  The coloured dissolved organic matter (CDOM) concentration is the standard
measure of humic substance in natural waters. CDOM measurements by remote
sensing is calculated using the absorption coefficient (a) at a certain
wavelength (e.g. 440nm). This paper presents a comparison of four machine
learning methods for the retrieval of CDOM from remote sensing signals:
regularized linear regression (RLR), random forest (RF), kernel ridge
regression (KRR) and Gaussian process regression (GPR). Results are compared
with the established polynomial regression algorithms. RLR is revealed as the
simplest and most efficient method, followed closely by its nonlinear
counterpart KRR.

Geophysics;Machine Learning