Physical sampling of water for off-site analysis is necessary for many applications like monitoring the quality of drinking water in reservoirs, understanding marine ecosystems, and measuring contamination levels in fresh-water systems. Persistent collection of physical water samples can be improved by automation. Robotic sampling makes it possible to strategically collect water samples based on real-time measurements of physical and chemical properties gathered with onboard sensors. In this paper, we present a multi-robot, data-driven, watersampling strategy, where autonomous surface vehicles plan and execute water sampling using the chlorophyll density as a cue for plankton-rich water samples. We use two Autonomous Surface Vehicles, one equipped with a water quality sensor and the other equipped with a physical water-sampling apparatus. The robot with sensors acts as an explorer, measuring and building a spatial map of chlorophyll density in the given region of interest. The robot equipped with water sampling apparatus makes decisions in real-time on where to sample the water, based on the suggestions made by the explorer robot. We evaluate our system in simulation and on real robots in an actual drinking-water reservoir, showing the effectiveness of the proposed system.