This study aims to maximize the informational return of mobile environmental data acquisition systems operating on road networks. We propose a two-step survey design framework that fuses spatially dense auxiliary data describing landscape heterogeneity with road network information and formulates route planning as a combinatorial optimization problem. An information-rich initial route is constructed using fuzzy clustering and orienteering optimization, followed by an economization step that preserves information content through convex hull analysis in auxiliary data space. The framework is evaluated using a large-scale (4,500 km²) case study of mobile Cosmic Ray Neutron Sensing (CRNS) soil moisture measurements in central Germany. The optimized route yields more representative spatial coverage and reduced extrapolation requirements when regionalizing sparse CRNS measurements into gravimetric soil moisture maps, thereby lowering ontological uncertainty in regression-based soil moisture mapping compared to an empirically designed route of similar length. The results demonstrate that explicitly information-driven route design substantially improves survey efficiency and data quality. The proposed framework offers a general and transferable alternative to convenience-based mobile sampling strategies in Earth and environmental sciences.