  Kernel-based machine learning regression algorithms (MLRAs) are potentially
powerful methods for being implemented into operational biophysical variable
retrieval schemes. However, they face difficulties in coping with large
training datasets. With the increasing amount of optical remote sensing data
made available for analysis and the possibility of using a large amount of
simulated data from radiative transfer models (RTMs) to train kernel MLRAs,
efficient data reduction techniques will need to be implemented. Active
learning (AL) methods enable to select the most informative samples in a
dataset. This letter introduces six AL methods for achieving optimized
biophysical variable estimation with a manageable training dataset, and their
implementation into a Matlab-based MLRA toolbox for semi-automatic use. The AL
methods were analyzed on their efficiency of improving the estimation accuracy
of leaf area index and chlorophyll content based on PROSAIL simulations. Each
of the implemented methods outperformed random sampling, improving retrieval
accuracy with lower sampling rates. Practically, AL methods open opportunities
to feed advanced MLRAs with RTM-generated training data for development of
operational retrieval models.
