Being able to sense and localize abnormalities in an aquatic environment efficiently and quickly is crucial for both the environmental scientists and government authorities to study the problem and take remediation measures. Conventional, statically-deployed buoy stations can only provide a sparse, low-resolution snap shoots of the environmental parameters of interest within a predefined area. Data interpolation between the stations, and extrapolation outside the area introduce estimation errors at best, and for the worst case, completely miss the environmental abnormality. We developed an adaptive sampling strategy using receding-horizon cross-entropy trajectory optimization on a Gaussian Process (GP). Specifically, we employ Upper-Confidence-Bound (UCB) search method to adaptively plan the vehicle’s path that exhibit an exploitation-exploration trade-off. Path planning at the initial stage is focus on exploring and learning a model of the environment, and later, to exploit the learned model to plan paths that increase sampling frequency around regions of high interest. Figure on the top showed the effectiveness of the path planning algorithm in generating paths to localize and sample at regions of “high interest”. Figure on the bottom-right showed a field experiment with an Unmanned Surface Vehicle (USV) equipped with an single-beam echo sounder demonstrated the approach’s capability to quickly localize and focuses the sampling at the deepest region (as surrogate for extrema of environmental features). Please refer to[bibcite key=Tan:icra2017] for more details.