A wavelet-enhanced inversion method for water quality retrieval from high spectral resolution data for complex waters

Optical remote sensing in complex waters is challenging
because the optically active constituents may vary independently
and have a combined and interacting influence on the
remote sensing signal. Additionally, the remote sensing signal is
influenced by noise and spectral contamination by confounding
factors, resulting in ill-posedness and ill-conditionedness in the
inversion of the model. There is a need for inversion methods
that are less sensitive to these changing or shifting spectral features.
We proposeWaveIN, a wavelet-enhanced inversion method,
specifically designed for complex waters. It integrates wavelettransformed
high-spectral resolution reflectance spectra in a multiscale
analysis tool. Wavelets are less sensitive to a bias in the
spectra and can avoid the changing or shifting spectral features
by selecting specific wavelet scales. This paper applied WaveIN
to simulated reflectance spectra for the Scheldt River. We tested
different scenarios, where we added specific noise or confounding
factors, specifically uncorrelated noise, contamination due to spectral
mixing, a different sun zenith angle, and specific inherent optical
property (SIOP) variation.WaveIN improved the constituent
estimation in case of the reference scenario, contamination due to
spectral mixing, and a different sun zenith angle. WaveIN could
reduce, but not overcome, the influence of variation in SIOPs.
Furthermore, it is sensitive to wavelet edge effects. In addition,
it still requires in situ data for the wavelet scale selection. Future
research should therefore improve the wavelet scale selection.
Index Terms--Chlorophyll-a, continuous wavelet transforms,
dissolved organic matter, hyperspectral remote sensing, multiscale,
optically complex waters, suspended matter.

Publication year:2014

Keywords:wavelet-enhanced inversion method, water quality retrieval, high spectral resolution data, complex waters