Chawla, S.

De Baets, B.

Desmet, N.

Knaeps, E.

Raymaekers, D.

Seuntjens, P.

Verhoest, N.

Open Marine Archive

 - 23 -  
Systematic sampling strategy for monitoring water quality by 
integrating in-situ and remote sensing data 
Chawla Sivee
1,2
, Nele Desmet
1
, Dries Raymaekers
1
, Els Knaeps
1
, Piet Seuntjens
1,2,3
, Bernard De Baets
2
 
and Niko Verhoest
2 
1
 Flemish Institute for Technological Research (VITO), Boeretang 200, 2400 MOL, Belgium 
E-mail: sivee.chawla@vito.be   
2
 Faculty of Bioscience Engineering, University Gent, Coupure Links 653, 9000 Gent, Belgium 
3
 Dept of Bioscience engineering, University Antwerp, Antwerp, Belgium 
 
Water quality monitoring is an important component of water management activities. Collecting 
samples is the first step in monitoring and an effective sampling design is important to ensure the 
quality of end results. The water components are heterogeneously distributed over water surface 
(Anttila et al., 2007). Also, the samples have spatial dependence or are autocorrelated over the 
water surface (Hedger et al., 2001). Usually, random sampling techniques are used for monitoring 
because of its ease of use and unbiased approach, but it ignores the heterogeneity and spatial 
dependence. Insufficient sample size also, contributes to sampling error. This research focuses on 
these sampling issues and proposes a systematic sampling strategy considering the spatial 
dependency, heterogeneity and sample size, to decrease error and increase accuracy.  
 
The methodology proposed in the research utilises geostatistical based techniques to analyse the 
spatial dependency of water components using variogram analysis on in-situ data. To capture 
heterogeneity, experiments were performed by varying grid size, spatial distribution of samples and 
number of samples, within the study area. Airborne hyperspectral remote sensing data, at different 
spatial resolutions, was used to perform the spatial analysis for calibration of the sampling scheme. 
For further calibration and validation, optical in-situ data was used.  
 
Final outcome of this research is an effective spatial sampling design which defines the sample size, 
the spatial distribution of samples and effective grid size for in-situ sampling to minimize error 
while estimating water quality parameters in a reservoir.  
 
For this study, Broechem drinking water reservoir is selected to monitor algae blooms by estimation 
chl-a concentration using in-situ and remote sensing data. Extensive in-situ and remote sensing 
data set were collected. In-situ monitoring was done by autonomous vehicle - Aqua drone (by VITO) 
by using probes- YSI 6600 V2 data sonde and fluoroprobe. Optical properties were captured using 
in-situ devices – ASD-spectroradiometer and in-house developed camera LICRIS, used for calibration. 
Also, synoptic view of study area (providing prior information to the input of the system) is available 
from hyperspectral remote sensing data of APEX, with flight time of around same time as that of in-
situ data.  
 
The study area is so chosen because of its hydrodynamic simplicity and extensive availability of 
data set. This has been taken as a test site with the idea to extend this study in more complex water 
bodies such as coastal waters which are both hydrodynamic ally complex and also, dominated by 
mixture of water constituents such as phytoplankton and sediment plumes.  
 
 
References
Anttila S., T. Kairesalo and P. Pellikka. 2007. A feasible method to assess inaccuracy caused by 
patchiness in water quality monitoring. Enivronment Monitoring and Assessment 142:11-22. 
Hedger R.D., P.M. Atkinson and T.J. Malthus. 2001. Optimizing sampling strategies for esitmating 
mean water quality in lakes using geostatistical techniques with remote sensing. Lakes & 
Reservoirs: Research and Management 6:279-288. 
 


Systematic sampling strategy for monitoring water quality by integrating in-situ and remote sensing data

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