Modeling of soil functions often faces rough estimations of soil properties. These rough estimations originate from coarse soil survey maps and miss to adequately represent small-scale soil heterogeneity. This study presents an approach to account for this heterogeneity to improve biomass production modeling. We employed two common proximal soil sensing (PSS) methods: ground penetrating radar (GPR) and electromagnetic induction (EMI) to describe soil depths over small spatial scales at the field site Wagna (Austria). The experiment encompasses highly heterogenic soil depth variations of fine topsoil above sandy subsoil. These variations are only roughly contained in the “standard” soil parameterization (STSP) although they strongly influence the local soil water balance. To adequately map small scale soil depth variation for the entire experiment we exploited the advantages of GPR (exact detection of boundary depth between top- and subsoil) and EMI (rapid scan on field scales to obtain distinct soil patterns). Both, the resulting small scale “geophysical adapted” soil map and the coarse scale STSP served as soil input for Carbon and Nitrogen Dynamics (CANDY) Plant Universal Simulator (PLUS) to model plant biomass production. The validation with “geophysical adapted” parameterization indicated an improved performance with a RMSE of the modeled grain biomass of 1762.7 kg ha−1 in comparison to 3584.6 kg ha−1 with STSP. The result demonstrated PSS to provide important small-scale soil data that can improve agro-ecosystem modeling.