This paper presents an oceanographic toolchain that can be used to generate multi-vehicle robotic surveys for large-scale dynamic features in the coastal ocean. Our science application targets Harmful Algal Blooms (HABs) which have significant societal impact to coastal communities yet are poorly understood ecologically. Bloom patches can be large spatially (in kms) and unpredictable in their extent. To understand their ecology, we need to be able to bring back water samples from the `right' places and times for lab analysis. In doing so, we target hotspots representative of intense biogeochemical activity for such sampling. Our approach uses remote sensing data to detect such hotspots using ocean color as a proxy, and advectively projects these patches spatio-temporally using surface current data from HF Radar stations. Experiments with satellite and Radar data sets are promising for large, coherent blooms. We show how these predictions can be used to select an appropriate sampling trajectory for an AUV.