This work grapples with the challenge of directing autonomous decision making by planetary rovers conducting science investigations. Most of the related work addresses obstacle avoidance and traversabilty, while less work seeks to directly improve science yield. This research develops a comprehensive approach for planetary rovers that accounts for both science investigation and mobility risk. We present a probabilistic framework that quantifies these two attributes of rover exploration and generates paths that constrain risk while increasing science return. Specifically, science productivity is measured and improved using formal principles from information theory and statistical learning for decision making. Risk is estimated using a probabilistic model that predicts rover wheel slippage based on geometric and semantic information. Our method is evaluated in a simulation study using real Mars surface data that is relevant for both science and terrain investigations. Experimental analysis verifies the effectiveness of our approach.