Many environments, such as unvisited planetary surfaces and oceanic regions, remain unexplored due to a lack of prior knowledge. Autonomous vehicles must sample upon arrival, process data, and either transmit findings to a teleoperator or decide where to explore next. Teleoperation is suboptimal, as human intuition lacks mathematical guarantees for optimality. This study evaluates an informative path planning algorithm for mapping a scalar variable distribution while minimizing travel distance and ensuring model convergence. We compare traditional open loop coverage methods (e.g., Boustrophedon, Spiral) with information-theoretic approaches using Gaussian processes, which update models iteratively with confidence metrics. The algorithm's performance is tested on three surfaces, a parabola, Townsend function, and lunar crater hydration map, to assess noise, convexity, and function behavior. Results demonstrate that information-driven methods significantly outperform naive exploration in reducing model error and travel distance while improving convergence potential.