This work utilizes Machine Learning (ML) regression and feature ranking techniques for water quality monitoring from remotely sensed data. The investigated regression methods include the Gaussian Process Regression (GPR), Suport Vector Regression (SVR) and Partial Least Squares Regression (PLSR). Feature relevance in the GPR model is as- sessed by the probabilistic Sensitivity Analysis (SA) approach.This ...