Machine Learning Regression Approaches for Colored Dissolved Organic Matter (CDOM) Retrieval with S2-MSI and S3-OLCI Simulated Data

The colored dissolved organic matter (CDOM) variable is the standard measure of humic
substance in waters optics. CDOM is optically characterized by its spectral absorption coefficient,
a CDOM at at reference wavelength (e.g., ≈ 440 nm). Retrieval of CDOM is traditionally done
using bio-optical models. As an alternative, this paper presents a comparison of five machine
learning methods applied to Sentinel-2 and Sentinel-3 simulated reflectance (R rs ) data for the
retrieval of CDOM: regularized linear regression (RLR), random forest regression (RFR), kernel
ridge regression (KRR), Gaussian process regression (GPR) and support vector machines (SVR).
Two different datasets of radiative transfer simulations are used for the development and training of
the machine learning regression approaches. Statistics comparison with well-established polynomial
regression algorithms shows optimistic results for all models and band combinations, highlighting
the good performance of the methods, especially the GPR approach, when all bands are used as
input. Application to an atmospheric corrected OLCI image using the reflectance derived form
the alternative neural network (Case 2 Regional) is also shown. Python scripts and notebooks are
provided to interested users.