This case study illustrates how to conduct geospatially Integrated sensitivity analysis for model predictions by varying the resolution of data using an integrated geospatial framework. The specific model used for this case study is the Revised Universal Soil Loss Equation (RUSLE), which is used to predict soil erosion potential, one of the key problems of our time. The RUSLE model is popular and commonly used in many regions of the world, where availability could be limited. Therefore, a systematic analysis will provide insights into the sensitivity of the model to the resolutions of input data which will, in turn, facilitate the adoption of appropriate data and resolution with the model for planning, monitoring, and management tasks. The selection of a model requires critical considerations to align the capabilities of models with the goals. The RUSLE model showed sensitivity and yielded different results for 2.5 m vs 30 m data. Finer resolutions of data predicted higher areas to have ‘very severe erosion or ERC5.’ There are circumstances where high resolution of data may not be available, and hence models can only use lower (coarser-resolution) data for some or all input parameters. It is important to know model prediction with coarser data may underestimate soil erosion potential. The use of an integrated geospatial framework can provide insight into the sensitivity of models to the resolution of input data.