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

TL;DR

This study presents a method combining satellite imagery and autonomous underwater vehicle data to adaptively track algal bloom fronts, validated through simulations and real-world experiments in the Baltic Sea.

Contribution

The paper introduces an adaptive sampling approach that integrates satellite data with AUV measurements for real-time algal bloom front tracking, including experimental validation.

Findings

The method effectively models chlorophyll a concentration online.

Satellite data improves front tracking accuracy.

Sensor noise impacts model performance.

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 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.