Compact Remote Sensing Ocean Models for Adaptive Sampling in the Coastal Ocean
Trygve Olav Fossum

Finding high-value locations for in-situ data collection is of great importance in ocean science, where diverse bio-physical processes interact to create dynamically evolving phenomenon. These often occupy a large spatial extant, are sparse, and difficult to predict. Autonomous robotic platforms can sustain themselves in harsh conditions with persistent presence, but require to be deployed at the right place and time. To that end, we consider the use of remote sensing images for building compact models that can improve skill in predicting sub-mesoscale features and inform onboard sampling. The model is established using a combination of dictionary learning and hierarchical clustering. The model is shown working in a real application using measurements from an autonomous surface vehicle (ASV), together with forecast and sampling strategies. Finally an analysis of the model prediction error is presented and compared across different paths and survey duration.