  This paper investigates using satellite data to improve adaptive sampling
missions, particularly for front tracking scenarios such as with algal blooms.
Our proposed solution to find and track algal bloom fronts uses an Autonomous
Underwater Vehicle (AUV) equipped with a sensor that measures the concentration
of chlorophyll a and satellite data. The proposed method learns the kernel
parameters for a Gaussian process model using satellite images of chlorophyll a
from the previous days. Then, using the data collected by the AUV, it models
chlorophyll a concentration online. We take the gradient of this model to
obtain the direction of the algal bloom front and feed it to our control
algorithm. The performance of this method is evaluated through realistic
simulations for an algal bloom front in the Baltic sea, using the models of the
AUV and the chlorophyll a sensor. We compare the performance of different
estimation methods, from GP to curve interpolation using least squares.
Sensitivity analysis is performed to evaluate the impact of sensor noise on the
methods performance. We implement our method on an AUV and run experiments in
the Stockholm archipelago in the summer of 2022.
