%0 conference paper
%@ 
%A Ruescas, A.B., Hieronymi, M., Koponen, S., Kallio, K., Camps-Valls, G.

%D 2017
%J Proceedings of IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017

%P 2187-2190 
%R doi:10.1109/IGARSS.2017.8127421
%T Retrieval of coloured dissolved organic matter with machine learing methods
%U https://doi.org/10.1109/IGARSS.2017.8127421
 
%X 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.