Today's planetary exploration robots rarely travel beyond the yesterday imagery. However, advances in autonomous mobility will soon permit single-command site surveys of multiple kilometers. Here scientists cannot see the terrain in advance, and explorer robots must navigate and collect data autonomously. Onboard science data understanding can improve these surveys with image analysis, pattern recognition, learned classification, and information-theoretic planning. We report on field experiments near Amboy Crater, California, that demonstrate fundamental capabilities for autonomous surficial mapping of geologic phenomena with a visible near-infrared spectrometer. We develop an approach to "science on the fly" that adapts the robot's exploration using collected instrument data. We demonstrate feature detection and visual servoing to acquire spectra from dozens of targets without human intervention. The rover interprets spectra onboard, learning spatial models of science phenomena that guide it toward informative areas. It discovers spatial structure (correlations between neighboring regions) and cross-sensor structure (correlations between different scales). The rover uses surface observations to reinterpret satellite imagery and improve exploration efficiency. C 2011 Wiley Periodicals, Inc.