Dynamic simulations models may enable for farmers the evaluation of crop and soil management strategies, or may trigger crop and soil management strategies if they are used as warning systems, e.g. for drought risks and for nutrients shortage. Predictions by simulation models may differ from field observations for a variety of reasons, and such deviations can be revealed instantly by traditional or by new field monitoring techniques. The objective of this study was to improve simulation results by integrating remote sensing observations during the growing season in the simulation (i.e. run-time calibration). The Rotask 1.0 simulation model was used as it simulates daily interactions between climate (radiation temperature, vapour pressure, wind speed, precipitation), soils (water holding capacities, soil organic matter dynamics, evaporation) and crops (light interception, dry matter production, nitrogen uptake, transpiration). Various run-time calibration scenarios for replacing simulated values by remotely observed values were tested. For a number of times in the growing season, simulated values of leaf area index (LAI) and canopy nitrogen contents were replaced with values estimated from remote sensing. Field experiments were carried out in the Netherlands in 1997 (validation) and 1998 (calibration) with potato variety Bintje. Destructive field samplings were performed to follow LAI and canopy nitrogen development in the growing season. Remote sensing observations at canopy level were taken by CropScan¿ equipment, covering the electromagnetic spectrum between 460¿810 nm in eight spectral bands. LAI and canopy nitrogen were monitored at various moments throughout the growing season by relating them with vegetation indices (VI) that were calculated from the combination of specific remote sensing bands. The results of this study show that run-time calibration of mechanistic simulation models may enhance simulation accuracy, depending on the method how additional information is integrated. It is advised to synchronize dry matter balances and internal nitrogen balances in accordance with adjustments to observed calibration variables (in this case LAI and canopy nitrogen content). It is shown an integrated approach follows the actual crop¿soil system more closely, which is helpful for specific crop management and precision agriculture in general. Run-time calibration with variables that can be estimated from remote sensing observations gives more accurate simulation results of variables that can not be observed directly, e.g. the evolution of soil inorganic nitrogen contents. High frequencies of remote sensing observations and interpolation in between them, allow reconstructing the evolution of LAI and canopy nitrogen contents to be integrated in the simulation, thereby increasing simulation accuracy of other model variables.