Modeling environmental processes with multi-robot teams allows for better understanding of natural phenomena and better performance in robotic applications. This talk suggests using methods from dynamical systems theory and machine learning to model environmental processes with robot teams. This talk will introduce kernel transfer operators. These operators can be approximated and analyzed to represent the key temporal and spatial characteristics of the environment. The talk will present preliminary results that demonstrate how the approximations of these operators from sensing data can allow for the use of multi-robot teams in real world scenarios with complex environments.