Adaptive Sampling of Algal Blooms Using Autonomous Underwater Vehicle and Satellite Imagery: Experimental Validation in the Baltic Sea

This repo serves as a showcase for the final paper. See Joana Fonseca's fork for implementation details.

Paper aviailable at arXiv

Abstract

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 (GP) 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.

Citation

If you find this work useful, please consider citing it:

@article{fonseca2025adaptive,
  author = {Joana Fonseca, Sriharsha Bhat, Matthew Lock, Ivan Stenius, Karl H. Johansson},
  title = {Adaptive Sampling of Algal Blooms Using Autonomous Underwater Vehicles and Satellite Imagery: Experimental Validation in the Baltic Sea},
  journal = {Journal TBD},
  year = {2025}
}