
GETIS, A., and ORD, J. K., 2010, The analysis of spatial association by use of distance statistics. Perspectives on Spatial Data Analysis, 127-145.
3.
HU, C., CHEN, Z., CLAYTON, T. D., SWARZENSKI, P., BROCK, J. C. & MULLER–KARGER, F. E. 2004. Assessment of estuarine water-quality indicators using MODIS medium-resolution bands: Initial results from Tampa Bay, FL. Remote Sensing of
Acknowledgement
USDA-SRE and the HED provided partial support for this study.
Environment, 93, 423-441.
4.
LPDAAC, 2010, Surface Reflectance Daily L2G Global 250m.
5.
LIU, Y., ISLAM, M. A., and GAO, J., 2003, Quantification of shallow water quality parameters by means of remote sensing. Progress in Physical Geography, 27, 24-43.
6.
WANG, J. J. & LU, X. 2010. Estimation of suspended sediment concentrations using Terra MODIS: An example from the Lower Yangtze River, China. Science of the Total Environment, 408, 1131-1138.
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