Ampe, E.

Batelaan, O.

Hestir, E.L.

Salvadore, E.

Triest, L.

Open Marine Archive

 - 9 -  
A wavelet approach for estimating chlorophyll-a from inland waters 
with reflectance spectroscopy 
Ampe Eva
1,3
, Erin L. Hestir
2
, Elga Salvadore
1,3
, Ludwig Triest
4
 and Okke Batelaan
1,5
 
1 
Department of Hydrology and Hydraulic Engineering, VUB, Pleinlaan 2, 1050 Brussels, Belgium  
E-mail: eva.ampe@vub.ac.be 
2
 CSIRO, Land and Water, Canberra ACT 2601, Australia 
3
 Flemish Institute for Technological Research (VITO), Boeretang 200, 2400 Mol, Belgium 
4 
Department of Biology, Plant Science and Nature Management, VUB, Pleinlaan 2, 1050 Brussels, 
Belgium 
5
 School of the Environment, Flinders University, GPO Box 2100, Adelaide, Australia 
 
This letter presents an application of continuous wavelet analysis, providing a new semi-empirical 
approach to estimate Chlorophyll-a (Chl-a) in optically complex inland waters. Traditionally spectral 
narrow band ratios have been used to quantify key diagnostic features in the remote sensing signal 
to estimate concentrations of optically active water quality constituents. However, they cannot cope 
easily with shifts in reflectance features caused by multiple interactions of variable absorption and 
backscattering effects that typically occur in optically complex waters. We used continuous wavelet 
analysis to detect Chl-a features at various wavelengths and frequency scales. We investigated 21 
small artificial shallow ponds in the Brussels Capital Region. Here, we collected in situ pond-level 
reflectance spectra and related them to in situ Chl-a concentration. We used the wavelet 
decomposition of these spectra to build a 2D correlation scalogram with the Chl-a concentration. 
We isolated the most informative wavelet regions via thresholding and could relate all five regions 
to known inherent optical properties. We selected the optimal feature per region and compared 
them to three well known narrow band ratio models. The wavelet approach can provide a flexible 
approach to analyse high spectral resolution data because wavelets analyse the signal at different 
scales and synthesize information across bands. Furthermore, we hypothesize that the wavelet 
features are less sensitive to confounding factors such as instrument noise, coloured dissolved 
organic matter (CDOM) and suspended matter. 
 


A wavelet approach for estimating chlorophyll-a from inland waters with reflectance spectroscopy

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